Paper on retrieval-augmented latent diffusion accepted to IEEE BigData 2025
Paper on extreme event prediction accepted to ICDM 2025
Paper on multi-modal vulnerability detection accepted to Information Fusion 2025
COLA 2024 Best Paper Award
Presidential Award for Faculty Excellence: Early Career Achievement (2025)
CECS Early Career Achievement Award (2025)
Paper on code similarity analysis accepted to IST 2025
Paper on cost-efficient vulnerability detection accepted to JSS 2025
Paper on FEA automation using LLMs and GNNs accepted to AAAI 2025
Paper on chatbots for autism interventions accepted to EAIT 2024
Paper on using LLMs to enhance few-shot GNNs accepted to IEEE BigData 2024
Student abstract on phishing detection using LLMs accepted to AAAI 2025
Paper on graph-based fraud detection accepted to ACML 2024
Paper on dual-reasoning LLMs accepted to ICONIP 2024
Paper on cross-modal adversarial reprogramming for vulnerability detection accepted to Information Sciences 2024
Two papers accepted to EMBS 2024
Two papers accepted to DASFAA 2024: energy-based bot detection and addressing heterophily in GNNs
Paper on class-imbalanced fraud detection accepted to IJCNN 2024
Served on NSF Panel (Dec 2023)
Paper on enhancing CV interpretability accepted to Sensors 2023
Paper on fooling AI explanations accepted to EMNLP 2023
Paper on vulnerability detection accepted to ESWA 2023
Paper on few-shot node classification accepted to ICDM 2023
Paper on class-imbalanced bot detection accepted to CIKM 2023
Paper on adversary for social good accepted to TKDD 2023
NSF CRII proposal funded (Mar 2023)
Paper on cross-modal adversarial reprogramming accepted to WWW 2023
Paper on hierarchical GNN accepted to PAKDD 2023
Background
Currently a tenure-track Assistant Professor in the Golisano College of Computing and Information Sciences at Rochester Institute of Technology (RIT)
Research interests lie at the intersection of machine learning and security, with emphases on:
Trustworthy machine learning: adversarial attacks and defenses, adversary for social good, model explanations, and other trustworthiness issues
Cybersecurity with ML/AI: detecting security threats (e.g., malware, fraud, bots, phishing, vulnerabilities) under challenging data scenarios (e.g., few-shot learning, class imbalance, heterophilic structures) using graph neural networks and language models
Data mining and data science: real-world applications in IoT, social networks, bioinformatics, education, information retrieval, and more