Research

  • Research

Trustworthy AI Service Systems

By leveraging my research activities built around service computing and smart services for the Internet of Things (IoT), my current research strategy since I joined PSU aims at developing a new chain of data analytical models and tools and platforms for designing and deploying “Trustworthy AI Service Systems.” To this end, I am investigating a multidisciplinary and systematic approach, integrating AI, IoT, and Blockchains. My current research projects fall under the following topics.

    1. AI-based Service Systems 
    2. Blockchains and AI-based Services
    3. Trustworthiness and AI Systems

Keywords: Applied Machine Learning and Reinforcement Learning, Blockchains, Service Computing, IoT. 

Research Funding and Grants

Managing Risks in AI Systems: Mitigating Vulnerabilities and Threats Using Design Tactics and Patterns
      • Team:
        • Youakim Badr (PI), School of Graduate Professional Studies
        • Raghu Sangwan, Satish Srinivasan, and Partha Mukherjee, (School of Graduate Professional Studies) (co-PIs)                
        • Prasenjit Mitra, College of IST, Technical Consultant
      • Program: 2020 industryXchange – Seed Grant
      • Budget: $50,000, Period: 10/01/2020 – 10/01/2021
Crowdlearning: Building Trustworthy AI Models from Crowdsourced Data and Edge Computing
      • Team:
        • Youakim Badr (PI), School of Graduate Professional Studies
        • Prasenjit Mitra (co-PI), College of Information Sciences and Technology
      • Program: Center for Security Research and Education – Impact Award
      • Budget: $57,467; Period: 01/11/2021 – 2/01/2022

Research Projects

AI-based Service Systems 

The key challenge under this topic revolves around new analytic methods and techniques to improve human-to-machine cognitive-based services in the Cloud or at the edge.

1) General Purpose Conversional AI Expert (Impulso)
      • Team: Atharva Mungee (MS) and Dr. Robin Qiu
      • Objective: build a Conversational AI service based on cognitive processes and learning objectives in a closed domain. The Conversational AI service continuously adapts conversions and answers based on accumulated knowledge acquired from interactions with humans and the assessment of their cognitive capabilities.
2) Non-Verbal Behavior Analyzer (NOVOR)
        • Team: Shraddha Maurya (RA), Sura Bondugula (RA), and Dr. Minyoung Cheong
        • Objective: develop a platform and models to detect patterns of non-verbal behaviors when humans interact with each other’s and/or with virtual assistants.

Blockchains and AI-based Services

The integration of Blockchains and AI still a largely undiscovered area and their combination have the potential to build new services in ways never before thought possible. To tackle this challenge, I am leading complementary research projects, covering AI for Blockchains, Blockchains for AI and distributed applications based on the integration of AI and Blockchains. 

1) Integration of AI and Blockchains 3.0 (chAIns)

      • Team: Vineeth Suhas Challagali (RA) and Dr. Partha Mukherjee
      • Objective: identify solutions to cope with challenges of existing blockchains in terms of interoperability, scalability, privacy and computational capabilities when using them to build end-to-end machine learning systems.

2) AI for Blockchains 

2.1)  Blockchain Data Analytics (Daan.chains)
          • Team: Akash Singh Baghel (RA) and Dr. Partha Mukherjee
          • Objective: build analytics pipelines to explore, understand and get insights from Ethereum and Bitcoin blockchains. 
2.1) Cryptocurrencies Exchange Rates Forecasting
          •  Team: Gauravi Bhalchandra Patil (RA), Mokkapati, Yogitha Siva (RA) and Dr. Partha Mukherjee
          •  Objective: deploy state of the art Deep Neural Networks models to forecast the Ethereum and Bitcoin Exchange Rates.

Trustworthiness and AI Systems

Recent advances in AI outperform in many cognitive tasks and become omnipresent in decision-making systems, self-driving cars, and critical systems. AI systems also present risks and biases and we must carefully consider their safety, trustworthiness, robustness, and dependability. In this topic, I am interested in AI risk management to identify vulnerability, assess, and mitigate risks at design and deployment time.

 1) Managing Risks in AI Systems

        • Team:
          • Youakim Badr (PI), School of Graduate Professional Studies (GV SGPS
          • Raghu Sangwan, Satish Srinivasan, and Partha Mukherjee, (GV SGPS) (co-PIs)                
          • Prasenjit Mitra, College of IST, Technical Consultant
        • Objective: This research proposal aims at developing Risk Management Framework for AI systems from holistic and multi-disciplinary perspectives considering risk assessment, fault tolerance and prediction, software testability, and mitigating vulnerabilities and threats using design tactics and patterns in distributed environments.
        • Funding: industryXchange 2020 seed grants ($50k)

2) Auto-Adversarial and Bias Vulnerability Detection

        • Team: Rahu Sharma (RA) and Suraj Bondugula (RA)
        • Objective: Build an automated tool to detect adversarial attacks and biases.

3) Smart and Secure Devices

        • Reinforcement Learning-based energy consumption Controller
          • Team: Anchal Gupta (MS), Dr. Robin Qiu, Dr. Ashkan Negahban
          • Objective: build and deploy Reinforcement Learning Controller on devices
        • Reinforcement Learning-based-Intrusion detection at the edge
          • Team: Wahid Khan Abzal (RA)
          • Objective: build and deploy an intrusion detector at the edge (Raspberry Pi)
  • Legends:
    RA – Research Assistant
    MS – MS graduate student