Harsha Kokel

Harsha Kokel is a Research Scientist at IBM Research. She obtained her Ph.D. at The University of Texas at Dallas under the guidance of Prof. Sriraam Natarajan. She is interested in knowledge-guided learning for structured, relational domains. Her research focuses on leveraging domain-specific expert knowledge to improve generalization in various AI/ML approaches. She is interested in sequential decision-making problems in structured domains and explores the combination of planning and reinforcement learning. Recently, she has also ventured into tabular representation learning for data discovery tasks.

Her research has been published in the following conferences: AAAI, ICAPS, IJCAI, NeurIPS, and ACS. Her work has been presented at various workshops at AAAI, NeurIPS, IJCNN, ICAPS, IJCAI and RLDM. She has reviewed papers for various journals, conferences, and workshops, including AAAI, IJCAI, NeurIPS, SDM, and DMKD. She serves as an assistant electronic publishing editor for JAIR. She co-organized the workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL) at IJCAI and ICAPS 2023.

Selected Publications

Action Space Reduction for Planning Domains,
Harsha Kokel, Junkyu Lee, Michael Katz, Kavitha Srinivas, Shirin Sohrabi, In IJCAI 2023.
paper | blog | code | DOI| video | cite

@inproceedings{KokelLKSS23,
title={Action Space Reduction for Planning Domains},
author={Kokel, Harsha and Lee, Junkyu and Katz, Michael and Srinivas, Kavitha and Sohrabi, Shirin},
publisher={International Joint Conferences on Artificial Intelligence Organization},
pages = {5394--5401},
year = {2023},
month = {8},
doi = {10.24963/ijcai.2023/599} booktitle = {Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, {IJCAI-23}},
}
LakeBench: Benchmarks for Data Discovery over Data Lakes (Preprint),
Kavitha Srinivas, Julian Dolby, Ibrahim Abdelaziz, Oktie Hassanzadeh, Harsha Kokel, Aamod Khatiwada, Tejaswini Pedapati, Subhajit Chaudhury, Horst Samulowitz, In ArXiv 2023.
paper | code | video
RePReL: A Unified Framework for Integrating Relational Planning and Reinforcement Learning for Effective Abstraction in Discrete and Continuous Domains,
Harsha Kokel, Sriraam Natarajan, Balaraman Ravindran, Prasad Tadepalli, In Neural Computing and Applications 2022.
paper | DOI | cite

@article{KokelNRT22,
author={Harsha Kokel and Sriraam Natarajan and Balaraman Ravindran and Prasad Tadepalli},
journal={Neural Computing and Applications},
title={RePReL: a unified framework for integrating relational planning and reinforcement learning for effective abstraction in discrete and continuous domains},
year={2022},
doi={https://doi.org/10.1007/s00521-022-08119-y},
}
LARA -- Human-guided collaborative problem solver: Effective integration of learning, reasoning and communication,
Harsha Kokel, Mayukh Das, Rakibul Islam, Julia Bonn, Jon Cai, Soham Dan, Anjali Narayan-Chen, Prashant Jayannavar, Janardhan Rao Doppa, Julia Hockenmaier, Sriraam Natarajan, Martha Palmer, Dan Roth, In The Tenth Annual Conference on Advances in Cognitive Systems (ACS) 2022.
paper | blog | supplemental | video | cite

@article{KokeletalACS22,
title={LARA -- Human-guided collaborative problem solver: Effective integration of learning, reasoning and communication},
author={Harsha Kokel, Mayukh Das, Rakibul Islam, Julia Bonn, Jon Cai, Soham Dan, Anjali Narayan-Chen, Prashant Jayannavar, Janardhan Rao Doppa, Julia Hockenmaier, Sriraam Natarajan, Martha Palmer, Dan Roth},
journal={The Tenth Annual Conference on Advances in Cognitive Systems (ACS)},
year={2022},
}
ORIENT: Submodular Mutual Information Measures for Data Subset Selection under Distribution Shift,^{* equal contributions}
Athresh Karanam^*, Krishnateja Killamsetty^*, Harsha Kokel^*, Rishabh K Iyer, In NeurIPS 2022.
paper | code | supplemental | DOI | cite

@inproceedings{KaranamKKI22,
author = {Athresh Karanam and Krishnateja Killamsetty and Harsha Kokel and Rishabh K Iyer},
title = {{ORIENT}: Submodular Mutual Information Measures for Data Subset Selection under Distribution Shift},
year = {2022},
booktitle = {NeurIPS},
}
Human-guided Collaborative Problem Solving: A Natural Language based Framework,
Harsha Kokel, Mayukh Das, Rakibul Islam, Julia Bonn, Jon Cai, Soham Dan, Anjali Narayan-Chen, Prashant Jayannavar, Janardhan Rao Doppa, Julia Hockenmaier, Sriraam Natarajan, Martha Palmer, Dan Roth, In ICAPS (demo track) 2021.
paper | blog | video | cite

