Member role: Industrial Fellow
Experienced Research Scientist with a demonstrated history of working on computer vision and image processing domains at well-known research institutes. Skilled in Computer Vision, Image Processing, Video Processing, Signal Processing, Feature Extraction, Deep Learning, Machine Learning, Biometrics, C++, Pattern Recognition, Python Programming, MATLAB and LaTeX. Strong research professional with a Doctor of Philosophy (Ph.D.) focused in facial biometrics (2D and 3D face recognition, face spoofing/anti-spoofing, disguise variations) at Multimedia Communications Department from Telecom ParisTech. Current research on multi-view action recognition using deep convolutional descriptors. Numerous well cited journals and conference papers in top journals and conferences.
Vittorio Ferrari is a Principal Research Scientist at Meta, working on realistic avatars for next-gen communication on wearable devices. In 2023-2025 he was the Director of Science at Synthesia, where he led R&D groups developing cutting-edge generative AI technology. Previously he built and led multiple research groups on computer vision and machine learning at Google (Principal Scientist), the University of Edinburgh (Full Professor), and ETH Zurich (Assistant Professor). He has co-authored over 160 scientific papers and won the best paper award at the European Conference in Computer Vision in 2012 for his work on large-scale segmentation. He received the prestigious ERC Starting Grant, also in 2012. He led the creation of Open Images, one of the most widely adopted computer vision datasets worldwide. While at Google his groups contributed technology to several major products (with launches e.g. on the Pixel phone, Google Photos, Google Lens). He was a Program Chair for ECCV 2018 and a General Chair for ECCV 2020. He is an Associate Editor of IEEE Pattern Analysis and Machine Intelligence, and formerly of the International Journal of Computer Vision. His recent research interests are in Generative Video, 3D Deep Learning, and Vision+Language models.
I am a research scientist and team lead in AI and robotics at Google DeepMind in London where I work on creating artificial intelligence for the physical world.
Principal Scientist at Merck Healthcare, part of Merck KGaA, Darmstadt.
Dr. sc ETH in Pharmaceutical Sciences, ETH Zurich.
Bachelor & Master in Chemistry at ETH Zurich, Switzerland.
Interested in AI for Drug Discovery and Development.
Dr. Kuldeep Singh is the CEO and Co-founder of Eka Labs AI, a deeptech startup building hyper-specialized Small Language Models (SLMs) for domain-specific intelligence.
Previously, he served as Director of AI Technology at Cerence AI, where he led the development and scaling of Generative AI and LLM-powered products from 0→1, enabling millions of user interactions across leading automotive OEMs, including Volkswagen, Audi, Toyota, Geely, JLR, and Ford, as well as Tier-1 suppliers. He also contributed to the development of Cerence’s automotive-grade LLM (CaLLM) and its deployment into premium infotainment systems, shaping next-generation voice-driven mobility experiences.
Dr. Singh holds a Ph.D. from the University of Bonn as a Marie Curie Fellow under Prof. Dr. Sören Auer. He later led Conversational AI research at Fraunhofer IAIS, Sankt Augustin in collaboration with Prof. Dr. Jens Lehmann. He has authored 50+ publications in top AI venues such as ICLR, AAAI, ACL, and The Web Conference, and holds two U.S. patents. His research focuses on knowledge distillation and Reinforcement learning for SLMs and graph representation learning, and he actively contributes in program committee of leading AI conferences.
Patrick van der Smagt is Head of AI & Engineering at Foundation Future Industries. He has led machine learning and robotics research across academia and industry, including directing AI research at Volkswagen Group and as professor for ML and robotics at TUM. His research spans probabilistic deep learning and inference for dynamical systems, optimal control, and robotics. He is affiliated with the LMU Graduate School of Systemic Neurosciences and serves as a professor at ELTE University (Budapest). He is an ELLIS and ELIAS fellow, and member of the Bavarian AI Council.
