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 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.
Keywords: Applied Machine Learning and Reinforcement Learning, Blockchains, Service computing, IoT. |
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)
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- 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.
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2) Non-Verbal Behavior Analyzer (NOVOR)
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- 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.
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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)
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- 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.
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2) AI for Blockchains
2.1) Blockchain Data Analytics (Daan.chains)
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- Team: Akash Singh Baghel (RA) and Dr. Partha Mukherjee
- Objective: build analytics pipelines to explore, understand and get insights from Ethereum and Bitcoin blockchains.
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2.1) Cryptocurrencies Exchange Rates Forecasting
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- 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.
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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
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- 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)
- Team:
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2) Auto-Adversarial and Bias Vulnerability Detection
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- Team: Rahu Sharma (RA) and Suraj Bondugula (RA)
- Objective: Build an automated tool to detect adversarial attacks and biases.
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3) Smart and Secure Devices
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- 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)
- Reinforcement Learning-based energy consumption Controller
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- Legends:
RA – Research Assistant
MS – MS graduate student