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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

Beyond my current research

Neuroscience and the adaptable brain

The brain is probably what I am most curious about beyond my current research. How does it learn from experience, reorganize itself, and make sense of the world? I am currently reading Livewired: The Inside Story of the Ever-Changing Brain by David Eagleman, and it has deepened my curiosity about neuroplasticity, perception, and memory. The idea that our brains are continually changing rather than operating like fixed machines fascinates me. I hope to understand what this biological adaptability might teach us about learning and artificial intelligence.

Geometry in nature

I have always loved finding geometric patterns in nature. Maybe it is a slightly unusual thing to notice, but the branching of trees and rivers, the symmetry of flowers, and the shapes hidden in seemingly chaotic landscapes regularly catch my attention. Growing up surrounded by the mountains and rivers of Kullu only strengthened that fascination. What interests me most is why these patterns emerge, and how mathematical and physical principles can produce recognizable structures across very different natural systems.

Cymatics and visible patterns of sound

I became interested in cymatics at an early age. Seeing sound-driven vibrations produce intricate geometric patterns in sand or water felt almost unbelievable to me. I am curious about how frequency, resonance, and the properties of materials determine the shapes that emerge. It is another example of an invisible process creating visible order, and I would love to explore the physics behind it more deeply.

These interests connect through a question that keeps pulling me in: how do complex patterns, structures, and intelligent behavior emerge from underlying interactions? I see them as open curiosities to explore alongside my current work in AI.

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.

Livewired: The Inside Story of the Ever-Changing Brain — David Eagleman

Currently reading; exploring the brain's capacity to adapt, learn, and reorganize itself.

Posters

Data Science Institute, Columbia University

Poster on Capstone — December 2023.

Technical Writing

Medium