By clicking “Accept All Cookies,” you agree to the storing of cookies on your device to enhance site navigation and analyze site usage.

Skip to main content

2023 Conference on Digital Experimentation @ MIT (CODE@MIT)

We are pleased to announce our 10th annual MIT Conference on Digital Experimentation (CODE@MIT).

Please scroll down to access agenda information and speaker bios.

Interested in becoming a CODE@MIT sponsor? Contact David Verrill: dverrill@mit.edu

November 10 - 11, 2023

8:00 am - 7:00 pm EST

Samberg Conference Center at MIT

50 Memorial Drive

Cambridge, MA 02142

About CoDE@MIT

The newly emerging capability to rapidly deploy and iterate micro-level, in-vivo, randomized experiments in complex social and economic settings at population scale is, in our view, one of the most significant innovations in modern social science. As more and more social interactions, behaviors, decisions, opinions and transactions are digitized and mediated by online platforms, our ability to quickly answer nuanced causal questions about the role of social behavior in population-level outcomes such as health, voting, political mobilization, consumer demand, information sharing, product rating and opinion aggregation is becoming unprecedented. This new toolkit portends a sea-change in our scientific understanding of human behavior and dramatic improvements in social and business policy as a result. When appropriately theorized and rigorously applied, randomized experiments are the gold standard of causal inference and a cornerstone of effective policy. But the scale and complexity of these experiments also create scientific and statistical challenges for design and inference. Different disciplines are approaching causal inference in contrasting, complementary ways. The purpose of the Conference on Digital Experimentation at MIT (CoDE) is to bring together leading researchers conducting and analyzing large scale randomized experiments in digitally mediated social and economic environments, in various scientific disciplines including economics, computer science and sociology, in order to lay the foundation for ongoing relationships and to build a lasting multidisciplinary research community.

Thank you to our CODE@MIT 2023 Organizers:

  • Sinan Aral
  • Dean Eckles
  • David Holtz
  • John Horton
  • Alex (Sandy) Pentland

Thank you to our CODE@MIT 2023 Technical Program Committee:

  • Jenny Allen
  • Doug Guilbeault
  • Madhav Kumar
  • Hannah Li
  • Benjamin Manning
  • Alex Moehring
  • Yuan Yuan

Agenda: Friday, November 10 - Day 1

Please note all plenary sessions only will be recorded and made available on the MIT IDE YouTube Channel post-event.

  • Please join us for breakfast and networking pre-conference. Take the elevators to the 7th floor where you will check in and find your name badge and visit our sponsor tables in Salon East (7th floor).

Agenda: Saturday, November 11 - Day 2 (subject to change)

Please note all plenary sessions only will be recorded and made available on the MIT IDE YouTube Channel post-event. The Fireside Chat at 11:45am on Nov. 11 will be live-streamed and made available to the public free of charge. Please check back closer to the event for livestream information and follow us @mit_ide for updates.

  • Please join us for breakfast and networking pre-conference. Visit our sponsor tables in Salon East (7th floor).

Thank you to our 2023 CoDE@MIT Premiere Sponsor:

Thank you to our 2023 CoDE@MIT Event Sponsors:

Thank you to our 2023 CoDE@MIT Reception Sponsor:

 

 

 

Speaker Bios

  • Dr. Payel Das is a Research Staff Member and manager in the AI Science Department of IBM Thomas J Watson Research Center in Yorktown Heights, NY. She is also an adjunct associate professor at the department of Applied Physics and Applied Mathematics (APAM), Columbia University.  She received her B.Sc. degree from Presidency College, Kolkata and M.Sc. degree from Indian Institute of Technology, Chennai in India. In 2002, Das came to USA to pursue a Ph.D. degree in theoretical physical chemistry with Prof. Cecilia Clementi at Rice University, Houston. Her Ph.D. thesis work was at the intersection of statistical physics and machine learning. During her Ph.D., she developed coarse-grained energy function and novel sampling techniques to directly explore low-dimensional manifold of a very complex, dynamic problem, such as protein folding.

    She has been a visiting fellow at the Institute of Pure and Applied Mathematics (IPAM) at UCLA, where she worked on extracting efficetive dimensionality of high-dimensional systems. She has also been a visting student in Princeton University, where she worked with Prof. Yannis Kevrekidis on developing a multi-scale simulation methodology, based on data mining tools for inferring low-dimensional reduction coordinates. During her years in India, she was a summer intern at National Chemical Laboratory, Pune, with Prof. Sourav Pal, where she worked on theoretical investigation of hard-soft acid base relation. Her research interest is in understanding deep neural networks from a statistical mechanics perspective as well as in developing novel interpretable deep learning algorthms.

    In her current role, she technically leads and manages research projects related to artificial intelligence (AI) for creativity and  discovery, with inspirations from and applications in material science, chemistry, physics, biology, and neuroscience. Many of these projects lie at the intersection of data-driven and physics-based  modeling. A major focus is to develop novel deep generative models for heterogenous data, which is abundant in real world applications. Prior to her current role, Dr. Das was a post-doctoral researcher at the Computational Biology Center at IBM TJ Watson Research, where she worked on free energy perturbation theory to study disease mechanisms.

    Das has co-authored over 40 peer-reviewed publications and several patent disclosures, given dozens of invited talks at several university colloquiums, department seminars, top rated conferences, and workshops. She serves in the editorial advisory board member of the ACS Central Science journal. Das is the recipient of IBM Outstanding Technical Achievement Award (the highest technical award at IBM), two IBM Research Division Awards, one IBM Eminence and Excellence Award, and two IBM Invention Achievement Awards.

CALL FOR ABSTRACTS

(The call for abstract deadline has passed for 2023. Please still register to join us as an attendee.)

Access PDF of CODE@MIT Call for Abstracts here.

Participants will be selected based on submissions of 3-page extended abstracts. Please submit an extended abstract of no more than 3 pages to the ONLINE PORTAL by September 15, 2023 Please contact (digital@mit.edu) with questions. Abstracts will be evaluated as they are submitted and evaluation will continue until the program is filled.  Space is limited, so interested researchers should submit their work as soon as possible. Authors of accepted abstracts will be notified on September 29, 2023 and will be expected to submit a final version as a PDF not to exceed 5 pages, including references and figures, by November 1, 2023.  Accepted abstracts will be distributed as informal working notes. Members of the press may attend the event, so please take this into account when choosing the work you submit.

KEY DATES

Conference: Nov. 10-11, 2023

Abstract Submission Deadline: Sept. 15, 2023

Notification to Authors: Sept. 29, 2023

Final Abstract Deadline (Accepted Authors Only): Nov. 1, 2023

Early Registration Deadline: Oct. 2, 2022

General Registration Deadline:  Nov.9, 2023