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Curriculum Vitae, Updated November 14th 2024.

🎓 Education

📚 Publications

📘 Book Chapters

  1. Kaushik Roy, Wu, S., Oltramari, M. (2025). “Neurosymbolic Cognitive Methods for Enhancing Foundation Model-based Reasoning.” (to appear in: The Handbook on Neurosymbolic AI and Knowledge Graphs, IOS Press) [chapter preprint].

  2. Kaushik Roy, Gaur, M., Soltani, M., Rawte, V., Kalyan, A., and Sheth, A. (2024). “Proknow: Process Knowledge for Safety Constrained and Explainable Question Generation for Mental Health Diagnostic Assistance in the Age of Large Language Models.” Science and Technology - Recent Updates and Future Prospects (to appear in Vol. 2) [chapter preprint].

📔 Journal Publications

  1. Venkataramanan, R., Tripathy, A., Kumar, T., Serebryakov, S., Justine, A., Shah, A., Bhattacharya, S., Foltin, M., Faraboschi, P., Kaushik Roy, and Sheth, A., Constructing a Metadata Knowledge Graph as an Atlas for Demystifying AI Pipeline Optimization (2025). Frontiers in Big Data (Vol. 7) [paper].

  2. Sheth, Amit, Kaushik Roy, Hemant Purohit, and Amitava Das (2024). “Civilizing and Humanizing Artificial Intelligence in the Age of Large Language Models.” IEEE Internet Computing (Vol. 28, Issue 5) [paper].

  3. Kaushik Roy. Healthcare Assistance Challenges-Driven Neurosymbolic AI (2024). Biomedical Journal of Science & Technical Research (Vol. 58, Issue 2) [paper].

  4. Shyalika, C., Kaushik Roy, et al. (2024). “RI2AP: Robust and Interpretable 2D Anomaly Prediction in Assembly Pipelines.” Sensors (Vol. 24, Issue 10) [paper].

  5. Sheth, A., and Kaushik Roy. (2024). “Neurosymbolic Value-Inspired Artificial Intelligence (Why, What, and How).” IEEE Intelligent Systems (Vol. 39, Issue 1) [paper].

  6. Sheth, A., Kaushik Roy, and Gaur, M. (2023). “Neurosymbolic Artificial Intelligence (Why, What, and How).” IEEE Intelligent Systems (Vol. 38, Issue 3) [paper].

  7. Kaushik Roy, Gaur, M., Soltani, M., Rawte, V., Kalyan, A., and Sheth, A. (2023). “Proknow: Process Knowledge for Safety Constrained and Explainable Question Generation for Mental Health Diagnostic Assistance.” Frontiers in Big Data (Vol. 5) [paper].

  8. Chaudhuri, S.K., Kleppinger, J.W., Nag, R., Kaushik Roy, et al. (2021). “A CdZnTeSe Gamma Spectrometer Trained by Deep Convolutional Neural Network for radioisotope identification.” In Hard X-Ray, Gamma-Ray, and Neutron Detector Physics XXIII (Vol. 11838) [paper].

  9. Sheth, A., Gaur, M., Kaushik Roy, Venkataraman, R., and Khandelwal, V. (2022). “Process Knowledge-Infused AI: Toward User-Level Explainability, Interpretability, and Safety.” IEEE Internet Computing (Vol. 26, Issue 5) [paper].

  10. Sheth, A., Gaur, M., Kaushik Roy, and Faldu, K. (2021). “Knowledge-intensive Language Understanding for Explainable AI.” IEEE Internet Computing (Vol. 25, Issue 5) [paper].

📰 Conference Publications

  1. Tiwari, A., Sinan, M., Kaushik Roy, Sheth, A., Saha, S., & Bhattacharyya, P. (2024, August). Towards a Contextualized and Semantics Infused Dialogue Generation Loss Function and Evaluation Metric. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 342-360). Cham: Springer Nature Switzerland [paper].

