Short Bio


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.


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News

04.2021 - Our work on SAND, a streaming subsequence anomaly detection method, was accepted at VLDB 2021.

03.2021 - Our work on CodecDB, an encoding-aware columnar database, was accepted at ACM SIGMOD 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.

01.2021 - I presented VergeDB, our system for enabling IoT and ML analytics on edge devices, at CIDR 2021.

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.

10.2020 - I serve on the program committees for the research tracks of ACM SIGMOD 2021, ICML 2021, and ICLR 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.

11.2019 - I serve on the program committees for the research tracks of IEEE ICDE 2020, ICML 2020, and ACM CIKM 2020.

08.2019 - I presented GRAIL, our work on efficiently learning representations for time series, at VLDB 2019.

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.

12.2018 - I serve on the program committees for the demo tracks of VLDB 2019 and IEEE 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.

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

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.

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.

Selected Publications

Recent selected referred publications in conferences and journals

VergeDB: A Database for IoT Analytics on Edge Devices

John Paparrizos, Chunwei Liu, Bruno Barbarioli, Johnny Hwang, Ikraduya Edian, Aaron J. Elmore, Michael J. Franklin, and Sanjay Krishnan

11th Conference on Innovative Data Systems Research (CIDR 2021)

PDF

Debunking Four Long-Standing Misconceptions of Time-Series Distance Measures

John Paparrizos, Chunwei Liu, Aaron J. Elmore, and Michael J. Franklin

ACM SIGMOD Conference on Management of Data (ACM SIGMOD 2020)

PDF

GRAIL: Efficient Time-Series Representation Learning

John Paparrizos and Michael Franklin

45th International Conference on Very Large Data Bases (VLDB 2019)

PDF

Band-limited Training and Inference for Convolutional Neural Networks

Adam Dziedzic1, John Paparrizos1, Sanjay Krishnan, Aaron Elmore, and Michael Franklin

36th International Conference on Machine Learning (ICML 2019)

1. Alphabetical order; Equal contribution

PDF

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