March 2020
Physics Guided Neural Networks for the Design and Understanding of Materials
Tian Xie (MIT)
online
Abstract: Climate change demands faster material innovations in multiple domains to reduce the carbon emissions of various industrial processes, but it currently takes 10-20 years to develop a single material with conventional human-driven approaches due to the large amounts of trail and errors needed. Machine learning approaches enable the direct learning of structure-property relations of materials from data, which have the potential to accelerate the search of materials and understand new scientific knowledge. However, one central challenge in developing…
Find out more »April 2020
Uncovering atomistic mechanisms of crystallization using Machine Learning
Rodrigo Freitas (Stanford University)
online
Abstract: Solid-liquid interfaces have notoriously haphazard atomic environments. While essentially amorphous, the liquid has short-range order and heterogeneous dynamics. The crystal, albeit ordered, contains a plethora of defects ranging from adatoms to dislocation-created spiral steps. All these elements are of paramount importance in the crystal growth process, which makes the crystallization kinetics challenging to describe concisely in a single framework. In this seminar I will introduce a novel data-driven approach to systematically detect, encode, and classify all atomic-scale mechanisms…
Find out more »[POSTPONED] Guido Imbens – The Applied Econometrics Professor and Professor of Economics, Graduate School of Business, Stanford University
E18-304
IDSS will host Prof. Guido Imbens as part of the Distinguished Speaker Seminar series. Prof. Guido Imbens’ primary field of interest is Econometrics. Research topics in which he is interested include: causality, program evaluation, identification, Bayesian methods, semi-parametric methods, instrumental variables.
Find out more »[POSTPONED] The Blessings of Multiple Causes
David Blei (Columbia University)
E18-304
*Please note: this event has been POSTPONED until Fall 2020* See MIT’s COVID-19 policies for more details. Title: The Blessings of Multiple Causes Abstract: Causal inference from observational data is a vital problem, but it comes with strong assumptions. Most methods require that we observe all confounders, variables that affect both the causal variables and the outcome variables. But whether we have observed all confounders is a famously untestable assumption. We describe the deconfounder, a way to do causal…
Find out more »May 2020
Data Science and Big Data Analytics: Making Data-Driven Decisions
online
Developed by 11 MIT faculty members at IDSS, this seven-week course is specially designed for data scientists, business analysts, engineers and technical managers looking to learn strategies to harness data. Offered by MIT xPRO. Course begins May 4, 2020.
Find out more »[POSTPONED] Michael Kearns – Professor and National Center Chair, Department of Computer and Information Science, University of Pennsylvania
E18-304
*Please note: this event has been POSTPONED until Fall 2020* See MIT's COVID-19 policies for more details.
Find out more »October 2020
SDSCon 2020
MIT Media Lab (E14-674)
SDSCon 2020 is the fourth annual celebration of the statistics and data science community at MIT and beyond, organized by MIT’s Statistics and Data Science Center (SDSC). Please see the conference website for registration information.
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