Abolfazl Asudeh,

A. Asudeh^* is an assistant professor of Computer Science at the University of Illinois Chicago and the director of Innovative Data Exploration Laboratory (InDeX Lab).
He serves as an Associate Editor for the IEEE Transactions on Knowledge and Data Engineering (TKDE) and is a regular PC member of Data Management flagship venues. He is a VLDB Ambassador, the VLDB Endowment's Liaison to NSF, and a senior IEEE member.
His research encompasses various aspects of Data Science, including Data Management, Information Retrieval, and Data Mining. He develops efficient, accurate, and scalable solutions by leveraging Approximation and Randomized Algorithms, and Computational Geometry.
Algorithmic Fairness and Data-centric Responsible AI are his major focus in research.
His research interests also include Ranking algorithms and indices, LLMs and Foundation Models, Social Networks, Machine Learning, and Misinformation Detection.

Sponsors

• 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: $300K).
• Google Research Scholar Award (2021 - 2022):
  An end-to-end system for detecting cherry-picked trendlines.
  Abolfazl Asudeh.
  $60k.

2025

• Sana Ebrahimi, Rishi Advani, Abolfazl Asudeh. Evaluating the Feasibility of Sampling-Based Techniques for Training Multilayer Perceptrons. In EDBT, 2025

2024

• Mahdi Erfanian, H. V. Jagadish, Abolfazl Asudeh. Chameleon: Foundation Models for Fairness-aware Multi-modal Data Augmentation to Enhance Coverage of Minorities. In VLDB, 2024
• Nima Shahbazi, Stavros Sintos, Abolfazl Asudeh. FairHash: A Fair and Memory/Time-efficient Hashmap. In SIGMOD, 2024
• Sana Ebrahimi, Nima Shahbazi, Abolfazl Asudeh. Requal-LM: Reliability and Equity through Aggregation in Large Language Models. In NAACL (Findings), 2024
• Nima Shahbazi, Abolfazl Asudeh. Reliability Evaluation of Individual Predictions: A Data-centric Approach. In VLDB Journal, 2024
• (Invited paper) Nima Shahbazi, Mahdi Erfanian, Abolfazl Asudeh. Coverage-based Data-centric Approaches for Responsible and Trustworthy AI. Data Engineering Bulletin 48(1), Special Issue on Data-centric Responsible AI, 2024
• Melika Mousavi, Nima Shahbazi, Abolfazl Asudeh. Data Coverage for Detecting Representation Bias in Image Data Sets: A Crowdsourcing Approach. In EDBT, 2024
• Suraj Shetiya, Ian Swift, Abolfazl Asudeh, Gautam Das. Shapley Values for Explanation in Two-sided Matching Applications.In EDBT, 2024
• Jiwon Chang, Bohan Cui, Fatemeh Nargesian, Abolfazl Asudeh, H. V. Jagadish. Data Distribution Tailoring Revisited: Cost-Efficient Integration of Representative Data. In VLDB Journal, 2024
• (Demo Paper) Nima Shahbazi, Mahdi Erfanian, Abolfazl Asudeh, Fatemeh Nargesian, Divesh Srivastava. FairEM360: A Suite for Responsible Entity Matching. In VLDB, 2024
• (Preprint) Mohsen Dehghankar, Rahul Raychaudhury, Stavros Sintos, Abolfazl Asudeh. Fair Set Cover. CoRR, 2024
• (Preprint) Sana Ebrahimi, Kaiwen Chen, Abolfazl Asudeh, Gautam Das, Nick Koudas. AXOLOTL: Fairness through Assisted Self-Debiasing of Large Language Model Outputs. CoRR, 2024

