Vasant Gajanan Honavar, PhD - Penn State Cancer Institute
Researcher Profile
Vasant Gajanan Honavar, PhD
Professor and Edward Frymoyer Chair of Information Sciences and Technology, College of Information Sciences and Technology
One Health Microbiome Center
Penn State Neuroscience Institute
One Health Microbiome Center
Penn State Neuroscience Institute
Scientific Program:Cancer Control
Recent Publications
2025
Jacobs, R, Morgan, D, Attarian, S, Meng, J, Shen, C, Wu, Z, Xie, CY, Yang, JH, Artrith, N, Blaiszik, B, Ceder, G, Choudhary, K, Csanyi, G, Cubuk, ED, Deng, B, Drautz, R, Fu, X, Godwin, J, Honavar, V, Isayev, O, Johansson, A, Martiniani, S, Ong, SP, Poltavsky, I, Schmidt, KJ, Takamoto, S, Thompson, AP, Westermayr, J, Wood, BM & Kozinsky, B 2025, 'A practical guide to machine learning interatomic potentials – Status and future', Current Opinion in Solid State and Materials Science, vol. 35, 101214. https://doi.org/10.1016/j.cossms.2025.101214
Rauer, E, Basu, S & Honavar, V 2025, Checking Consistency of CP-Theory Preferences in Polynomial Time. in T Walsh, J Shah & Z Kolter (eds), Special Track on AI Alignment. 14 edn, Proceedings of the AAAI Conference on Artificial Intelligence, no. 14, vol. 39, Association for the Advancement of Artificial Intelligence, pp. 15126-15133, 39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025, Philadelphia, United States, 2/25/25. https://doi.org/10.1609/aaai.v39i14.33659
Ashtekar, N, Zhu, J & Honavar, VG 2025, 'Class Incremental Learning from First Principles: A Review', Transactions on Machine Learning Research, vol. 2025.
Dalvi, A & Honavar, V 2025, Hyperdimensional Representation Learning for Node Classification and Link Prediction. in WSDM 2025 - Proceedings of the 18th ACM International Conference on Web Search and Data Mining. WSDM 2025 - Proceedings of the 18th ACM International Conference on Web Search and Data Mining, Association for Computing Machinery, Inc, pp. 88-97, 18th ACM International Conference on Web Search and Data Mining, WSDM 2025, Hannover, Germany, 3/10/25. https://doi.org/10.1145/3701551.3703492
2024
Xiao, T, Yuan, Y, Zhu, H, Li, M & Honavar, VG 2024, 'Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment', Advances in Neural Information Processing Systems, vol. 37.
Lekoubou, A, Petucci, J, Femi Ajala, T, Katoch, A, Hong, J, Sen, S, Bonilha, L, Chinchilli, VM & Honavar, V 2024, 'Can machine learning predict late seizures after intracerebral hemorrhages? Evidence from real-world data', Epilepsy and Behavior, vol. 157, 109835. https://doi.org/10.1016/j.yebeh.2024.109835
Dalvi, A, Ashtekar, N & Honavar, V 2024, 'Causal Matching using Random Hyperplane Tessellations', Proceedings of Machine Learning Research, vol. 236, pp. 688-702.
Roberts, DM, Schade, MM, Master, L, Honavar, VG, Nahmod, NG, Chang, AM, Gartenberg, D & Buxton, OM 2024, 'Corrigendum to “Performance of an open machine learning model to classify sleep/wake from actigraphy across ∼24-hour intervals without knowledge of rest timing” [Sleep Health 9 (2023) 596-610, (S2352721823001341), (10.1016/j.sleh.2023.07.001)]', Sleep health, vol. 10, no. 2, pp. 255-260. https://doi.org/10.1016/j.sleh.2023.11.008
Xiao, T, Zhu, H, Zhang, Z, Guo, Z, Aggarwal, CC, Wang, S & Honavar, VG 2024, 'Efficient Contrastive Learning for Fast and Accurate Inference on Graphs', Proceedings of Machine Learning Research, vol. 235, pp. 54363-54381.
