Learn about this year’s Keynote Speakers, Tutorial Speakers and Panelists:

Keynote Speakers

Moritz Hardt
MORITZ HARDT

Max Planck Institute for Intelligent Systems

Hardt is a director at the Max Planck Institute for Intelligent Systems, Tübingen. Previously, he was Associate Professor for Electrical Engineering and Computer Sciences at the University of California, Berkeley. His research contributes to the scientific foundations of machine learning and algorithmic decision making with a focus on social questions. He co-authored Fairness and Machine Learning: Limitations and Opportunities (MIT Press) and Patterns, Predictions, and Actions: Foundations of Machine Learning (Princeton University Press).

Helen Margetts
HELEN MARGETTS

University of Oxford

Helen Margetts is Professor of Society and the Internet and Professorial Fellow at Mansfield College. She is a political scientist specialising in the relationship between digital technology and government, politics and public policy. 

Since 2018, Helen has been Director of the Public Policy Programme at The Alan Turing Insitute, the UK’s national institute for data science and artificial intelligence. The programme works with policy-makers to research and develop ways of using data science and AI to improve policy-making and service provision, foster government innovation and establish an ethical framework for the use of data science in government. The programme comprises over 90 research projects involving 55 researchers across 20 universities, working with over 100 public sector agencies at national, regional, local and international levels of governance.

PATRICK PEREZ

Kyutai

Patrick Pérez is CEO at Kyutai, a non-profit open-science AI lab, based in Paris. Prior to this, Patrick was at Valeo as VP of AI and Scientific Director of valeo.ai (2018-2023), and with Technicolor (2009-2018), Inria (1993-2000, 2004-2009) and Microsoft Research Cambridge (2000-2004) as research scientist. His research interests lie in reliable multimodal AI for the benefit of all.

Prasanna (Sonny) Tambe
PRASANNA (SONNY) TAMBE

Wharton, University of Pennsylvania

Prasanna (Sonny) Tambe is an Associate Professor of Operations, Information and Decisions at the Wharton School at the University of Pennsylvania. His research focuses on the economics of technology and labor. Recent research projects focus on 1) understanding how firms compete for software developers, 2) how software engineers choose technologies in which to specialize, and 3) how AI is transforming HR management.

Much of this research has uses Internet-scale data sources to measure labor market activity at novel levels of granularity. His published papers have analyzed data from online job sites and other labor market intermediaries that generate databases of fine-grained information on workers’ skills and career paths or on employers’ job requirements. He is a co-author of “The Talent Equation: Big Data Lessons for Navigating the Skills Gap and Building a Competitive Workforce,” published by McGraw Hill in 2013.

Tutorial Speakers

HARIS KRIJESTORAC
HARIS KRIJESTORAC

HEC Paris

With the rise of voice-enabled digital interfaces, including Siri and Alexa, firms have growing interest in understanding how characteristics of a voice may affect business outcomes, such as engagement or sales. Drawing from the physics of sound, we will discuss the key acoustic properties of voices – including pitch, volume, harmonics, and tempo – with demonstrations on how to extract these features from voice clips. We will illustrate how to holistically analyze these acoustic features in the context of voice, through an example of research on the role of voice in consumer decision-making. Session attendees will have an opportunity to conduct similar analysis using a voice dataset, from which they will extract business insights.

Poonacha Medappa
POONACHA MEDAPPA

Tilburg University

The capabilities of machines are advancing rapidly, with examples such as ChatGPT’s human-like reasoning and creativity, Copilot’s capacity to become our peer-programmers, Facebook’s facial recognition technology, and Google’s new AI and ML frameworks like Tensorflow. With these advancements, researchers now have a large toolset of approaches to perform data-driven research and provide insights that were previously infeasible. But, as researchers, how will these advancements change our research identity and the nature of our research? For instance, face recognition algorithms do not follow predetermined rules for detecting certain pixel combinations that make up a face, based on human understanding. Instead, these algorithms utilize a vast dataset of labeled photos to estimate a function f (x), which predicts the presence y of a face based on pixels x. This approach has similarities to econometrics and raises important questions, which we will address in this workshop. Specifically, we will answer three questions – (a) Are these algorithms simply utilizing conventional methods to process extensive and innovative datasets? (b) If these are new empirical tools, how do they relate to existing knowledge? and, (c) How can we as researchers incorporate these methods into our own research?

 
The first half of the workshop will be an interactive lecture, where we will understand the background and implications of ML and AI techniques for economic research. In the second half of this workshop, we will have a hands-on exercise. Here, we will develop a data-driven research question using these new and advanced computational techniques. The idea here is to see the amazing power that we now have in conceptualizing new constructs and finding interesting insights.
Pablo Baquero
PABLO BAQUERO

HEC Paris

Pablo Marcello Baquero is Assistant Professor at HEC Paris and a Fellow at the Hi! Paris Center on Data Analytics and Artificial Intelligence for Science, Business and Society. He is a member of the Smart Law Hub at HEC Paris, is involved in different interdisciplinary academic communities focused on law and technology and collaborates closely with scholars across different disciplines, in projects at the intersection between law and AI.

