I work on the foundations of the next generation of data-intensive and machine-learning applications. As our cities, organizations, factories, houses, vehicles, and devices are becoming increasingly networked, it becomes a necessity to analyze and transform into actionable knowledge massive Internet-of-Things data collections. My work focuses on (i) designing scalable and accurate methods to achieve state-of-the-art performance for supervised and unsupervised tasks; and (ii) building intelligent tools that hardness such methods to solve real-world problems.
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