Academic: As Professor of Computer Science at BU, my academic
research centers on data-analytic and algorithmic challenges in two disciplines:
the science of computer networking (e.g., transport protocols, network architecture)
and the empirical study of Internet platforms (such as Airbnb, Groupon, and Yelp).
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 analytics that help power our incubated portfolio companies
from inception to profitability and beyond.
The list of
accepted papers for IMC '16 in Santa Monica is now available.
- 5/12/16: Submission deadline for IMC '16.
Looking forward to selecting a great program for the conference in Santa Monica this November.
- 2/29/16: Looking forward to having the BU XIA team build upon
last year's success
with the Google Summer of Code (GSoC) initiative again in 2016. Applicants worldwide are
invited to apply,
plus we are always looking to recruit BU students to conduct research on core XIA technology,
applications and deployments of XIA, and to help build out
the XIA implementation for Linux.
- 1/30/16: President Obama announces
Computer Science For All initiative.
Did you know that 22 states in the US do not allow CS courses to count towards high school graduation math or science requirements?
I'm looking at you, Massachusetts.
- 11/19/15: Presented our work on Airbnb and the sharing economy in Seoul at the
International Forum on Service Sector Advancement, sponsored by the
Korean Ministry of Strategy and Finance and the Korea Development Institute.
My academic research is currently 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.