MASTERS RESEARCH OR PROJECT

Prerequisite(s): Approval by a faculty member who agrees to supervise the work. Independent work involving experiments, computer programming, analytical investigation, or engineering design.

• Instructor: Daniel Bienstock
  h-index: 38 Info
• Instructor: Agostino Capponi
  h-index: 19 Info
• Instructor: Rachel Cummings
  h-index: 16 Info
• Instructor: Antonius B Dieker
  CULPA: 10 h-index: 19 Info
• Instructor: Adam N Elmachtoub
  CULPA: 1 h-index: 13 Info
• Instructor: Yuri Faenza
  h-index: 11 Info
• Instructor: Vineet Goyal
  h-index: 21 Info
• Instructor: Ali Hirsa
  h-index: 8 Info
• Instructor: Anran Hu
  Info
• Instructor: Garud Iyengar
  CULPA: 6 h-index: 30 Info
• Instructor: Hardeep Johar
  Info
• Instructor: Cedric Josz
  CULPA: 2 h-index: 13 Info
• Instructor: Soulaymane Kachani
  CULPA: 12 Info
• Instructor: Yaren Kaya
  Info
• Instructor: Christian Kroer
  h-index: 16 Info
• Instructor: Daniel Lacker
  CULPA: 4 h-index: 13 Info
• Instructor: Henry Lam
  h-index: 17 Info
• Instructor: Fabrizio Lecci
  Info
• Instructor: Tianyi Lin
  Info
• Instructor: Uday Menon
  Info
• Instructor: Bento Natura
  Info
• Instructor: Jay Sethuraman
  CULPA: 10 Info
• Instructor: Karl Sigman
  CULPA: 15 Info
• Instructor: Eric Stratman
  Info
• Instructor: Clifford Stein
  CULPA: 31 Wikipedia h-index: 53 Info
• Instructor: Wenpin Tang
  h-index: 6 Info
• Instructor: Kaizheng Wang
  CULPA: 1 h-index: 8 Info
• Instructor: Lily Xu
  Info
• Instructor: David D Yao
  CULPA: 5 Info
• Instructor: Yi Zhang
  CULPA: 2 Info
• Instructor: Xunyu Zhou
  h-index: 54 Info
• Instructor: Christine Chan
  Info
• Instructor: Chris Lee
  Info
• 1-3 points

MANAG TECH INNOV & ENTREPRENEURSHIP

MANAGING TECH INNOVATION

A required course for undergraduate students majoring in OR:EMS. Focus on the management and consequences of technology-based innovation. Explores how new industries are created, how existing industries can be transformed by new technologies, the linkages between technological development and the creation of wealth and the management challenges of pursuing strategic innovation.

• M 7 p.m.-9:30 p.m.
• Instructor: Brandon M Procak
  Info
• 3 points

FIELDWORK

Only for IEOR graduate students who need relevant work experience as part of their program of study. Final reports required. May not be taken for pass/fail credit or audited.

• 1-2 points

FIELDWORK

Only for IEOR graduate students who need relevant work experience as part of their program of study. Final reports required. May not be taken for pass/fail credit or audited.

• 1-2 points

FIELDWORK

Only for IEOR graduate students who need relevant work experience as part of their program of study. Final reports required. May not be taken for pass/fail credit or audited.

• 1-2 points

Advanced Reinforcement Learning

Adv Reinforcement Learnin

Theory of Markov Decision Processes (MDP) and Dynamic Programming. Design and convergence properties of Reinforcement Learning (RL) algorithms including Q-learning and Policy iteration methods.  Function approximation and deep RL algorithms: DQN, policy gradient, actor-critic methods. Exporation-Exploitation and regret bounds in RL. Multi-agent RL. RL with Human Feedback (RLHF). RL and Monte Carlo Tree Search (MCTS) for Agentic Systems.

Note: Only one of ORCS E4529 or 6529 may be taken for credit.

• MW 1:10 p.m.-2:25 p.m.
• Instructor: Shipra Agrawal
  h-index: 24 Info
• 3 points

OPTIMIZATION II

An introduction to combinatorial optimization, network flows and discrete algorithms. Shortest path problems, maximum flow problems. Matching problems, bipartite and cardinality nonbipartite. Introduction to discrete algorithms and complexity theory: NP-completeness and approximation algorithms.

• TR 10:10 a.m.-11:25 a.m.
• Instructor: Clifford Stein
  CULPA: 31 Wikipedia h-index: 53 Info
• 4.5 points

CONVEX OPTIMIZATION

Convex sets and functions, and operations preserving convexity. Convex optimization problems. Convex duality. Applications of convex optimization problems ranging from signal processing and information theory to revenue management. Convex optimization in Banach spaces. Algorithms for solving constrained convex optimization problems.

