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Design and Techno-Economic Analysis of PV Powered Irrigation System: Case Study in Rwanda
ISyE Seminar Series
Bayesian Pooling of Self- and Peer-Reports to Improve Measurement of Sensitive Behaviors
ISyE Seminar Series
Learning What Matters: Scalable Offline and Online Calibration of Digital Twins
ISyE Seminar Series
Modern Gaussian Processes for Neuroimaging Data Analysis
ISyE Seminar Series
Two-sided Assortment Optimization
ISyE Seminar Series
Infinite-State Partially Observable Markov Decision Processes with Applications to Inventory Control
ISyE Seminar Series
AI-Enabled In-Situ Quality Control: Learning Beyond the Known
Learning What Matters: Scalable Offline and Online Calibration of Digital Twins
Bayesian Pooling of Self- and Peer-Reports to Improve Measurement of Sensitive Behaviors
Design and Techno-Economic Analysis of PV Powered Irrigation System: Case Study in Rwanda
Dr. Michael Garland - A talk on new abstractions and programming models that make it easier to write high-performance kernels for matrix and tensor computations in modern AI systems.
Optimization under a Magnifying Glass
Making AI Impactful in Healthcare
Zoning in Emerging Logistics Systems: New Theory and Practice
Frontiers and Applications at the Interface of Discrete Optimization and Interpretable Machine Learning
A double decomposition algorithm for network planning and operations in deviated fixed-route microtransit
Bridging Machine Learning and Optimization for Human-Centered AI
GPU-Accelerated Linear Programming and Beyond
Systemic Consequences of Technology Choice in Clean Energy Supply Chains: Vulnerability and Competitiveness in Battery Critical Minerals
Uncertainty quantification for black-box models with conditional guarantees
Heterogeneous Treatment Effects in Panel Data: Applications to the Healthy Incentives Program
A Statistical Framework for Benchmarking Quantum Computers
Seeing the Forest for the Trees
From Democratizing Optimization with LLM to Improving LLM Performance with OR Techniques
Reconnecting Sampling, Design, and Causality: A Modern Perspective on Classical Foundations
Reducing Sample Complexity in Stochastic Derivative-Free Optimization via Tail Bounds and Hypothesis Testing
Forest Expression and Online Monitoring of Dynamic Networks
Gacha: A Simple Mechanism to Screen a Budget-Constrained Buyer
Automated Geometric Qualification of 3D-Printed Products
Incentive Aligned and Robust Distributed Learning Methods
Concentration Bounds for Statistical Learning for Time Dependent Data
Stochastic Modeling of Unified Resilience Metrics
Composite Likelihood for a Very Large Scale Binary Regression with Crossed Random Effects
Improving the Practical Scalability and Robustness of Zeroth-Order Optimization Solvers
Riemannian Proximal Sampler for High-accuracy Sampling on Manifolds
Bridging the Cyber–Physical Gaps in Health and Humanitarian Assistance
When Does Interference Matter? Decision-Making in Platform Experiments
Dealing with Ambiguity in Humanitarian Decision-Making
The Search for Parking for Commercial Last-Mile Delivery in Urban Environments (or should they?)
Analysis of the Genealogy Process in Forensic Investigative Genetic Genealogy
Linear regression using Hilbert-space valued covariates with unknown reproducing kernel
Smarter decisions for a secure world: opportunities and challenges for industrial engineering
Got (Optimal) Milk? Pooling Donations in Human Milk Banks with Machine Learning and Optimization
Learning with Local and Global Adversarial Corruptions
Modern Sampling Paradigms: from Posterior Sampling to Generative AI
On Principal Component Regression in High Dimension
Challenges and Opportunities in Assumption-free and Robust Inference
Yule’s “nonsense correlation”: Moments and density.
Simple menus in robust screening
Cooperation and the Design of Public Goods
Efficient Gradient Estimation for Overparameterized Stochastic Differential Equations
Propagation of Shocks on Networks: Can Local Information Predict Survival?
High-dimensional Clustering via A Latent Transformation Mixture Model
Covariate adjustment in randomized experiments with missing outcomes and covariates
Foundations of Private Optimization for Modern Machine Learning
Surgical Human-Robot Collaborations: Transforming Training, Skills, and Safety
Managing Tail Risk in Online Learning: When Safety Meets Efficiency
Advances in School District Design: Addressing Inequities and Planning for the Future
Integrative Artificial Intelligence for Healthcare
Online Contention Resolution Schemes for the Matching Polytope of Graphs'
Two-Stage Stochastic Multi-Objective Linear Programming
Algorithm and Incentive Design for Sustainable Resource Allocation: Beyond Classical Fisher Markets
Optimization under Uncertainty: Scheduling with Failover
Probing-enhanced stochastic programming
Mobilizing Demand Flexibility in Wholesale Electricity Markets with VPP Supply Functions
Diversity, equity and inclusion and racial and social justice in the field of operations research and analytics: Results from an examination of recent scholarship and university academic programs
Convexification and optimization of problems involving the Euclidean norm.
Multi-period mixed-integer quadratic programming
The (Surprising) Rate Optimality of Greedy Procedures for Large-Scale Ranking and Selection
Model-free selective inference: from calibrated uncertainty to trusted decisions
Statistical Methods for $mall Data Problems
The Human-Tech Duo: Augmenting Learning and Creativity with AI and Spatial Computing
Technology … Here, There, and Everywhere: The Need to Understand Human Interactions with Emerging Technologies
Recent Advances in Strongly Polynomial Algorithms for Linear Programming
Practicality meets Optimality: Real-Time Statistical Inference under Complex Constraints
Modeling Interference for Policy Evaluation in Stochastic Systems
Modeling and Mitigation of Network Cascades
Universal Learning for Decision-Making
Reliable Data-driven Decision Making
Epidemic Forecasting on Networks: Bridging Local Samples with Global Outcomes
