Stratos Idreos

Professor at Harvard University

Stratos Idreos is a distinguished computer scientist and educator at Harvard University. He holds the position of Gordon McKay Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS).1 2

Research and Expertise

Idreos leads the Data Systems Laboratory (DASlab) at Harvard, where his research focuses on:

Building a grammar for data systems
Developing workload and hardware-conscious data systems
Improving efficiency in relational data analytics, NoSQL, machine learning, blockchain, and data science applications1

His work aims to make big data processing more accessible and efficient, often achieving performance improvements of ten to a hundred times faster than existing systems.1

Academic Background

Ph.D. from the University of Amsterdam, Netherlands
Prior to Harvard, he was a tenure-track Scientific Staff Member at the Dutch National Research Center for Mathematics and Computer Science
Held research and visiting positions at Microsoft Research, IBM Almaden, EPFL, University of Trento, and National University of Singapore1 2

Awards and Recognitions

Idreos has received numerous prestigious awards for his contributions to computer science:

2011 ACM SIGMOD Jim Gray Doctoral Dissertation award
2011 ERCIM Cor Baayen award
2015 IEEE TCDE Rising Star Award
2020 ACM SIGMOD Contributions award
2022 ACM SIGMOD Test of Time Award
NSF Career Award
DOE Early Career Award
Sloan Research Fellowship
Capers W. McDonald and Marion K. McDonald Award for Excellence in Mentoring and Advising1 2 3

Recent Appointments

In July 2024, Stratos Idreos was appointed as Faculty Co-Director of the Harvard Data Science Initiative (HDSI). In this role, he will:

Foster interdisciplinary collaboration across Harvard's research landscape
Strengthen HDSI's relationships with data science researchers, especially at SEAS
Advance the understanding and application of data science in academia and beyond3 4

Stratos Idreos is known for his pioneering research at the intersection of database systems, machine learning, and data science infrastructure. His work aims to make data science more intuitive and interactive, with a focus on developing self-designing approaches for managing large data sets.3