I am a postdoctoral researcher at the
University of Chicago, where I work with
Prof. Michael Franklin.
I received my Ph.D. from
Columbia University under the supervision of
Prof. Luis Gravano. My work has been published in premier
data management (e.g., ACM SIGMOD and VLDB) and data science (e.g., ACM SIGKDD and ICML) conferences, and has received
multiple distinctions, including an ACM SIGMOD Research Highlight Award. My thesis on fast and accurate algorithms
for time-series analysis was recognized at the 2019 ACM SIGKDD Doctoral Dissertation Award competition. My research has
been covered by several media outlets, including the New York Times, the Washington Post, and the Guardian.
I work on the foundations of the next generation of data-intensive and machine-learning applications. In my research, I leverage principled ideas from databases and data science as scaffolds for understanding, analyzing, and transforming into actionable knowledge high-dimensional data, such as time-series, multimedia, text, and web data. I focus on (i) developing scalable and accurate computational methods to push the state of the art for supervised and unsupervised tasks; and (ii) building intelligent tools and systems that harness such methods to solve real-world problems.
04.2021 - Our work on SAND, a streaming subsequence anomaly detection method, was accepted at VLDB 2021.
03.2021 - I serve on the program committee for the research track of NeurIPS 2021.
01.2021 - I serve as Proceedings co-Chair of ACM SIGMOD 2022.
12.2020 - I serve on the senior program committee for the research track of IJCAI 2021.
11.2020 - I serve on the reproducibility committee of ACM SIGMOD 2021.
06.2020 - I presented our study on debunking four long-standing misconceptions in the time-series literature at ACM SIGMOD 2020.
04.2020 - Our work on an innovative storage method for decomposing string attributes in columnar stores was accepted at VLDB 2020.
02.2020 - I received an unrestricted research gift of $150,000 from Exelon Utilities.
12.2019 - I serve on the senior program committee for the research track of IJCAI 2020.
07.2019 - My thesis received an honorable mention for the 2019 ACM SIGKDD Doctoral Dissertation Award.
04.2019 - Our work on training and inference for CNNs in resource-constrained settings was accepted at ICML 2019.
03.2019 - I received an unrestricted research gift of $73,000 from Cisco Systems.
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."
2015ACM 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