Professor Dr. Nathan Kallus

Associate Professor

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Biography

Nathan Kallus is an Associate Professor in Operations Research and Information Engineering at Cornell Tech. His research interests encompass causal inference, particularly in conjunction with machine learning; statistics; optimization under uncertainty; sequential dynamic decision making; and algorithmic fairness. He is the author of the book "Applied Causal Inference Powered by Machine Learning and Artificial Intelligence". Kallus earned his Ph.D. in operations research from the Massachusetts Institute of Technology and holds a B.A. in mathematics and a B.S. in computer science from the University of California, Berkeley. Before joining Cornell, Kallus was a visiting scholar at the University of Southern California's Department of Data Sciences and Operations and served as a postdoctoral associate in MIT's Operations Research and Statistics group.

Research Interests

Causal Inference Machine Learning Statistics Optimization Under Uncertainty Sequential Dynamic Decision Making Algorithmic Fairness

Experience

Associate Professor

— Present

Cornell Tech • New York

Teaching and conducting research in Operations Research and Information Engineering.

Requirements for Cornell Tech

Master Program

Requirements

GPA Requirement
Required:3
GRE General
TOEFL
Listening
Required:15
Reading
Required:20
Writing
Required:20
Speaking
Required:22
Total
Required:100
IELTS
Overall
Required:7

Prerequisites

Linear Algebra Intermediate-level Probability and Statistics Programming course

Application Checklist

  • Academic Statement of Purpose
  • Personal Statement
  • Resume
  • Two letters of recommendation
  • Transcripts

Specialization Notes

The M.Eng. in ORIE at Cornell Tech focuses on Data Analytics and Operations Research in a tech environment.