Topics
Large Language Models, Deep Learning, Natural Language ProcessingBiography
Vivek Natarajan is a Research Scientist at Google leading research at the intersection of large language models (LLMs) and biomedicine. In particular, Vivek is the lead researcher behind Med-PaLM and Med-PaLM 2, which were the first AI systems to obtain passing and expert level scores on US Medical License exam questions respectively. Med-PaLM was recently published in Nature and has been featured in The Scientific American, Wall Street Journal, The Economist, STAT News, CNBC, Forbes, New Scientist among others. More recently, Vivek also led the development of Med-PaLM M, the first demonstration of a generalist biomedical AI system.
Over the years, Vivek’s research has been published in well-regarded journals and conferences like Nature, Nature Medicine, Nature Biomedical Engineering, JMLR, CVPR, ICCV and NeurIPS. It also forms the basis for several regulated medical device products under clinical trials at Google, including the NHS AI award winning breast cancer detection system Mammo Reader and the skin condition classification system DermAssist.
Prior to Google, Vivek worked on multimodal assistant systems at Facebook AI Research and published award winning research, was granted multiple patents and deployed AI models to products at scale with hundreds of millions of users.
Talk
Topics
Mathematics of Artificial Neural Networks, Biological Neural Networks, Deep LearningBiography
Johannes Schmidt-Hieber was born in Freiburg im Breisgau, Germany, in 1984. He received the master’s degree from the University of Göttingen, Germany, in 2007, and the joint Ph.D. degree from the University of Göttingen and the University of Bern, Switzerland, in 2010.,His Ph.D. degree was followed by two one-year post-doctoral visits at Vrije Universiteit Amsterdam, The Netherlands, and ENSAE, Paris, France. From 2014 to 2018, he was an Assistant Professor at the University of Leiden. Since 2018, he has been a Full Professor at the University of Twente, The Netherlands. His research interests include mathematical statistics, including nonparametric Bayes and statistical theory for deep neural networks. He serves as an Associate Editor for the Annals of Statistics, Bernoulli, and Information and Inference.
The Prof. Schmidt-Hieber’s ERC CoG grant has been selected by the ERC as one of four highlighted projects.
Talk
Recently a lot of progress has been made regarding the theoretical understanding of machine learning methods. One of the very promising directions is the statistical approach, which interprets machine learning as a collection of statistical methods and builds on existing techniques in mathematical statistics to derive theoretical error bounds and to understand phenomena such as overparametrization. The talk surveys this field and describes future challenges.
Topics
fine-tuning LLMs, Gemini, RL with human feedbackBiography
Senior Staff Research Scientist at Google DeepMind Paris, Research Scientist at Inria and Lecturer at MVA École Normale Supérieure Paris-Saclay. Michal is primarily interested in designing algorithms that would require as little human supervision as possible. This means 1) reducing the “intelligence” that humans need to input into the system and 2) minimizing the data that humans need to spend inspecting, classifying, or “tuning” the algorithms. That is why he is working on methods and settings that are able to deal with minimal feedback, such as deep reinforcement learning, bandit algorithms, or self-supervised learning. Michal is actively working on representation learning and building worlds models. He is also working on deep (reinforcement) learning algorithm that have some theoretical underpinning. He has also worked on sequential algorithms with structured decisions where exploiting the structure leads to provably faster learning. He received his Ph.D. in 2011 from the University of Pittsburgh under the supervision of Miloš Hauskrecht and after was a postdoc of Rémi Munos before taking a permanent position at Inria in 2012.
https://www.esetscienceaward.sk/en/envoys/michal-valko
Current research topics: Gemini, fine-tuning LLMs, reinforcement learning with human feedback.
