I am a research who loves to work on new and challenging problems in the world of Machine learning, Math, or Health AI. I often enjoy challenges that come from learning in settings where data is limited or very noisy, investigativng causal relations using ML techniques, or in optimizing gathering new data via active learning or bayesian optimisation methods. I also love researching LLM agents and swarms.
I am a Machine Learning Researcher with expertise in developing innovative AI solutions. I obtained my bachelors in Biological sciences at the University of Calgary in 2021 and my masters in Computing Sciences at the University of Alberta in 2025 specializing in Statistical Machine Learning. My thesis work regards finding near optimal way to gather and use data in hard settings (with limnited information) using Active Learning, Optimisation, and Survival analysis.
More recently I have been working in areas of transfer learning, as well as using LLM agents in efficient ways. I enjoy collaborating with diverse teams and continuously learning new techniques and frameworks.
Alberta Machine Intelligence Institute (AMII)
Researched Bayesian optimization and transfer learning techniques for life sciences applications. Designed models to improve media formulation for cell growth across multiple cell types.
Alberta Machine Intelligence Institute (AMII)
Developed scalable ML pipelines using SQL and Python. Delivered prototype ML applications and actionable insights to industry partners.
Alberta Machine Intelligence Institute (AMII)
Work Integrated Learning Oppportunity (WILO) where I researched and helped compose a Coursera course teaching ML scientist effective dimension reduction techinques for using Genentic datasets.
University of Alberta
Built statistical models and time series analyses to predict disease diagnosis timelines. Developed optimal algorithm for data gathering in survival settings under budget constraints.
University of Alberta
Engineered data pipelines and developed classification models for alloy properties prediction, achieving 90% accuracy.
Calgary Chess School
Designed and taught Python and data visualization curriculum. Created open-source educational materials for students and the broader community.
Presented at RISE Conference
Developed and compared synchronous vs asynchronous updates in soft actor-critic algorithms using the FRANKA robotic arm.
Read PaperPublished in *Biology*, MDPI
Conducted a meta-analysis of Alzheimer's mechanisms and the role of herbal compounds in altering disease pathways.
Read PaperSubmitted to ACM
Investigates the emergence of intelligence through the aggregation of LLM agents like Qwen2 and LLaMA 3.2 using a new mathematical framework.
Read PaperSubmitted to Bioinformatics
Predicts time to first bowel resection in pediatric Crohn's patients using clinical and lab data. In collaboration with medical professionals.
View ReportSubmitted to ICML
Proposes a novel data acquisition algorithm guaranteeing a 63% optimal convergence rate under budget constraints. Validated on medical and financial datasets.
Read PaperSubmitted to ANTS conference
Proposes a novel benchmark in evaluating how llm agents collaborate and deal with complexity via np hard problems.
Read PaperAI-powered safety classification tool for chemistry and biology labs. Offers real-time risk assessment (Safe, Caution, Hazardous) with contextual reasoning using both rule-based and GPT-4o RAG systems.
Technologies: GPT-4o, RAG, FAISS, ChromaDB, Streamlit, GitHub Pages, Python
Live Demo View CodeRunner-up at NatHacks Hackathon. Built a multi-modal AI model for real-time sentiment and emotion analysis using speech and facial cues.
Technologies: OpenCV, Hugging Face, Amazon Polly, Python
View ProjectUsed K-means clustering on Happiness Report data to visualize socio-economic trends among countries.
Technologies: Streamlit, Pandas, Scikit-learn, Python
View Project