About
Innovative Data Exploration Laboratory (InDeX Lab) is an academic research group directed by Dr. A. Asudeh, at the Computer Science department of the University of Illinois Chicago.
At InDeX Lab, we design Efficient, Accurate, and Scalable Algorithms for Data Science and AI.
At InDeX Lab, we design Efficient, Accurate, and Scalable Algorithms for Data Science and AI.
Highlights
- 2025: seven full papers, including papers in KDD (two papers), ICML, VLDB, and EDBT.
- 2024: nine full papers in SIGMOD, VLDB, NAACL, VLDB Journal (two papers), EDBT (two papers), ICKG, and one invited paper in Data Engineering Bulletin; and one Demo paper in VLDB.
- 2023: three full papers in VLDB, KDD, and ALGORITHMICA, one survey paper in ACM COMPUTING SURVEYS, one tutorial in WSDM, and two workshop papers in Fairness workshop at SDM'23.
- 2022: five full papers (two in VLBD, one in ICDE, and two journal papers in ACM TODS and Expert Systems) and presented one tutorial (in SIGMOD), in collaboration with University of Rochester, UTA, U-M, and Google Brain.
- 2021:
- Four full papers (two in SIGMOD, one in VLBD, and one in ICDE), one invited paper in Data Engineering Bulletin, and one workshop paper (in SIGMOD-DEEM), in collaboration with the DBGroup@UM, ChuDataLab@GaTech, University of Rochester, Politecnico di Torino, and Google Research (Structured Data Group).
- Google's Research Scholar award for our work on Cherry-picked Trendlines.
- Communications of the ACM featured our work on "Signal Reconstruction at Scale" as its Research Highlight in February 2021, 64.2: 106-115.
- ACM SIGMOD Blog featured our article "Enabling Responsible Data Science in Practice", Jan. 2021. - 2020: five papers in VLBD 2020 (three full reseach papers, one tutorial, and one demo), 1 demo paper in SIGMOD 2020, and 1 invited paper in VLBDJ (Special Issue on Best of VLDB'18), in collaboration with the DBGroup@UM and DBXLAB@UTA.
- 2019:
- Three papers in SIGMOD 2019 (two full reseach papers and one demo), 2 papers in VLDB 2019 (one full research paper and one demo), 1 full research paper in ICDE 2019, 1 in PAKDD 2019, 1 demo paper in CIKM 2019, and 1 invited paper in Data Engineering Bulletin.
- ACM SIGMOD Research Highlight Award 2019.
News and Announcements
- Congratulations to Mohsen Dehghankar on his KDD'25 paper, “Rank It, Then Ask It: Input Reranking for Maximizing the Performance of LLMs on Symmetric Tasks”.
- Congratulations to Mohsen Dehghankar and Mahdi Erfanian on their ICML'25 paper, “An Efficient Matrix Multiplication Algorithm for Accelerating Inference in Binary and Ternary Neural Networks”.
- Congratulations to Nima Shahbazi on the “2024-2025 College of Engineering Exceptional Research Promise Award”.
- Nima Shahbazi will be a Research Intern at Microsoft Research, Gray Systems Lab in Summer 2025.
- Congratulations to Mohsen Dehghankar on his VLDB'25 paper, “Mining the Minoria: Unknown, Under-represented, and Under-performing Minority Groups”.
- Congratulations to Mohsen Dehghankar on his KDD'25 paper, “Fair Set Cover”.
- Congratulations to Mahdi Erfanian on his VLDB'24 paper, “Chameleon: Foundation Models for Fairness-aware Multi-modal Data Augmentation to Enhance Coverage of Minorities”.
- Congratulations to Sana Ebrahimi and Rishi Advani on their EDBT'25 paper, “Evaluating the Feasibility of Sampling-Based Techniques for Training Multilayer Perceptrons”.
