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

These publication tags are generated from the output of this researcher. Click any tag below to view other Huck researchers working on the same topic.

Proteins Experiments Protein Rna Machine Learning Sequence Homology Health Conformations Therapeutics Datasets Rna Binding Proteins Methodology Decision Making Deep Neural Networks Reinforcement Learning Ovarian Neoplasms Model Graph In Graph Theory B Lymphocyte Epitopes Docking Artificial Intelligence Lenses Neural Networks Biological Science Disciplines Time Series

Most Recent Publications

Junjie Liang, Weijieying Ren, Sahar Hanifi, Vasant Honavar, 2024, on p. 8

EsaCL: An Efficient Continual Learning Algorithm

Weijieying Ren, Vasant Honavar, 2024, on p. 9

License Forecasting and Scheduling for HPC

Burak Gulhan, Gulsum Gudukbay, Amit Amritkar, J Sampson, Vasant Honavar, Adam Focht, Chuck Pavlovski, Mahmut Kandemir, 2024, on p. 8

Daniel M. Roberts, Margeaux M. Schade, Lindsay Master, Vasant G. Honavar, Nicole G. Nahmod, Anne Marie Chang, Daniel Gartenberg, Orfeu M. Buxton, 2024, Sleep Health

Causal Effect Estimation using Random Hyperplane Tessellations

Abhishek Dalvi, Neil Ashtekar, Vasant Honavar, 2024,

Samik Basu, Vasant Honavar, Ganesh Ram Santhanam, Jia Tao, 2023, on p. 206-213

Amogh Subbakrishna Adishesha, Lily Jakielaszek, Fariha Azhar, Peixuan Zhang, Vasant Honavar, Fenglong Ma, Chandra Belani, Prasenjit Mitra, Sharon Xiaolei Huang, 2023, IEEE Journal of Biomedical and Health Informatics on p. 3645-3656

Daniel Roberts, M Gray, Margeaux Schade, Lindsay Master, Vasant Honavar, Nicole Nahmod, Anne-Marie Chang, Daniel Gartenberg, Orfeu Buxton, 2023, Sleep Health on p. 596-610

A Lekoubou Looti, Justin Petucci, Avnish Katoch, Vasant Honavar, 2023, Annals of Neurology on p. S128

A Simple, Fast Algorithm for Continual Learning from High-Dimensional Data

Neil Ashtekar, Vasant Honavar, 2023,

Most-Cited Papers

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

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

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

Aria Khademi, David Foley, Sanghack Lee, Vasant Honavar, 2019, on p. 2907-2914

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

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

Yimin Zhou, Yiwei Sun, Vasant Honavar, 2019, on p. 283-293

Yasser El-Manzalawy, Drena Dobbs, Vasant G. Honavar, 2017, on p. 255-264

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

Yiwei Sun, Suhang Wang, Tsung Yu Hsieh, Xianfeng Tang, Vasant Honavar, 2019, on p. 3527-3533

News Articles Featuring Vasant Honavar

Millennium Café series to feature special editions in October, November

The Millennium Café, held every Tuesday by the Materials Research Institute (MRI) featuring two talks by Penn State researchers that serve as an exchange of ideas and solutions, will hold three special sessions in October and November.

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.​