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

Links

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

Inducing Clusters Deep Kernel Gaussian Process for Longitudinal Data

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

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)]

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,

Representing and Reasoning with Multi-Stakeholder Qualitative Preference Queries

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

Forecasting User Interests Through Topic Tag Predictions in Online Health Communities

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

Performance of an open machine learning model to classify sleep/wake from actigraphy across∼ 24-hour intervals without knowledge of rest timing.

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

How Well Can Machine Learning Predict Late Seizures after Intracerebral Hemorrhages? Evidence from Real-World Data

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

Computational prediction of protein interfaces: A review of data driven methods

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

Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach

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

Fairness in algorithmic decision making: An excursion through the lens of causality

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

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 06 Biological Sciences 0604 Genetics

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

Improving image captioning by leveraging knowledge graphs

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

In silico prediction of linear B-cell epitopes on proteins

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

Megan: A generative adversarial network for multi-view network embedding

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

News Articles Featuring Vasant Honavar

Sep 16, 2022

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.

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Oct 04, 2021

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.

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Jul 17, 2019

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.

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Jul 17, 2019

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?

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Jul 15, 2019

AI to fight unfair discrimination

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

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Jul 12, 2019

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.

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Jul 12, 2019

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.

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Jul 11, 2019

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.

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Jul 11, 2019

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.

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Apr 02, 2019

'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.​

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