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Anirban Mondal

Associate Professor of Computer Science, Ashoka University

Ph.D. National University of Singapore

Dr. Anirban Mondal is an Associate Professor of Computer Science at Ashoka University. He has a Ph.D. in Computer Science from the National University of Singapore, an MBA from the University of Massachusetts Amherst (UMass) and a B.Tech. (Hons.) in Computer Science & Engineering from the Indian Institute of Technology (IIT) Kharagpur, India. During the past 18+ years, he has led multiple key projects for envisioning, designing and architecting end-to-end systems in domains such as urban informatics (smart cities), spatial databases and financial analytics. His technological expertise coupled with his business capabilities as well as his ability to create a big vision and execute it to completion in diverse multi-cultural settings make him an exciting innovator. 

His research interests include big data analytics, mobile and ubiquitous data management, incentive-based mobile crowdsourcing, spatial databases, database indexing, Big Data, IoT, distributed databases and large-scale data management in distributed systems such as P2P environments. He has an established reputation, key presence and high visibility in the international research community and contributes pro-actively to local research communities as well. He has numerous publications in key conferences/journals and is actively involved as a PC Chair/Co-chair, PC member, journal reviewer as well as keynote/tutorial speaker at reputed international conferences/workshops. More recently, he has served as the General Chair for BDA 2020, and he is serving as the one of the General Chairs of DASFAA 2022. He has a proven track record in establishing international research collaborations. His research collaborations include prestigious Universities in Japan, Singapore, USA, Canada, Australia and India.

He has extensive work experience in both academia and industry. His work experience includes 7 years as a Research Associate at the University of Tokyo (Japan), 3+ years as an Associate Professor at the Indraprastha Institute of Information Technology Delhi (IIIT Delhi, India), and 3 years as a Senior Research Scientist at Xerox Research Center India. He has spearheaded industry research projects in domains such as urban informatics and finance, leading to four granted patents by the USPTO (US Patent and Trademark Office) as well as several patent filings. He has also been a Fellow of the prestigious Japan Society for Promotion of Science (JSPS) as well as an ACM India Eminent Speaker.

  • Cost-Effective Air Quality Monitoring with Decision Support (Funded by Mphasis Labs at Ashoka and 3CS)
    • PI: Anirban Mondal (Ashoka University, India); Girish Agrawal (O.P. Jindal Global University) and P. Krishna Reddy, Professor, IIIT Hyderabad

The goal of this project is to provide a visual tool both for the public, and for administrators and planners charged with improving air quality. The tool will allow the public to assess in real-time the air quality around them, and work with their local representatives and government officials to take steps to improve their local air quality. The final product will also be an easy-to-use tool for state, regional and local administrators and planners to evaluate various scenarios for improving air quality, the impact of various interventions on air quality, by performing what-if analyses, and achieve SDG targets related to improving public health through improved air quality.

  • COLOURS: A COgnition-enabLed information system for personalized tOURistic experienceS in India (Funded by Technology Innovation Hub, IIIT Delhi).
    • PI: Anirban Mondal (Ashoka University, India); Co-PIs: Mukesh Mohania (IIIT Delhi, India) and Ladjel Bellatreche (ENSMA, Poitiers, France,)

COLOURS is a personalized cognitive computing and social sensing based tourist assistance information system for creating a highly enhanced and immersive tourism experience in India. COLOURS collects hyperlocal information from local people, experts and historical/cultural documents to build a cognitive knowledge base.

  • Utility Mining for itemset placement in the retail industry: This project examines research issues and challenges for addressing the effective placement of itemsets in retail stores for maximizing the revenue of the retailer. In particular, this project concerns research issues including (but not limited to) products/items of different sizes, retail slot premiumness, itemset diversification, retail inventory management, market segmentation, spatial placement of itemsets and so on.
  • Financial Analytics on 10K reports/annual reports and social media: The goal of this FinTech project is to understand the financial health of firms by effectively analyzing the data from multiple sources such as 10K reports/annual reports and social media. In particular, we examine the data from the four key financial statements (Balance Sheet, Income Statement, Statement of Cash Flow and Statement of Equity) and try to interpret the significance of the data in these statements in the light of macro-environmental factors, which can be analyzed from news and social media.

DBLP: https://dblp.org/pid/61/780.html

Google Scholar page: https://scholar.google.co.in/citations?user=WGjWgoEAAAAJ&hl=en

Selected Recent Publications (Journals/Books)

