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Machine Learning · Research · Engineering

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.

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Research Interests

My primary research interest is Neuro-Symbolic AI, particularly where it connects with Knowledge Graphs and Deep Learning. I am also deeply curious about Neuroscience and how ideas about learning, memory, and reasoning in biological systems can inspire better intelligent systems. My second research interest is Uncertainty Quantification.

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Research Experience

My primary research experience comes from industry, where I have worked on applied machine learning, language models, knowledge-grounded reasoning, and AI systems. My academic research experience complements that work through 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.

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Teaching

Columbia University

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

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Industry Experience

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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.

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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.

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Posters

Data Science Institute, Columbia University

Poster on Capstone — December 2023.

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Publications

Medium