Biography:
Pierre Ménard is a Research Scientist at Meta, where he works on large language model agents. His research focuses on reinforcement learning, with particular interests in multi-agent systems, unsupervised learning, and reinforcement learning from human feedback. Before joining Meta, he was a postdoctoral researcher, most recently at ENS Lyon. He earned his PhD in machine learning from Université Toulouse III – Paul Sabatier, where he studied exploration-exploitation trade-offs in sequential decision-making problems.

Abstract:
We introduce Meta Agents Research Environments (ARE), a research platform for scalable creation of environments, integration of synthetic or real applications, and execution of agentic orchestrations. ARE provides simple abstractions to build complex and diverse environments, each with their own rules, tools, content, and verifiers, helping to bridge the gap between model development and real-world deployment. We also propose Gaia2, a benchmark built in ARE and designed to measure general agent capabilities. Beyond search and execution, Gaia2 requires agents to handle ambiguities and noise, adapt to dynamic environments, collaborate with other agents, and operate under temporal constraints. Unlike prior benchmarks, Gaia2 runs asynchronously, surfacing new failure modes that are invisible in static settings. Our experiments show that no system dominates across the intelligence spectrum: stronger reasoning often comes at the cost of efficiency, and budget scaling curves plateau, highlighting the need for new architectures and adaptive compute strategies. Perhaps more importantly, ARE abstractions enable continuous extension of Gaia2 to other environments, empowering the community to rapidly create new benchmarks tailored to their domains. In AI's second half, progress increasingly depends on defining meaningful tasks and robust evaluations to drive frontier capabilities forward.

Biography:
Rebekka leads the Relational Machine Learning Group at the Helmholtz Center CISPA since 2021. Their research on sparse deep learning is supported by an ERC starting grant since December 2023 and Apple Research since August 2025. From 2019-2021, she was a PostDoc at the Biostatistics Department of the Harvard T.H. Chan School of Public Health working with John Quackenbush, from 2017-2018 at the Institute for Machine Learning at ETH Zurich with Joachim Buhmann, and from 2016-2017 at the Chair of Systems Design at ETH Zurich with Frank Schweitzer. Her PhD research at the ETH Risk Center was supervised by Frank Schweitzer and co-supervised by Hans J. Herrmann from 2013-2016. Her thesis on systemic risk won the Zurich Dissertation Prize and our work on international maize trade received the CSF Best Contribution Award. She studied Mathematics and Physics at TU Darmstadt.

Abstract:
Deep learning continues to achieve impressive breakthroughs across disciplines but relies on increasingly large neural network models that are trained on massive data sets. Their development inflicts costs that are only affordable by a few labs and prevent global participation in the creation of related technologies. In this talk, we will ask the question whether it really has to be like this and discuss some of the major challenges that limit the success of deep learning at smaller scales. Last but not least, we will also provide an overview over our most promising solution strategies.

Biography:
After graduating from École Polytechnique, Louis worked on multimodal generative models at the University of Cambridge. He is now the CEO of Retab, a platform used by AI and operations teams, from fast-growing startups to Fortune 500 companies, to automate their hardest document workflows.

Abstract:
Most back-office work is built around processing documents at scale. Insurance companies review thousands of claim packets per day. Logistics firms reconcile shipping documents across millions of orders per year. Underwriting teams extract the same fields and check the same rules - thousands of times. These aren't one-off analytical tasks. They're high-volume, repetitive workflows that companies staff entire teams to run manually. Vibe coding a "document agent" demo with OCR + Gemini is easy. Scaling it to production with minimal human review is hard. When your extraction accuracy is 90% instead of 99%, every output needs human verification - and review time approaches the time of doing it yourself. This was the fundamental limitation of traditional IDP, and it hasn't gone away just because the models got better. This talk will cover the latest on building multi-stage VLM pipelines that scale to millions of documents while minimizing human review: schema-driven structured extraction, context management for large files, k-LLM consensus for uncertainty quantification, and agentic optimization loops that maximize F1 scores. The session ends with a live demo.

Biography:
Dr.Marina Esteban-Medina is a postdoctoral researcher at the ETH Zürich AI Center and ETH Zürich’s Department of Biosystems Science and Engineering (Switzerland). She works at the intersection of AI and translational biomedicine, developing machine-learning and statistical models to understand tumor heterogeneity and treatment response, with a particular focus on pediatric cancer. She earned a degree in Biotechnology and an MSc in Computational Biology from Universidad Carlos III de Madrid, followed by a PhD in Computer Science from the University of Córdoba. During her doctoral work in a Computational Medicine platform, she built predictive models that integrated clinical and genomic data from the Andalusian healthcare system with public resources to identify therapeutic targets in cancer and rare diseases, collaborating with the Francis Crick Institute (London) and Institut Curie (Paris). Her research has been supported by competitive international funding, including an EMBO Scientific Exchange Grant and an ETH AI Center Postdoctoral Fellowship. She contributes to international initiatives such as the Disease Map and several ELIXIR communities as well as the Immune Digital Twins. Her interests lie in bridging robust AI methodology with clinically actionable insights.

Abstract:
Predicting tumor drug response is a fundamental challenge in precision oncology, complicated by cellular heterogeneity, where minor resistant sub-populations drive clinical relapse. While single-cell transcriptomics offers a high-resolution view of these mixtures, a significant domain gap remains: most drug-response labels originate from bulk cell-line screens, whereas clinical deployment targets single-cell patient data. In this talk, I will explore how to bridge this gap through biologically grounded domain adaptation. I will examine why standard alignment-focused deep transfer methods often fail when they overlook the complex biological realities of the transfer problem. Robust translation requires moving beyond predicting a single sensitivity score toward modeling response as a structured change in cellular state. In this direction, recent generative frameworks have opened promising paths toward a comprehensive modeling of these cellular responses. Finally, I will highlight the importance of uncertainty estimation as a guardrail, ensuring that predictions in new single-cell datasets are safely bounded by the limits of the model's training knowledge.

Biography:
Joann is a PhD researcher in Computer Science at the Institute of Computational Perception, Johannes Kepler University. She holds a Bachelor's degree (BM) in Music Performance (Violin) from The University of Texas at Austin and a Master’s degree (MSc) in Music Technology from the Georgia Institute of Technology. She previously worked as a Research Assistant at the Music and AI Lab at Academia Sinica in Taipei, Taiwan. Her research focuses on Music Emotion Recognition, with an emphasis on understanding how emotional expression varies across performances, which lies at the intersection of audio signal processing, computational music analysis, and music cognition. With a background in music performance, she continues to engage in community outreach through music. She is also a member of the Philharmonia Moments Musicaux in Taiwan, where she previously served as Principal Second Violin and remains involved when visiting.

Abstract:
Music Emotion Recognition (MER) aims to model and understand how music conveys emotion, and lies within the broader field of Music Information Retrieval (MIR). Despite recent advances in machine learning, MER remains challenging due to issues such as limited data and generalization across datasets. In this talk, she will provide a high-level introduction to MIR and discuss recent research on MER utilizing audio and symbolic modalities to model emotional content. She will present findings on the generalization gap across datasets and explore methods to mitigate this gap. She will also discuss ongoing work investigating how performance differences between musicians influence perceived emotion, and briefly highlight potential applications such as therapeutic uses of emotion-aware music technologies.

Biography:
Ondřej Dušek is an Assistant Professor at Charles University in Prague, working on natural language generation and human-computer dialogue. His research focuses on generative language models including large language models, mostly applied to the data-to-text and dialogue response generation tasks. He is specifically interested in evaluating the quality of generated content, specifically its semantic accuracy. After obtaining his PhD in Prague, Ondřej spent 2 years as a postdoc at Heriot-Watt University in Edinburgh. Back in Prague, he is currently the PI of an ERC Starting Grant which aims to produce fluent, accurate and explainable natural language generation systems.

Abstract:

Biography:
I am an assistant professor in Natural Language Processing at the University of Edinburgh and an affiliated lecturer at the University of Cambridge. Over the past year, I was a visiting professor at NVIDIA. My research focuses primarily on efficient and modular architectures for foundation models, especially with respect to adaptive memory and end-to-end tokenization. Previously, I was a visiting postdoctoral scholar at Stanford University and a postdoctoral fellow at Mila Montreal and Mc Gill University. In 2021, I obtained a PhD from the University of Cambridge. My research has been featured the Economist and Scientific American, among others. I received a Google Research Faculty Award and several awards (Highlight Awards at ACL 2025 and Best Paper Awards at EMNLP 2021 and Repl4NLP 2019). I am recipient of an ERC Starting Grant and a £2M ARIA grant. I am a Scholar of the European Lab for Learning and Intelligent Systems (ELLIS) and part of the TACL journal editorial team.

