$query = new WP_Query( array( ‘cat’ => ’40’ ) );


Avigdor Gal is the Benjamin and Florence Free Chaired Professor of Data Science at the Faculty of Industrial Engineering & Management at the Technion is a Technion graduate and an expert on information systems. His research focuses on effective methods of integrating data from multiple and diverse sources, which affect the way businesses and consumers seek information over the Internet.

His current work zeroes in on schema matching — the task of providing communication between databases, and connecting such communication to real-world concepts. Another line of research involves the identification of complex events such as flu epidemics, biological attacks, and breaches in computer security, and its application to disaster and crisis management. He has applied his research to European and American projects in government, eHealth, and the integration of business documents.

Born in Tel Aviv-Jaffa, Prof. Gal received his bachelor’s degree in Computer Science in 1990, and in 1995 earned his doctorate in information systems engineering — both from the Technion. During his doctoral studies in the area of temporal active databases, he received the Miriam and Aaron Gutwirth Scholarship for three consecutive years (1993-1995). Alongside his studies, he served in the Israeli Air Force reserves as an information systems consultant.

After a two-year stint from 1995-1997 as a post-doctoral fellow at the University of Toronto in the Department of Computer Science, Prof. Gal started his academic career as an assistant professor at Rutgers University. He joined the Technion in 2001 and has been active in numerous Technion activities including having served as Vice Dean for Teaching from 2008-2011.

Prof. Gal has published more than 100 papers in leading professional journals (e.g. Journal of the ACM (JACM), ACM Transactions on Database Systems (TODS),  IEEE Transactions on Knowledge and Data Engineering (TKDE), ACM Transactions on Internet Technology (TOIT), and the VLDB Journal) and conferences (SIGMOD, VLDB, ICDE, BPM, DEBS, ER, CoopIS) and books (Schema Matching and Mapping). He authored the book Uncertain schema Matching in 2011, serves in various editorial capacities for periodicals including theJournal on Data Semantics (JoDS), Encyclopedia of Database Systems and Computing, and has helped organize professional workshops and conferences nearly every year since 1998.

He has won the IBM Faculty Award each year from 2002-2004, several Technion awards for teaching, the 2011-13 Technion-Microsoft Electronic Commerce Research Award, and the 2012 Yanai Award for Excellence in Academic Education, and others.

Among his many professional activities, he is a senior member of the Institute of Electrical and Electronics Engineers (IEEE), a member of the Association for Computing Machinery (ACM) and an affiliate member of BPM Center, supported by Queensland University of Technology (Australia) and TU Eindhoven (Netherlands). He has served on several advisory boards including DEBS (Distributed Evenet-Based Systems) and CooplS (Cooperative Information Systems).


  • 2019
    JPMorgan AI Faculty Award for “Teaching the Machine to Solve Matching Problems“
  • 2018
    DEBS'2018 Test of Time Award for the paper “Complex Event Processing over Uncertain Data“ (DEBS 2008)
  • 2018
    Accenture Research Award for $50,000.
  • 2017
    BPM'2017 best paper award for the paper “Temporal Network Representation of Event Logs for Improved Performance Modelling in Business Processes“
  • 2014
    Audience Award for the paper “Grand Challenge: Scalable Stateful Stream Processing for Smart Grids” at DEBS’2014
  • 2014
    ICAPS 2014 Honorable Mention for the Outstanding Paper Award for the paper “Goal Recognition Design.”
  • 2013
    Audience Award for the paper “Grand Challenge: The TechniBall System” at DEBS’2013
  • 2012
    Yanai Award for Excellence in Academic Education
  • 2011, 2012
    Technion-Microsoft Electronic Commerce Research Award for $15,000
  • 2010, 2011, 2013, 2014,2015
    Technion Excellence in Teaching
    top 12% Technion-wide in teaching
    evaluations for the course category
  • 2009
    Opossum (created jointly with Eran Toch, Iris Reinhartz Berger, and Dov Dori) won the 3rd International Semantic Service Selection Contest
    at the Third International Workshop SMR2
    2009 on Service Matchmaking and Resource Retrieval in the Semantic Web as the fastest
    OWL-S matchmaker.
  • 2009, 2012
    Technion Award for Outstanding Achievements in Teaching
    top 4% Technion-wide
    in teaching evaluations for the course category
  • 2006-present
    IEEE Computer Society Senior member
  • 2002-2004
    Alexander Goldberg Academic Lectureship in Industrial Engineering and Management - England
    including an award for $800 annualy
  • 2002-2004
    IBM Faculty Award for $20,000 annualy
  • 1998
    Best Paper Award for the paper “Information Services for the Web: Building and Maintaining Domain Models” at CoopIS’98
  • 1993-1995
    The Miriam and Aaron Gutwirth for Outstanding Research Achievements
    Highly Outstanding Research Achievements for 1995
  • 1990
    President’s List for Highly Outstanding Achievements- Technion, Israel Institute of Technology
  • 1987-1989
    Dean’s List for Outstanding Achievements- Technion, Israel Institute of Technology
November 2022: A talk and an interview during a visit to Cornell University at the beginning of October are available on YouTube:  https://www.youtube.com/watch?v=Ryb9fteSioQ, https://www.youtube.com/watch?v=_Kae9_j3RNc  
October 2022: The power of co-joint deep learning! The paper “FlexER: Flexible Entity Resolution for Multiple Intents”, co-authored by Bar Genossar, Roee Shraga, and Avigdor Gal, was accepted to SIGMOD2023 (Seattle, June 2023). The paper tells the story of multiple intents in Entity Resolution and how GNN can jointly learn how to identify tuple duplicates for different intents. A Technical Report can be downloaded from https://arxiv.org/abs/2209.07569
August 2021: The paper “PoWareMatch: a Quality-aware Deep Learning Approach to Improve Human Schema Matching”, authored by Roee Shraga and Avigdor Gal, has been accepted to the Journal of Data & Information Quality (JDIQ), Special Issue on Deep Learning for #DataQuality. The paper tackles the problem of #schemamatching, the ability to generate quality matches among data concepts (e.g., database attributes), a core task of any data integration process. The paper examine a novel angle on the behavior of humans as matchers, studying match creation as a process. We analyze the dynamics of common evaluation measures (precision, recall, and f-measure) and highlight the need for unbiased matching, a newly defined concept that highlights the problematic assumption that human decisions represent reliable assessments of schemata correspondences. PoWareMatch makes use of a #deeplearning mechanism to calibrate and filter human matching decisions, which are then combined with algorithmic matching to generate better match results.
WP Twitter Auto Publish Powered By : XYZScripts.com