Prakhar Gupta
Technical Lead - Document Intelligence at Hexagon AB
Winterthur, Zurich, Switzerland
Summary
🤩
Rockstar🎓
Top SchoolPrakhar Gupta is a Technical Lead in Document Intelligence with 12 years of experience applying NLP and deep learning to real-world problems, backed by a PhD from EPFL and top-ranked MTech from IIT Bombay. He has advanced research credentials—postdoctoral and senior researcher roles—spanning efficient static and contextual representations, multilingual models, and LLMs, and has applied these methods to domains as diverse as historical analysis and public health. At Hexagon he leads document intelligence efforts while contributing to generative AI initiatives, bridging cutting-edge research with production needs. An active contributor to educational open-source material (notably EPFL’s Optimization for ML course labs), he combines strong theoretical foundations with practical ML engineering across modern frameworks. Known for turning complex NLP research into deployable systems, he is open to opportunities in both applied deep learning and research.
12 years of coding experience
2 years of employment as a software developer
Indian Institute of Technology BombayDoctor of Philosophy - PhD, Computer and Communication Sciences, Doctor of Philosophy - PhD, Computer and Communication Sciences at EPFL (École polytechnique fédérale de Lausanne)Bachelor of Technology - BTech, Aerospace Engineering, Bachelor of Technology - BTech, Aerospace Engineering at IIT Kanpur
Github Skills (7)
jupyter-notebook10
machine-learning10
gradient-descent10
python10
implement9
algorithm9
algorithms9
Programming languages (10)
Github contributions (5)
epfml/OptML_course
Feb 2018 - Jul 2022
EPFL Course - Optimization for Machine Learning - CS-439
Role in this project:
ML Engineer
ML EngineerContributions:224 commits, 226 pushes, 1 branch in 4 years 5 months
Contributions summary:Prakhar's commits focus on developing and implementing machine learning models within the EPFL Optimization for Machine Learning course. The primary contribution is the publishing of initial lab assignments (lab01), including a notebook with basic operations. Subsequent commits involve the creation of a template for lab 3 on gradient descent, followed by solutions for the lab. The final commit includes publishing mini-project descriptions.
optimizationmachine-learningepfl
nathansimonis1612/ML_course
Aug 2019 - Feb 2021
EPFL Machine Learning Course, Fall 2019
Contributions:90 commits in 1 year 5 months
falldata-sciencemachine-learning-coursemachine-learning2019-fall
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