Abstract:

Biography:
Ivan Cimrák is a Professor at University of Žilina (Slovakia), where he works at the intersection of artificial intelligence and biomedical engineering. His recent research focuses on neural networks for medical imaging, with an emphasis on mammography—including analyses of mammography datasets for deep learning and methods that leverage longitudinal screening information to support earlier and more reliable detection. In collaboration with clinical partners, he also develops AI-assisted workflows for digital pathology, such as computer-supported scoring of proliferation markers (e.g., Ki67) from routine histology slides. Alongside AI, he has a strong background in computational modelling of blood flow and cell mechanics, and he has contributed widely used open-source implementations for simulating deformable cells in fluid environments. His work has been recognized by multiple awards, including the Slovak “Cena za vedu a techniku” (Science and Technology Award) 2024 in the category Personality of Science and Technology, and he was a finalist of the ESET Science Award 2024 (Outstanding Academic).

Abstract:
If AI can match or even outperform radiologists in benchmark studies, why is it still not running screening programs? This talk looks at mammography as a stress test for applied ML: low signal-to-noise ratio, rare positives, subtle findings, multimodal context, and high-stakes decisions. I will show that success in retrospective studies is only part of the story. The real challenge is bridging the gap between strong models and trustworthy clinical deployment.

Biography:
I am a PhD candidate in Computer Science at AGH University of Krakow, and the leader of Machine Learning and Chemoinformatics Lab (MLCIL). My research concerns fair evaluation, graph representation learning, graph classification, chemoinformatics, and molecular property prediction. I'm also interested in time series, NLP, and MLOps, and I'm also teaching all of those things at AGH. I also work at Placewise as Data Science Engineer, and at MatGen as Senior Chemoinformatician. Beside my professional work, I train Historical European Martial Arts (HEMA) with messer and longsword, and like reading and tabletop RPGs.

Abstract:
Carcinogenicity - ability of molecules to cause cancer - is the critical regulatory endpoint for both drugs in medicinal chemistry and pesticides in agrochemistry. Testing it for novel compound candidates is long, hard, and expensive. At the same time, cancer safety is of paramount importance - glyphosate cancer-related court cases alone resulted in almost $10 billion in settlements so far. Over the years, many methods have been proposed, spanning feature engineering, graph neural networks (GNNs), and NLP-inspired transformers for molecules. However, all of them have been tested almost exclusively on medicinal chemistry data. What happens if we try to apply them to pesticides? As we will show, all of those models break down. Even slight data distribution shift causes existing models to fall in performance. While pesticides are similar to small molecule drugs, no current solutions have acceptable quality to predict their carcinogenicity. We will cover data analysis and machine learning approaches in chemoinformatics that allow us to quantitatively evaluate those issues, and how we can alleviate those problems with data augmentation, transfer learning, and out-of-distribution learning methods.

Biography:
Human-centric AI researcher at the IDEAS Research Institute, working in the Phenomenology and Computational Psychiatry group. She holds a Research Master’s degree in Neuroscience and Artificial Intelligence from the University of Amsterdam. Her work bridges cognitive science, psychology, and deep learning to study AI’s (mis)alignment with human cognition. She evaluates LLMs’ functional capacities for introspection, metacognition, and situation modelling in relation to human cognitive abilities. In parallel, she uses NLP to analyse and model human lived experience, particularly in complex clinical contexts.

Abstract:
Growing reliance on LLMs for psychiatric self-assessment raises questions about their ability to interpret qualitative patient narratives. We present the first direct comparison between state-of-the-art LLMs and mental health professionals in diagnosing Borderline (BPD) and Narcissistic (NPD) Personality Disorders utilizing Polish-language first-person autobiographical accounts. We show that the top-performing Gemini Pro models surpassed human professionals in overall diagnostic accuracy by 21.91 percentage points (65.48% vs. 43.57%). While both models and human experts excelled at identifying BPD (F1 = 83.4 & F1 = 80.0, respectively), models severely underdiagnosed NPD (F1 = 6.7 vs. 50.0), showing a reluctance toward the value-laden term "narcissism." Qualitatively, models provided confident, elaborate justifications focused on patterns and formal categories, while human experts remained concise and cautious, emphasizing the patient's sense of self and temporal experience. Our findings demonstrate that while LLMs are highly competent at interpreting complex first-person clinical data, they remain subject to critical reliability and bias issues.

Biography:
He is a computer science engineer and artificial intelligence master's graduate, passionate about coding, automation, and AI agents. He is dedicated to knowledge sharing through talks, articles, and video courses. His journey in AI began with DeepMind's AlphaZero chess system, reflecting his love for the game. His interests later evolved to include computer vision and autonomous vehicles, and eventually expanded to large language models and agentic systems. With professional experience in HRTech and MedTech startups focusing on process automation, he is currently developing MisterCar — an agentic framework for researchers and developers. When not immersed in AI, he enjoys watching and playing chess, his favorite games including Stronghold and S.T.A.L.K.E.R., and listening to epic soundtrack music from movies and games.

Abstract:
MisterCar is a configuration-driven framework for building AI agents that interact with virtual environments. This talk demonstrates practical applications through four complete imitation learning examples. I'll showcase how MisterCar enables rapid experimentation in computer vision research by transforming video games into AI testbeds. Through live demonstrations of bots trained for Space Waves, Pacman, ElectricMan, and Euro Truck Simulator 2, you'll see how the same framework adapts to different ML tasks — from binary classification for timing-based games, through multiclass navigation, to multilabel combat systems and regression for continuous control. The presentation focuses on the complete development workflow: data collection from gameplay demonstrations, exploratory data analysis, neural network training with PyTorch Lightning, and real-time agent deployment. Each example illustrates how MisterCar's modular architecture — sensors, transforms, planners, and executors — composes into production-ready agents entirely through JSON configuration, eliminating the need for custom training loops or inference pipelines. Key takeaways include understanding when to apply different classification approaches, handling temporal context in visual decision-making, and leveraging configuration-driven development for rapid AI prototyping. Whether you're a CV researcher seeking experimental environments or a developer interested in practical imitation learning applications, this talk demonstrates how sophisticated AI agents can be built through composition rather than custom programming.

Biography:
Mateusz is a PhD candidate at the Faculty of Computer Science, AGH University of Krakow, and a member of the AGH ML and Chemoinformatics Group. His research focuses on molecular representation learning and fair evaluation in cheminformatics. In his most recent study, he benchmarked 25 foundation models for molecular property prediction across 25 datasets using hierarchical Bayesian statistical testing. Beyond academia, Mateusz works as a Senior Data Science Engineer at Sabre Labs R&D, developing machine learning models for the airline and hospitality industries.

Abstract:
When comparing machine learning models, statistical testing helps us understand whether the observed differences are real, or just caused by random noise. Most researchers rely on null hypothesis significance testing (NHST) when analyzing their results, however many authors have raised concerns about it. NHST cannot describe the probability of one model being better than another, it cannot confirm whether two models perform practically equivalently, and a statistically significant result does not always mean the difference is meaningful in practice. Bayesian statistical testing offers a more informative alternative. It gives you the actual probability that one algorithm outperforms another, lets you formally claim that two models are practically equivalent, and makes uncertainty explicit, all without setting an arbitrary p-value threshold. In this talk, we will go through the differences between the two approaches, how Bayesian testing handles multi-model, multi-dataset evaluation scenarios, and show how you can add Bayesian testing to your own workflow with just a few lines of Python code.

Biography:
I'm a Data Scientist and the Manager of the Risk Machine Learning Team at AllegroPay , where I specialize in credit risk and fraud detection within the financial sector. Before moving into management, I held senior data science roles at institutions like PKO BP and Hexaware / AXA GO. During that time, my work heavily focused on building robust machine learning models using Gradient Boosting and Neural Networks. My technical foundation includes a Master of Computer Science (and Automatic Control and Robotics) from the Warsaw University of Technologies and an MBA in Innovation and Data Analysis. I'm highly passionate about the ethical implications of AI, which is why I am currently pursuing Doctoral Studies in Economics at the Warsaw School of Economics. There, my thesis research focuses specifically on fairness in credit risk modeling.

