Hello, my name is

Harshal Chaudhari

I'm a PhD candidate in Computer Science at Boston University, co-advised by Prof. John Byers and Prof. Evimaria Terzi. My research interests include problems in algorithmic data mining, operations research and their applications in urban transportation systems.


  • Explainable A.I. Systems
  • Reinforcement Learning
  • Robust Optimization
  • Causal Inference


  • Python, Java, C++
  • Stata, R
  • Apache Spark, Hadoop
  • SQL, MongoDB


  • Statistical ML
  • Advanced Algorithms
  • Optimization Theory
  • Operations Research

Latest News

Oct 2020: Our work "Learn to Earn: Enabling Coordination Within a Ride-Hailing Fleet" has been accepted at IEEE BigData, 2020.

Aug 2020: My work with Zillow Group on Fairness in Multistakeholder Recommendations has been accepted at FATREC workshop held alongside RecSys 2020.

Aug 2019: I will be visiting Anchorage, Alaska for KDD 2019. Hope to see you there!

Oct 2018: After spending a very worthwhile summer interning at Zillow, we have decided to continue our research together remotely.

May 2018: Excited to spend my summer at Zillow Group as an AI intern on the Personalization team.

Feb 2018: Our paper "Putting Data in the Driver's Seat: Optimizing Earnings for On-Demand Ride-Hailing" featured on The Morning Paper.

Jan 2018: Our paper "Markov Chain Monitoring" accepted at SIAM International Conference on Data Mining (SDM18), San Diego.

Dec 2018: Our paper "Impact of free app promotion on future sales" accepted at TSMO 2018: Workshop on Two-sided Marketplace Optimization

Oct 2017: Our paper "Putting Data in the Driver's Seat: Optimizing Earnings for On-Demand Ride-Hailing" accepted at WSDM 2018, Los Angeles.

Research projects

A General Framework for Fairness in Multistakeholder Recommendations
  • Authors:
  • Harshal A. Chaudhari,
  • Sangdi Lin,
  • Ondrej Linda

Traditionally, multistakeholder recommendations problems have been formulated as integer linear programs which compute recommendations in an offline fashion, by incorporating provider constraints. Such approaches can lead to unforeseen biases wherein certain users consistently receive low utility recommendations in order to meet the global coverage constraints. To remedy this situation, we propose a general formulation that incorporates provider coverage objectives alongside individual user objectives, in a real-time personalized recommender system.

Robust LSM-Tree backed Key-Value Stores (ongoing)
  • Authors:
  • Harshal A. Chaudhari,
  • Evimaria Terzi,
  • Manos Athanassoulis

Modern LSM-tree backed key-value stores co-tune merge policies, buffer sizes and the false positive rates for the Bloom filters across different levels of LSM-tree. These systems typically minimize the costs for fixed workloads. We augment them to make them robust to perturbations in design parameters and workloads.

  • GitHub
  • PDF
  • Proceedings
  • (Coming soon)
Learn to Earn: Enabling Coordination Within a Ride-Hailing Fleet
  • Authors:
  • Harshal A. Chaudhari,
  • John W. Byers,
  • Evimaria Terzi

We combine the interpretability of vanilla reinforcement learning with combinatorial optimization techniques to propose a systematically tunable, scalable and effective framework to maximize earnings of a fleet of ride-share drivers.

Markov Chain Monitoring
  • Authors:
  • Harshal A. Chaudhari,
  • Michael Mathioudakis,
  • Evimaria Terzi

Given an initial distribution of items over the nodes of a Markov chain, we wish to estimate the distribution of items at subsequent times. In deriving these estimates, we issue queries to retrieve partial information on the distribution of items.

Putting Data in the Driver's Seat
  • Authors:
  • Harshal A. Chaudhari,
  • John W. Byers,
  • Evimaria Terzi

We formalize the problem of devising a strategy to maximize expected earnings of ride-hailing service driver, describe a series of algorithms to solve the problem, and exemplify the methods on a large scale simulation of driving for Uber in NYC ... Read more.

Impacts of free app promotion
  • Authors:
  • Harshal A. Chaudhari,
  • John W. Byers

Amazon's Free App of the Day program, aimed at improving app visibility using daily free promotions, is a compelling experiment in the 'economics of free'. We investigate its longer-term consequences on the performance of apps on Amazon Appstore.


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My fullform CV is available here.

You can also find me on the following channels