Krishna Poddar
Mumbai, Maharashtra, India
Summary
🤩
Rockstar🎓
Top SchoolKrishna Poddar is a software engineer and founder with 14 years of experience building product-first systems at the intersection of ML, distributed systems, and fintech. He has shipped 0→1 projects in industry roles at Flexport and Google internships focusing on performance and infrastructure, and contributed hands-on computer vision coursework material to Stanford’s CS131 release demonstrating practical NumPy and image-processing skills. As founder of an AdTech DOOH startup and a stealth AI learning assistant for education, he blends entrepreneurial drive with applied ML to tackle real-world problems. His academic background includes an MEng and BS from Cornell in Financial Engineering, Operations Research and Computer Science, where he also served as a teaching assistant for quantitative trading and stochastic processes. Not obvious at first glance: he pairs production engineering experience with academic research and teaching, making him comfortable translating theoretical models into deployable systems. Based in Mumbai, he brings a global perspective from U.S. research environments to Indian startup markets.
14 years of coding experience
3 years of employment as a software developer
International Baccalaureate Diploma Program, International Baccalaureate Diploma Program at Chinmaya International Residential SchoolMaster of Engineering - MEng Financial Engineering Operations Research and Information Engineering, Master of Engineering - MEng Financial Engineering Operations Research and Information Engineering at Cornell University
Github Skills (7)
computer-vision10
python10
image-processing10
numpy10
matplotlib9
jupyter-notebook8
linear-algebra8
Programming languages (7)
Github contributions (5)
StanfordVL/CS131_release
Oct 2017 - Oct 2019
Released assignments for the Stanford's CS131 course on Computer Vision.
Role in this project:
ML Engineer
ML EngineerContributions:18 commits, 1 PR, 15 pushes in 2 years
Contributions summary:Krishna contributed to the release of assignments for a computer vision course. Their primary work involved adding and modifying Python code, specifically within Jupyter Notebook files. The commits show implementation of image processing techniques, including convolutions, padding, and color space manipulations within the context of a computer vision curriculum. The user's work demonstrates familiarity with image manipulation, linear algebra concepts relevant to computer vision, and the use of NumPy and Matplotlib.
stanfordpythonvisiondeep-learningcomputer-vision
leobxpan/CS131_release
Sep 2018 - Oct 2019
Released assignments for the Stanford's CS131 course on Computer Vision.
Contributions:15 commits in 1 year
stanfordpythonvisiondeep-learningcomputer-vision
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