• MW 4:10 p.m.-5:25 p.m.
• Instructor: Bento Natura
  Info
• 3 points

STOCHASTIC MODELING II

Continuation of IEOR E6711, covering further topics in stochastic modeling in the context of queueing, reliability, manufacturing, insurance risk, financial engineering, and other engineering applications. Topics from among generalized semi-Markov processes; processes with a non-discrete state space; point processes; stochastic comparisons; martingales; introduction to stochastic calculus.

• TR 2:40 p.m.-3:55 p.m.
• Instructor: David D Yao
  CULPA: 5 Info
• 4.5 points

ADVANCED TOPICS IN IEOR

AI & Algo for Soc Good

Selected topics in IEOR. Content varies from year to year. May be repeated for credit.

• MW 11:40 a.m.-12:55 p.m.
• Instructor: Lily Xu
  Info
• 3 points

ADVANCED TOPICS IN IEOR

Log-concave probability

Selected topics in IEOR. Content varies from year to year. May be repeated for credit.

• MW 2:40 p.m.-3:55 p.m.
• Instructor: Daniel Lacker
  CULPA: 4 h-index: 13 Info
• 3 points

ADVANCED TOPICS IN IEOR

High-dimensional Prob w/Applic

Selected topics in IEOR. Content varies from year to year. May be repeated for credit.

• TR 4:10 p.m.-5:25 p.m.
• Instructor: Kaizheng Wang
  CULPA: 1 h-index: 8 Info
• 3 points

ADVANCED TOPICS IN IEOR

Dynamic Learning & Opt

Selected topics in IEOR. Content varies from year to year. May be repeated for credit.

• MW 10:10 a.m.-11:25 a.m.
• Instructor: Vineet Goyal
  h-index: 21 Info
• 3 points

ADVANCED TOPICS IN IEOR

Exploring Diffusion Model

Selected topics in IEOR. Content varies from year to year. May be repeated for credit.

• TR 11:40 a.m.-12:55 p.m.
• Instructor: Wenpin Tang
  h-index: 6 Info
• 3 points

ADVANCED TOPICS IN IEOR

Healthcare Apps of ML

Selected topics in IEOR. Content varies from year to year. May be repeated for credit.

• R 2:40 p.m.-5:10 p.m.
• Instructor: Rachel Cummings
  h-index: 16 Info
• 3 points

RESEARCH

Before registering, the student must submit an outline of the proposed work for approval by the supervisor and the chair of the Department. Advanced study in a specialized field under the supervision of a member of the department staff. May be repeated for credit.

• Instructor: Anish Agarwal
  Info
• Instructor: Shipra Agrawal
  h-index: 24 Info
• Instructor: Eric Balkanski
  h-index: 13 Info
• Instructor: Daniel Bienstock
  h-index: 38 Info
• Instructor: Agostino Capponi
  h-index: 19 Info
• Instructor: Rachel Cummings
  h-index: 16 Info
• Instructor: Antonius B Dieker
  CULPA: 10 h-index: 19 Info
• Instructor: Adam N Elmachtoub
  CULPA: 1 h-index: 13 Info
• Instructor: Yuri Faenza
  h-index: 11 Info
• Instructor: Vineet Goyal
  h-index: 21 Info
• Instructor: Ali Hirsa
  h-index: 8 Info
• Instructor: Anran Hu
  Info
• Instructor: Garud Iyengar
  CULPA: 6 h-index: 30 Info
• Instructor: Hardeep Johar
  Info
• Instructor: Cedric Josz
  CULPA: 2 h-index: 13 Info
• Instructor: Soulaymane Kachani
  CULPA: 12 Info
• Instructor: Yaren Kaya
  Info
• Instructor: Christian Kroer
  h-index: 16 Info
• Instructor: Daniel Lacker
  CULPA: 4 h-index: 13 Info
• Instructor: Henry Lam
  h-index: 17 Info
• Instructor: Fabrizio Lecci
  Info
• Instructor: Tianyi Lin
  Info
• Instructor: Uday Menon
  Info
• Instructor: Bento Natura
  Info
• Instructor: Jay Sethuraman
  CULPA: 10 Info
• Instructor: Karl Sigman
  CULPA: 15 Info
• Instructor: Eric Stratman
  Info
• Instructor: Clifford Stein
  CULPA: 31 Wikipedia h-index: 53 Info
• Instructor: Wenpin Tang
  h-index: 6 Info
• Instructor: Kaizheng Wang
  CULPA: 1 h-index: 8 Info
• Instructor: Lily Xu
  Info
• Instructor: David D Yao
  CULPA: 5 Info
• Instructor: Yi Zhang
  CULPA: 2 Info
• Instructor: Xunyu Zhou
  h-index: 54 Info
• Instructor: Christine Chan
  Info
• Instructor: Winsor Yang
  Info
• 1-6 points