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Coffee break, posters, and 1-on-1 discussions
Yangyi Lu · Daniel Chen · Hongseok Namkoong · Marie Charpignon · Maja Rudolph · Amanda Coston · Julius von Kügelgen · Niranjani Prasad · Paramveer Dhillon · Yunzong Xu · Yixin Wang · Alexander Markham · David Rohde · Rahul Singh · Zichen Zhang · Negar Hassanpour · Ankit Sharma · Ciarán Lee · Jean Pouget-Abadie · Jesse Krijthe · Divyat Mahajan · Nan Rosemary Ke · Peter Wirnsberger · Vira Semenova · Dmytro Mykhaylov · Dennis Shen · Kenta Takatsu · Liyang Sun · Jeremy Yang · Alexander Franks · Pak Kan Wong · Tauhid Zaman · Shira Mitchell · min kyoung kang · Qi Yang

Sat Dec 14 10:15 AM -- 11:00 AM (PST) @
in “Do the right thing”: machine learning and causal inference for improved decision making »

Author Information

Yangyi Lu (University of Michigan)
Daniel Chen (Toulouse School of Economics / The Institute for Advanced Study in Toulouse)

Daniel Chen received his BA and MS in applied math and economics from Harvard College (1999, summa cum laude) and his JD (2009) from Harvard Law School. He earned his PhD in economics from MIT (2004). He is a professor at the Institute for Advanced Study in Toulouse / Toulouse School of Economics, Senior Research Associate/Fellow at LWP at Harvard Law School, and Project Advisor at NYU Courant Institute of Mathematical Sciences Center for Data Science. He was previously Chair of Law and Economics and co-founder of the Center of Law and Economics at ETH Zurich, Assistant Professor of Law, Economics, and Public Policy at Duke University, and Kauffman Fellow at the University of Chicago Law School. He has lines of research on law and legitimacy, how market forces interact with moral beliefs, behavioral influences on judicial decision-making, and measuring the consequences of law via the random assignment of judges. He has papers published in Econometrica, Journal of Political Economy, Quarterly Journal of Economics, American Economic Review, RAND Journal of Economics, and several law reviews. He maintains an interest in methodology through high-dimensional statistical approaches for causal inference and through the development of oTree---an open source platform for online, lab, and field experiments.

Hongseok Namkoong (Stanford University)
Marie Charpignon (MIT)

Marie grew up in Burgundy, France and studied engineering in Paris. She moved to the US to study Computational and Mathematical Engineering at Stanford University for her master’s (’16). She is passionate about statistics for education and healthcare. After graduation, she joined Microsoft as a data scientist focusing on education technology. There, she built models to better understand online collaboration, studied the impact of technology usage at school and organized workshops for high school girls. She is currently a second-year graduate student in the MIT PhD program in Social & Engineering Systems. Her work on causal inference for drug repurposing using Electronic Health Records combines mathematical modelling, data analysis and policy.

Maja Rudolph (BCAI)
Amanda Coston (Carnegie Mellon University)
Julius von Kügelgen (University of Cambridge and Max Planck Institute for Intelligent Systems)

I am a PhD student with Bernhard Schölkopf at the Max Planck Institute for Intelligent Systems in Tübingen. As part of the Cambridge-Tübingen programme I am also co-supervised by Adrian Weller at the University of Cambridge, where I spent the first year of my PhD. My research interests lie at the intersection of causal inference and machine learning. Previously, I studied Mathematics (BSc+MSci) at Imperial College London and Artificial Intelligence (MSc) at UPC Barcelona in Spain and at TU Delft in the Netherlands. I am originally from the beautiful Hamburg in northern Germany.

Niranjani Prasad (Princeton University)
Paramveer Dhillon (University of Michigan)

Assistant Professor at the University of Michigan

Yunzong Xu (MIT)
Yixin Wang (Columbia University)
Alexander Markham (University of Vienna)

I'm a third year PhD student in the Neuroinformatics research group at the University of Vienna. My research focuses on causal discovery methods and especially their application to the brain sciences.

David Rohde (Criteo)
Rahul Singh (MIT)

Rahul Singh is a PhD candidate in Economics and Statistics at MIT. His research interests are causal inference and statistical learning theory.

Zichen Zhang (University of Alberta)
Negar Hassanpour (University of Alberta)
Ankit Sharma (Tata Consultancy Services)
Ciarán Lee (Babylon)
Jean Pouget-Abadie (Harvard University)
Jesse Krijthe (Radboud University Nijmegen)
Divyat Mahajan (Indian Institute of Technology Kanpur)

I am a Research Fellow at Microsoft Research Lab India, where I work with Amit Sharma on Machine Learning and Causal Inference. Prior to joining MSR, I completed my undergraduate double major program in Mathematics and Computer Science from the Indian Institute of Technology, Kanpur. Interests: Machine Learning | Causal Inference | Explainability, Generalization and Robustness in Deep Learning

Nan Rosemary Ke (MILA, University of Montreal)
Peter Wirnsberger (DeepMind)
Vira Semenova (MIT)
Dmytro Mykhaylov (Criteo)
Dennis Shen (Massachusetts Institute of Technology)
Kenta Takatsu (University of Massachusetts Amherst)
Liyang Sun (MIT)
Jeremy Yang (MIT)
Alexander Franks (University of California, Santa Barbara)
Pak Kan Wong (Hong Kong Applied Science and Technology Research Institute)

I obtain my Ph.D. degree in Computer Science and Engineering from The Chinese University of Hong Kong focused on Evolutionary Computation and Deep Learning. My current research focuses on designing state-of-the-art computer vision systems to solve challenging problems in FinTech.

Tauhid Zaman (Yale University)

Tauhid is an Associate Professor of Operations Management at the Yale School of Management. He received his BS, MEng, and PhD degrees in electrical engineering and computer science from MIT. His research focuses on solving operational problems involving social network data using probabilistic models, network algorithms, and modern statistical methods. Some of the topics he studies in the social networks space include combating online extremists and assessing the impact of bots. His broader interests cover data driven approaches to investing in startup companies, algorithmic sports betting, and biometric data. His work has been featured in the Wall Street Journal, Wired, Mashable, the LA Times, and Time Magazine.

Shira Mitchell (NYC Mayor's Office of Data Analytics)
min kyoung kang (Microsoft)
Qi Yang (MIT)

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