News
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.
About 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
under 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
of Athens.
Research interests
My main research interest lies in datadriven algorithmics and applications.
Specifically, I am interested in the theoretical foundations of data science,
and in applications involving datadriven 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
Singlepass streaming algorithms for realtime analytics
Graphs and networks
Theory: Graph theory, random graphs and graph algorithms
Applications: Mining realworld datasets, including the Web graph, social networks and
biological datasets
Pattern theory
Efficient optimization techniques
Machine learning and data mining
Bayesian probability theory
