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.

Grants

• NSF IIS-2107290 (2021 - 2024):
  III: Medium: Collaborative Research: Fairness in Web Database Applications.
  Abolfazl Asudeh (Lead PI - UIC), H. V. Jagadish (UofM), and Gautam Das and Shirin Nilizadeh (UTA).
  $1M (UIC portion: $292K).
• Google Research Scholar Award (2021 - 2022):
  An end-to-end system for detecting cherry-picked trendlines.
  Abolfazl Asudeh.
  $64K.

2022

• Suraj Shetiya, Ian Swift, Abolfazl Asudeh, Gautam Das. Fairness-Aware Range Queries for Selecting Unbiased Data. ICDE, 2022

2021

• (Invited Blog) Abolfazl Asudeh. Enabling Responsible Data Science in Practice. ACM SIGMOD Blog, 2021.
• (Invited Paper) Abolfazl Asudeh, You (Will) Wu, Cong Yu, H. V. Jagadish. Perturbation-based Detection and Resolution of Cherry-picking. Data Engineering Bulletin, Vol. 45(3), pages 39–51, 2021, Special Issue on Challenges in Combating Misinformation.
• (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.
• Fatemeh Nargesian, Abolfazl Asudeh, H. V. Jagadish. Tailoring Data Source Distributions for Fairness-aware Data Integration. PVLDB, Vol. 14(11), pages 2519–2532, 2021, VLDB Endowment.
• 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. SIGMOD DEEM Workshop, 2021, ACM.

2020

• (Tutorial) Abolfazl Asudeh and H.V. Jagadish. Fairly Evaluating and Scoring Items in a Data Set. PVLDB, 13(12): 3445-3448, 2020, VLDB Endowment.
• Yin Lin, Yifan Guan, Abolfazl Asudeh, HV Jagadish. Identifying Insufficient Data Coverage in Databases with Multiple Relations. PVLDB, Vol. 13(11), 2020, VLDB Endowment.
• Abolfazl Asudeh, H.V. Jagadish, You (Will) Wu, Cong Yu. On Detecting Cherry-picked Trendlines.
  PVLDB, Vol. 13(6), pages 939–952, 2020, VLDB Endowment.
• Suraj Suresh Shetiya, Abolfazl Asudeh, Sadia Ahmed, Gautam Das. A Unified Optimization Algorithm For Solving “Regret-Minimizing Representative” Problems. PVLDB, Vol. 13(3), pages 239–251, 2020, VLDB Endowment.
• (Invited Paper) Abolfazl Asudeh, Azade Nazi, Jees Augustine, Saravanan Thirumuruganathan, Nan Zhang, Gautam Das, Divesh Srivastava. Scalable Algorithms for Signal Reconstruction by Leveraging Similarity Joins. The VLDB Journal, Vol. 29(2), pages 681–707, 2020, Special Issue on best of VLDB’18.
• Zhongjun Jin, Mengjing Xu, Chenkai Sun, Abolfazl Asudeh, HV Jagadish. MithraCoverage: A System for Investigating Population Bias for Intersectional Fairness. SIGMOD (Demo), 2020, ACM.
• Jees Augustine, Suraj Suresh Shetiya, undefined , Abolfazl Asudeh, Saravanan Thirumuruganathan, Azade Nazi, Nan Zhang, Gautam Das, Divesh Srivastava. Orca-SR: A Real-Time Traffic Engineering Framework leveraging Similarity Joins. VLDB (Demo), 2020, VLDB Endowment.
• (Preprint) Abolfazl Asudeh, Tanya Berger-Wolf, Bhaskar DasGupta, Anastasios Sidiropoulos. Maximizing coverage while ensuring fairness: a tale of conflicting objective. CoRR, 2020, Arxiv/2007.08069.
• (Preprint) Hadis Anahideh, Abolfazl Asudeh, Saravanan Thirumuruganathan. Fair Active Learning. CoRR, 2020, Arxiv/2001.01796.

