Vedic Partap
Manager - Quant Risk at Graviton Research Capital LLP
Gurugram, Haryana, India
Summary
👤
Senior🎓
Top SchoolVedic Partap is a quant risk manager with 9 years of experience applying machine learning, statistical modeling, and software engineering to financial risk and trading systems. After an IIT Kharagpur CSE degree, he progressed through quantitative strategist roles at Goldman Sachs—where he sped up risk engines from minutes to seconds and built ML-based bucketing and diagnostic frameworks for mortgage and securities businesses—now leading quant risk at Graviton Research Capital. His background spans production-grade ML, NLP research, materials-science deep learning, and swarm-robotics simulation, reflecting an unusual blend of research rigor and hands-on systems optimization. Known for turning complex data and models into fast, auditable risk infrastructure, he combines strong technical delivery with domain fluency in global mortgages and trading risk. Based in Gurugram, he brings both industrial and academic collaborations to bear on practical, low-latency risk solutions.
9 years of coding experience
2 years of employment as a software developer
Science, Science at O.S.D.A.V. Public SchoolBachelor of Technology - BTech, Computer Science, Bachelor of Technology - BTech, Computer Science at Indian Institute of Technology, Kharagpur
Github Skills (22)
convolutional-neural-networks10
event-driven8
deep-learning7
git-remote7
tensor6
data-structures4
python3
dependency-parsing3
natural-language-processing3
cpp3
machine-learning2
sequence-labeling2
named-entity-recognition2
pytorch2
corenlp2
Programming languages (4)
Github contributions (5)
vedic-partap/vedic-partap.github.io
Aug 2019 - Jul 2025
Contributions:12 pushes, 1 branch in 6 years
vedic-partap/Event-Driven-Stock-Prediction-using-Deep-Learning
Dec 2017 - Oct 2019
A deep learning method for event driven stock market prediction. Deep learning is useful for event-driven stock price movement prediction by proposing a novel neural tensor network for learning event embedding, and using a deep convolutional neural network to model the combined influence of long-term events and short-term events on stock price movements
Contributions:18 commits, 5 PRs, 11 pushes in 1 year 10 months
convolutional-neural-networksdeep-learningevent-driventensor
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