Short Bio


I am currently a Postdoctoral Researcher in Computer Science at the University of Chicago, where I work with Prof. Michael Franklin. I received my Ph.D. in Computer Science from Columbia University. My advisor was Prof. Luis Gravano and I was member of the Database Research Group.

The overarching goal of my research is to develop computational methods, tools, and systems needed to help analyze and transform into actionable knowledge large collections of evolving data (i.e., data that change over time, such as time series, time-varying measurements, and sequences of streams). To achieve this goal, my work focuses on designing scalable, accurate, and data-aware algorithms; and on building data-driven systems that harness principled methods to solve real-world problems.


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Education

2018

Columbia University

Ph.D. in Computer Science

2015

Columbia University

M.Phil. in Computer Science

2011

EPFL

M.S. in Computer Science

2009

Aristotle University

B.S. in Computer Science

Professional Experience

2015

Microsoft Research

Research Intern

2014

Microsoft Research

Research Intern

2011

Yahoo! Labs

Research Intern

2010

Logitech

Project Management Intern

Selected Achievements, Awards, and Fellowships

2018NetApp Faculty Fellowship Award

NetApp supports our effort towards learning to compress time series to accelerate Internet of Things (IoT) data analytics.

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.

Selected Publications

Recent selected referred publications in conferences and journals

Fast and Accurate Time-Series Clustering

John Paparrizos and Luis Gravano

ACM Transactions on Database Systems (ACM TODS 2017)


"Best of SIGMOD 2015" Special Issue

PDF

Screening for Pancreatic Adenocarcinoma Using Signals From Web Search Logs

John Paparrizos, Ryen W. White, and Eric Horvitz

Journal of Oncology Practice (JOP 2016)


PDF

Predicting the Impact of Scientific Concepts Using Full Text Features

Kathy McKeown,1 Hal Daume,1 Snigdha Chaturvedi,2 John Paparrizos,2 Kapil Thadani,2 et al.

Journal of the American Society for Information Science and Technology (JASIST 2016)

1. Lead PIs 2. Lead student authors in alphabetic order


Nominated for the "ISSI Paper of the Year Award"
PDF

Detecting Devastating Diseases in Search Logs

John Paparrizos, Ryen W. White, and Eric Horvitz

ACM SIGKDD Conference on Knowledge Discovery and Data Mining (ACM SIGKDD 2016)

PDF

k-Shape: Efficient and Accurate Clustering of Time Series

John Paparrizos and Luis Gravano

ACM SIGMOD Conference on Management of Data (ACM SIGMOD 2015)


Invited to "Best of SIGMOD 2015" Special Issue of ACM TODS

Received the "2015 ACM SIGMOD Research Highlight Award"

PDF

List of Publications

Publications in peer-reviewed conferences and journals