Teaching and research in Operations Research and Financial Engineering (ORFE) focus on foundations of data science, probabilistic modeling, and optimal decision-making under uncertainty, with applications in economics and finance, energy and the environment, healthcare management, physical and biological sciences, social networks, and transportation. The graduate and undergraduate programs provide strong technical grounding in statistics, stochastics, and optimization, which are central in the analysis of massive datasets that arise in areas as diverse as the human genome and financial markets, and for applications ranging from clinical trials to robotics.
Award for Excellence honors graduate student achievement
Jonathan Conway, assistant professor of chemical and biological engineering, received an innovation grant for the project “Engineering symbiotic plant-microbe interactions for sustainable agriculture.” Photo by Sameer A. Khan/Fotobuddy
Since 2008, innovation funds have fostered research in AI fairness, sustainable agriculture, drug discovery and more
Class of 2024 award winners and Distinguished Teaching Award recipient Maria Garlock. Photos by Frank Wojciechowski
Class Day marks achievement, determination and optimism
Junior faculty awards recognize outstanding teaching and research
Boris Hanin, talking here with postdoctoral researchers Gage DeZoort and Mufan Li, is applying his background in mathematical physics to understand the structural properties of large neural networks that power AI systems. Photo by Sameer A. Khan/Fotobuddy
Statistics start to untangle AI networks
Teagan Mathur, left, a graduate student in mechanical and aerospace engineering, receives her award from Leena Memon ’25, vice president of the Engineering Council. Photos by Tori Repp/Fotobuddy
Engineering students recognize exceptional teachers and mentors
Events
December 02
12:25 pm
Do LLMs Solve Novel Tasks? An Empirical Investigation of Out-of-distribution Generalization
December 03
4:30 pm
Approximate Message Passing Algorithms for High-dimensional Estimation and Inference
December 04
4:30 pm
A Quantitative Approach to Optimal Impact Portfolios
December 05
4:30 pm
Machine Learning for Algorithm Design
December 12
4:30 pm
The Magic Of Monotone Integer Programs: Polynomial Time Solvability With Min-Cut Procedure, Fast High Quality Solutions for Related Hard Problems and the New Incremental Parametric Cut Algorithm For Densest Subgraph and Other Monotone Ratio Problems
