Abstract: 

Contract theory is the study of economic incentives when parties transact in the presenceof private information.  We augment classical contract theory to incorporate a role forlearning from data, where the overall goal of the adaptive mechanism is to obtain desiredstatistical behavior.  We consider applications of this framework to problems in federatedlearning, the delegation of data collection, and principal-agent regulatory mechanisms.

Short biography:

Michael I. Jordan is the Pehong Chen Distinguished Professor at the University of California, Berkeley.  His research interests bridge the computational, statistical, cognitive, biological and social sciences.  Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering, a member of the American Academy of Arts and Sciences, and a Foreign Member of the Royal Society.  He is a Fellow of the American Association for the Advancement of Science.  He was the inaugural winner of the World Laureates Association (WLA) Prize in 2022.  He was a Plenary Lecturer at the International Congress of Mathematicians in 2018.  He has received the Ulf Grenander Prize from the American Mathematical Society, the IEEE John von Neumann Medal, the IJCAI Research Excellence Award, the David E. Rumelhart Prize, and the ACM/AAAI Allen Newell Award.  

Abstract:

A purported “AI Singularity” has been much in the public eye recently,

especially since the release of ChatGPT last November, spawning social media “AI Breakthrough” threads promoting Large Language Model (LLM) achievements.  Alongside this, mass media and national political attention focused on “AI Doom” hawked by social media influencers, with twitter personalities invited to tell congresspersons about the coming ``End Times’’.   

In my opinion, “AI Singularity” is the wrong narrative; it drains time and energy with pointless speculation. We do not yet have general intelligence, we have not yet crossed the AI singularity, and the remarkable public reactions signal something else entirely.

Something fundamental to science really has changed in the last ten years. In certain fields which practice Data Science according to three principles I will describe, progress is simply dramatically more rapid than in those fields that don’t yet make use of it.

Researchers in the adhering fields are living through a period of very profound transformation, as they make a transition to frictionless reproducibility. This transition markedly changes the rate of spread of ideas and practices, and marks a kind of singularity, because it affects mindsets and paradigms and erases memories of much that came before.  Many phenomena driven by this transition are misidentified as signs of an AI singularity. Data Scientists should understand what's really happening and their driving role in these developments.

Short biography: 

David Donoho is Anne T. and Robert M. Bass Professor of Humanities and Sciences and Professor of Statistics at Stanford University. He has studied the exploitation of sparse signals in signal recovery, including for denoising, superresolution, and solution of underdetermined equations. He coined the notion of compressed sensing which has impacted many scientific and technical fields, including magnetic resonance imaging in medicine, where it has been implemented in FDA-approved medical imaging protocols and is already used in millions of actual patient MRIs. In recent years he has been studying large-scale covariance matrix estimation, large-scale matrix denoising, detection of rare and weak signals among many pure noise non-signals, compressed sensing and related scientific imaging problems, and most recently, empirical deep learning.

Professor Donoho is an elected member of the National Academy of Sciences, the American Academy of Arts and Sciences, and the American Philosophical Society, and also an elected foreign associate of the French Académie des Sciences. He was a MacArthur Fellow, and was the recipient of: 1994 COPSS Presidents' Award, 2001 John von Neumann Prize of SIAM, 2010 Norbert Wiener Prize in Applied Mathematics, 2013 Shaw Prize for Mathematics, 2018 Gauss Prize from IMU, and 2022 IEEE Jack S. Kilby Signal Processing Medal.

Abstract:

Massive data collection holds the promise of a better understanding of complex phenomena and ultimately, of better decisions. Representation learning has become a key driver of deep learning applications, since it allows learning latent spaces that capture important properties of the data without requiring any supervised annotations. While representation learning has been hugely successful in predictive tasks, it can fail miserably in causal tasks including predicting the effect of an intervention. This calls for a marriage between representation learning and causal inference. An exciting opportunity in this regard stems from the growing availability of interventional data (in medicine, advertisement, education, etc.). However, these datasets are still miniscule compared to the action spaces of interest in these applications (e.g. interventions can take on continuous values like the dose of a drug or can be combinatorial as in combinatorial drug therapies). In this talk, we will present initial ideas towards building a statistical and computational framework for causal representation learning and its application towards optimal intervention design in the context of the biomedical sciences.

Short biography:

Caroline Uhler is a full professor in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society at MIT. In addition, she is a core institute member at the Broad, where she co-directs the Eric and Wendy Schmidt Center. She holds an MSc in mathematics, a BSc in biology, and an MEd all from the University of Zurich. She obtained her PhD in statistics from UC Berkeley in 2011 and then spent three years as an assistant professor at IST Austria before joining MIT in 2015. She is a SIAM Fellow, a Simons Investigator, a Sloan Research Fellow, and an elected member of the International Statistical Institute. In addition, she received an NIH New Innovator Award, an NSF Career Award, a Sofja Kovalevskaja Award from the Humboldt Foundation, and a START Award from the Austrian Science Foundation. Her research lies at the intersection of machine learning, statistics, and genomics, with a particular focus on causal inference, representation learning, and gene regulation.

Abstract:

Large networks that change dynamically over time are ubiquitous in various areas such as social networks, and epidemiology. These networks are often modeled by random dynamics which, despite being relatively simple, give a quite accurate macroscopic description of real networks. "Network archaeology"  is an area of combinatorial statistics in which one studies statistical problems of inferring the past properties of such growing networks. In this talk we discuss some simple network models and review recent results on revealing the past of the networks.

Short biography:

Gabor Lugosi received his Ph.D. from the Hungarian Academy of Sciences in 1991. Since 1996, he has been at the Department of Economics, Pompeu Fabra University, Barcelona where in 2006 he became an ICREA research professor. His research interests include the theory of machine learning, nonparametric statistics, inequalities in probability, random structures, and information theory.