About Me

I am a Data Scientist - Research at Google. I'm part of the core Data Science team in AIM (Analytics, Insights and Measurement) working on privacy-centric machine learning for ads. My work lies at the intersection of differential privacy, machine learning, statistical modeling, and mathematical optimization.

Before this, I was a Clinical Assistant Professor in the Operations group at the Kenan-Flagler Business School at UNC Chapel Hill. Prior to that, I obtained my Ph.D. in Operations Research from the Operations Research Center at MIT advised by Professor Dimitris Bertsimas.

As part of my PhD, I worked on developing methods for prescriptive analytics - using data-driven techniques from optimization, statistics, and machine learning to solve problems that lead to better decisions and outcomes. My main application areas of focus were healthcare analytics and operations management.

During my PhD, I spent a summer as an intern at Google Research, NYC . Before starting graduate school at MIT, I worked in quantitative finance for a year developing statistical arbitrage based strategies at WorldQuant LLC, India.

I obtained my undergraduate degree (B.Tech) in Chemical Engineering along with a minor in Applied Statistics and Informatics from the Indian Institute of Technology Bombay in Mumbai, India. During my undergraduate, I spent a summer each working on problems in computational systems biology in EPFL, Switzerland and process control in NUS, Singapore.

Papers

[Link to Google Scholar page]

All authors in alphabetical order.

• Optimizing Hierarchical Queries for the Attribution Reporting API   [Paper]
  Matthew Dawson, Badih Ghazi, Pritish Kamath, Kapil Kumar, Ravi Kumar, Bo Luan, Pasin Manurangsi, Nishanth Mundru, Harikesh Nair, Adam Sealfon and Shengyu Zhu
  Proc. of the AdKDD Workshop at the 29th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (AdKDD’23), 2023
  [Talk]   [Slides]   [ArXiv paper (longer version)]
• Data-Driven Surgical Tray Optimization to Improve Operating Room Efficiency   [Paper]
  V. Deshpande, N. Mundru, S. Rath, M. Knowles, D. Rowe and B. Wood
  Operations Research, 2023.
  Finalist, INFORMS Innovative Application in Analytics Award (2022) [Link]
  Selected for POMS College of Healthcare Operations Management Research-in-Practice Showcase Presentations (2022)
  Media Coverage [Collaborating to Cut Hospital Costs]
• Optimization-based Scenario Reduction for Data-Driven Two-stage Stochastic Optimization   [Paper]
  D. Bertsimas and N. Mundru
  Operations Research 71 (4), 1343-1361, 2023.
• Sparse Convex Regression   [Paper]
  D. Bertsimas and N. Mundru
  INFORMS Journal on Computing 33 (1), 262-279, 2021.
• Optimal Prescriptive Trees   [Paper]
  D. Bertsimas, J. Dunn and N. Mundru
  INFORMS Journal on Optimization 1(2), 164-183, 2019.
• The Airlift Planning Problem   [Paper]
  D. Bertsimas, A. Chang, V. V. Mišić and N. Mundru
  Transportation Science 53(3), 773-795, 2019.
• Computation of Alarm Relevant Probabilities Using Geometric Random Variable Modeling   [Paper]
  N. Mundru, R. Ghosh and M. Bhushan
  IFAC-Papers Online 50(1), 2866-2871, 2018.

Teaching Experience

Course Instructor (at Kenan-Flagler)

• BUSI 403 Operations Management, Undergraduate class
• Fall 2020 (3 sections)
• Spring 2020 (2 sections)

Teaching Assistant (at MIT)

• 15.761 Introduction to Operations Management, MBA class, Fall, 2018. (Instructor: Nikos Trichakis)
• 15.071x The Analytics Edge EdX class, Summer, 2017. (Instructors: Dimitris Bertsimas and Allison O'Hair)
• 15.053 Optimization Methods for Business Analytics, Undergraduate class, Spring, 2016. (Instructor: James Orlin)
• 15.071 The Analytics Edge MBA class, Spring, 2015 (Instructors: Rob Freund and John Silberholz)
• 15.094 Robust Modeling, Optimization, and Computation Ph.D. class, Spring, 2014. (Instructor: Dimitris Bertsimas)