Background

Academic: As Professor of Computer Science at BU, my academic research centers on data-analytic and algorithmic challenges in two disciplines: the empirical study of Internet platforms (such as Airbnb, Groupon, Uber and Yelp), and the science of computer networking (e.g., transport protocols, network architecture).

Entrepreneurial: As founding Chief Scientist at the Cogo Labs technology incubator (founded in 2005), I help research and design our own technology platform: we provide proprietary methods for online advertising, algorithmic marketing, and data science analytics that help power our incubated portfolio companies from inception to profitability and beyond.

In the News

• 6/18/18: EverQuote, a company we incubated at Cogo Labs, has officially commenced its IPO.
  
  
• 5/7/18: Presented a technical overview talk, "Targeted Online Advertising: A View from the Inside", at the BU Law School as part of the Cyber Alliance talk series.
  Please email Tyler Gabrielski (tgabs@bu.edu) for a link to the video recording.
  
  
• 4/23/18: We have six new students joining us to work this summer on our open-source Linux XIA project, all funded by Google Summer of Code. Welcome Ka Ming Hui, Pranjan Sana, Vibhav Pant, Daivik Dave, Hrishav Mukherjee, and Prashant Kumar Prajapati!
  This marks the fourth consecutive summer we've participated in GSoC.
  
  
• 4/17/18: It's Innovation Week at BU!
  Photo of me presenting my recent research on unicorns (the startup kind) at the Global Disruptors kickoff event.
  
  
• 3/21/18: Our EdX course, Business Analytics for Data-Driven Decision Making (QD602x, part of BU Questrom's MicroMasters in Digital Innovation), launched today. See also: QD602x featured in a BU Daily Free Press article last year.
  
  
• 3/10/18: Looking forward to serving again on the ACM Internet Measurement Conference program committee in 2018. IMC is returning to Boston this fall!
  
  
• 2/20/18: Our WSDM 2018 paper was reviewed as *the morning paper* on Adrian Colyer's blog of the same name.
  Long story short: we devise methods for Uber and Lyft drivers to construct real-time contingency plans to maximize their earnings during their work week. The key is to effectively leverage historical ridership data (which has significant spatiotemporal variation in our NYC-based study) to get a big earnings boost.
  
  
• 2/16/18: Harshal presented our work on "app of the day" Appstore promotions at the Workshop on Two-Sided Marketplace Optimization, colocated with WSDM 2018 in LA.
  
  
• 2/15/18: Harshal presented our paper "Putting Data in the Driver's Seat: Optimizing Earnings for On-Demand Ride-Hailing" as a Long presentation at WSDM 2018 in LA.
  
  
• 12/5/17: I was elected by the BU Faculty Council to serve on the Questrom (business school) Dean Search Advisory Committee.
  
  
• 11/29/17: Today marks the official launch of Innovate @ BU! See also: article in BU Today.
  
  
• 10/17: Our Airbnb impacts paper appeared in this month's AMA Journal of Marketing Research (JMR).
  
  
• 7/13/17: Excited to be a member of the inaugural class of Boston University Data Science Fellows. See this Q&A on my perspective.

Research and Affiliations

I'm a Computer Science Professor and a Data Science Fellow at BU. I'm a member of the Networks Research group and the DBLab at BU, both of which hold open weekly talk series. Please join us! I also hold appointments as Fellow at the Hariri Institute and a courtesy appointment in the Department of Marketing in BU Questrom.

My academic research is supported in part by grants from the National Science Foundation, the Hariri Institute at Boston University, and by a Google Faculty Research Award.

At Cogo Labs, I've been working on a fascinating problem domain since the company's founding: the algorithmic, data management, and systems-building challenges that arise on the buy side of pay-per-click advertising, a canonical Big Data optimization problem. A great example of a successful launch from Cogo Labs using this technology is EverQuote, the Cambridge-based auto insurance marketplace.

I've also collected a few useful pointers to helpful advice for prospective or current graduate students.

Teaching

• CS 131 - Combinatoric Structures (Fall '18) , co-taught with Babis Tsourakakis.
• CS 330 - Intro to Algorithms (Spring '18) , co-taught with Dora Erdos.
• CS 591 - Networks and Markets: Theory and Applications (Fall '16)
• CS 112 - Intro to Data Structures (Spring '12)
• CS 455/655 - Computer Networks (Fall '10)
• CS 559 - Algorithmic Aspects of Computer Networks (Spring '10)
• CS 591 - Electronic Commerce (Fall '15)
• CS 697 - Computer Science Graduate Initiation Course (Spring '10)