Experience

Research in medical AI, applied machine learning, and teaching at Queen's University and beyond.

Research ExperienceIndustry ExperienceTeaching Experience
Academic research & internships

Research Experience

– Present
BAM Lab · Queen's School of Computing

Computing Graduate Research Fellow

Research on explainable radiology vision-language models for multi-label, long-tail cancer identification and visual grounding. This includes interpretation and evaluation of biomedical VLMs under class imbalance.

Faculty of Health Science, Queen's University

Supervisors: Prof. Farhana Zulkernine and Dr. Nicholas Cofie

Automated extraction and evaluation of resident Entrustable Professional Activities from assessor notes, including sentiment analysis and Spearman correlation between assessor feedback and committee comments.

School of Rehabilitation Therapy, Queen's University

Supervisors: Prof. Farhana Zulkernine and Assoc. Prof. Nora Fayed

Automated literature filtration, information extraction, and classification across 8,000 clinical randomized controlled trial articles using domain-specific BERT and a domain-specific language model, with reported 95% specificity. Contributed to grant writing.

–
Pfizer Canada · MITACS Accelerate

ML Research Intern

  • Extracted, transformed, and analyzed unstructured electronic health records to study patient characteristics, demographics, and comorbidities.
  • Used Pandas, MySQL, NumPy, scikit-learn, and seaborn; applied k-means clustering and logistic regression.
  • Contributed to a 2025 study on Alzheimer's disease diagnosis and management in Ontario primary care.
Applied machine learning & research software

Industry Experience

–
EvenUp · Legal AI

Associate Machine Learning Engineer Internship

  • Explored GEPA evolutionary prompt optimization and its effectiveness for legal AI workflows.
  • Created a firmwide relevance dataset with LLM-as-judge evaluation to monitor precision@k, recall@k, and NDCG@k.
  • Implemented cross-encoder, listwise, and setwise reranking, improving retrieval metrics by 12% over the baseline.
–
CritLab@Queen's ECE · Smith School of Electrical and Computer Engineering

Front-end Developer

Manager: Dr. Sean Kauffman

  • Co-created a visualization interface for space log analysis and nfer, using Python Socket.IO, HTML, CSS, and D3.js to explore streaming events and temporal interval hierarchies.
  • Documented the tool in work accepted at the 2025 NASA Formal Methods Symposium.
–
Silicon Orchard Ltd · Dhaka, Bangladesh

Machine Learning Engineer

  • Built an internal HR analytics tool based on BERT.
  • Developed stylometric authorship attribution and deep reinforcement-learning recommendation methods.
  • Developed speech recognition for smart-home systems.
Queen's University · School of Computing

Teaching Experience

– Present
School of Computing · Queen's University

Graduate Teaching Assistant

Supported students through marking, proctoring, lab sessions, and office hours across software engineering, cybersecurity, data structures, and data analytics.

Courses supported

  • CISC 423Software Requirements
  • CISC 447Introduction to Cybersecurity
  • CISC 235Data Structures
  • CISC 251Data Analytics