Innovative Data Exploration Laboratory (InDeX Lab) is an academic research group directed by Dr. Abolfazl Asudeh, at the Computer Science department of the University of Illinois Chicago. At InDeX Lab, we study different aspects of Big Data and Data Science, including data management, data analytics, and data mining, for which we aim to find efficient, accurate, and scalable algorithmic solutions.

  • 2023:
    - We published 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:
    - We published 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:
    - We published 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).
    - We received 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:
    - We published 5 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:
    - We published 3 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.
    - We received the ACM SIGMOD Research Highlight Award 2019.
  • Congratulations to Nima Shahbazi on his VLDBJ'24 paper, “Reliability Evaluation of Individual Predictions: A Data-centric Approach”.
  • Congratulations to Nima Shahbazi on his SIGMOD'24 paper, “FairHash: A Fair and Memory/Time-efficient Hashmap”.
  • 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.

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