T-CAIREM Conference
Presented conditional VAE research on polysomnography-to-ECG reconstruction and shared early fidelity benchmarks for clinical trial cohorts.
Mithun Manivannan
MSc Data Science | ML for Healthcare + Machine Learning for Healthcare, ECG Reconstruction, Biomedical Signal Processing, and Clinical AI
Visual Stories & Projects
Snapshots from labs, conferences, and the creative routines that keep my research grounded.
Spatial Omics Clustering
Nonparametric Bayesian spatial clustering maps tumor microenvironments to inform breast cancer phenotyping work at McMaster University.
Preventive Assessment Tools
PAT dashboards map lifestyle, clinical, and biomarker inputs into risk models so clinicians can explore protective factors in real time.
Music & Dragonboating
Between research sprints I compose, arrange for McMaster’s The Macaellas, and race with my dragonboat crew — the rhythm keeps me focused.
ecg-reconstruction
conditional-vae-polysomnography
spatial-omics-clustering
cardiac-vae-risk
pat-platform
pokemon-battle-sim
Publications & Presentations
A selection of peer-reviewed publications, conference presentations, and collaborative research contributions.
Publication entries are syncing from shared portfolio data.
Awards & Recognition
Faculty of Science Graduate Scholarship
Carleton University · $12,000 · 2025
TCAIREM Studentship
University of Toronto · $10,000 · 2025
Faculty of Science Award of Excellence
McMaster University · $3,000 · 2020
Dean's List
McMaster University · 4× · 2020–2023
About Mithun
I'm Mithun Manivannan, a graduate researcher pursuing my MSc in Data Science, Analytics & AI at Carleton University. My work sits at the intersection of machine learning, biomedical signal processing, and healthcare analytics.
Currently, I'm developing novel approaches for ECG reconstruction from wearable devices at Sunnybrook Health Sciences Centre's Schulich Heart Program, building real-time signal fidelity algorithms and physiologic outcome predictors. My research spans from conditional neural VAEs for polysomnography-to-ECG reconstruction to Bayesian spatial clustering for tumor microenvironment analysis.
I'm passionate about building interpretable ML pipelines that translate complex biomedical data into actionable clinical insights. Whether it's PyTorch models for cardiac risk stratification, forecasting algorithms for rare-disease trials, or automated annotation systems for clinical cohorts — I love the challenge of making AI work in real healthcare settings.
Beyond research, I've worn many hats: teaching health research methods at Carleton, developing population forecasting models for policy work, and leading vocal arrangements for McMaster's A Cappella club. Outside of work, you'll find me at the piano, exploring VR experiments, or diving into the latest papers on ethical AI in medicine.
Music & Performance
Music has always been a central part of my life. I was part of The Macaellas, McMaster University's men's a cappella group, where I performed as a vocalist and arranged music for the ensemble. I also compose my own original pieces, blending elements from classical, jazz, and contemporary styles.
I'm also an avid dragon boater, competing in local and regional races. The discipline, teamwork, and rhythm of paddling complements my research work, teaching me about coordination, endurance, and pushing limits both individually and as part of a team.
Skills & tools
Generative AI & Deep Learning
- LLMs (Llama 2, GPT-4 APIs)
- RAG Pipelines
- Variational Autoencoders (VAEs)
- Transformers & Attention
- PyTorch
- TensorFlow
Biomedical Signal Processing
- ECG Reconstruction
- Polysomnography Analysis
- Time-series Forecasting
- Signal Denoising
- Biosignal Interpretation
MLOps & Cloud
- Docker & Containers
- Kubernetes
- AWS (SageMaker, EC2)
- GCP (Vertex AI, BigQuery)
- MLflow
- CI/CD for ML
Statistical & Spatial Analysis
- Bayesian Inference
- Spatial Omics
- Survival Analysis
- Causal Inference
- R (tidyverse, Bioconductor)
Data Engineering
- SQL & NoSQL
- dbt
- Apache Airflow
- PostgreSQL
- Data Pipelines
Education
MSc Data Science & Artificial Intelligence
Carleton University · 2025–2027
BSc (Honours) – Mathematical Sciences
McMaster University · 2020–2025
BSc (Honours) – Kinesiology
McMaster University · 2020–2025
