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Assistant Professor


Education : Ph.D. (IIT Madras)

Dr. Vinu E. Venugopal is currently an Assistant Professor at the International Institute of Information Technology, Bangalore, India. His research interests include various aspects of data management and have focused on scalable Big Data processing architectures, large-scale knowledge graphs and ontologies. His work has a technological focus and includes building systems for high-velocity stream data processing, distributed deep learning and reasoning.

He received his Ph.D. in Computer Science and Engineering (Dec 2017) from the Indian Institute of Technology Madras (IITM), India. Before joining IIIT Bangalore, he held positions as a Research Associate (Post-doc) in the Big Data, Data Science, and Databases research group at the University of Luxembourg (Mar 2018 - Dec 2021), and as an external lecturer for the Master in Data Science Program at the Leuphana University, Lüneburg, Germany.

He is a recipient of the IITM Institute Research Award in 2017 and has been selected for the Leonardo Research Fellowship in 2020. He has authored several international journals and conference publications, and two of them were nominated for the best paper awards. He has served as a PC member, a reviewer and an organizer for several international conferences, workshops and journals, including VLDB, SIGMOD, SWJ, CIKM, ICML, and many others.

More details about his broad industrial associations and educational background are given on his webpage (https://sites.google.com/site/vinueviitm).

My current research at IIITB has a technological focus that includes building systems for high-velocity stream data processing, distributed deep learning and scalable reasoning. I am broadly interested in working in the various facets of Large-scale Information/Data Management (such as distributed query processing, semantic indexing, information extraction and information retrieval), Knowledge Graphs, Neurosymbolic AI and Semantic Web Technologies. 

Recent Publications

  • AI 551 Foundations of AI Term-II Jan-May Since 2024 (co-teaching with Prof. Badrinath)

  • CS839 NoSQL Systems Term-II Jan- May Since 2022

  • CS876 Streaming Data Systems Term-I Aug-Dec Since 2022

  • CS838 Cloud Computing Term-II Dec-2021 - May-2022 (not offering anymore)

  • Laboratoire des Sciences du Numérique de Nantes (LS2N) CNRS, France, Prof. Dr. Guillaume RASCHIA and Prof. Jose MARINEZ | StreamSpan: Advancing Stream Data Systems with Spanning Events | Funding agency: CEFIPRA

  • Machine Intelligence & Robotics CoE (MINRO), IIITB, India, Prof. G R Sinha | Machine Learning based study of Community Wellness | Funding agency: MINRO, IIITB

  • Center for Technology Research and Innovation (CTRI), IIITB, India, Prof. Chandrashekar Ramanathan An AI-Driven Scalable DataLake Framework | Funding agency: Center for E-Governance, IIITB

  • Center for Internet of Ethical Things (CIET), IIITB, India, Prof. Chandrashekar Ramanathan | Ethics-aware Framework for Large-Scale IoT Data Processing | Funding agency: Gov. of Karnataka.

  • Web Science Lab, IIITB, India, Prof. Srinath Srinivasa & Prof. Sridhir M Karnataka Data Lake | Funding agency: Gov. of Karnataka

  • University of Luxembourg and Max-Planck-Institut für Informatik, Germany, Prof. Dr. Gerhard WEIKUM & Prof. Dr. Martin THEOBALD BigText - A Distributed Graph Database for Large-Scale Text Analytics | Funding agency: FNR CORE

Please refer: https://bda-lab.github.io/projects/ for updated project details.

Scalable Data Science and AI (ScaDS.ai) Lab  (https://scads.iiitb.net/)

The Scalable Data Science and AI (ScaDS.ai) Lab was established by Prof. Vinu E. Venugopal in 2021 at the International Institute of Information Technology Bangalore (IIITB). Our research team is dedicated to creating scalable solutions for a variety of big data challenges.

Our current research efforts are concentrated on two main areas:

  1. Advancing Neuro-Symbolic AI: We are developing robust AI algorithms to enhance reasoning capabilities.

  2. Distributed Streaming Data Systems: We are designing architectures to efficiently process spanning events, which are events associated with a time interval rather than a single timestamp.

Lab members:

PhD Students

  • Naseela Jehan (full-time, Aug 2023 - Present)

  • Vadivelan Balasubramanian (part- time, Jan 2023 - Present)

MS Students

  • Prachi Naik (Aug2022-Present)

  • AniketMitra (Jan2023-Present)