Vasant Honavar

Huck Chair in Biomedical Data Sciences and AI; Professor and Edward Frymoyer Chair of Information Sciences and Technology

Vasant Honavar

Research Summary

Statistical machine learning algorithms for predictive modeling; modeling and inference of biological networks; characterization and prediction of protein-protein, protein-RNA, and protein-DNA interactions.

Huck Affiliations


Publication Tags

Proteins Protein Rna Machine Learning Conformations Sequence Homology Learning Systems Time Series Experiments Classifiers Scoring Prediction Education Amino Acid Sequence Homology Rna Binding Proteins Neoplasms Recommender Systems Deep Neural Networks Unsupervised Learning Scalability Artificial Intelligence Evidence Structural Similarity Perovskites Neural Networks

Most Recent Papers

Shedding light into the darknet

Rupesh Prajapati, Vasant Honavar, Dinghao Wu, John Yen, Michalis Kallitsis, 2021, on p. 469-470

Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns to Attend to Important Variables As Well As Time Intervals

Tsung Yu Hsieh, Suhang Wang, Yiwei Sun, Vasant Honavar, 2021, on p. 607-615

SrVARM: State regularized vector autoregressive model for joint learning of hidden state transitions and state-dependent inter-variable dependencies from multi-variate time series

Tsung Yu Hsieh, Yiwei Sun, Xianfeng Tang, Suhang Wang, Vasant G. Honavar, 2021, on p. 2270-2280

Functional autoencoders for functional data representation learning

Tsung Yu Hsieh, Yiwei Sun, Suhang Wang, Vasant Honavar, 2021, on p. 666-674

Longitudinal Deep Kernel Gaussian Process Regression

Junjie Liang, Yanting Wu, Dongkuan Xu, Vasant Honavar, 2021,

Dynamical Gaussian Process Latent Variable Model for Representation Learning from Longitudinal Data

Thanh Le, Vasant Honavar, 2020, on p. 183-188

Two-dimensional hybrid organic-inorganic perovskites as emergent ferroelectric materials

Yuchen Hou, Congcong Wu, Dong Yang, Tao Ye, Vasant G. Honavar, Adri C.T. Van Duin, Kai Wang, Shashank Priya, 2020, Journal of Applied Physics

The Virtual Data Collaboratory

Manish Parashar, Anthony Simonet, Ivan Rodero, Forough Ghahramani, Grace Agnew, Ron Jantz, Vasant Honavar, 2020, Computing in Science and Engineering on p. 79-92

Adversarial Attacks on Graph Neural Networks via Node Injections

Yiwei Sun, Suhang Wang, Xianfeng Tang, Tsung Yu Hsieh, Vasant Honavar, 2020, on p. 673-683

Commuting Network Spillovers and COVID-19 Deaths Across US Counties

Christopher Seto, Aria Khademi, Corina Graif, Vasant Honavar, 2020, arXiv preprint arXiv:2010.01101

Most-Cited Papers

Mobile health technology evaluation

Santosh Kumar, Wendy J. Nilsen, Amy Abernethy, Audie Atienza, Kevin Patrick, Misha Pavel, William T. Riley, Albert Shar, Bonnie Spring, Donna Spruijt-Metz, Donald Hedeker, Vasant Honavar, Richard Kravitz, R. Craig Lefebvre, David C. Mohr, Susan A. Murphy, Charlene Quinn, Vladimir Shusterman, Dallas Swendeman, 2013, American Journal of Preventive Medicine on p. 228-236

Computational prediction of protein interfaces

Li C. Xue, Drena Dobbs, Alexandre M J J Bonvin, Vasant Honavar, 2015, FEBS Letters on p. 3516-3526

RNABindRPlus: a predictor that combines machine learning and sequence homology-based methods to improve the reliability of predicted RNA-binding residues in proteins.

