Photo of Asudeh, Abolfazl

Abolfazl Asudeh

Assistant Professor

Department of Computer Science


Building & Room:

SEO 1131


851 S. Morgan St, MC 152, Chicago, IL, 60607

Office Phone:

(312) 996-4860


My research spans to different aspects of Big Data and Data Science, including data management, analytics, and mining, for which I aim to find efficient, accurate, and scalable algorithmic solutions.
Responsible Data Science and Algorithmic Fairness is my current major focus.

Selected Publications


  • (Invited Blog) Abolfazl Asudeh. Enabling Responsible Data Science in Practice. ACM SIGMOD Blog, 2021.
  • (Invited Paper) Abolfazl Asudeh, Jees Augustine, Saravanan Thirumuruganathan, Azade Nazi, Nan Zhang, Gautam Das, Divesh Srivastava. Scalable Signal Reconstruction for a Broad Range of Applications. Communications of the ACM (CACM) Research Highlight, Vol. 64(2), pages 106–115, 2021, ACM.
  • Abolfazl Asudeh, Nima Shahbazi, Zhongjun Jin, H. V. Jagadish. Identifying Insufficient Data Coverage for Ordinal Continuous-Valued Attributes. SIGMOD, 2021, ACM.
  • Hantian Zhang, Xu Chu, Abolfazl Asudeh, Shamkant Navathe. OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learning. SIGMOD, 2021, ACM.
  • Matteo Corain, Paolo Garza, Abolfazl Asudeh. DBSCOUT: A density-based method for scalable outlier detection in very large datasets. ICDE, 2021, IEEE.
  • Hantian Zhang, Nima Shahbazi, Xu Chu, Abolfazl Asudeh. FairRover: Explorative Model Building for Fair and Responsible Machine Learning. DEEM Workshop at SIGMOD, 2021, ACM.






Notable Honors

2021, Google Research Scholar Award, Google Research

2021, Communications of the ACM's Research Highlight, ACM

2019, ACM SIGMOD Research Highlight Award, ACM

2020, VLDB Journal Special Issue on Best of VLDB, VLDB

2017, ACM SIGMOD Most Reproducible Paper Award, ACM