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Research & Seminars

The area of Quantitative Methods is dedicated to excellent research and outstanding teaching in the Krannert School’s undergraduate and graduate programs. The students in our programs have opportunities to participate in inter-collegiate case competitions, experiential learning initiatives, and student-led club activities. U.S. News & World Report has consistently ranked the Krannert undergraduate program in the quantitative methods/analysis specialty among the top programs along with MIT, Columbia, Carnegie Mellon, University of Pennsylvania, UC Berkeley, and other peer institutions having a strong STEM (Science, Technology, Engineering, and Mathematics) focus.

Krenicki Center for Business Analytics & Machine Learning

Quantitative Methods Research Seminars:

Date Speaker Institution Topic
December 9th, 2022 Uday Shanbhag Department of Industrial and Manufacturing Engineering, Pennsylvania State University Probability Maximization via Minkowski Functionals: Convex Representations and Tractable Resolution
December 2nd, 2022 Dmitriy Drusvyatskiy Department of Mathematics, University of Washington Optimization algorithms beyond smoothness and convexity
November 18th, 2022 Steve Hanneke Department of Computer Science, Purdue University A Theory of Universal Learning
November 11th, 2022 Guanghui (George) Lan Georgia Institute of Technology Policy mirror descent for online reinforcement learning
November 4th, 2022 Avetik Karagulyan  Department of Computer Science, KAUST Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition
October 28th, 2022 Ben Grimmer Department of Applied Mathematics and Statistics, Johns Hopkins University Scalable, Projection-Free Optimization Methods
October 21st, 2022 Mateo Diaz Computing and Mathematical Sciences, Caltech Clustering a mixture of Gaussians with unknown covariance
October 14th, 2022 Yining Wang Naveen Jindal School of Management, University of Texas at Dallas Differential Privacy in Personalized Pricing with Nonparametric Demand Models
October 7th, 2022 Yichen Zhang Krannert School of Management Information Design and Order Smoothing in Supply Chain Management (A New Perspective to a Mature Problem)
September 30th, 2022 Ilias Diakonikolas Department of Computer Science, Wisconsin-Madison Learning with Massart Noise
September 23rd, 2022 Paul Valiant Department of Computer Science, Purdue University Mean Estimation in Low and High Dimensions
September 9th, 2022 Will Wei Sun Krannert School of Management Trustworthy Reinforcement Learning for Online Decision Making
February 14th, 2022 Yiqiao Zhong Stanford University Why interloping neural nets generalize well: Recent insights from neural tangent model
February 7th, 2022 Sebastian Perez-Salazar Georgia Tech Robust Online Selection with Uncertain Offer Acceptance
January 31st, 2022 Alex Wang Carnegie Mellon Accurately and Efficiently Solving Structured Nonconvex Optimization Problem
January 28th, 2022 Weibin Mo University of North Carolina at Chapel Hill Efficient Learning of Optimal Individualized Treatment Rules
January 24th, 2022 Zhan Lian Cornell University Labor Cost Free-Riding in the Gig Economy
January 21st, 2022 Xiaowu Dai University of California, Berkeley Learning Strategies in Decentralized Matching Markets under Uncertain Preferences
January 19th, 2022 Changhwa Lee University of Pennsylvania Optimal Recommender System Design
January 14th, 2022 Feng Ruan University of California, Berkeley Designing Better Nonconvex Models for Modern Statistical Applications
November 30, 2021 Roberto Imbuzeiro Oliveira IMPA The contact process over a switching random d-regular graph
November 16, 2021 Prof. Frank E. Curtis Department of Industrial and Systems Engineering, Lehigh University Algorithms for Deterministically Constrained Stochastic Optimization
November 9, 2021 Prof. Mert Gürbüzbalaban Rutgers Business School, Rutgers University Heavy tails arising in stochastic gradient descent methods in deep learning
October 29, 2021 Prof. Nathan Kallus School of Operations Research and Information Engineering, Cornell University Smooth Contextual Bandits
October 19, 2021 Prof. Gabor Lugosi Department of Economics, Pompeau Fabra University Learning the structure of graphical models by covariance queries
October 7, 2021 Prof. Francesco Orabona Dept. of Electrical and Computer Engineering, Boston University Parameter-free Stochastic Optimization of Variationally Coherent Functions
September 21, 2021 Prof. Po-Ling Loh Statistical Laboratory, Centre for Mathematical Sciences, University of Cambridge A modern take on Huber regression
August 24th, 2021 Prof. Stanislav Minsker Dep. of Mathematics, University of Southern California Robust and Efficient Mean Estimation
November 30, 2018 Prof. Simge Küçükyavuz Department of Industrial Engineering and Management Sciences, Northwestern University Risk-Averse Set Covering Problems
November 16, 2018 Prof. Emerson Melo Department of Economics, Indiana University Bloomington A Variational Approach to Network Games
October 19, 2018 Prof. Siddhartha Banerjee School of Operations Research and Information Engineering, Cornell University Online Decision-Making Using Prediction Oracles
March 23, 2018 Prof. Santanu Dey School of Industrial and Systems Engineering, Georgia Institute of Technology
Theoretical Analysis of the Role of Sparsity in Cutting-Plane Selection
March 2, 2018 Prof. Ariel Procaccia Department of Computer Science, Carnegie Mellon University Extreme Democracy
September 15, 2017 Prof. Yihong Wu Department of Statistics and Data Science, Yale University Polynomial Approximation, Moment Matching and Optimal Estimation of the Unseen
September 1, 2017 Prof. Jyrki Wallenius Aalto University School of Business Accounting for Political Opinions, Power, and Influence: A Voting Advice Application
April 28, 2017 Prof. Adam Wierman Department of Computing and Mathematical Sciences, California Institute of Technology Platforms & Networked Markets: Transparency & Market Power
November 4, 2016 Prof. Venkatesan Guruswami Department of Computer Science, Carnegie Mellon University (2+eps)-SAT is NP-Hard, and Further Results on Promise Constraint Satisfaction
September 30, 2016 Prof. Regina Liu Department of Statistics and Biostatistics, Rutgers University Fusion Learning: Fusing Inferences from Multiple Sources for More Powerful Findings

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