Machine Learning Researcher & AI Ethics Advocate
Developing scalable, interpretable, and trust-aware AI systems. Bridging academia and industry through ethical, human-centric machine intelligence grounded in over a decade of R&D experience.
I am a PhD student in Computer Science at the University of Calgary, working on scalable and interpretable trust-aware modeling for user-generated online content. My research spans machine learning, NLP, and responsible AI.
With over a decade of industry experience across fintech, enterprise software, and AI research, I bring a rare combination of theoretical depth and practical engineering to complex problems. I transitioned from leading software teams at Midland Bank to cutting-edge ML research — a journey that has shaped my commitment to building technology that genuinely serves people.
I am passionate about AI transparency, ethical data practices, and accessible research. As a Teaching Assistant for courses in security and database systems, I work to instill responsible AI thinking in the next generation of CS students.
Trust-aware modeling, deceptive content detection, LLM-driven opinion mining, trustworthy AI
Graduate Research & Teaching Assistant — TA for CPSC 329 & CPSC 471 at University of Calgary
Public health trust, parliamentary NLP, AI in education, financial technology
Calgary, Alberta, Canada
A novel framework leveraging trust vectors for detecting fake and deceptive reviews in online platforms.
Applying large language models to mine and analyze public trust signals from social and political discourse.
Benchmarking classical ML against deep neural architectures on news categorization tasks.
Adaptive transfer learning pipeline for improving clinical accuracy in brain tumor classification from MRI.
Framework addressing transparency, fairness, and accountability in autonomous AI systems.
Comprehensive survey of XAI and ethical frameworks applied to computer vision in social media contexts.
Open to research collaborations, speaking engagements, and academic discussions around trustworthy AI, NLP, and responsible machine learning.