Abstract:
Machine learning models consume raw, tabular data, but business stakeholders think in complex concepts and relationships. This mismatch creates a "Semantic Gap," which quietly builds up "Semantic Debt" across ML systems over time. In this presentation, I will walk you through how to bridge that gap using Knowledge Graphs. Moving beyond standard data dictionaries, I'll show you the practical mechanics of mapping ML features to structured semantic layers, using frameworks like FIBO as a reference point. We will look at actual use cases where this semantic mapping directly improves the ML lifecycle. You will see how it guides smarter feature selection, highlights blind spots in feature engineering, and dramatically improves Explainable AI (XAI) by aggregating raw variables into intuitive, human-readable concepts. Finally, we'll briefly cover how structuring your data this way provides the necessary foundation for building reliable, autonomous AI agents.

Biography:
I have over 18 years of experience in the IT industry, specializing in data analysis, data processing, and data architecture. For more than 8 years, I have worked in Data Science, gaining strong expertise across corporate finance, capital markets, pharmaceuticals, and medical devices. Certified Microsoft Data Science Associate and Microsoft Data Analyst Associate. I Have over 10 years of mentoring experience, with a particular focus on Data Science, supporting specialists in their professional growth and the practical application of data-driven solutions.

Abstract:
What if we could see deeper, clearer, and faster beneath the Earth’s surface? We explore how modern AI techniques—particularly advanced U-Net architectures—are transforming seismic data interpretation through superresolution. Geophysical data is often noisy, low-resolution, and expensive to process. By applying attention mechanisms, improved loss functions (MSE, MS-SSIM, Gradient and Frequency losses), and transitioning from 2D to 3D models, we significantly enhance image quality, improve signal-to-noise ratio, and preserve critical geological structures such as faults and reflectors. Beyond theory, this session focuses on practical engineering decisions: mixed precision training, kernel optimization, dynamic signal scaling, and cost-efficient deployment. We also discuss evaluation strategies combining quantitative metrics with expert-driven geological validation. This talk is ideal for AI practitioners and domain experts who want to see how deep learning moves from research papers into high-impact industrial applications—delivering sharper insights, faster analysis, and measurable business value in geophysics.

Biography:
Maciej Szymkowski, PhD, is a Senior Machine Learning Engineer at Future Processing, specializing in the intersection of advanced research and industrial AI application. With a career spanning institutions such as Bialystok University of Technology (2018-2026), Warsaw University of Technology (2021-2022), and AGH University in Cracow (2021-2022), he brings a deep academic foundation to complex engineering challenges. Previously, Maciej served as the Head of AI Development at the Łukasiewicz Research Network – Poznan Institute of Technology (2022-2024). His extensive professional portfolio includes high-impact roles at SoftServe, Hemolens Diagnostics, and Transition Technologies, where he transitioned cutting-edge algorithms into production-ready solutions. His specialization is Computer Vision with a focus on medical diagnostics and transport systems (aviation and automotive sector). Moreover, he is an author or co-author of over 45 research papers published in JCR journals and international conference proceedings. When not advancing the field of AI, Maciej is a dedicated sports enthusiast, closely following Real Madrid, the New York Knicks, and the Pittsburgh Penguins.

Abstract:
In 2016, Professor Geoffrey Hinton famously predicted that AI would soon replace radiologists. A decade later, the human expert remains central to clinical practice, yet the technological landscape has been fundamentally reshaped. This presentation provides a critical evaluation of this "prophecy" by contrasting classical methodologies with cutting-edge AI breakthroughs. The talk is structured into two technical phases – in the first, I will concentrate on classical foundations - traditional signal and image processing pipelines (based on algorithms like filtering and thinning) are utilized for initial pathological change detection in anatomical scans (e.g., liver radiology scans). The second part of the talk will concentrate on novel generative frontier. I will explore the transition to modern architectures, evaluating object detection models (YOLO) and specialized foundation models like MedSAM for zero-shot medical segmentation and changes observation. Furthermore, we investigate the reasoning capabilities of Vision-Language Models (VLMs), including Gemini, GPT, Qwen, and InternVL, in interpreting complex diagnostic data. By synthesizing classical processing, deep learning pipelines, and multimodal reasoning, this talk concludes with a data-driven assessment of the current state of the art. Is it finally time to replace the radiologist, or has AI instead created the ultimate diagnostic collaborator?

Biography:
Mateusz Olko is a doctoral researcher at the University of Warsaw and AKCES NCBR. He earned both his Bachelor’s and Master’s degrees in Computer Science from the University of Warsaw, specializing in machine learning. He explores topics in causal machine learning and causal discovery, with a particular interest in how they can be connected to deep learning. More broadly, he is interested in computational reasoning and how learning systems can better capture structure and support decision-making. His research has been recognized at top-tier AI conferences, including NeurIPS and ICML.

Abstract:
Foundation models for tabular data have recently gained a lot of attention in the machine learning community, driven by their impressive performance across a wide range of tabular tasks. In particular, prior-fitted networks — models trained once on synthetic data and then used for in-context prediction without any further fine-tuning — have demonstrated superior performance across a wide range of tabular benchmarks. TabPFN is a prominent example of this paradigm. In this talk, we explore how this new approach can be applied to causal discovery: the task of inferring causal structure from observational data. We discuss how TabPFN's in-context inference opens fresh perspectives on some of the field's long-standing challenges and what opportunities this direction brings to the causal discovery community.

Biography:
I'm working as AI Research Manager (NLP) at Fastr.ai. I am educated in a variety of disciplines, as I've earned my Master's at Warsaw School of Economics, Advanced Analytics - Big Data, and my Bachelor’s Degree at the Faculty of Physics of University of Warsaw, specialization Molecular Biophysics. I'm passionate about Machine Learning, and it's potential practical and scientific applications in our world.

Abstract:
This presentation shares practical lessons from building a dense retrieval system for candidate-job matching at scale—tens of millions of candidates across tens of thousands of jobs in seconds. We introduce transformer architecture modifications for processing long documents and a modified margin pairwise loss function that reflects annotation ambiguity. A central theme is training data quality: we describe a human-in-the-loop annotation framework that made an overwhelming task manageable for a small team with limited budget, and share failed training attempts demonstrating how data quality shapes what models can learn. As a practical bonus, we show how contrastive training can run on a single GPU with reasonable batch size, and how to reduce vector database memory footprint up to 32-fold.

Biography:
For over 12 years, he has been professionally involved in data analytics as a data scientist, team leader, and project manager. He has participated in over 200 advanced analytics and AI projects. His main experience and interests lie in the pharmaceutical and healthcare industries, but he has also worked on projects in various other sectors, including finance, media, retail, energy, and agriculture. Currently, he is responsible for designing and implementing data solutions, primarily in the areas of machine learning and artificial intelligence. He shares his knowledge as a data science trainer, lecturer, and conference speaker. He is a co-author of scientific papers, mainly in the fields of medicine and statistics.

Abstract:
Boxing punch detection project was an experimental initiative aimed at developing a real-time computer vision system for boxing, with the ultimate goal of enabling a Video Assistant Referee (VAR) tailored to the sport's dynamic nature. Unlike traditional VAR systems in sports with fixed-camera setups and discrete events, boxing presents unique challenges due to its fluid motion, variable lighting, and interpretative complexity. The project focuses on building a scalable machine learning pipeline capable of detecting and analyzing key combat events, such as punches, blocks, dodges, and infractions. Finally, as in any machine learning projects, the data processing was a crucial step and preparing structured dataset of annotated boxing clips will also be discussed.

Biography:
Dominik has over 11 years of hands-on experience in Machine Learning, Data Exploration, and Business Analysis projects, primarily in the FMCG industry. As a seasoned technical leader, he excels in setting strategic goals and crafting detailed roadmaps for complex projects. Dominik holds a PhD with distinction from the Warsaw University of Technology, where he specialized in neural networks for image processing. Passionate about bridging the commercial and academic worlds, he combines cutting-edge research with practical applications to drive innovation. For over 2 years, Dominik has been deeply immersed in the Generative AI space, successfully delivering multiple proof-of-concept projects and advancing several initiatives to production. His expertise and curiosity make him a key driver of transformative AI solutions.

Abstract:
As Large Visual Language Models (LVLMs) become increasingly central to visual understanding in retail and marketing contexts, critical limitations in their ability to interpret detailed visual information are emerging. This presentation examines the “visual blind spots” of generative AI systems when applied to tasks requiring high precision and contextual awareness. Use cases such as promotional shelf analysis, point-of-sale marketing material recognition, and full-shelf product parsing reveal common failure modes, including confusion between visually similar products, imprecise localization, and sensitivity to image size and perspective. To mitigate these issues, the presentation explores strategies that leverage classical computer vision techniques to enhance LVLM inputs. These include preprocessing methods such as depth filtering, morphological operations, and perspective normalisation, as well as text-guided segmentation and region-of-interest (ROI) refinement. This hybrid approach aims to improve clarity and accuracy in downstream tasks while reducing reliance on large volumes of training data, paving the way for more robust and scalable multimodal systems.

