KDD 2023 Workshop on Knowledge-infused Learning

I am an assistant professor of Computer Science and an affiliate faculty member of Ebiquity Research Group at University of Maryland Baltimore County (UMBC). In the past, I have hold the position of Senior AI Research Scientist with the Knowledge and Dialog team within AI Center at Samsung Research America. I was a visiting researcher at the Alan Turing Institute. I have been awarded PhD in Computer Science from the Artificial Intelligence Institute at The University of South Carolina in 2022, where I was advised by Dr. Amit Sheth. My Ph.D. research was supported by University of Chicago's Data Science for Social Good Fellowship, AI For Social Good Fellowship from Dataminr Inc., EPSRC-UKRI grant through Alan Turing Institute, and NSF-EAGER on Knowledge-infused Learning. Prior to this, I completed bachelors and masters in Computer Science and Software Engineering from Netaji Subhas University of Technology(East Campus) and Delhi Technological University, India, respectively.

🚨At UMBC CSEE, I will be starting a reading+research group: Knowledge-infused AI and Inference( KAI²). For preface, there are some useful Youtube Videos on Knowledge-infused Learning. KAI² Website Coming Soon. Till then, please drop me an email if you would like to join.

Research Interests

I have broad interests in knowledge graphs and machine learning (in particular deep learning) for application into natural language processing, recommender systems, and computational social data science. My research is mostly directed towards:

• Knowledge-infused Learning: is a class of Neuro-Symbolic AI techniques that utilize a variety of knowledge (lexical, linguistic, domain specific, commonsense, process knowledge and constraint-based) in different forms and abstractions into deep neural network. It improves upon data-centric statistical learning to reduce training, reduce computing needs, and broaden coverage, resulting in improved performance, safety and model interpretation, and providing user-level explanations.
• Knowledge Intensive Language Understanding: is a new unified benchmark to help AI researchers build models that are better able to leverage real-world knowledge to accomplish a broad range of tasks. Models that are better able to leverage real-world knowledge do well in tasks o

I recently gave a tutorial on Knowledge-infused Reinforcement Learning at Knowledge Graph Conference, which is one of core AI areas I am interested in. I am fascinated about interdisciplinary research as it provide practical means to make real world impact. I collaborate with domain experts in mental health, crisis informatics, conversational AI, virtual health assistant, and digital security. I also have been a co-organizer and a strong proponent of two communities: Knowledge-infused Mining and Learning and Computational Linguistics and Clinical Psychology.

Contact

Prospective Students:

I am actively looking for skilled and self-motivated student to join my group! If you are interested in working with me, please apply through the UMBC PhD program and mention me in your application.

UMBC Students:

1. If you are already a graduate student at UMBC, feel free to reach out.
2. I am also accepting undergraduate students for senior thesis advising or independent work. I also recommend you take CMSC 678 (or at least one of Machine Learning or NLP or Knowledge Graph classes) before getting into knowledge-infused learning research.

I also have weekly Open Session for UMBC and Prospective students (undergraduate and graduate). If you would like to discuss any topic with me, feel free to sign up for them (15-minute each; Multiple booking is also possible.)

Quick Links

• CV
• Academic Research Statement
• Academic Teaching Statement
• Google Scholar Profile
• Ph.D. Dissertation Video

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