@article{KokeletalDemo,
title={Human-guided Collaborative Problem Solving: A Natural Language based Framework},
author={Harsha Kokel, Mayukh Das, Rakibul Islam, Julia Bonn, Jon Cai, Soham Dan, Anjali Narayan-Chen, Prashant Jayannavar, Janardhan Rao Doppa, Julia Hockenmaier, Sriraam Natarajan, Martha Palmer, Dan Roth},
journal={Thirty First International Conference on Automated Planning and Scheduling ({ICAPS}) Demo Track},
year={2021},
}
A Probabilistic Approach to Extract Qualitative Knowledge for Early Prediction of Gestational Diabetes,
Athresh Karanam, Alexander L. Hayes, Harsha Kokel, David M. Haas, Predrag Radivojac, Sriraam Natarajan, In AIME 2021.
paper | blog | DOI| video | cite

@inproceedings{KaranamHKHRN21,
author = {Athresh Karanam and Alexander L. Hayes and Harsha Kokel and David M. Haas and Predrag Radivojac and Sriraam Natarajan},
title = {A Probabilistic Approach to Extract Qualitative Knowledge for Early Prediction of Gestational Diabetes},
year = {2021},
publisher = {Springer International Publishing},
pages = {497--502},
booktitle = {Artificial Intelligence in Medicine},
isbn = {978-3-030-77211-6},
DOI = {10.1007/978-3-030-77211-6_59},
}
RePReL: Integrating Relational Planning and Reinforcement Learning for Effective Abstraction,
Harsha Kokel, Arjun Manoharan, Sriraam Natarajan, Balaraman Ravindran, Prasad Tadepalli, In ICAPS 2021.
paper | blog | code | supplemental | DOI| video | cite

@article{KokelMNRT21,
title={RePReL: Integrating Relational Planning and Reinforcement Learning for Effective Abstraction},
author={Kokel, Harsha and Manoharan, Arjun and Natarajan, Sriraam and Balaraman, Ravindran and Tadepalli, Prasad},
journal={Proceedings of the International Conference on Automated Planning and Scheduling},
number={1},
volume={31},
year={2021},
month={May},
pages={533--541},
url={https://ojs.aaai.org/index.php/ICAPS/article/view/16001},
}
Graph Sparsification via Meta-Learning,^{* contributed talk (4/22 accepted paper)}
Guihong Wan, Harsha Kokel, In Deep Learning for Graphs (DLG) Workshop at AAAI 2021.
paper | slide | video | cite

@article{WanK21,
title={Graph Sparsification via Meta-Learning},
author={Wan, Guihong and Kokel, Harsha},
journal={Deep Learning for Graphs (DLG) Workshop at AAAI},
year={2021}
}
A Unified Framework for Knowledge Intensive Gradient Boosting: Leveraging Human Experts for Noisy Sparse Domain,
Harsha Kokel, Phillip Odom, Shuo Yang, Sriraam Natarajan, In AAAI 2020.
paper | blog | code | supplemental | poster | slide | DOI | cite

@article{KokelOYN20,
author = {Harsha Kokel and Phillip Odom and Shuo Yang and Sriraam Natarajan},
title = {A Unified Framework for Knowledge Intensive Gradient Boosting: Leveraging Human Experts for Noisy Sparse Domains},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence}
volume = {34},
number = {04},
pages = {4460--4468},
year = {2020},
month = {Apr.},
DOI = {10.1609/aaai.v34i04.5873},
url = {https://aaai.org/ojs/index.php/AAAI/article/view/5873},
}

Talks

Networking talk, Integrated Planning and Reinforcement Learning at Almaden Women’s Interest and Networking Group (AWING) at IBM, Jun 7, 2023.
slide
Invited talk, Integrated Planning and Reinforcement Learning for Compositional Domains at AI Seminar at OSU, May 26, 2023.
slide | video
Invited talk, Dynamic probabilistic logic models for effective task-specific abstractions in RL at Robotics Lab at the Brown University, Sep 16, 2022.
slide | video
Presented paper, Hybrid Deep RePReL: Integrating Relational Planning and Reinforcement Learning for Information Fusion at IEEE 25th International Conference on Information Fusion (FUSION), Linköping, Sweden, Jul 5, 2022.
slide | video
Invited talk, RePReL: Integrating Relational Planning and Reinforcement Learning for Effective Abstractions at Eleventh RBCDSAI Workshop on Recent progress in Data Science and AI, IIT Madras, India, Nov 17, 2021.
slide | video
Ph.D. mixer talk, Human Allied Artificial Intelligence at UT Dallas, USA, Sep 10, 2021.
slide
Invited talk, A Unified Framework for Knowledge Intensive Gradient Boosting: Leveraging Human Experts for Noisy Sparse Domain at AAIR Lab Spring Meeting, ASU, USA, Jan 22, 2021.