GHOST Day: Applied Machine Learning Conference

About the Conference

'25 edition

Spanning two exhilarating days and featuring an impressive lineup of keynote presentations, over 30 stimulating talks, and a diverse and dynamic audience numbering in the hundreds, the sixth installment of GHOST Day: AMLC once again redefined the machine learning community experience. Building upon the resounding success of previous events, we amplified our efforts, forging partnerships with numerous businesses and delivering an even richer tapestry of insights from globally renowned speakers.

Across the two conference days, we had the privilege of hosting trailblazing researchers representing industry titans such as Mistral AI, DeepL, OpenAI, Meta, NASA, Google DeepMind, Anthropic, along with experts from prestigious academic institutions like University of Cambridge, Stanford University, Technical University of Munich, and Poznan University of Technology.

The thematic sessions on offer spanned a wide spectrum of subjects, ranging from LLMs and Code Generation to Reinforcement Learning and AI Safety. In keeping with tradition, we provided an invaluable platform for emerging researchers during the Students' sessions. In the brief interludes between sessions, our engaged audience actively participated in an enriching panel discussion on Research in Academia vs. Industry.

Download the booklet

Our Speakers

who was speaking

Max Welling

CTO | CuspAI & Professor | University of Amsterdam

Oscillators and Traveling Waves in Machine Learning

Traveling waves of neural activity are increasingly observed in biological brains. In this talk I will discuss a number of models that support traveling waves and explore how they can be used to build better ML models. In particular, the AKOrN model is able to synchronise collections of neurons and bind them together to represent larger, coarse ...

Prof. Dr. Max Welling is a full professor and research chair in machine learning at the University of Amsterdam and a Merkin distinguished visiting professor at Caltech. He is co-founder and CTO of the startup CuspAI in Materials Design. He is a fellow at the Canadian Institute for Advanced Research (CIFAR) and the European Lab for Learning and ...

Andrew Fitzgibbon

Engineering Fellow | Graphcore

AI Hardware and Real-world AI

AI is fast becoming a significant consumer of the world’s computational power, so it is crucial to use that power wisely and efficiently. Our approaches to doing so must span all levels of the research stack: from fundamental theoretical understanding of the loss surfaces and regularization properties of machine learning models, to efficient ...

Andrew Fitzgibbon is an Engineering Fellow at Graphcore, working on the future of computing hardware and programming for AI and numerical computing. He is best known for his work on computer vision: he was a core contributor to the Emmy-award-winning 3D camera tracker “boujou”, having co-founded the company “2d3”, with Andrew Zisserman, ...

Maya Bechler-Speicher

Research Scientist | Meta

Graph Neural Networks Use Graphs When They Shouldn’t

Learning over graph-structured data is central to a wide range of domains, including social networks, biological systems, and medicine. Graph Neural Networks (GNNs) have emerged as the leading methodology for such tasks, predicated on the assumption that the input graph encodes information relevant to the predictive objective. Nevertheless, there ...

Maya is a Research Scientist at Meta and a last-year Ph.D. candidate in Computer Science at Tel Aviv University, where she is also a Lecturer, teaching "Machine Learning with Graphs." She is a domain expert in Graph Machine Learning, with research spanning both theoretical and applied aspects, including its integration with LLMs and ...

Baptiste Roziere

Researcher and Code Generation Team Leader | Mistral AI

LLMs for code

This talk explores how Large Language Models (LLMs) can revolutionize software development by enhancing programmer productivity. We will discuss key applications such as in-IDE code completion and code-assistant chatbots, demonstrating how LLMs can automate tasks and allow developers to focus on high-level design.

Baptiste is leading the code generation team at Mistral AI. Previously, he was a research scientist in the codegen team at Meta AI in Paris working. He contributed to Llama and led Code Llama. During his PhD at Meta AI and Université Paris Dauphine, Baptiste conducted research on unsupervised translation of programming languages and model ...

Fatima Sanchez-Cabo

IP Computational Systems Biomedicine Lab | CNIC

AI enhances cardiovascular research

AI is transforming research information changing our life and the way we work in science, particularly in cardiovascular research. In my talk, I will be presenting several AI-based methods that might help improving the diagnostic, treatment and study of cardiovascular research. In particular, I will present EN-PESA, a Machine Learning algorithm ...

Dr. Fátima Sánchez Cabo graduated in Mathematics from the Complutense University of Madrid in 2000. From there she moved to the University of Manchester where she obtained a grant from the BBSRC to develop her doctoral work on statistical analysis and mathematical data modeling of microarrays. In 2005 she joined the Institute of Genomics and ...

Babak Ehteshami Bejnordi

Research Scientist | Qualcomm AI Research

Efficient Deployment of Large Language Models on Edge Devices

Deploying large language models (LLMs) on edge devices has emerged as a compelling solution to achieve reduced costs, lower latency, enhanced privacy, and personalized user experiences. However, on-device deployment introduces unique challenges due to the resource constraints of edge devices, which differ significantly from high-throughput ...

Babak Ehteshami Bejnordi is a Research Scientist at Qualcomm AI Research in the Netherlands, leading a research group focusing on conditional computation for efficient deep learning. His primary research focus lies in the realm of efficient Deep Learning for Large Language Models (LLMs) and Computer Vision. His recent research works have been in ...

Stefan Haufe

Head of the UNIML group & Professor | Technische Universität Berlin

How can explainable AI provide quality control for ML?

There is a desire for humans to ""understand"" decisions of machine learning (ML) models. Explainable AI (XAI) seemingly addresses this need by providing information beyond a model's output. XAI methods have been proposed to help identifying and correcting failure modes in models and data, to facilitate scientific discovery by identifying ...

Stefan Haufe is a joint Associate Professor of Uncertainty, Inverse Modeling, and Machine Learning at Technische Universität Berlin and Physikalisch-Technische Bundesanstalt Berlin. He also serves as a Group Leader at Charité - Universitätsmedizin Berlin, where his research focuses on developing advanced computational methods. His group ...