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Deep Learning · Neuro-Symbolic Systems · Knowledge Representations

Vritansh Kamal

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About Me

I am from Kullu, Himachal Pradesh, a small Himalayan valley in northern India surrounded by mountains, rivers, orchards, and a culture I remain closely connected to. It is the place I call home and a big part of how I think about curiosity, simplicity, and staying grounded.

See Kullu on Google Images

I spent several years working in industry across technology, analytics, and machine learning before moving to the United States for my M.S. in Data Science at Columbia University. Before Columbia, I completed my B.Tech. in Computer Science at SRM Institute of Science and Technology.

Outside work, I enjoy reading, learning about neuroscience, and following questions about how humans learn, reason, remember, and make decisions.

Research Interests

I am interested in building learning systems that combine strong representations, structured knowledge, graph-based analysis, and principled treatment of uncertainty.

Deep Learning Neuro-Symbolic Systems Knowledge Representations Graph Analytics Uncertainty Quantification

Research Experience

My primary research experience comes from industry, where I have worked on applied machine learning, language models, neuro-symbolic and knowledge-grounded reasoning, and AI systems. My healthcare and academic research experience complements that work through longitudinal healthcare modeling, medical imaging, and applied deep learning.

Primary · Industry Research Experience

Oracle

Working on neuro-symbolic and knowledge-graph-grounded approaches for reliable multi-step AI reasoning in telecommunications.

CVS Health

Worked on applied machine learning and time-series forecasting for healthcare pricing and underwriting problems.

Secondary · Academic Research

GE Research

Master's capstone on PET/CT lesion detection using DETR and Conditional-DETR for medical imaging.

HIPPO Labs

Research Assistant — worked on machine-learning systems for large-scale deployments and parallel data processing.

Teaching

Columbia University

Teaching Assistant II, Quantitative Methods in Social Sciences — Fall 2023.

Industry Experience

Projects

Data Discovery Pro

Built a Python library for automated exploratory data analysis, preprocessing, and dataset visualization.

Pose Estimation

Developed deep-learning pose-estimation experiments using custom CNNs and ResNet-based transfer learning.

Readings

Reading is one of the things I genuinely enjoy outside work. These are a few books that have stayed with me, and I would highly recommend them to anyone looking for thoughtful perspectives on people, habits, ambition, money, and decision-making.

Posters

Data Science Institute, Columbia University

Poster on Capstone — December 2023.

Technical Writing

Medium