  • Anirban Mondal, Samant Saurabh, Parul Chaudhary, Raghav Mittal and P. Krishna Reddy. A retail itemset placement framework based on Premiumness of Slots and Utility Mining. IEEE Access, 2021
  • Anirban Mondal, Ayaan Kakkar, Nilesh Padhariya and Mukesh Mohania. Efficient Indexing of Top-k Entities in Systems of Engagement with Extensions for Geo-tagged Entities, Data Science and Engineering, 2021
  • Samant Saurabh, Sanjay Madria, Anirban Mondal, Ashok Singh Sairam and Saurabh Mishra. An analytical model for information gathering and propagation in social networks using random graphs. Data and Knowledge Engineering (DKE). 129: 101852, 2020 
  • Parul Chaudhary, Anirban Mondal, Polepalli Krishna Reddy. An improved scheme for determining top-revenue itemsets for placement in retail businesses. International Journal of Data Science and Analytics, 10(4): 359-375, 2020 
  • Periodic Pattern Mining: Theory, Algorithms, and Applications. Editors: Uday Kiran Rage, Philippe Fournier-Viger, Jose Maria Luna., Jerry Chun-Wei Lin and Anirban Mondal (Eds.) Publisher: Springer (Book published by Springer), 2020
  • Mohamed Hamlich, Ladjel Bellatreche, Anirban Mondal and Carlos Ordonez. Smart Applications and Data Analysis – Third International Conference, SADASC 2020, Marrakesh, Morocco, Proceedings. Communications in Computer and Information Science 1207, Springer 2020, ISBN 978-3-030-45182-0 (Conference Proceedings/Book)
  • Ladjel Bellatreche, Vikram Goyal, Hamido Fujita, Anirban Mondal, P. Krishna Reddy. Big Data Analytics – 8th International Conference, BDA 2020, Sonepat, India, December 15-18, 2020, Proceedings. Lecture Notes in Computer Science 12581, Springer 2020, ISBN 978-3-030-66664-4 (Conference Proceedings/Book)
  • Lakshmi Gangumalla, P. Krishna Reddy, Anirban Mondal. Multi-location visibility query processing using portion-based transactional modeling and pattern mining. Data Mining Knowledge Discovery 33(5): 1393-1416, 2019

Selected Recent Publications (Conferences)

  • Raghav Mittal, Anirban Mondal, Parul Chaudhary and P. Krishna Reddy. An Urgency-aware and Revenue-based Itemset Placement Framework for Retail Stores. Proceedings of the Database and Expert Systems Applications (DEXA) Conference, 2021 
  • Anirban Mondal, Raghav Mittal, Vrinda Khandelwal, Parul Chaudhary and P.K. Reddy. PEAR: A Product Expiry-Aware and Revenue-Conscious Itemset Placement Scheme. Proceedings of the IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2021
  • Shadaab Siddiqie, Anirban Mondal and P. Krishna Reddy. An Improved Dummy Generation Approach for Enhancing User Location Privacy. International Conference on Database Systems for Advanced Applications. Proceedings of the International Conference on Database Systems for Advanced Applications (DASFAA), 2021: 487-495 
  • Anirban Mondal, Nilesh Padhariya and Mukesh Mohania. Towards Efficient Retrieval of Top-k Entities in Systems of Engagement. Proceedings of the 21st International Conference on Web Information Systems Engineering (WISE), 2020: 52-67 
  • Pooja Gaur, P. Krishna Reddy, Mittapally Kumara Swamy and Anirban Mondal. A Revenue-Based Product Placement Framework to Improve Diversity in Retail Businesses. Proceedings of the International Conference on Big Data Analytics (BDA), 2020: 289-307 
  • Akhil Ralla, Shadaab Siddiqie, P. Krishna Reddy and Anirban Mondal. Coverage Pattern Mining Based on MapReduce. Proceedings of the ACM India Joint International Conference on Data Science and Management of Data (CoDS-COMAD), 2020: 209-213
  • Shadaab Siddiqie, Akhil Ralla, P. Krishna Reddy, Anirban Mondal: Sensor-based Framework for Improved Air Conditioning Under Diverse Temperature Layout. Proc. ICDCN 2020: 50:1-50:6 
  • Chinmay Bapna, Polepalli Krishna Reddy, Anirban Mondal. Improving Product Placement in Retail with Generalized High-Utility Itemsets. Proceedings of the International Conference on Data Science and Advanced Analytics (DSAA), 2020: 60-69 
  • Shadaab Siddiqie, Akhil Ralla, P. Krishna Reddy, Anirban Mondal: Sensor-based Framework for Improved Air Conditioning Under Diverse Temperature Layout. Proc. ICDCN 2020: 50:1-50:6 
  • Parul Chaudhary, Anirban Mondal, Polepalli Krishna Reddy. An Efficient Premiumness and Utility-Based Itemset Placement Scheme for Retail Stores. Database and Expert Systems Applications – 30th International Conference, DEXA 2019, Linz, Austria, August 26-29, 2019
  • P. Revanth Rathan, P. Krishna Reddy, Anirban Mondal. Discovering Diverse Popular Paths Using Transactional Modeling and Pattern Mining. Database and Expert Systems Applications – 30th International Conference, DEXA 2019, Linz, Austria, August 26-29, 2019
  • Akhil Ralla, P. Krishna Reddy, Anirban Mondal. An Incremental Technique for Mining Coverage Patterns in Large Databases. Proceedings of the International Conference on Data Science and Advanced Analytics – DSAA 2019: 211-220 
Study at Ashoka

Study at Ashoka