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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 8th, 2023 Shixin Wang Department of Decisions, Operations and Technology, CUHK Business School, The Chinese University of Hong Kong The Power of Simple Menus in Robust Mechanism Design
December 1st, 2023 Cong Ma Department of Statistics, University of Chicago The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing
November 17th, 2023 Cong (Alex) Shi Department of Management Science, Miami Herbert Business School, University of Miami Marrying Stochastic Gradient Descent with Bandits: Learning Algorithms for Inventory Systems with Fixed Costs
November 10th, 2023 Yonghan Jung Department of Computer Science, Purdue University Estimating Joint Treatment Effects from Marginal Experiments
November 3rd, 2023 Zhengling Qi Department of Decision Sciences, GW School of Business, The George Washington University Reinforcement Learning under Unmeasured Confounding
October 27th, 2023 Guanting Chen Department of Statistics and Operations Research, University of North Carolina at Chapel Hill Learning to Make Adherence-Aware Advice
October 20th, 2023 Nathan Kallus School of Operations Research and Information Engineering, Cornell University What's the Harm? Bounding Disparities in Treatment Effects
October 13th, 2023 Ruohan Zhan Industrial Engineering and Decision Analytics, The Hong Kong University of Science and Technology Post-Episodic Reinforcement Learning Inference
October 6th, 2023 Johannes Wiesel Department of Mathematics, Carnegie Mellon University The Out-of-Sample Prediction Error of the Square-Root LASSO and Related Estimators
September 29th, 2023 Promit Ghosal Department of Mathematics, Brandeis University Solving Linear Programming via Diagonal Linear Networks
September 22nd, 2023 Alexander Teytelboym Department of Economics, University of Oxford Approximate Auctions
September 15th, 2023 Alex L. Wang Quantitative Methods Area, Daniels School of Business, Purdue University Sharp Exact Penalty Formulations in Signal Recovery
September 8th, 2023 Rui Gao Department of Information, Risk, and Operations Management, McCombs School of Business, University of Texas at Austin Multistage Distributionally Robust Optimization with Adapted Wasserstein Distance
September 1st, 2023 Shuangning Li Department of Statistics, Harvard University Inference and Decision-Making in the Presence of Interference
August 25th, 2023 Thanh Nguyen Quantitative Methods Area, Daniels School of Business, Purdue University Equilibrium Existence and Implementability
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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