He teaches courses on Digital Assets and Blockchain, Tech Law, Digital Innovation Law and Contract Law.

His scholarly interests are in the fields of law and technology (particularly law and artificial intelligence and smart contracts), private law and international business transactions. His perspective is comparative and interdisciplinary, considering the intersection of law with computer science, economics, business and sociology.

His research examines how legal institutions and technologies can support practices of innovation in a socially and economically inclusive way, contributing to disseminate to most firms the opportunities to produce in the frontiers of innovation and extending the benefits of advanced technologies to society at large in a lawful and ethical way.

Johan Hombert
JOHAN HOMBERT

HEC Paris

This tutorial starts with a brief introduction of fintech lending and the use of credit scoring in credit markets. The main part of the tutorial is an interactive game in which participants play the role of a fintech lender. Context: Banks increasingly use alternative data and machine learning to screen customers and set interest rates. For example, a lender using digital footprints to predict loan default will have a competitive edge over traditional lenders. However, there are important pitfalls to avoid when using alternative data and machine learning to score consumers, such as the winner’s curse and discrimination. This tutorial and its interactive game provide an introduction to these issues.
PIETRO GORI

Télécom Paris

Pietro Gori is an Assistant Professor (Maître de Conférences) in Artificial Intelligence and Medical Imaging at Télécom Paris, part of the IMAGES group in the LTCI lab. Additionally, he collaborates with the Ima-Brain research team at the Institute of Psychiatry and Neuroscience of Paris (IPNP) and NeuroSpin at the CEA.

His academic background includes working with notable researchers at Inria in the ARAMIS Lab in Paris and at Neurospin (CEA). He holds an MSc in Mathematical Modelling and Computation from the DTU in Copenhagen and an MSc in Biomedical Engineering from the University of Padova.

He contributed to developing the deformetrica software suite for statistical shape analysis and the Clinica platform for clinical neuroimaging studies. His research primarily focuses on machine learning, AI, medical imaging, and computational anatomy.

ALAIN RAKOTOMAMONJY

INSA Rouen – Criteo AI Lab

Alain Rakotomamonjy was a professor at the University of Rouen and is now affiliated with the Criteo AI Lab. He is a researcher in the field of machine learning, with expertise in areas such as optimal transport and brain-computer interfaces. He has recently contributed to the development of effective algorithms for domain adaptation, generative models and private sliced-Wasserstein distances.

GAEL VAROQUAUX

Inria

Gaël Varoquaux is a research director working on data science at Inria (French computer science national research) where he leads the Soda team.
Varoquaux’s research covers fundamentals of artificial intelligence, statistical learning, natural language processing, causal inference, as well as applications to health, with a current focus on public health and epidemiology. He also creates technology: he co-funded scikit-learn, one of the reference machine-learning toolboxes, and helped build various central tools for data analysis in Python.
Varoquaux has worked at UC Berkeley, McGill, and university of Florence. He did a PhD in quantum physics supervised by Alain Aspect and is a graduate from Ecole Normale Superieure, Paris. 

MARTIN TAKAC

Mohamed bin Zayed University of Artificial Intelligence

Martin Takac is an Associate Professor at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), UAE. Before joining MBZUAI, he was an Associate Professor in the Department of Industrial and Systems Engineering at Lehigh University, where he has been employed since 2014. He received his B.S. (2008) and M.S. (2010) degrees in Mathematics from Comenius University, Slovakia, and Ph.D. (2014) degree in Mathematics from the University of Edinburgh, United Kingdom. He received several awards during this period, including the Best Ph.D. Dissertation Award by the OR Society (2014), Leslie Fox Prize (2nd Prize; 2013) by the Institute for Mathematics and its Applications, and INFORMS Computing Society Best Student Paper Award (runner up; 2012). His current research interests include designing and analyzing algorithms for machine learning, AI for science, understanding protein-DNA interactions, and using ML for energy. Martin received funding from various U.S. National Science Foundation programs (including through a TRIPODS Institute grant awarded to him and his collaborators at Lehigh, Northwestern, and Boston University) and recently was awarded a grant with the Weizmann Institute of Science. He served as an Associate Editor for Mathematical Programming Computation, Journal of Optimization Theory and Applications, and Optimization Methods and Software, and as area chair for ICLR and AISTATS. Martin currently serves as an area chair at ICML and NeurIPS.

VALENTIN DE BORTOLI

Valentin De Bortoli is a chargé de recherche (equiv. to assistant professor) on leave from CNRS and currently a research scientist at Google DeepMind. He was previously a postdoctoral researcher at Oxford University and received his PhD from ENS Paris-Saclay. He has published papers in ICASSP, COLT, UAI, ICML, ICLR, NeurIPS, TMLR and JMLR. His research lies at the intersection between applied probability, statistics and machine learning with a recent focus on the interplay between stochastic control, optimal transport and diffusion models.