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 |
