Selva Nadarajah []

Note to prospective students

I enjoy working with graduate students. PhD students that I collaborate with me are strong in algorithms and programming or have a strong mathematical background, ideally both. They are passionate about solving challenging business problems using reinforcement learning, optimization, and high dimensional statistics. Masters students that I work with are computationally strong and self-motivated to learn new technical material and methods. Please see below for past students, their profiles, and nature of work. Before emailing me, I recommend that you do the following:
  • Browse through my research publications and working papers to identify any past projects or ongoing work that is of interest to you. Read such papers more carefully and include specifics of what you find interesting in your email.

  • I welcome new ideas outside my ongoing or past work. If you are contacting me with a new idea for collaboration, kindly indicate why my involvement would be useful (it may not be!).

  • If applicable, please consult with your PhD advisor and make sure they are onboard.

PhD students

  • Parshan Pakiman, Information and Decision Sciences, University of Illinois at Chicago (In progress; PhD student since 2017)
    Background: BSc in Applied Mathematics, University of Tehran
    Research interests: Self-adapting reinforcement learning, Operations-Finance Interface.

  • Alessio Trivella, PhD in Management Engineering, Denmark Technical University (Graduated in Fall 2018; co-supervisors: David Pisinger and Stein-Erik Fleten)
    Background: BSc and MSc in Mathematics, University of Milan
    Thesis title: Decision making under uncertainty in sustainable energy operations and investments
    First position: Post-doctoral fellow, ETH Zurich
    Current position: Assistant Professor of Operations Research, University of Twente

  • Danial Mohseni Taheri, Information and Decision Sciences, University of Illinois at Chicago (Graduated in Spring 2021)
    Background: BSc in Industrial Engineering, Amirkabir University of Technology
    Thesis title: Reinforcement learning for real options: Interpretable planning under uncertainty and limited data
    First position: Senior Machine Learning Scientist, JP Morgan

  • Andreas Kleiven, Industrial Economics and Technology Management, NTNU (Graduated in Spring 2022; co-supervisors: Stein-Erik Fleten)
    Background: BSc in Statistics and MSc in Applied Physics and Statistics, NTNU
    Thesis title: Investment and operational planning under uncertainty
    First position: Power Associate, Citadel

  • Bo Yang, Operations Management, Carnegie Mellon University (Graduated in Spring 2022; co-supervisors: Nicola Secomandi)
    Background: BS in Industrial Engineering, University of Shanghai for Science and Technology; MS in Management Sciences and Engineering, Shanghai Jiao Tong University
    Thesis title: A pathwise optimization approach for reinforcement learning in merchant energy operations
    First position: Post-doctoral fellow, Columbia University

Masters students (list incomplete)
  • Abhilash Chenreddy, Masters of Science in Business Analytics, University of Illinois at Chicago (Graduated in Fall 2018)
    Research interests: Inverse reinforcement learning and shopper marketing
    First position: Associate Data Scientist, ZS Associates
    Current position: PhD student and IVADO Fellow, HEC Montreal and MILA

  • Akshay Balachandran, Masters of Science in Industrial Engineering, University of Illinois at Chicago (Graduated in Spring 2017)
    Research interests: Retail operations and optimization
    First position: Management Consultant, CGN Global
    Current position: Vice President, Alix Partners

  • Karthikeyan Venugopal, Masters of Science in Information Systems, University of Illinois at Chicago (Graduated in Fall 2015; co-supervisor: Negar Soheili)
    Research interests: Retail operations and optimization
    First position: Supply Chain Analyst, Home Depot
    Current position: Manager of Advanced Analytics, Walmart