Ren, W & Honavar, VG 2024, EsaCL: An Efficient Continual Learning Algorithm. in S Shekhar, V Papalexakis, J Gao, Z Jiang & M Riondato (eds), Proceedings of the 2024 SIAM International Conference on Data Mining, SDM 2024. Proceedings of the 2024 SIAM International Conference on Data Mining, SDM 2024, Society for Industrial and Applied Mathematics Publications, pp. 163-171, 2024 SIAM International Conference on Data Mining, SDM 2024, Houston, United States, 4/18/24. https://doi.org/10.1137/1.9781611978032.19
Xiao, T, Cui, C, Zhu, H & Honavar, VG 2024, GeomCLIP: Contrastive Geometry-Text Pre-training for Molecules. in M Cannataro, H Zheng, L Gao, J Cheng, JL de Miranda, E Zumpano, X Hu, Y-R Cho & T Park (eds), Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024. Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024, Institute of Electrical and Electronics Engineers Inc., pp. 1250-1256, 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024, Lisbon, Portugal, 12/3/24. https://doi.org/10.1109/BIBM62325.2024.10822346
Xiao, T, Li, M, Yuan, Y, Zhu, H, Cui, C & Honavar, VG 2024, How to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning Perspective. in Y Al-Onaizan, M Bansal & Y-N Chen (eds), EMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference. EMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, Association for Computational Linguistics (ACL), pp. 13413-13426, 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024, Hybrid, Miami, United States, 11/12/24. https://doi.org/10.18653/v1/2024.emnlp-main.744
Liang, J, Ren, W, Sahar, H & Honavar, V 2024, 'Inducing Clusters Deep Kernel Gaussian Process for Longitudinal Data', Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 12, pp. 13736-13743. https://doi.org/10.1609/aaai.v38i12.29279
Ren, W, Li, X, Chen, H, Rakesh, V, Wang, Z, Das, M & Honavar, V 2024, 'TabLog: Test-Time Adaptation for Tabular Data Using Logic Rules', Proceedings of Machine Learning Research, vol. 235, pp. 42417-42427.
2023
Adishesha, AS, Jakielaszek, L, Azhar, F, Zhang, P, Honavar, V, Ma, F, Belani, C, Mitra, P & Huang, SX 2023, 'Forecasting User Interests Through Topic Tag Predictions in Online Health Communities', IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 7, pp. 3645-3656. https://doi.org/10.1109/JBHI.2023.3271580
Schade, MM, Roberts, DM, Honavar, VG & Buxton, OM 2023, Machine learning approaches in sleep and circadian research. in Encyclopedia of Sleep and Circadian Rhythms: Volume 1-6, Second Edition. Elsevier, pp. 53-62. https://doi.org/10.1016/B978-0-12-822963-7.00383-2
Jung, Y, Geng, C, Bonvin, AMJJ, Xue, LC & Honavar, VG 2023, 'MetaScore: A Novel Machine-Learning-Based Approach to Improve Traditional Scoring Functions for Scoring Protein–Protein Docking Conformations', Biomolecules, vol. 13, no. 1, 121. https://doi.org/10.3390/biom13010121
Roberts, DM, Schade, MM, Master, L, Honavar, VG, Nahmod, NG, Chang, A-M, Gartenberg, D & Buxton, OM 2023, 'Performance of an open machine learning model to classify sleep/wake from actigraphy across ∼24-hour intervals without knowledge of rest timing', Sleep health. https://doi.org/10.1016/j.sleh.2023.07.001
Basu, S, Honavar, V, Santhanam, GR & Tao, J 2023, Representing and Reasoning with Multi-Stakeholder Qualitative Preference Queries. in K Gal, K Gal, A Nowe, GJ Nalepa, R Fairstein & R Radulescu (eds), ECAI 2023 - 26th European Conference on Artificial Intelligence, including 12th Conference on Prestigious Applications of Intelligent Systems, PAIS 2023 - Proceedings. Frontiers in Artificial Intelligence and Applications, vol. 372, IOS Press BV, pp. 206-213, 26th European Conference on Artificial Intelligence, ECAI 2023, Krakow, Poland, 9/30/23. https://doi.org/10.3233/FAIA230272