2019

• (Invited Paper) Abolfazl Asudeh, H.V. Jagadish, Julia Stoyanovich. Towards Responsible Data-driven Decision Making in Score-Based Systems. Data Engineering Bulletin, Vol. 42(3), pages 76–87, 2019, Special Issue on Fairness, Diversity, and Transparency in Data Systems.
• Abolfazl Asudeh, Azade Nazi, Nan Zhang, Gautam Das, H.V. Jagadish. RRR: Rank-Regret Representative. SIGMOD, 2019, ACM.
• Abolfazl Asudeh, HV Jagadish, Gerome Miklau, Julia Stoyanovich. On obtaining stable rankings. PVLDB, Vol. 12(3), pages 237–250, 2019, VLDB Endowment.
• Abolfazl Asudeh, H.V. Jagadish, Julia Stoyanovich, Gautam Das. Designing Fair Ranking Schemes. SIGMOD, 2019, ACM.
• Abolfazl Asudeh, Zhongjun Jin, HV Jagadish. Assessing and Remedying Coverage for a Given Dataset. ICDE, 2019.
• (Invited Paper) Abolfazl Asudeh, Azade Nazi, Jees Augustine, Saravanan Thirumuruganathan, Nan Zhang, Gautam Das, Divesh Srivastava. Efficient Signal Reconstruction for a Broad Range of Applications. Special Issue on 2018 ACM SIGMOD Research Highlights, 2019.
• Abolfazl Asudeh, Azade Nazi, Nick Koudas, Gautam Das. Maximizing Gain over Flexible Attributes in Peer to Peer Marketplaces. PAKDD, pages 327–345, 2019, Springer.
• Yifan Guan, Abolfazl Asudeh, Pranav Mayuram, HV Jagadish, Julia Stoyanovich, Gerome Miklau, Gautam Das. MithraRanking: A System for Responsible Ranking Design. SIGMOD (Demo), 2019, ACM.
• Chenkai Sun, Abolfazl Asudeh, H. V. Jagadish, Bill Howe, Julia Stoyanovich. MithraLabel: Flexible Dataset Nutritional Labels for Responsible Data Science. CIKM (Demo), 2019, ACM.
• Sona Hasani, Faezeh Ghaderi, Shohedul Hasan, Saravanan Thirumuruganathan, Abolfazl Asudeh, Nick Koudas, Gautam Das. ApproxML: Efficient Approximate Ad-Hoc ML Models Through Materialization and Reuse. VLDB (Demo), 2019.

2018

• Abolfazl Asudeh, Azade Nazi, Jees Augustine, Saravanan Thirumuruganathan, Nan Zhang, Gautam Das, Divesh Srivastava. Leveraging similarity joins for signal reconstruction. PVLDB, Vol. 11(10), pages 1276–1288, 2018, VLDB Endowment.
• Sona Hasani, Saravanan Thirumuruganathan, Abolfazl Asudeh, Nick Koudas, Gautam Das. Efficient construction of approximate ad-hoc ML models through materialization and reuse. PVLDB, Vol. 11(11), pages 1468–1481, 2018, VLDB Endowment.
• Ke Yang, Julia Stoyanovich, Abolfazl Asudeh, Bill Howe, HV Jagadish, Gerome Miklau. A nutritional label for rankings. SIGMOD (Demo), 2018, ACM.

2017

• Abolfazl Asudeh, Azade Nazi, Nan Zhang, Gautam Das. Efficient Computation of Regret-ratio Minimizing Set: A Compact Maxima Representative. SIGMOD, pages 821–834, 2017, ACM.
• Md Farhadur Rahman, Abolfazl Asudeh, Nick Koudas, Gautam Das. Efficient Computation of Subspace Skyline over Categorical Domains. CIKM, pages 407–416, 2017, ACM.
• Yeshwanth Durairaj Gunasekaran, Abolfazl Asudeh, Sona Hasani, Nan Zhang, Ali Jaoua, Gautam Das. QR2: A third-party query reranking service over web databases. ICDE (Demo), 2017, ACM.

2016-

• Abolfazl Asudeh, Nan Zhang, Gautam Das. Query Reranking as a Service. PVLDB, Vol. 9(11), pages 888–899, 2016, VLDB Endowment.
• Abolfazl Asudeh, Saravanan Thirumuruganathan, Nan Zhang, Gautam Das. Discovering the skyline of web databases. PVLDB, Vol. 9(7), pages 600–611, 2016, VLDB Endowment.
• Ning Yan, Sona Hasani, Abolfazl Asudeh, Chengkai Li. Generating preview tables for entity graphs. SIGMOD, pages 1797–1811, 2016, ACM.
• Abolfazl Asudeh, Gensheng Zhang, Naeemul Hassan, Chengkai Li, Gergely V. Zaruba. Crowdsourcing Pareto-Optimal Object Finding By Pairwise Comparisons. CIKM, pages 753–762, 2015, ACM.