Biography:
I am a machine learning researcher and computer vision specialist currently working as an ML Expert at MIM Fertility, where I focus on applied deep learning and end-to-end model development in medical settings for IVF. Right now, I’m working on EmbryoAid with a particular focus on detecting division times during embryo development. My previous experience includes ML research and engineering work on remote sensing at AI Clearing, where I worked with multiple products at various stages of development. On a less professional note, I’m quite active in the Polish ML community. I served as Vice President of the KNSI Golem AI student club and co-founded the Google Developer Student Club at Warsaw University of Technology. I also helped organize some great events, such as MLinPL and the EnsembleAI hackathon. When I had more time, I also did some open-source contributions (Darts is a great Time Series library 10/10) and took part in quite a few hackathons (results varied)

Abstract:
This talk presents a computer vision–based approach for automatically classifying embryo division times in in vitro fertilization (IVF) time-lapse imaging. I provide an overview of the problem domain, highlighting the clinical importance of accurate timing annotations for embryo assessment and selection. As healthcare data is becoming a rapidly growing domain in machine learning, driven by the increasing availability of medical imaging and digital health records, methods that can reliably analyze such data are gaining significant relevance. The presentation will describe the nature of the problem domain, along with key preprocessing, post-processing steps, and an outline of the modeling pipeline. I’ll also discuss practical challenges such as annotation quality, class imbalance, and generalization within this domain. Overall, this talk is intended to serve as a gentle introduction to the unique characteristics and challenges of applying machine learning in the healthcare domain.

Biography:
Maciej Łachut is a student and researcher driven by a fundamental interest in the complexities of audio and speech processing. His work is rooted in the challenge of how machines perceive and interpret the human voice, moving beyond simple data points to capture the nuanced acoustic signals that define our communication. While his research spans various aspects of signal analysis, Maciej has focused specifically on Speech Emotion Recognition (SER) to address the inherent difficulties of cross-lingual environments. This interest led to his proceedings paper on inference-only speaker adaptation, which secured 1st place in the POL-EVAL 2025 Task 4 and is indexed in the ACL Anthology. By utilizing self-supervised models and novel clustering techniques, he explores how AI can calibrate to an individual's unique vocal range without intensive retraining. Maciej remains a dedicated student of the field, eager to explore how refined audio understanding can bridge the gap between raw signal processing and more authentic human-centric technology.

Abstract:
To truly synthesize a human voice, an AI must first understand the "baseline" of human feeling. But how do we teach machines to recognize emotions when every speaker’s voice is a moving target? Cross-lingual Speech Emotion Recognition (SER) often hits a wall because models struggle to separate a person’s unique vocal quirks from their actual emotional state. In this session, we dive into the architecture that secured 1st place in the POL-EVAL 2025 Task 4. We move beyond rigid models to introduce a novel Greedy Cluster Assignment Algorithm that adapts to a speaker’s voice on the fly at inference time. Using a frozen HuBERT encoder, our method "calibrates" to an individual's unique vocal range without any retraining—an essential step for building truly responsive and empathetic AI. By boosting Macro F1 scores from 0.619 to 0.753, we demonstrate how unsupervised clustering can finally disentangle ambiguous categories like Fear and Surprise. Join us to see how mastering Inference-Only Speaker Adaptation is the key to bridging the gap between raw acoustic data and authentic emotional intelligence.

Biography:
I am a student at PUT. Focused on Physical AI. My technical expertise is centered on the intersection of physical hardware and advanced AI, specifically focusing on Vision-Language-Action (VLA) policies. Currently, I am developing an autonomous waste-collection robot that utilizes a 6-DOF robotic arm, focusing on the intersection of advanced control theory and computer vision. My work emphasizes the transition from simulation to real-world deployment, utilizing frameworks such as ROS2 and LeRobot.

Abstract:
As the field of robotics shifts from rigid automation toward general-purpose Physical AI, the challenge remains: how do we enable robots to interact with unstructured, real-world environments? This talk explores the development and deployment of an autonomous waste-collection robot, focusing on the integration of Vision-Language-Action (VLA) policies to handle diverse and unpredictable objects.

Biography:
Sebastian is an ELLIS/IMPRS-IS PhD student at the University of Tübingen. Prior to returning to academia, he conducted research in industry, where he worked as a scientist in Microsoft's Mixed Reality and AI Lab, and an AI Resident at Microsoft Research Cambridge. In a previous life, he spent several years working as a software engineer. His primary research interest lies in efficiently learning compositional representations in computer vision.

Abstract:
Synthetic data in machine learning is attracting increased interest, as it frequently offers a faster and cheaper alternative to traditional data collection and annotation in applications ranging from autonomous vehicles to healthcare. It is typically used to supplement real data and often requires domain adaptation to bridge the gap between simulation and reality. In this talk, I will show that it is possible to perform real-world face analysis tasks, such as landmark detection and semantic parsing, with synthetic images alone and without domain adaptation. In addition, I will outline several other applications of synthetic data and discuss how it helps overcome legal, ethical, and privacy issues that arise when collecting real data and enables new approaches in cases where manual labelling is impossible.

Biography:
Pola got her PhD in 2022 from the Technical University of Denmark doing research on probabilistic machine learning, invariance learning and AI ethics. She has recently joined AWS in Berlin as an Applied Scientist where she works on responsible AI.

Abstract:
Machine learning algorithms work by extracting patterns from large amounts of training data and projecting correlations forward. This mechanism by which ML works becomes a problem when training data is scarce: In the low data regime, performance usually drops dramatically. Data augmentation (DA), i.e. the practice of introducing additional, synthetic data into the training set is one way to mitigate this problem, and has become a standard tool in the deep learning pipeline. DA is usually performed ad hoc, i.e., based on assumptions rather than using a statistical model. Such ad hoc DA cannot guarantee optimal augmentation and might be difficult to do for data that is not easily interpreted by humans. This talk formalises the practice in order to provide a principled model for learning data augmentation using Bayesian techniques. We close with an outlook on how data augmentation can play a role for algorithmic fairness and de-biasing ML algorithms.

Biography:
Graduated with Master degree from NTUU "KPI" in Kyiv in 2013, worked in Samsung (2012 - 2014), Amazon (2015 - 2023), Grammarly (2023 - now). Mostly experienced with speech technologies with gradual shift towards natural language processing.

Abstract:
Generative modeling is an exciting area of deep learning that has the ability to convert randomness into meaningful outputs. It is rapidly expanding and pushing the boundaries of artificial intelligence, becoming an increasingly important area of study in various industries. In this presentation, we will provide an overview of four main approaches to generative modeling: auto-regressive models, generative adversarial networks, normalizing flows, and diffusion models. We will highlight the key concepts behind each approach, and demonstrate how they are used in practice.

Biography:
Maria Ganzha is working as Associate Professor at the Warsaw University of Technology. She has received her M. A. and Ph. D. in mathematics at the Moscow State University in Russia, and a Doctor of Science degree in Computer Science from the Polish Academy of Sciences. Maria has published nearly 200 scientific articles, is editor in chief of a book series and a member of the editorial boards of 5 journals. She has been invited to the program committees of over 250 conferences. Her scientific interest is focused on agent-based technology, semantic technology, and machine learning. She is Technical Coordinator of EU project ASSIST-IoT, and principal investigator of SRIPAS team in another EU project aerOS. From 2020 she is a President of Mazovian Chapter of Polish Information Processing Society (Polskie Towarzystwo Informatyczne).

Abstract:
Proliferation of digital artifacts with various computing capabilities, development of very high speed networks, along with the emergence of edge computing paradigm, offer new possibilities for the development of Machine Learning solutions. One of the most popular of them is Federated Learning (FL). However, edge computing brings also a number of problems that need to be dealt with. For instance, in real-life deployments, sudden client dropout, low/varying network bandwidth, or divergent local data distributions need to be considered. Interestingly, some of these problems can be partially managed by selecting an appropriate FL topology. In the talk different aspects of realization of Federated Learning in real-world-grounded scenarios will be discussed.

Biography:
Michał Kuźba is a Machine Learning Scientist at Expedia Group in Switzerland. He has more than 5 years of professional and research experience in Machine Learning and Data Science in companies such as Facebook, Microsoft and Ml2DataLab. He describes himself as an enthusiast of Machine Learning, Natural Language Processing, explainability and human aspects of AI and Data Science. His recent projects include: “Automatic processing and trends extraction from documents on the topic of AI regulations and Responsible AI”, “Conversational explanations of Machine Learning models using chatbots” and “Travel recommendations using graph embeddings representation”. When not expanding his knowledge, Michał shares his expertise with other people. He is also passionate about cartography, geography and travelling.