Rasna R. Walia, Li C. Xue, Katherine Wilkins, Yasser El-Manzalawy, Drena Dobbs, Vasant Honavar, 2014, PLoS One on p. e97725

Predicting protein-protein interface residues using local surface structural similarity

Rafael A. Jordan, Yasser El-Manzalawy, Drena Dobbs, Vasant Honavar, 2012, BMC Bioinformatics

Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art

Rasna R. Walia, Cornelia Caragea, Benjamin A. Lewis, Fadi Towfic, Michael Terribilini, Yasser El-Manzalawy, Drena Dobbs, Vasant Honavar, 2012, BMC Bioinformatics

Unambiguity regularization for unsupervised learning of probabilistic grammars

Kewei Tu, Vasant Honavar, 2012, on p. 1324-1334

IScore: A novel graph kernel-based function for scoring protein-protein docking models

Cunliang Geng, Yong Jung, Nicolas Renaud, Vasant Honavar, Alexandre M.J.J. Bonvin, Li C. Xue, 2020, Bioinformatics on p. 112-121

Min-redundancy and max-relevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data.

Yasser El-Manzalawy, Tsung Yu Hsieh, Manu Shivakumar, Dokyoon Kim, Vasant Honavar, 2018, BMC Medical Genomics on p. 71


Li C. Xue, Rafael A. Jordan, El Manzalawy Yasser, Drena Dobbs, Vasant Honavar, 2014, Proteins: Structure, Function and Genetics on p. 250-267

A user similarity-based Top- N recommendation approach for mobile in-application advertising

Jinlong Hu, Junjie Liang, Yuezhen Kuang, Vasant Honavar, 2018, Expert Systems with Applications on p. 51-60

News Articles Featuring Vasant Honavar

Vasant Honavar named Huck Chair in Biomedical Data Sciences and AI

Vasant Honavar, professor in the College of Information Sciences and Technology, has been named the Dorothy Foehr Huck and J. Lloyd Huck Chair in Biomedical Data Sciences and Artificial Intelligence by the University’s Huck Institutes of the Life Sciences.

Researchers deploy AI to detect bias in AI and humans

Researchers have developed a tool using artificial intelligence (AI) to detect unfair bias in protected areas such as race or gender. The tool could be used in finding bias in AI systems or even bias by human decisions makers, according to the researchers at Penn State and Columbia University.

What is bias in AI really, and why can’t AI neutralize it?

Selection algorithms everywhere are exhibiting traits that appear to be racist, sexist, and otherwise discriminatory. Have neural networks already developed their own neuropathy? Or are people somehow the problem?

AI to fight unfair discrimination

Researchers developed a new artificial intelligence (AI) tool for detecting unfair discrimination such as race or gender.

Penn State researchers develop bias-detecting technology

Researchers at Penn State and Columbia University have developed artificial intelligence technology that can detect unfair discrimination within specific demographics.

Artificial intelligence tool can identify gender and racial bias

Scientists have developed a new artificial intelligence (AI) tool for detecting unfair discrimination—such as on the basis of race or gender.

Researchers Create New AI Tool for Detecting Unfair Discrimination

Penn State and Columbia University researchers have created a new artificial intelligence tool to detect unfair discrimination based on gender and race. For example, a long-standing concern of civilized societies has been preventing unfair treatment of individuals based on gender, race, or ethnicity.

US researchers create AI tool that can detect discrimination on basis of race, gender

A team of researchers at Pennsylvania State and Columbia University created an artificial intelligence (AI) tool for detecting discrimination with respect to a protected attribute, such as race or gender.

'AI will see you now': Panel to discuss the AI revolution in health and medicine

​This month’s CyberScience Seminar, organized by the Institute for CyberScience (ICS), will be held from 1:30–3 p.m. on Thursday, April 11, in 233B HUB-Robeson Center and will feature a panel of Penn State experts who will discuss the benefits — and the risks — of using AI in the healthcare industry.​

Is artificial intelligence affecting the job market?

The term Artificial Intelligence elicits images of cyborgs and terminators; human-like robots intent on pursuing death and destruction. Hollywood representations distort what AI really is and the technological achievements that scientists are making.