I'm completing my MS in Data Science (thesis track) at the University of Massachusetts Dartmouth,
working as a Graduate Research Assistant under
Dr. Gokhan Kul on Department of Defense-funded cybersecurity research
in collaboration with the U.S. Military Academy at West Point.
My thesis research falls under the DoD-funded project
"Resilience Engineering of ML-enabled Open World Recognition for Network Intrusion Detection Systems"
(Grant W911NF-22-2-0160). My work centers on two core contributions:
MITRE ATT&CK-based attack chain prediction using hybrid LSTM-Markov models, and
Adversarial Risk Analysis (ARA-OSID), a decision-theoretic framework with ATT&CK-derived
utility functions for defender-attacker scenarios in NIDS. My earlier research addressed what I call the
False Champion Problem: ML-based intrusion detection systems that achieve high aggregate accuracy
while failing catastrophically on critical attack classes. I've authored four first-author papers across these areas (1 accepted at DSN, 3 under review).
Before graduate school, I spent 3+ years as a Data Scientist and Software Engineer building
production ML pipelines, time series models, and cloud-native services on AWS. I'm seeking
research scientist, ML engineer, and cybersecurity engineer roles where I can
apply both research depth and engineering skills. STEM OPT eligible with 3 years of U.S. work authorization.
Research Interests
Adversarial Machine Learning
Network Intrusion Detection
Deep Learning Robustness
Cyber Threat Intelligence
Synthetic Data Generation
AI Security & Safety
