Chinmaya Pancholi
Low Latency Software Developer
Singapore
Summary
👤
Senior🎓
Top SchoolChinmaya Pancholi is a Low Latency Software Developer with 9 years of experience building high-performance trading systems at Tower Research Capital, grounded in a dual B.Tech/M.Tech in Computer Science from IIT Kharagpur. He blends systems-level engineering for ultra-low-latency production environments with a strong background in NLP and ML tooling, evidenced by a Google Summer of Code contribution that integrated and extended the widely used Gensim library (including adding fastText and word2vec enhancements). His early research and product work spans large-scale text processing, question-answering, and production backends for consumer assistants, showing both academic rigor and product focus. Based in Singapore, Chinmaya is comfortable moving between research code and production-ready systems, and has a track record of shipping features that bridge ML models and real-time applications.
9 years of coding experience
5 years of employment as a software developer
Secondary School Certificate (SSC) Examination, 10/10 CGPA, Secondary School Certificate (SSC) Examination, 10/10 CGPA at Maharaja Sawai Man Singh VidyalayaHigh School Diploma, Mathematics and Science, 94%, High School Diploma, Mathematics and Science, 94% at Step By Step High SchoolB.Tech and M.Tech (Dual degree), Computer Science and Engineering, 9.6/10, B.Tech and M.Tech (Dual degree), Computer Science and Engineering, 9.6/10 at Indian Institute of Technology, Kharagpur
English, Hindi, French
Github Skills (6)
word-embeddings10
machine-learning10
nlp10
word2vec10
python10
natural-language-processing10
Programming languages (5)
Github contributions (5)
piskvorky/gensim
Mar 2017 - Nov 2017
Topic Modelling for Humans
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:167 commits, 29 PRs, 162 comments in 8 months
Contributions summary:Chinmaya contributed significantly to the `gensim` library, primarily focusing on enhancing the `word2vec` model. They updated the descriptions of functions, specifically in the `word2vec.py` file, demonstrating an understanding of the model's inner workings. Furthermore, the user added features, like the `predict_output_word` function, with the goal of improving the functionality related to negative sampling.
pythonword-similarityword-embeddingsdata-miningfor-humans
chinmayapancholi13/chinmayapancholi13.github.io
Mar 2017 - Jun 2021
Website to update and share information.
Contributions:48 pushes, 1 branch in 4 years 3 months
updatepythonshare
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