  2. Pallagani, V., Kaushik Roy, et al. (2024). “On the Prospects of Incorporating Large Language Models in Automated Planning and Scheduling.” In Proceedings of the International Conference on Automated Planning and Scheduling (Vol. 34, pp. 432-444) [paper].

  3. Kaushik Roy, Khandelwal, V., Vera, V., Surana, H., Heckman, H., & Sheth, A. (2024, March). GEAR-Up: Generative AI and External Knowledge-Based Retrieval: Upgrading Scholarly Article Searches for Systematic Reviews. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 21, pp. 23823-23825) [paper].

  4. Venkataramanan, R., Kaushik Roy, Raj, K., Prasad, R., Zi, Y., Narayanan, V., & Sheth, A. (2023, October). Cook-gen: Robust Generative Modeling of Cooking Actions from Recipes. In 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 981-986) [paper].

  5. Kaushik Roy, Garg, T., Palit, V., Zi, Y., Narayanan, V., and Sheth, A. (2023, June). Knowledge Graph Guided Semantic Evaluation of Language Models for User Trust. In 2023 IEEE Conference on Artificial Intelligence (CAI) (pp. 234-236) [paper].

  6. Kaushik Roy, Khandelwal, V., Goswami, R., Dolbir, N., Malekar, J., & Sheth, A. (2023, September). Demo alleviate: Demonstrating Artificial Intelligence Enabled Virtual Assistance for Telehealth: The Mental Health Case. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 13, pp. 16479-16481) [paper].

  7. Gaur, M., Kaushik Roy, Sharma, A., Srivastava, B., and Sheth, A. (2021). “Who can help me?: Knowledge Infused Matching of Support Seekers and Support Providers during COVID-19 on Reddit.” In 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI) (pp. 265-269) [paper]

  8. Kaushik Roy, Zhang, Q., Gaur, M., & Sheth, A. (2021). Knowledge-infused Policy Gradients with Upper Confidence Bound for Relational Bandits. In Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Springer International Publishing (pp. 35-50) [paper].

  9. Bhat, A., & Kaushik Roy. (2016, August). Optimized KNN for Sequential Execution. In 2016 International Conference on Inventive Computation Technologies (ICICT) (Vol. 1, pp. 1-6) [paper].

  10. Nasreen, A., Kaushik Roy, Roy, K., & Shobha, G. (2015, June). Key Frame Extraction and Foreground Modelling using K-means Clustering. In 2015 7th International Conference on Computational Intelligence, Communication Systems and Networks (pp. 141-145) [paper].

  11. Bhat, A. G., Kaushik Roy, Anchalia, P. P., & Jeevith, H. M. (2015, March). Design and Implementation of a Dynamic Intelligent Traffic Control System. In 2015 17th UKSim-AMSS International Conference on Modelling and Simulation (UKSim) (pp. 369-373) [paper].

  12. Anchalia, P. P., Kaushik Roy, & Roy, K. (2015, March). Two Class Fisher’s Linear Discriminant Analysis Using MapReduce. In 2015 17th UKSim-AMSS International Conference on Modelling and Simulation (UKSim) (pp. 463-469) [paper].

  13. Anchalia, P. P., & Kaushik Roy (2014, January). The K-nearest Neighbor Algorithm using MapReduce Paradigm. In 2014 5th International conference on intelligent systems, modelling and simulation (pp. 513-518) [paper].

📜 Symposium and Workshop Publications

  1. Kaushik Roy, Zi, Y., & Sheth, A. (2024, November). Towards Pragmatic Temporal Alignment in Stateful Generative AI Systems: A Configurable Approach. In Proceedings of the AAAI Symposium Series (Vol. 4, No. 1, pp. 388-390) [paper].

  2. Kaushik Roy, Oltramari, A., Zi, Y., Shyalika, C., Narayanan, V., & Sheth, A. (2024, May). Causal Event Graph-Guided Language-based Spatiotemporal Question Answering. In Proceedings of the AAAI Symposium Series (Vol. 3, No. 1, pp. 227-233) [paper].