2023

• Nima Shahbazi, Nikola Danevski, Fatemeh Nargesian, Abolfazl Asudeh, Divesh Srivastava. Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching. Proceedings of the VLDB Endowment, 2023.
• Rishi Advani, Paolo Papotti, Abolfazl Asudeh. Maximizing Neutrality in News Ordering. In KDD, 2023.
• Nima Shahbazi, Yin Lin, Abolfazl Asudeh, H. V. Jagadish. Representation Bias in Data: A Survey on Identification and Resolution Techniques. ACM COMPUTING SURVEYS (CSUR), 2023.
• Abolfazl Asudeh, Tanya Berger-Wolf, Bhaskar DasGupta, Anastasios Sidiropoulos. Maximizing coverage while ensuring fairness: a tale of conflicting objective. ALGORITHMICA, SN 1432-0541, 2023.
• (Tutorial) Fatemeh Nargesian, Abolfazl Asudeh, H. V. Jagadish. Next-generation Challenges of Responsible Data Integration. In WSDM, 2023, ACM.
• (Preprint) Rishi Advani, Abolfazl Asudeh. Online Maximum Independent Set of Hyperrectangles. CoRR, 2023.
• (Preprint) Suraj Shetiya, Shohedul Hasan, Abolfazl Asudeh, Gautam Das. Efficient Strongly Polynomial Algorithms for Quantile Regression. CoRR, 2023.
• (Workshop) Khanh Duy Nguyen, Nima Shahbazi, Abolfazl Asudeh. PopSim: An Individual-level Population Simulator for Equitable Allocation of City Resources. In SDM Workshop on Algorithmic Fairness in Artificial intelligence, Machine learning, and Decision making, 2023.
• (Workshop) Francesco Di Carlo, Nazanin Nezami, Hadis Anahideh, Abolfazl Asudeh. FairPilot: An Explorative System for Hyperparameter Tuning through the Lens of Fairness. In SDM Workshop on Algorithmic Fairness in Artificial intelligence, Machine learning, and Decision making, 2023.

2022

• Ian Swift, Sana Ebrahimi, Azade Nova, Abolfazl Asudeh. Maximizing Fair Content Spread via Edge Suggestion in Social Networks. Proceedings of the VLDB Endowment, Vol. 15(11), pages 2692 – 2705,2022.
• Abolfazl Asudeh, Fatemeh Nargesian. Towards Distribution-aware Query Answering in Data Markets. Proceedings of the VLDB Endowment, Vol. 15(11), page 3137 – 3144, 2022.
• (Tutorial) Fatemeh Nargesian, Abolfazl Asudeh, H. V. Jagadish. Responsible Data Integration: Next-generation Challenges. SIGMOD, 2022, ACM.
• Suraj Shetiya, Ian Swift, Abolfazl Asudeh, Gautam Das. Fairness-Aware Range Queries for Selecting Unbiased Data. ICDE, 2022.
• Abolfazl Asudeh, G. Das, H.V. Jagadish, Shangqi Lu, A. Nazi, Yufei Tao, Jianwen Zhao, N. Zhang. On Finding Rank Regret Representatives. ACM TODS, 2022.
• Hadis Anahideh, Abolfazl Asudeh, Saravanan Thirumuruganathan. Fair Active Learning. Expert Systems with Applications (ESWA), 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. Proceedings of the VLDB Endowment, Vol. 14(11), pages 2519–2532, 2021.
• 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. Proceedings of the VLDB Endowment, 13(12): 3445-3448, 2020.
• Yin Lin, Yifan Guan, Abolfazl Asudeh, HV Jagadish. Identifying Insufficient Data Coverage in Databases with Multiple Relations. Proceedings of the VLDB Endowment, Vol. 13(11), 2020.
• Abolfazl Asudeh, H.V. Jagadish, You (Will) Wu, Cong Yu. On Detecting Cherry-picked Trendlines.
  Proceedings of the VLDB Endowment, Vol. 13(6), pages 939–952, 2020.
• Suraj Suresh Shetiya, Abolfazl Asudeh, Sadia Ahmed, Gautam Das. A Unified Optimization Algorithm For Solving “Regret-Minimizing Representative” Problems. Proceedings of the VLDB Endowment, Vol. 13(3), pages 239–251, 2020.
• (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. In VLDB (Demo), 2020.

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. Proceedings of the VLDB Endowment, Vol. 12(3), pages 237–250, 2019.
• 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. In VLDB (Demo), 2019.

2018

• Abolfazl Asudeh, Azade Nazi, Jees Augustine, Saravanan Thirumuruganathan, Nan Zhang, Gautam Das, Divesh Srivastava. Leveraging similarity joins for signal reconstruction. Proceedings of the VLDB Endowment, Vol. 11(10), pages 1276–1288, 2018.
• Sona Hasani, Saravanan Thirumuruganathan, Abolfazl Asudeh, Nick Koudas, Gautam Das. Efficient construction of approximate ad-hoc ML models through materialization and reuse. Proceeding of the VLDB Endowment, Vol. 11(11), pages 1468–1481, 2018.
• 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. Proceedings of the VLDB Endowment, Vol. 9(11), pages 888–899, 2016.
• Abolfazl Asudeh, Saravanan Thirumuruganathan, Nan Zhang, Gautam Das. Discovering the skyline of web databases. Proceedings of the VLDB Endowment, Vol. 9(7), pages 600–611, 2016.
• 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.