Swaminathan Sankaran | ML Engineer

About

A bit about me

I'm a Master's student in Data Science at the University at Buffalo, and curiosity has shaped a lot of how I learn and work. I enjoy understanding how things work, whether that is a technical system, a human decision, or a bigger idea in philosophy, psychology, and rationality.

A lot of that mindset comes from the things I've always enjoyed outside of work. I used to be a competitive gamer, which taught me how to think strategically, stay composed, and keep improving through practice. I'm also a speedcuber, so I've always liked patterns, problem solving, and the challenge of getting better with focus and repetition. I enjoy reading, exploring different perspectives, and learning through food, culture, and the ways people think and live.

That same curiosity is what drew me to data science and machine learning. It also shapes how I approach my work. I enjoy building systems that are thoughtful, useful, and reliable, and I'm especially interested in turning ideas into practical solutions that create real impact.

I'm currently looking for internship opportunities where I can keep learning, contribute meaningfully, and grow as both a problem solver and engineer.

Projects

Selected Work

MLOps / Production Systems

Drift-Aware MLOps Pipeline

Drift-aware MLOps pipeline built to detect silent model degradation, trigger automated retraining, and improve model reliability under changing data distributions
Built and containerized the system using FastAPI, Airflow, MLflow, PostgreSQL, and Evidently AI with Docker Compose, with Kubernetes-ready orchestration in mind
Monitored system health and pipeline behavior in real time using Prometheus and Grafana dashboards
Validated end-to-end performance by simulating covariate and concept drift, triggering automated retraining that recovered shifted-distribution PR-AUC from 0.37 to 0.89 while preserving historical ROC-AUC at 0.75

Multimodal Deep Learning

Multi-Modal Molecular Similarity Regression

Modeled molecular similarity for drug discovery to improve candidate selection and ranking of chemical compounds
Designed a multi-modal architecture fusing 2D image features using ResNet-18, 3D geometry using SchNet, and fingerprint embeddings, trained with contrastive learning across 291,742 molecules
Achieved ~0.92 Pearson correlation on 200 expert-annotated pairs, outperforming the Tanimoto similarity baseline on unseen molecular pairs
Built the system to combine complementary molecular views for stronger ranking quality in downstream discovery workflows

Medical Imaging

Patch-Level CT Tamper Classification

Built to detect tampering at the patch level within CT scans, where localized manipulation is harder to catch than whole-scan forgeries
Jointly trained a 3D convolutional compressor with an ImageNet-pretrained ResNet-18 across 169 volumetric lung CT scans to convert 16-slice 3D patches into compact 2D feature maps
Focused on patch-level manipulation, where tampering is more localized and more difficult to detect than whole-scan forgery
Achieved 0.95 validation AUC, outperforming 2.5D, 3D, and projection-based baselines at lower computational cost

Experience

Where I've Worked

Machine Learning Engineer, Intern

Zolvit

Feb 2024 — Aug 2024

Eliminated ~23 hours/week of manual data entry by building an OCR + T5-large extraction pipeline for legal documents
Improved legal retrieval precision by ~28% across 10,000+ documents using hybrid Elasticsearch keyword + Pinecone vector search
Built a document routing classifier achieving 92% accuracy on TF-IDF/Doc2Vec features, automating intake workflows
Containerized ML services with Docker and deployed on AWS EC2, maintaining 99.9% uptime with Grafana monitoring

Education

Academic Background

University at Buffalo

MS in Engineering Science (Data Science)

Aug 2025 — Dec 2026

Relevant Coursework

Statistical Learning I & II Machine Learning Probability Theory Database Data Science Data Models & Query Languages Numerical Methods

Vellore Institute of Technology

B.Tech in Computer Science and Engineering (AI & ML)

Aug 2019 — Jul 2023

Relevant Coursework

Machine Learning Deep Learning Reinforcement Learning Computer Vision Applied Linear Algebra Statistics Data Structures & Algorithms Database Management Systems

Skills