Abstract:
At Expedia, we offer travel products on a global scale. It means we deal with numerous hotels and even more hotel rooms. But what do we do if we want to compare them, cluster, or learn relations and representations? In this talk, I will talk about similarity learning for hotel rooms. We will compare multiple techniques, including the classification approach, siamese networks and triplet loss. We will see how to learn the similarity function directly or through representation learning (embeddings). We will also see how different perspectives (traveller, hotelier, domain experts) can provide various ground truths for the model to learn. Finally, we will show the benefits of using interpretability techniques in model development and model selection.

Biography:
Award-winning computer scientist and data analyst. One of the founding members of deepsense.ai. He specializes in applying data structures and algorithms in artificial intelligence.

Abstract:
"GPT can be extremely impressive, but we turn that impression around with a few carefully crafted prompts. The wrong answers are given with an almost human-like confidence. We will discuss the various mistakes of GPT models in relation to their training processes."

Biography:
Sebastian is a postdoctoral researcher at IDEAS NCBR and also an assistant professor at the Gdańsk University of Technology, where he earned his PhD. Previously, he was employed as an Applied Scientist at Amazon and contributed to projects such as the visual perception system for the autonomous robot Amazon Scout. He has extensive experience in a variety of computer science topics and has worked for Moody's Analytics on mathematical modeling. His research focuses on the real-world generalization and efficient computation of machine learning algorithms. In addition, he is collaborating with the Medical University of Gdańsk on a project aimed at early cancer diagnosis through the use of liquid biopsies.

Abstract:
Continual Learning is an emerging paradigm in machine learning where models learn new tasks in sequence without access to previously acquired knowledge. The key challenge in this field is addressing the problem of catastrophic forgetting of previous information. This is crucial because in many real-world scenarios, models must be able to adapt to constantly evolving data and tasks. During this presentation, I will discuss the latest developments in Continual Learning research and explain why it is an important research area of focus for us at IDEAS NCBR.

Biography:
Michał is a Research Engineer at Allegro Machine Learning Research team and a graduate of EPFL and Poznań University of Technology. During his career, he worked on a variety of ML projects, notably in the field of natural language processing, text generation, and recommender systems - both in academia and industry.

Abstract:
Did you know that Allegro, the largest e-commerce platform in Poland, is now operating in multiple languages? The key step for a successful international expansion is having accurate and fast translations of product offers and descriptions. In this talk, we are excited to present how the Machine Learning Research department of Allegro developed an in-house Machine Translation solution, beating the most popular commercial translation tools on chosen language pairs and bringing significant cost savings to the company every day. This achievement has been made possible through a combination of careful data preparation, modern evaluation metrics, and cutting-edge ML techniques, such as deep transformer-based models, and knowledge distillation.

Biography:
Agata works as Senior AI Scientist in Pearson. She has couple of years of experience in Artificial Intelligence/Machine Learning. Her professional interests are connected with Natural Language Processing. She obtained her Master degree in Applied Mathematics from Wroclaw University of Science and Technology. In her free time, she loves hiking and travelling. Mateusz Otmianowski is a Principal AI Scientist at Pearson. He is focused on solving education problems with Machine Learning solutions. Before joining Pearson, he worked in various roles in e-commerce and the financial sector.

Abstract:
Models from GPT family offer state of the art performance on many tasks and are accessible through simple API. This makes them an interesting technology to experiment with and build products around. We believe that one of the more interesting areas to use GPT models is education, as they make it easier to deliver personalized learning at scale. That is why we spent much time experimenting with GPT models in the past months, using them in various contexts like learning content generation and as teaching agents. In our talk we would like to share lessons learned from those experiments, discuss what worked and what didn’t and point at the areas we believe still need to be researched.

Biography:
Juggling work, family, friends, health and soul. I'm passionate about the intersection between computing infrastructure and artificial intelligence. With experience in the IT, Financial and Pharmaceutical industries I have a wide spectrum of the different challenges that need to be taken into account when exposing ML in production. I'm totally inspired by my current role since I can contribute back to society in a more direct manner by helping accelerate drug discovery from my MLOPs engineer trench. I work with Python, Kubeflow and GitLab, and I'm constantly looking for new cloud-native technologies that can help us bridge the gap to production while minimizing maintenance. Part of a novel team of MLOPs engineers at Roche with a common passion and a clear goal.

Abstract:
Machine Learning Operations (MLOps) has been maturing over the last two years, and we are finally beginning to observe the rewards of focus and synergy from multiple groups and we finally have produced an enterprise MLOps reference architecture. The road towards this company success has been though trial and error and after investing multiple resources into this effort. This talk will focus on the roadmap towards obtaining such a reference architecture, summarizing lessons learned, technologies used and present a reference architecture.

Biography:
Kacper is a Graphics Software Development engineer at Intel, where he is involved in various computer vision-related projects. His main area of expertise and interest is in image processing, where he has already authored several projects. Before joining Intel, Kacper worked on object detection at FlexLink, where he was responsible for developing and improving the object detection system. Kacper is passionate about technology in general and gaining new skills. In 2021, he earned his Bachelor's degree in Automatic Control and Robotics from Poznan University of Technology.

Abstract:
There are many types of visual corruption. The simplest such as a Black Screen, are easily detected using the basics of Computer Vision. Everything changes when we are going to detect visual corruption in games. Sometimes Black Screen is part of gameplay, sometimes not. As problems become more complex, recognizing them comes to checking multiple images at once. In that case, Machine learning becomes the only solution.

Biography:
Piotr Biliński is a Senior Applied Scientist at Amazon Alexa. He obtained his PhD in Computer Science with first class honours from University of Nice in 2014. Then, he was a Postdoctoral Research Fellow at INRIA and a Postdoctoral Researcher at University of Oxford. Further professional appointments include work at Microsoft Research, Honeywell Laboratory, as AI mentor at NASA FDL and FDL Europe, and as Assistant Professor at University of Warsaw. His research interests include deep learning and machine learning. His latest results deal with generative models and disentangle representation learning for text-to-speech and video generation. Piotr has authored US patent and 30+ research papers at main speech/audio, computer vision and machine learning venues (ICASSP, Interspeech, CVPR, AAAI, IJCAI, NeurIPS, ECCV, WACV, and others). He serves as chair and reviewer in major computer science conferences and journals. He is a member of IEEE, AAAI and Computer Vision Foundation. He supports the work on the Polish AI strategy and he is an expert of National Science Center and Foundation for Polish Science.

Abstract:
In this talk I will focus on the concept of speech disentanglement for controllable text-to-speech synthesis. Firstly, I will briefly give introduction to text-to-speech and explain why is it so important research area. Then, I will explain what speech disentanglement is. And then, I will dive deeper into text-to-speech technology and how we leverage deep learning and various speech disentanglement technologies to control text-to-speech synthesis process.

Biography:
Albert Wolant is software development engineer working on DALI team at NVIDIA. He has experience both in Deep Learning and GPGPU software development. In the past he did research work in parallel algorithms and data structures. Kamil received his master's degree from the University of Warsaw, where he focused on formal verification of smart contracts.

Abstract:
Data augmentation is a crucial technique in deep learning, allowing us to enrich training data from existing samples by applying various transformations to the input images. This technique not only increases the effective size of the training dataset but also improves the model's robustness to variations and reduces overfitting. However, designing an effective set of augmentation operations (policies) manually can be a challenging task, and the optimal policy can vary from dataset to dataset. AutoAugment is an approach to automatically search for the best data augmentation policies for a given dataset and task using reinforcement learning. By applying machine learning to itself, AutoAugment can discover new augmentation policies that can significantly improve the performance of deep learning models. In this presentation, we will focus on the importance of data augmentation. We will demonstrate how various automatic augmentation techniques can be integrated with NVIDIA DALI on GPU, enabling fast and effective data augmentation for deep learning models. In summary, this presentation will provide a comprehensive overview of how automatic augmentations work and how to efficiently run them on the GPU with the help of NVIDIA DALI. We will showcase how these techniques can significantly improve the performance of deep learning models and discuss the importance of data augmentation for achieving state-of-the-art results.

Biography:
Karolina has been working a Data Scientist since 2017, mainly in the area of machine learning for the financial sector. The first models she built dealt with predicting the departure of the bank's corporate clients and their behavioral segmentation. As part of her PhD studies, she is currently working on the prediction of corporate financial condition using deep learning and NLP methods.

