Impact
Awards, Grants, News & Open Source Contributions
Recognition, funding, and contributions to the AI security community
AI4SEC Lab
Making Impact in AI Security Research
🏆 Awards & Honors
NeurIPS 2024 Spotlight Paper
Top 2% of accepted papers
CTIBench: A Large-Scale Benchmark
Aspen Institute AI Fellow
Selected for prestigious fellowship
Best Paper Award - NDSS 2024
Network and Distributed System Security Symposium
Best Paper Award - ACSAC 2023
Annual Computer Security Applications Conference
Best Paper Award - UPSTAT 2023
Upstate New York AI & ML Conference
International Women in Cybersecurity Award
Recognition for contributions to the field
Industry Adoption
Research used by Google, Microsoft, Cisco, TrendMicro
15+ Awards Total
Best papers, fellowships, and recognitions
💰 Research Grants
$395,000+ in active external funding from NSF, Toyota, IBM, and Argonne National Lab
NSF SaTC TTP Small - DeFake
Deepfake Detection Research
National Science Foundation
IBM AI Research
Trust & Threat Intelligence
Two-phase collaboration
Toyota Infotech
Explainable ML for Autonomous Vehicles
Two-phase project
Argonne National Laboratory
LLM Cybersecurity Advisor
Department of Energy
📰 News & Media Coverage
CTIBench Featured by Major Tech Companies
Our NeurIPS 2024 Spotlight paper on benchmarking LLMs for cybersecurity has been featured and adopted by Google, Microsoft, Cisco, and TrendMicro for evaluating their threat intelligence systems.
TwiML AI Podcast Interview
Featured on the This Week in Machine Learning & AI podcast discussing explainable AI for cybersecurity applications and the importance of interpretability in security-critical systems.
Podcast • InterviewTechTarget Feature
Research on automated threat intelligence featured in TechTarget's coverage of emerging AI technologies in cybersecurity.
Article • Industry NewsInfoSecurity Magazine
Work on malware detection and threat intelligence discussed in InfoSecurity Magazine's analysis of AI-powered security solutions.
Article • Analysis🌐 Open Source Projects
LADDER - Android Malware Dataset
A comprehensive dataset containing 250+ cyberthreat intelligence (CTI) reports related to Android malware. Includes structured information extracted from CTI reports, malware samples, and associated metadata.
CyNER - Cybersecurity Named Entity Recognition
A specialized Named Entity Recognition (NER) library designed for cybersecurity domain. Automatically extracts security-relevant entities from text including malware names, CVE IDs, attack techniques, and IOCs.
TINKER - Knowledge Graph
A comprehensive threat intelligence knowledge graph containing 52K triples and 30K entities. Represents relationships between security concepts, threats, and mitigation strategies for automated reasoning.