I am a postdoctoral researcher at the
University of Chicago, where I work with
Prof. Michael Franklin.
I received my Ph.D. in
Computer Science from
Columbia University under the supervision of
Prof. Luis Gravano.
I work on the foundations of the next generation of data-intensive and machine-learning applications. As our cities, factories, houses, vehicles, and devices are becoming increasingly networked, it becomes a necessity to analyze and transform into actionable knowledge massive high-dimensional Internet-of-Things data collections. My work focuses on (i) developing scalable and accurate methods to push the state of the art for supervised and unsupervised tasks; and (ii) building intelligent tools that harness such methods to solve real-world problems.
12.2019 - I serve on the senior program committee for the research track of IJCAI 2020.
11.2019 - I serve on the program committees for the research track and the demo track of ICDE 2020.
11.2019 - I serve on the program committee for the research track of ICML 2020.
10.2019 - I will give a guest lecture on data indexing at the "Databases" course at the University of Chicago.
07.2019 - My thesis received an honorable mention for the 2019 ACM SIGKDD Doctoral Dissertation Award.
06.2019 - Our vision paper on AI/ML systems for resource-constrained or shared environments was accepted at ACM SIGOPS Operating Systems Review.
05.2019 - I received an unrestricted research gift of $73,000 from Cisco Systems.
04.2019 - Our paper on training and inference for CNNs in resource-constrained settings was accepted at ICML 2019.
03.2019 - I serve on the program committee for the demo track and the "Best Demo" selection committee of VLDB 2019.
12.2018 - I serve on the program committee for the demo track of ICDE 2019.
10.2018 - I was awarded a NetApp Faculty Fellowship of $50,000. (First time awarded to a postdoctoral researcher.)
10.2018 - I received my Ph.D. degree from Columbia University.
Selected Achievements, Awards, and Fellowships
20192019 ACM SIGKDD Doctoral Dissertation Award, Honorable Mention
ACM SIGKDD dissertation awards recognize "outstanding work done by graduate students in the areas of data science, machine learning, and data mining."
2018NetApp Faculty Fellowship Award
NetApp supports our effort towards learning to compress time series to accelerate Internet of Things (IoT) data analytics.
2016Research coverage by Popular Press
Our research on "Screening for Pancreatic Cancer Using Signals From Web Search Logs" was covered by New York Times, Washington Post, The Guardian, MIT Technology Review, and Fortune; and our research on "Social Dynamics of Language Change in Online Networks" was covered by FastCompany.
2016Nomination for the "ISSI Paper of the Year Award"
Our work on "Predicting the Impact of Scientific Concepts Using Full Text Features" is one of the ten papers selected across papers published in 2015 or 2016 for consideration for the "ISSI Paper of The Year Award," which "recognizes high quality research in the field of Scientometrics and Informetrics."
2016ACM SIGMOD Research Highlight Award
Our paper "k-Shape: Efficient and Accurate Clustering of Time Series" was selected across papers published in premier database conferences (i.e., SIGMOD, VLDB, ICDE, PODS, EDBT, and ICDT) for the "ACM SIGMOD Research Highlight Award," which recognizes research papers that "address an important problem, represent a definitive milestone in solving the problem, and have the potential of significant impact."
2015Best of ACM SIGMOD
Our paper "k-Shape: Efficient and Accurate Clustering of Time Series" was selected as one of the two best papers of the ACM SIGMOD International Conference on Management of Data.
2014Onassis Foundation Fellow
Recognition for Greek students with outstanding academic record.
Recent selected referred publications in conferences and journals