I am currently a Ph.D. candidate at the University of Illinois at Chicago, Department of Information and Decision Sciences. Here at UIC, I am working with Prof. Nadarajah. My research focuses on developing and applying machine learning and decision making algorithms for various business-oriented problems. These include user behavior modeling, procurement, and investment decisions.
- Our paper "Interpretable User Models via Decision-rule Gaussian Processes" got accepted in Advances in Approximate Bayesian Inference (NIPS Workshop).
- Meeting Corporate Renewbale power targets, Under review in Management Science (with S. Nadarajah, A. Trivella) [PDF]
In this paper, we design procurement portfolios for companies which have committed to procuring a percentage of future power demand from renewable sources. Computing multi-stage procurement portfolios for these companies results in intractable Markov decision processes. Using methodologies in approximate dynamic programming, we develop a novel and near-optimal heuristic for decision making on realistic instances.
- Interpretable User Models via Decision-rule Gaussian Processes, Submitted to NIPS 2019 Workshop (with S. Nadarajah, T. Tulabandhula) [Draft upon request]
Models of user behavior are critical inputs in many prescriptive settings and can be viewed as decision rules that transform state information available to the user into actions. Gaussian processes (GPs), as well as nonlinear extensions thereof, provide a flexible framework to learn user models in conjunction with approximate Bayesian inference. However, the resulting models may not be interpretable in general. We propose decision-rule GPs (DRGPs) that apply GPs in a transformed space defined by decision rules that have immediate interpretability to practitioners. We illustrate this modeling tool on a real application and show that structural variational inference techniques can be used with DRGPs. We find that DRGPs outperform the direct use of GPs in terms of both out-of-sample performance and the quality of optimized decisions. These performance advantages continue to hold when DRGPs are combined with transfer learning.
- Investment under Limited Long-Term Information, Working Paper (with S. Nadarajah, A. Kelevin, S-E. Feleten )
The limited availability of long-term information about the uncertainties for decision-making problems (i.e., investment in Hydropower plant capacity) increases the potential impact of model misspecification and often lead to suboptimal decisions. To overcome this issue, we propose an algorithm that leverages statistical information in the medium-term and handles the lack of long-term information using robust optimization.
- Overbooking in Network of Storage Assets, Technical report (with S. Nadarajah, T. Tulabandhula )
SKILLS AND BACKGROUND
- Machine Learning
- Reinforcement Learning
- decision-making algorithms
- Python (Pandas, Pytorch, Tensorflow, Keras, Scikit-Learn, Scipy)
- R, MATLAB
- SQL, Spark
- Unix, AWS
PhD in Information and Decision Sciences, University of Illinois at Chicago
BSc in Industrial Engineering, AmirKabir University, 2015
Graduate Reaserch Assistant in Artificial Intelligence
- Worked on human behavior modeling, procurement, and investment problems using machine learning and decision making algorithms.
Graduate Teaching Assistant in Business Analytics
- Topics: Computer vision, Natural Language Processing, Bayesian inference, reinforcement learning
- Topics: Linear and integer programming in Julia, forecasting in R
- Provided tutorials in Pytorch
Project Adviser for Industry Projects
- Cloudbakers: Designing and implementing a predictive model using natural language processing along with analysts to evaluate the credibility of repositories in GitHub, Fall 2019
- Varuna (Startup): Collaborated with co-founders and analysts on business requirements, gathering data, and discussing findings to build a predictive model for forecasting system failures in water purification plants, Spring 2019
|Interpretable User Models via Decision-rule Gaussian Processes INFORMSAnnual Meeting, Seattle, Washington||OCT 2019|
|Meeting Corporate Renewable Power Targets INFORMSAnnual Meeting, Seattle, Washington||OCT 2019|
|Overbooking in Network of Storage Assets Production and Operations Management Society Annual Conference, Washington D.C. (POMS)||May 2019|
|Meeting Corporate Renewable Power Targets Production and Operations Management Society Annual Conference (POMS)||May 2019|
|Investment under Limited Long-Term Information Production and Operations Management Society Annual Conference (POMS)||May 2019|
|Dual Reoptimization based Approximate Dynamic Programming INFORMS Annual Meeting, Phoenix, Arizona||Nov 2019|
|Meeting Corporate Renewable Power Targets Production and Operations Management Society Annual Conference, Houston, Texas (POMS)||May 2019|
|Meeting Corporate Renewable Power Targets Manufacturing & Service Operations Management Annual Conference, Dallas, Texas (MSOM)||Jul 2018|