Dominik Janzing is a Principal Research Scientist at Amazon Research Tübingen, Germany, where he works on causal inference for monitoring AWS cloud services.
Education / degrees:
– 1995: “Diplom” in Physics, Universität Tübingen
– 1998: Dr. in Mathematics, Universität Tübingen
– 2006: “Habilitation” (teaching permission) in Computer Science, Universität Karlsruhe (now KIT)
Research topics:
– 1995 – 2006: quantum information and thermodynamics
– Since 2003: causal inference – foundations and applications
His contributions range from the formalization of the independence of mechanisms principle to the formalization of root cause analysis, quantification of causal influence, and evaluation of causal discovery methods without ground truth. The textbook Elements of Causal Inference received the ”Causality in Statistics Education Award” from the American Statistical Association.
Selected publications:
1) Janzing, Wocjan, Zeier, Geiss, Beth: Thermodynamic cost of reliability and low temperatures, tightening Landauer’s principle and the second law, Int. J. Physics, 2000.
2) Janzing and Schölkopf: Causal inference using the algorithmic Markov condition, IEEE TIT 2010.
3) Janzing, Grosse-Wentrupp, Balduzzi, Schölkopf: Quantifying causal influences, Annals of Statistics 2013
4) Peters, Janzing, Schölkopf: Elements of Causal Inference, MIT Press 2017.
5) Budhathoki, Minorics, Blöbaum, Janzing: causal structure-based root cause analysis of outliers, ICML 2022.
6) Faller, Vankadara, Mastakouri, Locatello, Janzing: Self-compatibility: evaluating causal discovery without ground truth, AISTATS 2024.
Jean-Philippe Vert is the co-founder and CEO of Bioptimus, an AI-first tech company pioneering the use of foundation models to transform our understanding of biology and accelerate biomedical innovation. He is also a professor (currently on leave) at PSL University. A recognized leader in AI for biology, Jean-Philippe brings over 25 years of experience at the cutting edge of machine learning and life sciences. Before founding Bioptimus, he served as Chief R&D Officer at Owkin and was a Research Lead at Google Brain. Prior to transitioning to industry, he held academic positions at ENS Paris, the Curie Institute, and Mines ParisTech, and was a Fullbright and Miller visiting professor at the University of California, Berkeley. He began his research career at Kyoto University. Jean-Philippe graduated from École Polytechnique and the Corps des Mines, and earned a PhD in mathematics from Paris University. He has authored over 190 scientific publications and is internationally recognized for his contributions to statistical learning, artificial intelligence, biomedical data modeling, and translational research. He is an ELLIS Fellow, a member of the National Academy of Technologies of France, and has received several prestigious honors, including the CNRS Bronze Medal, a Grand Prize from the National Academy of Sciences of France, and a European Research Council (ERC) grant.
Letitia Parcalabescu has an academic background in Physics and Computer Science, and holds a PhD in Computational Linguistics. Her doctoral research focused on benchmarking and interpreting the internal processes and explanations of multimodal AI models. Currently, she is an AI researcher at Aleph Alpha Research, working on training interpretable reasoning models by design, as well as curating and synthesizing data for large-scale pre-training. She created the “AI Coffee Break with Letitia” YouTube channel where she breaks down complex AI concepts. Topics range from newest research results in natural language processing, computer vision, to the broader societal impact of AI.
Ola Engkvist, PhD, is Executive Director and Head of Molecular AI within Discovery Sciences at AstraZeneca R&D, where he leads the development and application of machine learning and artificial intelligence to accelerate drug design. He has published over 180 peer-reviewed scientific articles and is an ELLIS fellow at the European Laboratory for Learning and Intelligent Systems and a 2025 Clarivate Highly Cited Researcher. He holds an adjunct professorship in machine learning and AI for drug design at Chalmers University of Technology, serves as a Trustee of the Cambridge Crystallographic Data Centre, and is recognized for his work in pharmaceutical innovation.