Abstract:
One of the characteristics of working with textual data is its vagueness. Capturing the semantics of language requires more advanced models, and the very concept of fuzzy logic can be the basis for creating such technical solutions. The presentation will therefore cover the ways of working with fuzzy logic-based language processing methods.

Biography:
Jędrzej leads the Machine Learning team at Appsilon. His focus centers around applications of computer vision techniques in the analysis of human shape and motion. Additionally, he is involved in numerous collaborations with life scientists and in particular ecologists, in which computer vision is used to aid the study of life on earth. More about the ML projects ran at Appsilon can be found here: https://appsilon.com/ai-research/ He started his career as a theoretical physicist, made a turn into data science and finally focused on computer vision. Privately, Jędrzej enjoys skiing, as well as ice-skating, and tries to turn any conversation into a discussion about one of those.

Abstract:
During the talk I will present recent results concerning counting nests of Antarctic birds in images captured from drones. The study is based on magnificent data (with spatial resolutions reaching 0.6cm/px) which contains crucial information about the well-being of the still understudied ecosystem of the Antarctic. This in turn, is vital to monitoring the rapid changes the ecosystem is undergoing due to the climate crisis. I will discuss the statement of the problem (interesting application of object detection), the key challenges and the solutions we have developed to tackle them. The results were obtained by the Appsilon team in collaboration with scientists from the Arctowski Polish Antarctic Station. I will wrap-up the talk with a quick overview of the recent results of projects the Appsilon’s ML team was involved in globally: regarding Arctic oceans and African savannas - I will be happy to discuss them during the conference.

Biography:
Roman Prykhodchenko - Data Scientist at WIDMO Spectral Technologies, Ph.D. candidate at the AGH Doctoral School. Passionate about building complex systems, ML, and computer vision, and its application in the domain of Ground Penetrating Radar data. Have a strong interest in automation and data visualization. Have a background in autonomous robotics and computer vision. Mentor of the 4th Robotic Craftsmanship International Academy in 2019.

Abstract:
We are a company that has been developing the innovative Spectral Ground Penetrating Radar (SGPR). Our aim is to revolutionize the current state of underground detection by application of cloud, machine learning, and deep learning solutions with connection to our state-of-the-art SGPR device’s data. Our work focuses on applications of computer vision, ML, and DL techniques in the analysis of Spectral Ground Penetrating data, and data visualization. Data that are recorded by radar are complex and usually require some level of expertise in the domain of geophysics to be analyzed, labeled, and understood. The tangible information, which our customers get from the spectral ground penetrating radar is helping them to answer the question of what is underground without using invasive methods or having prior knowledge in the geophysics domain. During the talk, we will try to answer the following questions: - What are we looking for underground? - What type of data and how to process SGPR data before training ML models? - How to get labeled datasets based on knowledge discovery and other techniques? - Which model architecture to use? - How to evaluate model predictions?

Biography:
Senior Research Engineer in the Machine Learning Research team at Allegro, where he works on applying and advancing NLP methods in the e-commerce domain. He obtained his PhD from the University of Warsaw, in which he focused on machine learning methods for histopathology.

Abstract:
The problem of label noise is critically important in supervised learning - after all, when the training data is corrupted, it may be hard to recover the correct training signal. A simple solution, i.e. more thorough data annotation, is unfortunately rarely possible in practice, as machine learning projects have both time and cost constraints. As such, it is necessary to accept the presence of noise in training data and develop robust models. We evaluated a set of methods that increase the robustness of machine learning models to various forms of label noise in the context of extreme text classification. This setting is novel for the problem of label noise, as the majority of existing research focused on small-scale image classification. We show that simple symmetric noise is easy to overcome even for very high noise ratios, while more complex noise patterns are much harder to deal with. In addition to experimental results, we discuss the relation between robustness, memorization and training dynamics.

Biography:
Data scientist with extensive professional experience at the intersection of statistical inference and machine learning. His research path oscillates closely around time series analysis and stochastic processes. During his consultancy career Lukasz has advised global companies in key projects related to data management and machine learning.

Abstract:
The problem of applying artificial neural networks (ANN) to nonstationary trending time series remains practically unsolved. No improvements to the optimization procedures proposed so far have proven fully satisfactory. We present a new and original solution involving the transformation of original data into a new series in form of a sequence of jumps defined as time distances between succeeding maxima reached by a variable. This new series is representable by a compound Poisson distribution and is stationary in contrast to the original data generating process. Simulation experiments confirm that the jump process can serve as a feature of the ANN. It retains the most important information about the nature of the original time series, particularly about the composition of stochastic and deterministic trends causing nonstationarity.

Biography:
For the past 10 years I have been involved in data analytics in the broadest sense, starting with spatial data and later moving on to more standard data e.g tabular data. For the past 4 years I have been leading a team as Data Science Manager, where we implement projects for our clients holistically, i.e. starting with the analysis of requirements and business processes and ending with the implementation and, if necessary, maintenance and development of the solution. In my work I am a bridge between the world of business and the world of technology. I specialize in translating technical and analytical issues into business language.

Abstract:
For many people, time series is often a rather trite topic, and forecasting sales or orders is trivial. And either the issue ends with ARIMA or, conversely, there is a belief that it is enough to throw the data into neural networks. After all, it can't be that hard, since logistics and retail companies somehow manage to do it, and on Kaggle the data is so nice. Nothing could be further from the truth. With an assortment of a few dozen or thousands products, several to several thousand locations, and daily granularity, the issue is no longer so simple. When you're modeling a demand forecast, a number of questions arise - how to deal with infrequent sales, what evaluation metric to choose, how much impact business decisions have on the process, and others. What can happen on this type of project, what will surprise you with the data and the business and what are the options for solving them I will tell you in the presentation.

Biography:
Tomasz Pędzimąż is a machine learning professional with expertise in natural language processing and speech recognition technologies and methods. As the Head of Development Team, he leads to the creation of innovative technologies used in voicebot products and solutions. He specializes in statistical language models, which he applies to improve the accuracy and performance of speech recognition systems. He leads a highly skilled machine learning team that provides market-ready services in the speech domain.

Abstract:
Developing a human-like voice for diverse business applications is an ongoing challenge. In this presentation, we will journey through the continuous advancements of Techmo TTS, highlighting new features and enhancements such as IPA alphabet support, phonetic lexicons, improved prosody, SSML tags usage, and voice personalization. We will examine the role machine learning plays in managing these features and elevating the overall TTS experience. Additionally, we will introduce TTS Studio, a custom web app offering a suite of tools designed to optimize accessibility and quality across various prompts and applications. TTS Studio delivers natural-sounding voices, real-time synthesis, and other valuable features that enrich the TTS experience. Attendees will gain insightful knowledge of the latest innovations in Techmo TTS technology and learn how to leverage these advancements in their own applications.

Biography:
Marcin Mosiolek is the Chief Data Scientist at SII, where he coordinates the work of AI engineers on automated document reading projects. He also leads a novel research project related to adding new objects to already recorded videos. He has been working in the industry for more than 12 years, developing successful AI-based products for both startups and large enterprises, including large-scale document processing and several search-related products. Marcin has also led initiatives in autonomous driving and augmented reality.

Abstract:
ChatGPT has brought more attention to natural language processing than any other AI model. However, relying entirely on ChatGPT’s responses is extremely risky due to its tendency to hallucinate non-existent facts, which effectively precludes its use in critical domains such as healthcare or law. Fortunately, it’s possible to take advantage of the excellent conversational capabilities of modern large language models while restricting their responses to strong evidence present in a set of specific documents. Retrieval-Augmented Generative Question Answering is an approach that combines retrieval-based methods, such as semantic search, with conversational models, making the above possible. We used this approach to augment our in-house semantic search with a chatbot that provides answers in natural language. The talk aims to present practical applications of concepts such as large language models, semantic similarity of text, semantic search engines and finally retrieval augmented generative question answering. The presentation shares experience collected in a customer facing projects.

Biography:
Karol Piniarski has been specialising in computer vision for almost 10 years. Involved in many research and commercial projects in the field of image generation, deep learning, object detection, image compression and ADAS. Machine Learning and Computer Vision consultant at Lingaro Sp. z o. o. Additionally, in constant cooperation with the Poznań University of Technology (PUT) as an assistant professor at the Institute of Automation and Robotics. He received his doctoral degree in 2022 at the Poznań University of Technology.

