I will be an assistant professor at Boston University. I am looking for graduate students.
Please send me an email with your CV if you are interested in working with me.
I am a Postdoctoral Fellow of the Center
for Research on Computation and Society (CRCS)
at Harvard University.
I am also affiliated with the Theory of Computation Group, the EconCS Group, and Berkman center for Internet & Society.
Here at Harvard University, I work closely with
Professor Michael Mitzenmacher and
Professor David Parkes.
Before joining Harvard, I was a Postdoctoral Fellow at Brown University
(CS and ICERM)
where I had the fortune
to work with Professor Eli Upfal.
I obtained my Ph.D. from the ACO program and a MSc from the
Machine Learning program, both from
Carnegie Mellon University.
I carried out my dissertation
Mathematical and Algorithmic Analysis of Network and Biological Data
the supervision of Professor Alan Frieze.
With respect to the biological side, I was fortunate to collaborate with
Professor Russell Schwartz,
MD Stanley Shackney and Professor Gary Miller on a
computational biology project. Specifically, I developed computational methods for
mining various types of cancer datasets
including aCGH, microarray
and FISH datasets.
I have worked with Professor Christos Faloutsos on the PEGASUS project,
which is now used by Microsoft.
I have worked at Yahoo! Research and Microsoft Research
in the past. My work at Microsoft was one of their research highlights.
I did my undergraduate studies in Electrical and Computer Engineering
at the National Technical University
My main research interest lies in data-driven algorithmics and applications.
Specifically, I am interested in the theoretical foundations of data science,
and in applications involving data-driven discovery. Application domains I am interested in
include social networks, the World Wide Web and healthcare.
Big data analytics
Methods, efficient algorithms, theory and implementations to analyze large datasets
Single-pass streaming algorithms for real-time analytics
Graphs and networks
Theory: Graph theory, random graphs and graph algorithms
Applications: Mining real-world datasets, including the Web graph, social networks and
Efficient optimization techniques
Machine learning and data mining
Bayesian probability theory