Vasant Honavar
Huck Chair in Biomedical Data Sciences and AI; Professor and Edward Frymoyer Chair of Information Sciences and Technology
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E335 Westgate
University Park, PA - vuh14@psu.edu
- 814-865-3141
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
- Center for Molecular Immunology and Infectious Disease
- Bioinformatics and Genomics
- Microbiome Center
- Neuroscience Institute
Links
Publication Tags
Proteins Machine Learning Experiments Health Protein Rna Scanning Sequence Homology Telescopes Time Series Deep Neural Networks Neural Networks Conformations Education Rna Binding Proteins Classifiers Visualization Model Predictive Modeling Evidence Methodology Marketing Lenses Brain Graph In Graph TheoryMost Recent Publications
Connected in health: Place-to-place commuting networks and COVID-19 spillovers
Christopher H. Seto, Corina Graif, Aria Khademi, Vasant G. Honavar, Claire E. Kelling, 2022, Health and Place
Detecting and Interpreting Changes in Scanning Behavior in Large Network Telescopes
Michalis Kallitsis, Rupesh Prajapati, Vasant Honavar, Dinghao Wu, John Yen, 2022, IEEE Transactions on Information Forensics and Security on p. 3611-3625
Shedding light into the darknet: scanning characterization and detection of temporal changes
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
FARE: Enabling Fine-grained Attack Categorization under Low-quality Labeled Data
Junjie Liang, Wenbo Guo, Tongbo Luo, Vasant Honavar, Gang Wang, X Xing, 2021,
Feeding the machine: Challenges to reproducible predictive modeling in resting-state connectomics
Andrew Cwiek, Sarah M. Rajtmajer, Bradley Wyble, Vasant Honavar, F Hillary, Emily Grossner, G Grossner, Frank G. Hillary, 2021, Network Neuroscience on p. 29-48
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, on p. 8556-8564
Longitudinal Deep Kernel Gaussian Process Regression
Junjie Liang, Yanting Wu, Dongkuan Xu, Vasant G. Honavar, 2021, on p. 8556-8564
Most-Cited Papers
Mobile health technology evaluation: The mHealth evidence workshop
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: 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
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
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
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
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
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
In silico prediction of linear B-cell epitopes on proteins
Yasser El-Manzalawy, Drena Dobbs, Vasant G. Honavar, 2017, on p. 255-264
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
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.
Full Article
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.
Full Article
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.
Full Article
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?
Full Article
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.
Full Article
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.
Full Article
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.
Full Article
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.
Full Article
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.
Full Article
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.
Full Article