Abstract:
Over the past year, there has been an incredibly rapid development of model architectures that generate higher quality images. Therefore, the new opportunities arise for business applications, but also challenges that must be addressed. In our project, we propose utilisation of automatic image generation for the marketing purposes. We prepare innovative framework that includes many steps: natural-language processing, text to image generation based on recent diffusion models, fine-tuning, super-resolution and reranking. Thanks to this, we are able to automatically generate advertisement images adopted to a given customer segment. This project is supported by the National Centre for Research and Development with grant number: POIR.01.01.01-00-0062/20.

Biography:
Currently a Data Scientist (with NLP) at deepsense.ai which is one of the largest and most recognized companies in Poland preparing complex solutions to practical customers' problems using both state‑of‑the‑art solutions and implementation of own ideas in the field of artificial intelligence. Master's degree in artificial intelligence obtained in September 2022 at Poznan University of Technology. Honoured with the prestigious award/medal of the university awarded by the Rector to the selected top 5 graduates of the university. Presenter of a self-developed method achieving state-of-the-art results for imbalanced data at the poster session of an international conference (MLinPL). Machine learning enthusiast, interested in continuous development, and deepening both theoretical and practical knowledge. Eager to undertake difficult challenges and tasks.

Abstract:
Aspect Sentiment Triplet Extraction (ASTE) is a task in natural language processing that focuses on automatically identifying aspect phrases, opinion phrases, and sentiment polarity from a given text. The goal of ASTE is to accurately extract and categorize the opinion expressed in a text, which is crucial for sentiment analysis and opinion mining applications. This work presents a new approach to ASTE. The approach, which involves a learnable extractor of spans and a differentiable span selector, improves upon existing methods by capturing interrelations between spans during model output construction. Experiments show that this approach outperforms other state-of-the-art methods in terms of the F1-score.

Biography:
Michal Tyrolski is a Deep Learning Researcher at DeepFlare and the University of Warsaw. He graduated from the University of Warsaw and currently works in Piotr Miłoś' research lab on reinforcement learning. Michal's areas of interest include meta and model-based reinforcement learning. He has previously worked at companies such as Nvidia, Microsoft, and Samsung.

Abstract:
Complex reasoning problems contain states that vary in the computational cost required to determine a good action plan. Taking advantage of this property, we propose Adaptive Subgoal Search (AdaSubS), a search method that adaptively adjusts the planning horizon. To this end, AdaSubS generates diverse sets of subgoals at different distances. A verification mechanism is employed to filter out unreachable subgoals swiftly, allowing to focus on feasible further subgoals. In this way, AdaSubS benefits from the efficiency of planning with longer subgoals and the fine control with the shorter ones, and thus scales well to difficult planning problems. We show that AdaSubS significantly surpasses hierarchical planning algorithms on three complex reasoning tasks: Sokoban, the Rubik's Cube, and inequality proving benchmark INT.

Biography:
Sylwana has 5+ years of experience, mostly in the image domain (super resolution, synthetic images, augmented reality) and predictive maintenance. She’s been working mostly in the automotive, semiconductor, telecommunication and financial sectors, for companies like AMD, T-Mobile, TomTom, AT&T. Sylwana is also a content creator and a former TEDx speaker on the topics of Machine Learning. Joanna has 4+ years of experience in research and commercial application of ML solutions in such fields as chemistry, radars, network traffic, sales and military. She has a broad ML overview having developed projects in NLP, preventive maintenance, computer vision and - most lately - point cloud computing. Joanna has a biomedical and computer science university background.

Abstract:
Point clouds enable us to present the shape of objects in 3D space, being an omnipresent type of data used for space mapping in meteorology, Earth observation, or autonomous vehicles, to name only a few. Just like in typical Data Science projects, point clouds are subject to classification and segmentation tasks. What’s not trivial though is, how to deal with noise and bad quality of such data during those projects? It turns out that such a topic is still almost undiscovered. The majority of research on it focuses mostly on artificially added Gaussian noise, which is simply not enough. Such noise is too simplistic and doesn’t cover real-world scenarios, such as ours: point cloud denoising of mobile built-in sensor outputs. Thus, we had to develop some solutions on our own. Fortunately, we discovered that real noise is quite an interesting problem to tackle. Firstly, because data quality can be affected by such physical phenomena as wave interferences, and knowledge of that can really help. And secondly, because when we apply deep learning models on top of that (Pointfilter being one of them), those methods can actually work.

Biography:
He completed his architectural studies with elements of programming at Dessau International Architecture, Hochschule Anhalt in Germany. He was a DAAD scholarship recipient. In 2018, he defended his PhD with honors at the Lodz University of Technology, presenting his work on ""Optimization of Continuum Material Topology in Search of Architectural Forms. Adaptation of Selected Engineering Methods for Architectural Purposes"". Since 2019, he has been working at Pixel Technologists, first as a Data Scientist, and a year later, he took on the position of Head of Data Science, leading the Data Science team. During this time, he has been involved in the development of tools for anatomical segmentation of the brain, lungs from various studies, including MRI and CT. He is a co-author of an automatic system for detecting changes typical for the Sars-Cov-2 on CT images. Since 2021, he has been a certified Scrum Master.

Abstract:
Multiple Sclerosis (MS) is a debilitating neurological disease that affects millions of people worldwide. Tracking the progression of MS is critical to providing timely and effective treatment to patients. However, traditional methods of monitoring MS using MRI can be time-consuming and subjective. In response, we developed the FLAIR Lesions and Atrophy Report (FLAAR), an automated pipeline that provides neurologists with fast and reliable tracking of MS progression. FLAAR pipeline is based on a AirFlow DAG, allowing for comparative analysis of brain MRI images using FLAIR, T1w, and T1w-Contrast Enhanced images. The system's core is composed of two UNet-like neural networks that have been trained on an in-house dataset by highly-trained radiologists. These models enable accurate segmentation of both anatomical brain structures and demyelinating lesions. To validate the stability and repeatability of our anatomical brain segmentation solution, we conducted a Test-Retest experiment on over 40 healthy brain MRIs. In addition, we created percentile grids of healthy population brain structure volumes using the GAMLSS model to enhance atrophy tracing. This information enables neurologists to better compare patient trends and disease progression. The results of our research are converted to HL7 FHIR standard and published to the hospital VNA system, where specialists can evaluate results on interactive web-based panels. By automating MRI analysis and providing accurate and reliable tracking of MS progression, FLAAR is poised to become an indispensable tool for neurologists working with MS patients. Our innovative approach to MS monitoring using FLAAR provides a fast and reliable solution that can improve patient outcomes while saving valuable time for medical professionals. We believe that our research has the potential to revolutionize the way MS is monitored and treated.

Biography:
Rafal Pytel is a Senior Machine Learning Engineer at Reasonfield Lab. He received MSc in Computer Science from the Delft University of Technology under the supervision of Jan van Gemert from Computer Vision Lab, with expertise in Human Pose Estimation. His research focus lies in a variety of fields, from fine-grained Image Classification to Semantic Segmentation. In his professional career, he got experience in applying deep learning solutions in the agriculture and automotive industry. In his free time, Rafal enjoys windsurfing, skiing and travelling around the World.

Abstract:
Have you ever wondered what this famed Explainable AI (XAI) is? Did you also wonder about their usage in other domains than dogs vs cats? Or you heard something about GradCAM and wondered how you could use it in your project. In this presentation, using the brand-new, rapidly developing library FoXAI, you will learn about some algorithms (not only GradCam) used for Computer Vision (e.g. Object Detection or Human Pose Estimation), how to use them and what kind of adjustments they need for domains other than Image Classification. Additionally, we will explore some mistakes in explanations and some methods to quantify the quality of the produced explanation without the need for the tedious and expensive work of domain experts. To check the library before the talk, explore https://github.com/softwaremill/FoXAI.

Biography:
Dominik has over 8 years of hands-on experience in Machine Learning, Deep Learning, Data Exploration and Business Analysis projects primarily in the FMCG industry. He is a technical leader setting goals and preparing road maps for projects. He is also a PhD candidate at Warsaw University of Technology where he focuses on the study of neural networks for image processing. He tries to be a bridge between commercial and academic worlds. His main research interest is digital image processing in context of facilitating adoption of deep learning algorithms in business context where training data is scarce or non-existing.

Abstract:
The presentation covers recent advances in data augmentation via mixing images for computer vision. It builds understanding of the two main classes of augmentation (pixel-wise mixing & patch-wise mixing) and pass intuition on what types of problems those are most useful for as well as to what other modalities (e.g. text) those can be generalized to. All this is accompanied with visual aspect (what does the resulting augmented image look like?) and empirical evaluation of the methods. The presentation is based on a recent survey (https://link.springer.com/article/10.1007/s10462-022-10227-z).

