Vasant Honavar

About

Vasant Honavar is a Professor and Edward Frymoyer Chair of Information Sciences and Technology at Pennsylvania State University. He serves as the Director of the Center for Big Data Analytics and Discovery Informatics and the Director of the Artificial Intelligence Research Laboratory. Additionally, he is the Associate Director of the Institute for CyberScience and a Graduate Faculty member in Social Data Analytics, as well as a C-SoDA Faculty Affiliate. His research focuses on social data analytics, artificial intelligence, and big data discovery. Honavar's work involves advancing the understanding and application of data-driven methods in social sciences and informatics, contributing to the development of innovative approaches in these fields. His professional profile is accessible through the university's faculty webpage and other online platforms, reflecting his active engagement in research and academic leadership.

Research topics

• Computer Science
• Machine Learning
• Artificial Intelligence
• Data Mining
• Political Science
• Computer Security
• Sociology
• Engineering
• Medicine
• Condensed matter physics

Selected publications

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

  2020 · 161 citations

  Senior authorCorresponding

  Graph Neural Networks (GNN) offer the powerful approach to node classification in complex networks across many domains including social media, E-commerce, and FinTech. However, recent studies show that GNNs are vulnerable to attacks aimed at adversely impacting their node classification performance. Existing studies of adversarial attacks on GNN focus primarily on manipulating the connectivity between existing nodes, a task that requires greater effort on the part of the attacker in real-world a…

  DOI

• Explainable Multivariate Time Series Classification

  2021 · 54 citations

  Senior authorCorresponding

  Many real-world applications, e.g., healthcare, present multi-variate time series prediction problems. In such settings, in addition to the predictive accuracy of the models, model transparency and explainability are paramount. We consider the problem of building explainable classifiers from multi-variate time series data. A key criterion to understand such predictive models involves elucidating and quantifying the contribution of time varying input variables to the classification. Hence, we int…

  DOI

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

  Journal of Applied Physics · 2020 · 43 citations

  Hybrid organic–inorganic perovskite (HOIP) materials have attracted significant attention in photovoltaics, light emission, photodetection, etc. Based on the prototype metal halide perovskite crystal, there is a huge space for tuning the composition and crystal structure of this material, which would provide great potential to render multiple physical properties beyond the ongoing emphasis on the optoelectronic property. Recently, the two-dimensional (2D) HOIPs have emerged as a potential candid…

  DOI

• Connected in health: Place-to-place commuting networks and COVID-19 spillovers

  Health & Place · 2022 · 16 citations

  PublisherOA PDFDOI

• Regression with Large Language Models for Materials and Molecular Property Prediction

  arXiv (Cornell University) · 2024-09-09 · 8 citations

  preprintOpen access

  We demonstrate the ability of large language models (LLMs) to perform material and molecular property regression tasks, a significant deviation from the conventional LLM use case. We benchmark the Large Language Model Meta AI (LLaMA) 3 on several molecular properties in the QM9 dataset and 28 materials properties. Only composition-based input strings are used as the model input and we fine tune on only the generative loss. We broadly find that LLaMA 3, when fine-tuned using the SMILES representa…

  PublisherOA PDFDOI

Recent grants

• EAGER: Towards a Computational Infrastructure for Analysis of Sensitive Data

  NSF · $232k · 2015–2019

• BD Spokes: SPOKE: NORTHEAST: Collaborative Research: Integration of Environmental Factors and Causal Reasoning Approaches for Large-Scale Observational Health Research

  NSF · $95k · 2017–2021

• Penn State Biomedical Big Data to Knowledge (B2D2K) Training Program

  NIH · $1.2M · 2016–2021

Frequent coauthors

• Drena Dobbs

  Iowa State University

  59 shared

• Yasser EL‐Manzalawy

  Geisinger Health System

  46 shared

• Doina Caragea

  44 shared

• Samik Basu

  Indian Statistical Institute

  38 shared

• Adrian Silvescu

  34 shared

• Ganesh Ram Santhanam

  Iowa State University

  29 shared

• Jie Bao

  Tsinghua University

  28 shared

• Karthik Balakrishnan

  Stanford Health Care

  27 shared

Education

• PhD, Computer Science

  University of Wisconsin Madison

  1990

• M.S., Computer Science

  University of Wisconsin Madison

  1989

• M.S., Electrical and Computer Engineering

  Drexel University

  1984

• B.E., Electronics Engineering

  Bangalore University

  1982

Similar researchers at Pennsylvania State University

• Duane Alwinh-64
• Stephanie Lanzah-56
• Prasenjit Mitrah-51