- Congratulations to Nima Shahbazi and Mahdi Erfanian on their VLDB'24 demo paper, “FairEM360: A Suite for Responsible Entity Matching”.
- Congratulations to Nima Shahbazi on his VLDB Journal (2024) paper, “Reliability Evaluation of Individual Predictions: A Data-centric Approach”.
- Our (invited) paper Coverage-based Data-centric Approaches for Responsible and Trustworthy AI was published in Data Engineering Bulletin Vol. 48(1), March 2024.
- Congratulations to Sana Ebrahimi and Nima Shahbazi on their NAACL'24 paper, “Reliability and Equity through Aggregation in Large Language Models”.
- Congratulations to Nima Shahbazi on his (Megagon) internship project's acceptance in ICDE'24.
- Congratulations to Nima Shahbazi on his SIGMOD'24 paper, “FairHash: A Fair and Memory/Time-efficient Hashmap”.
- Nima Shahbazi will be a Research Scientist Intern at Megagon Labs in Summer 2023.
- Congratulations to Nima Shahbazi on his VLDB'23 paper, “Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching”.
- Congratulations to Melika Mousavi and Nima Shahbazi on their EDBT'24 paper, “Data Coverage for Detecting Representation Bias in Image Data Sets: A Crowdsourcing Approach”.
- Congratulations to Rishi Advani on his KDD'23 paper, “Maximizing Neutrality in News Ordering”.
- Congratulations to Nima Shahbazi on his ACM COMPUTING SURVEYS (CSUR) paper, “A Survey on Techniques for Identifying and Resolving Representation Bias in Data”.
- Congratulations to Khanh Duy Nguyen on his SDM workshop paper, “PopSim: An Individual-level Population Simulator for Equitable Allocation of City Resources”.
- Congratulations to Ian Swift and Sana Ebrahimi on their VLDB 2022 paper, “Maximizing Fair Content Spread via Edge Suggestion in Social Networks”.
- Here are the slides and other information about our SIGMOD'22 tutorial on "Responsible Data Integration: Next-generation Challenges".
- Congratulations to Ian Swift on his ICDE 2022 paper, “Fairness-Aware Range Queries for Selecting Unbiased Data”.
- A big Thank you to Google for supporting our work on Cherry-picked Trendlines with the Research Scholar award!
- Congratulations to Nima Shahbazi on his SIGMOD 2021 paper, “Identifying Insufficient Data Coverage for Ordinal Continuous-Valued Attributes”.[paper][slides][video]
- Congratulations to Matteo Corain on his ICDE 2021 paper, “DBSCOUT: A density-based method for scalable outlier detection in very large datasets”. This paper is the outcome of his joint MS thesis with Politecnico di Torino (Italy), co-adviced by Dr. Paolo Garza.
📌 Pinned Systems and Repositories 📌
Needle🪡🔍 is a deployment-ready open-source image retrieval database with high accuracy that can handle complex queries in natural language.
It is Fast, Efficient, and Precise, outperforming state-of-the-art methods.
Born from high-end research, Needle is designed to be accessible to everyone while delivering top-notch performance.
Whether you are a researcher, developer, or an enthusiast, Needle opens up innovative ways to explore your image datasets. ✨
📖 Detailed installation instructions: Getting Started .
RSR 🧮: Efficient Matrix Multiplication for Accelerating Inference in Binary and Ternary Neural Networks
This project aims to provide a fast and efficient approach to low-bit matrix multiplication. The code repository implements Redundant Segment Reduction (RSR), a fast matrix multiplication algorithm designed for matrices in binary and ternary networks. The RSR method optimizes computation efficiency by a log(n) factor, making it particularly useful for applications in low-bit deep learning and efficient inference. The codebase provides ready-to-use C++ and NumPy-based implementations, as well as PyTorch implementations with both CPU and GPU support, enabling scalable and optimized matrix operations in deep learning environments. It includes sample experiments on various `1.58bit` models and LLMs.✨Sponsors