2022
Seto, CH, Graif, C, Khademi, A, Honavar, VG & Kelling, CE 2022, 'Connected in health: Place-to-place commuting networks and COVID-19 spillovers', Health and Place, vol. 77, 102891. https://doi.org/10.1016/j.healthplace.2022.102891
Kallitsis, M, Prajapati, R, Honavar, V, Wu, D & Yen, J 2022, 'Detecting and Interpreting Changes in Scanning Behavior in Large Network Telescopes', IEEE Transactions on Information Forensics and Security, vol. 17, pp. 3611-3625. https://doi.org/10.1109/TIFS.2022.3211644
Cwiek, A, Rajtmajer, SM, Wyble, B, Honavar, V, Grossner, E & Hillary, FG 2022, 'Feeding the machine: Challenges to reproducible predictive modeling in resting-state connectomics', Network Neuroscience, vol. 6, no. 1, pp. 29-48. https://doi.org/10.1162/netn_a_00212
2021
Hsieh, TY, Wang, S, Sun, Y & Honavar, V 2021, Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns to Attend to Important Variables As Well As Time Intervals. in WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining. WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining, Association for Computing Machinery, Inc, pp. 607-615, 14th ACM International Conference on Web Search and Data Mining, WSDM 2021, Virtual, Online, Israel, 3/8/21. https://doi.org/10.1145/3437963.3441815
Liang, J, Guo, W, Luo, T, Honavar, V, Wang, G & Xing, X 2021, FARE: Enabling Fine-grained Attack Categorization under Low-quality Labeled Data. in 28th Annual Network and Distributed System Security Symposium, NDSS 2021. 28th Annual Network and Distributed System Security Symposium, NDSS 2021, The Internet Society, 28th Annual Network and Distributed System Security Symposium, NDSS 2021, Virtual, Online, 2/21/21. https://doi.org/10.14722/ndss.2021.24403
Hsieh, TY, Sun, Y, Wang, S & Honavar, V 2021, Functional autoencoders for functional data representation learning. in SIAM International Conference on Data Mining, SDM 2021. SIAM International Conference on Data Mining, SDM 2021, Siam Society, pp. 666-674, 2021 SIAM International Conference on Data Mining, SDM 2021, Virtual, Online, 4/29/21. https://doi.org/10.1137/1.9781611976700.75
Liang, J, Wu, Y, Xu, D & Honavar, VG 2021, Longitudinal Deep Kernel Gaussian Process Regression. in 35th AAAI Conference on Artificial Intelligence, AAAI 2021. 35th AAAI Conference on Artificial Intelligence, AAAI 2021, vol. 10A, Association for the Advancement of Artificial Intelligence, pp. 8556-8564, 35th AAAI Conference on Artificial Intelligence, AAAI 2021, Virtual, Online, 2/2/21. https://doi.org/10.1609/aaai.v35i10.17038
Prajapati, R, Honavar, V, Wu, D, Yen, J & Kallitsis, M 2021, Shedding light into the darknet: scanning characterization and detection of temporal changes. in CoNEXT 2021 - Proceedings of the 17th International Conference on emerging Networking EXperiments and Technologies. CoNEXT 2021 - Proceedings of the 17th International Conference on emerging Networking EXperiments and Technologies, Association for Computing Machinery, Inc, pp. 469-470, 17th ACM International Conference on emerging Networking EXperiments and Technologies, CoNEXT 2021, Virtual, Online, Germany, 12/7/21. https://doi.org/10.1145/3485983.3493347
Hsieh, TY, Sun, Y, Tang, X, Wang, S & Honavar, VG 2021, SrVARM: State regularized vector autoregressive model for joint learning of hidden state transitions and state-dependent inter-variable dependencies from multi-variate time series. in The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021. The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021, Association for Computing Machinery, Inc, pp. 2270-2280, 30th World Wide Web Conference, WWW 2021, Ljubljana, Slovenia, 4/19/21. https://doi.org/10.1145/3442381.3450116