SUJIT GUJAR


Currently, I am working as Assistant Professor at Machine Learning Laboratory, International Institute of Information Technology, Hyderabad. 


Prior to this, I was a post-doctoral researcher with Prof Boi Faltings, LIA, EPFL, Lausanne (Jan'14-Oct'15) and a Sr Research Associate with Prof Y Narahari (Nov'15-Apr'16).  I was a Research Scientist at Xerox Research Centre India (Jan'11-Nov'13).   I completed my Ph.D. in the Department of Computer Science and Automation @ Indian Institute of Science, Bangalore. I worked with Prof Y Narahari, Game Theory Lab. I am a recipient of the Alumni Medal of IISc for the Best Thesis in CSA for the academic year 2011-12' for my Ph.D. Dissertation, Novel Mechanisms for Allocation of Heterogeneous Items in Strategic Settings.  During my Ph.D. I was a recipient of the Infosys Doctoral Fellowship. (For Curriculum Vite)



Research Interests

  • Game Theory and Mechanism Design 
  • Application of Game Theory to Artificial Intelligence (AI)
    • Social networks
    • Internet advertising
    • Crowdsourcing
    • Crowdfunding
    • Intelligent transportation
    • Online education
  • AI and Machine Learning
  • Cryptographic Game Theory (Securing Auctions) 
  • Distributing trust: Block-chain Technology
  • Game Theory and Machine Learning

Recent Updates

  • Our following papers got accepted in the Workshop on Game Theory in Blockchain, GTiB@WINE2020
    • ASHWAChain: A Fast, Scalable and Strategy-proof Committee-based Blockchain Protocol
    • Block Rewards, Not Transaction Fees Keep Miners Faithful In Blockchain Protocols
  • Our following paper got accepted in IJCAI 2020
    • FNNC: Achieving Fairness through Neural Networks
  • Our following papers got accepted in AAMAS 2020 as Extended Abstracts
    • BitcoinF: Achieving Fairness For Bitcoin In Transaction Fee Only Model
    • Designing Truthful Contextual Multi-Armed Bandits based Sponsored Search Auctions
    • Ballooning Multi-Armed Bandits
  • Our following paper got accepted in INFOCOM 2020
    • Mneme: A Mobile Distributed Ledger
  • Our two papers are accepted in AAAI 2020
    • A Multiarmed Bandit Based Incentive Mechanism for a Subset Selection of Customers for Demand Response in Smart Grids
    •  Bidding in Smart Grid PDAs: Theory, Analysis and Strategy

Openings

I have few openings for highly motivated Master and Ph.D. students to work on fascinating research problems in applied game theory as well as research challenges which lie at the intersection of Machine Learning and Game Theory and block-chain technology.  I am excited about how to design protocols/ algorithms for modern marketplaces strategy-proof, that is, strategic agents do not manipulate it. I am also intrigued by the fact that with appropriate incentive engineering, blockchain technology has not just solved distributed consensus, but enabled to spread the trust across the whole network. Note you must be admitted to IIITH if you are interested in working with me as a regular student. If you are interested, you can drop an email to schedule a meeting. Currently, I do not have any internship openings for Bachelor's or Master's students. 

Professional Activities

        SPC Member: IJCAI 2021

        PC member:   IJCAI 2020, 2019, 2018,2017,2016. AAMAS 2020,2019,2018,2017. AAAI 2021, 2020. WINE 2017.

        Reviewer: Conferences   TheWebConf 2019, FSTCCS'15, ACM EC'15, AAAI'15, ICOR'12 WINE'12, 

                       Journals        MACH, AI, GEB, EJOR, TMC, ECRA, IEEE SMC, JAAMAS

Teaching


  • CSE435: Advanced Communication Networks (Monsoon 2018, Monsoon 2019) (IIITH)
  • CSE512: Distributing Trust and Blockchains (Spring 2018, Monsoon 2018, Monsoon 2019) (IIITH)
  • CSE481: Optimization Methods (Spring 2017, Spring 2018) (IIITH)
  • CSE498: Introduction to Game Theory (Monsoon 2016, Monsoon 2017, Spring 2019)  (IIITH)
  • Advanced Topics in Algorithmic Game Theory and Mechanism Design (Spring 2015) (EPFL)
   

Recent Publications

  • ASHWAChain: A Fast, Scalable and Strategy-proof Committee-based Blockchain Protocol, GTiB@WINE 2020
  • Block Rewards, Not Transaction Fees Keep Miners Faithful In Blockchain Protocols, GTiB@WINE 2020
  • FNNC: Achieving Fairness through Neural Networks, IJCAI 2020
  • Ballooning Multi-Armed Bandits, AAMAS 2020 and ALA,AAMAS 2020
  • Orthos: A Trustworthy AI Framework For Data Acquisition, EMAS, AAMAS'20
  • Designing Refund Bonus Schemes for Provision Point Mechanism in Civic Crowdfunding, GAIW, AAMAS'20
  • BitcoinF: Achieving Fairness For Bitcoin In Transaction Fee Only Model, AAMAS 2020
  • Designing Truthful Contextual Multi-Armed Bandits based Sponsored Search Auctions, AAMAS 2020
  • Mneme: A Mobile Distributed Ledger, INFOCOM 2020
  • Human Machine Collaboration for Face Recognition, CoDS-COMAD 2020 (Best Paper Runner-up)
  • A Multi-armed Bandit Based Incentive Mechanism for a Subset Selection of Customers for Demand Response in Smart Grids, AAAI 2020
  • Bidding in Smart Grid PDAs: Theory, Analysis and Strategy, AAAI 2020
  • FaRM: Fair Reward Mechanism for Information Aggregation in Spontaneous Localized Settings, IJCAI 2019
  • Civic Crowdfunding for Agents with Negative Valuations and Agents with Asymmetric Beliefs,  IJCAI 2019
  • HRCR: Hidden Markov-based Reinforcement to Reduce Churn in Question Answering Forums, PRICAI 2019
  • Thompson Sampling Based Multi-Armed-Bandit Mechanism Using Neural Networks, AAMAS 2019
  • A Truthful, Privacy-Preserving, Approximately Efficient Combinatorial Auction For Single-minded Bidders, AAMAS 2019
  • Aggregating Citizen Preferences for Public Projects Through Civic Crowdfunding, AAMAS 2019
  • A Reinforcement Learning Based Broker Agent for a Power Trading Competition: Design and Performance, AAAI 2019

Elsewhere


 

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