  3. Zi, Y., Kaushik Roy, Narayanan, V., & Sheth, A. (2024, May). Exploring Alternative Approaches to Language Modeling for Learning from Data and Knowledge. In Proceedings of the AAAI Symposium Series (Vol. 3, No. 1, pp. 279-286) [paper].

  4. Raj, K., Kaushik Roy, Bonagiri, V., Govil, P., Thirunarayan, K., Goswami, R., & Gaur, M. (2024, May). K-PERM: Personalized Response Generation Using Dynamic Knowledge Retrieval and Persona-Adaptive Queries. In Proceedings of the AAAI Symposium Series (Vol. 3, No. 1, pp. 219-226) [paper].

  5. Zi, Y., Veeramani, H., Kaushik Roy, & Sheth, A. P. (2023, December). RDR: The Recap, Deliberate, and Respond Method for Enhanced Language Understanding. In Neuro-Symbolic Learning and Reasoning in the era of Large Language Models [paper].

  6. Shiri, A., Kaushik Roy, Sheth, A., & Gaur, M. (2024, January). L3 Ensembles: Lifelong Learning Approach for Ensemble of Foundational Language Models✱. In Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD) (pp. 592-594) [paper].

  7. Kaushik Roy, Zi, Y., Gaur, M., Malekar, J., Zhang, Q., Narayanan, V., & Sheth, A. (2023, October). Process Knowledge-infused Learning for Clinician-friendly Explanations. In Proceedings of the AAAI Symposium Series (Vol. 1, No. 1, pp. 154-160) [paper].

  8. Tsakalidis, A., Chim, J., Bilal, I. M., Zirikly, A., Atzil-Slonim, D., Nanni, F., …, Kaushik Roy, … & Liakata, M. (2022, July). Overview of the CLPsych 2022 Shared Task: Capturing Moments of Change in Longitudinal User Posts. In Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology (pp. 184-198) [paper].

  9. Gupta, S., Agarwal, A., Gaur, M., Kaushik Roy, Narayanan, V., Kumaraguru, P., & Sheth, A. (2022). Learning to Automate Follow-up Question Generation Using Process Knowledge for Depression Triage on Reddit Posts. In Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology (pp. 137-147) [paper].

  10. Kaushik Roy, Rawte, V., Chakraborty, M., Gaur, M., Faldu, K., Kikani, P., … & Sheth, A. P. TDLR: Top Semantic-Down Syntactic Language Representation. In NeurIPS’22 Workshop on All Things Attention: Bridging Different Perspectives on Attention [paper].

  11. Lokala, U., Lamy, F., Dastidar, T. G., Kaushik Roy, Daniulaityte, R., Parthasarathy, S., & Sheth, A. (2021). eDarkTrends: Harnessing Social Media Trends in Substance Use Disorders for Opioid Listings on Cryptomarket. In ICLR AI for Public Health Workshop 2021 [paper].

📹 Tutorials and Talks

  1. Kaushik Roy, Megha Chakraborty, Yuxin Zi, Manas Gaur, and Amit Sheth. “Neurosymbolic Customized and Compact CoPilots”, International Semantic Web Conference, November 2024 [Website]

  2. Kaushik Roy. “Healthcare Assistance Challenges-Driven Neurosymbolic AI,” invited talk at Ontolog Summit 2024, April 2024 [Abstract][Slides][Video].

  3. Kaushik Roy. Knowledge-infused Neurosymbolic Artificial Intelligence for Mental Healthcare. Intelligent Clinical Care Center, University of Florida, January 2024 [Slides].

  4. Lokala, U., Kaushik Roy, Jaimini, U., and Sheth, A. (2023). “Causal AI for Web and Health Care.” ACM Web Conference [Website].