Biography:
Michal Mikolajczak is a founder and Tech Lead at datarabbit.ai – data/machine learning focused software house, that helps organizations utilize their data and gain competitive advantage by designing, building, and shipping AI and data driven solutions for their businesses. Due to working there on a variety of projects from different industries, he possesses a broad range of diversified ML experience, with a focus on its productization. But his primary background is image processing – for a couple of years he was working in the medical imaging field, including being a CTO of a startup that was successfully acquired by a NASDAQ company. Privately a big fan of BI/any kind of data visualization systems that allow storytelling with data and Pratchett works enjoyer.

Abstract:
AI and ML technologies have been on the rise in recent years, developing and popularizing very rapidly. But in the recent past, it turned out, that there are multiple ML-specific challenges other than just models’ performance that are important when bringing ML systems to production. As data is the lifeblood of ML solutions, many of those challenges are associated with it – a related key area on which we will focus during this talk is data versioning. Keeping a track of the data utilized and versioning it in a reliable way is mandatory in some of the regulated industries (e.g. medical devices), but due to the value it brings, it starts to become a necessity in more mature/production projects – and incredibly helpful addition even in small/personal ones. During this talk, we will go through the key concepts of data versioning, what value (and how exactly) it brings, as well as important factors and challenges one might need to consider when dealing with its practical implementation.

Biography:
Maths and computers guy and a leader working in R&D. Graduated from Gdańsk University of Technology and University of Bristol, where he obtained PhD in Engineering Mathematics. In his day-to-day work he enjoys combining his analytical expertise with programming skills, especially as to Machine Learning. Through his career he managed R&D teams in many companies working in fields of technology, media, pharmaceutical and petrochemical. He is mostly interested in implementing math and science in business environment. Jakub originally has C/C++ background but currently works mostly in JVM and Python world. Finally, he is an educator in topics connecting engineering and science in the field of Data and AI/ML.

Abstract:
Recommender systems are by all means a quite mature topic in the AI/ML community. Nonetheless, they are of a paramount importance for business, bringing direct value for marketing, sales, customer satisfaction to name a few. In the talk I will review the modern approaches to recommender systems from both scientific and business world and point out which algorithms are useful for a particular business problem. Finally, I will present how we approach a less typical recommendation problem we have in Amazon Ring.

Biography:
I hold a BSc degree in Algorithmic Computer Science and am currently in the 2nd year of MSc studies in AI at Wrocław University of Science and Technology. At the university I lead an ML team in the research group SOLVRO. I work as an AI engineer at the med-tech company Hemolens Diagnostics. My main area of research and interest lies in applications of geometric deep learning, mainly neural networks dealing with 3D point clouds and meshes, in medical image processing. I am an author of two papers in this domain which were published at MLMI 2022 and GeoMedIA 2022 respectively - both conferences endorsed by MICCAI.

Abstract:
The development of automated medical image processing methods is an essential step in providing non-invasive accurate diagnostic tools. Medical images, such as Computed Tomography (CT) or Magnetic Resonance Imaging (MRI), take the form of 3D voxel grids, for analysis of which Convolutional Neural Networks (CNNs) are a default choice. In recent years, many novel techniques for less commonly used 3D data structures in deep learning, such as point clouds and meshes, have been proposed. These approaches already have proven to be a great complement to the CNNs in the field of medical image processing. In this talk, I would like to introduce the concept of neural networks used to process 3D point clouds via the blueprint of geometric deep learning and showcase the most common architectures PointNet and PointNet++. I will discuss the benefits of using point clouds instead of voxel grids with a focus on medical image processing. At last, I will showcase applications of point clouds in vascular medical images by discussing my publication at GeoMedIA 2022 titled Eigenvector grouping for vessel labeling and ongoing research in this area.

Biography:
I'm Jan Gruszczyński and I work as an Artificial Intelligence engineer at WTT where I specialize in unsupervised learning. After work I try to actively connect my secondary degree studies in AI with my passion for biology and medicine, by pursuing scientific projects connecting natural sciences with machine learning in collaboration with Poznan University of Medical Science.

Abstract:
For the past year, me and my colleagues have been working with a team of pathologists, trying to develop a practical nuclei instance segmentation tool for histopathological tissue slices. Join our presentation to learn more details such as: - Explore with us all of the unexpected technical but also formal difficulties that were encountered. - Learn about the usefulness of deep learning in the field of pathology but also about the feasibility of bringing such solutions to production. - Discover how we collaborated with a team of physicians to create one of its kind dataset, in a multi stage pipeline. - Study our trials to beat current state-of-the-art solutions in the field.

Biography:
Dawid Plaskowski is enrolled as a master’s student of Artificial Intelligence at Poznań University of Technology. Before pursuing his degree, he worked as a data scientist specializing in drone swarms and predictive maintenance. Currently, he's an R&D specialist in Natural Language Processing at Łukasiewicz - Poznań Institute of Technology where his expertise stems from his experience as the leader of the “Transformer” seminar group at GHOST. His research interests are: large language models, reinforcement learning and interpretability.

Abstract:
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Biography:
I'm a final year MSc Eng. student of Computing with AI specialization at Poznań University of Technology and part-time AI Intern at InterLAN. My research interests are broad and include machine learning applications, preference learning, metaheuristics, natural language processing and computer vision. The AI Tech project is a vital part of my studies and allowes me to engage in many extracurricular activities, e.g. participation in ECML 2022. During my work at InterLAN I focus on developing machine learning models which enhance our software solutions for logistics companies. In my free time I love to play the clarinet in a concert band.

Abstract:
In order to determine an efficient route for a shipping truck, InterLAN's optimization software needs to possess detailed information about the delivery. This includes an estimation of how much time will the vehicle spend at the loading/unloading facilities, or the so-called 'dwell time'. The task was approached as a classic regression problem with tabular input data acquired from InterLAN's SPEED application. As it was raw real-life freight data, it required thorough cleansing and feature engineering before use. The main challenge was obtaining ground truth values of dwell time as they were not recorded directly. It was solved by comparing the GPS log of the truck with known coordinates of shipment's origin and destination. Finally, a wide selection of machine learning algorithms was tested and promising results were achieved by the best alternative - a stacking ensemble of models based on random forest and gradient boosting approaches.

Biography:
Michał is a 3rd year Computer Science student at AGH UST. Member and lecturer of BIT student organization at AGH. Recent winner in AI category at BITEhack 2023 hackathon. Enthusiast of ML and its applications in medicine. Personally, fan of sailing, skiing and classical music.

Abstract:
Many real-world datasets in machine learning have mixed type variables: numerical, categorical nominal and ordinal, and even asymmetrical binary features. Most algorithms only support numerical variables, requiring one-hot encoding and introducing problems such as high sparsity and curse of dimensionality. In this talk, we present an alternative for both supervised and unsupervised learning problems: Gower metric. It is highly flexible, supporting all common variable types, and we additionally present original ideas for improving its performance. We present practical applications to classification and clustering of mixed variable type datasets. Also, we describe our journey from proof-of-concept Python implementation to a highly efficient version, further accelerated by Numba, providing tips on improving performance.

Biography:
Mateusz is a master's student at the Poznań University of Technology and currently works as an AI Engineer Intern at Wunderman Thompson Technology. He is mainly interested in Natural Language Processing, but he also deals with Computer Vision tasks on a daily basis and has the opportunity to explore a broad range of fields, from Stable Diffusion to Graph Neural Networks. Dawid Plaskowski is enrolled as a master’s student of Artifficial Intelligence at Poznań University of Technology. Before pursuing his degree, he worked as a data scientist specializing in drone swarms and predictive maintenance. Currently, he's an R&D specialist in Natural Language Processing at Łukasiewicz - Poznań Institute of Technology where his expertise stems from his experience as the leader of the “Transformer” seminar group at GHOST. His research interests are: large language models, reinforcement learning and interpretability.

Abstract:
While the popularity of question answering chatbots is on the rise, many problems in these types of systems still remain unsolved. In our work, we tackled with the knowledge hallucination problem, which stands for when the model confidently provides non-credible answers to questions, as well as the problem of outdated knowledge, stemming from the time cutoff on the model’s training dataset. We approach these using a selection of prompting techniques, using n-shot learning, information retrieval, and multi-stage answering strategies. We present our implementations of the methods in the form of a Python library and an Open System web application. Furthermore, we also show the results of experiments on various prompting techniques performed on question answering benchmarks OpenBookQA and HotpotQA. The authors of the work are: Dawid Plaskowski, Mateusz Politycki, Marcin Korcz and Alex Terentowicz