Caroline Uhler
at Great Learning
Henry L. & Grace Doherty Associate Professor, Electrical Engineering and Computer Science at MIT Professional Education
Schools
- Sloan School of Management
- MIT Professional Education
- Great Learning
Expertise
Links
Biography
Great Learning
Caroline Uhler joined the MIT faculty in 2015 as an assistant professor in EECS and IDSS. She holds an MSc in Mathematics, a BSc in Biology, and an MEd in High School Mathematics Education from the University of Zurich. She obtained her PhD in Statistics from UC Berkeley in 2011. Before joining MIT, she spent short postdoctoral positions at the Institute for Mathematics and its Applications at the University of Minnesota and at ETH Zurich, and 3 years as an assistant professor at IST Austria. Her research focuses on mathematical statistics and computational biology, in particular on graphical models, causal inference and algebraic statistics, and on applications to learning gene regulatory networks and the development of geometric models for the organization of chromosomes. She is an elected member of the International Statistical Institute and she is the recipient of a Sloan Research Fellowship, an NSF Career Award, a Sofja Kovalevskaja Award from the Humboldt Foundation, and a START Award from the Austrian Science Fund.
MIT Professional Education
Caroline Uhler joined the MIT faculty in 2015 as an assistant professor in EECS and IDSS. She holds an MSc in Mathematics, a BSc in Biology, and an MEd in High School Mathematics Education from the University of Zurich. She obtained her PhD in Statistics from UC Berkeley in 2011. Before joining MIT, she spent short postdoctoral positions at the Institute for Mathematics and its Applications at the University of Minnesota and at ETH Zurich, and 3 years as an assistant professor at IST Austria. Her research focuses on mathematical statistics and computational biology, in particular on graphical models, causal inference and algebraic statistics, and on applications to learning gene regulatory networks and the development of geometric models for the organization of chromosomes. She is an elected member of the International Statistical Institute and she is the recipient of a Sloan Research Fellowship, an NSF Career Award, a Sofja Kovalevskaja Award from the Humboldt Foundation, and a START Award from the Austrian Science Fund.
RESEARCH AREAS
Mathematical statistics (algebraic statistics, multivariate analysis, graphical models, causal inference, maximum likelihood estimation); Convex optimization; Applied algebraic geometry; Mathematical biology (chromosome packing models, inference of gene regulatory networks)
Videos
Seminar on Applied Geometry and Algebra (SIAM SAGA): Caroline Uhler
Caroline Uhler (MIT) -- Causal inference through permutation-based algorithms
MIA: Caroline Uhler, Geometry and Gene Regulation
Session 9: Caroline Uhler
LIDS@80: Session 3 Introduction — Caroline Uhler (MIT)
From causal inference to autoencoders, memorization & gene regulation - Caroline Uhler, MIT
Caroline Uhler: Causal inference in the light of drug repurposing for COVID-19
Caroline Uhler, Multi Domain Data Integration From Observations to Mechanistic Insights
Caroline Uhler: AI Cures Drug Discovery Conference
Caroline Uhler: Autoencoders & causality in the Light of Drug Repurposing for COVID19 | IACS Seminar
Stochastics and Statistics Seminar Fall 2020 - Caroline Uhler, MIT
Courses Taught
Applied Data Science Program
MIT Professional Education
Online
May 18
No Code AI and Machine Learning: Building Data Science Solutions
MIT Professional Education
Online
May 18
Professional Certificate Program in Machine Learning & Artificial Intelligence
MIT Professional Education
Cambridge, Massachusetts, United States
Jun 5
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