  5. Kaushik Roy, Gaur, M., Zhang, Q., and Sheth, A. (2022). “Knowledge-infused Reinforcement Learning.” Knowledge Graph Conference [Website].

  6. Kaushik Roy, Lokala, U., Gaur, M., and Sheth, A.P. (2022). “Neuro-symbolic AI for Mental Healthcare.” International Conference on AI-ML Systems [Website].

  7. Lokala, U., Gaur, M., Kaushik Roy, and Sheth, A. (2021). “Knowledge infused Natural Language understanding for Public Health, Epidemiology, and Substance Use.” IJCAI Workshop on Mining Actionable Insights from Social Networks [Video].

  8. Sheth, A., Kaushik Roy, Gaur, M., and Lokala, U. (2021). “Knowledge In-Wisdom Out-Explainable Data for AI in Cyber Social Threats and Public Health.” AAAI Conference on Web and Social Media [Website].

  9. Gaur, M., and Kaushik Roy (2020). “Knowledge-infused Statistical Learning for Social Good Applications.” PyData Berlin [Video].

🏫 Teaching and Academic Service

📹 Guest Lectures

  1. Language Models - BERT, Mamba, and GPT, CSCE 771 - Computer Processing of Natural Language Processing, University of South Carolina (2024) [Slides]
  2. AI in Healthcare (with a focus on using Knowledge-infused Neurosymbolic AI), CSCE 791 - Seminar in Advances in Computing, University of South Carolina (2024) [Slides]
  3. Knowledge-infused Neurosymbolic AI: Knowledge Graphs for Enhanced Semantics, CMSC-691 - Knowledge-powered NeuroSymbolic AI for Explainability, Interpretability, and Safety, University of Maryland Baltimore County (2024) [Slides].
  4. Constrained Optimization, CSCE 590 - Optimization, University of South Carolina (2024).
  5. Transformers and Liquid Time Networks, CSCE 790 - Neural Networks and Their Applications, University of South Carolina (2023).
  6. Recurrent Neural Networks, Long-term Short-term Memory, Neural Ordinary Differential Equations, and Positional Encodings, CSCE 790 - Neural Networks and Their Applications, University of South Carolina (2023).
  7. Automatic Differentiation and Optimization Strategies, CMSC-478 - Introduction to Machine Learning, University of Maryland Baltimore County (2023).
  8. Interpretable Machine Learning and Hoeffdings Inequality, CMSC-478 - Introduction to Machine Learning, University of Maryland Baltimore County (2022) [Video].

🏛️ Administrative Roles

  1. Guest Editor for IEEE Internet Computing Special Issue on Civilizing and Humanizing AI [Website]
  2. Guest Editor for Information Special Issue on Methodsfor Integrating Information in Data, Language Models, and Knowledge Graphs for Neurosymbolic Learning and Reasoning [Website]
  3. Program Committee, @AAAI 2024: AAAI Fall Symposium on Large Language Models for Knowledge Graph and Ontology Engineering [Website]
  4. Organizing Committee, KiL 2023@KDD: 3rd International Workshop on Knowledge-infused Learning [Website].
  5. Organizing Committee, Collaborative Assistants for the Society’s (CASY) 2022: 2nd Edition [Website].
  6. Hackathon Committee, Knowledge Graph and Semantic Web Conference (KGSWC) 2021 [Website]

💡 Proposal Contributions

  1. NSF EAGER Award #2335967 - Knowledge-guided Neurosymbolic AI with Gaurdrails for Safe Virtual Health Assistants for 2023-2025. Principle Investigator: Amit Sheth.
  2. NSF EAGER Award #2133842 - Advancing Neurosymbolic AI with Deep Knowledge-infused Learning for 2022-2024. Principle Investigator: Amit Sheth.
  3. EPSRC-UKRI grant on Time-sensitive Sensing of Language and User-generated Content awarded by the Alan Turing Institute for 2021-22. Principle Investigator: Manas Gaur.