Pranay Mukherjee
Chief Executive Officer at LocusChat
Bengaluru, Karnataka, India
Summary
👤
Senior🎓
Top SchoolPranay Mukherjee is a CEO and deep learning engineer with 8 years of experience building AI-driven products and advising startups across healthcare, fintech, and e-commerce. He founded LocusChat and previously delivered NLP and deep learning solutions in oncology at SymphonyAI, pairing research rigor with product-focused execution. His academic internships produced conference-quality computer vision work and practical studies in hate speech and cyberattack analysis, reflecting a blend of applied research and domain breadth. An active open-source contributor, he implemented and optimized the Jaro-Winkler distance in the widely used NLTK library, demonstrating attention to algorithmic detail and code quality. Based in Bengaluru, he combines hands-on model development with digital marketing instincts and a track record of translating research into production-ready systems.
8 years of coding experience
4 years of employment as a software developer
High School, 3.9/4.0 GPA, High School, 3.9/4.0 GPA at Hem Sheela Model School - IndiaBachelor of Technology, Electrical, Electronics and Communications Engineering, 8.5/10.0, Bachelor of Technology, Electrical, Electronics and Communications Engineering, 8.5/10.0 at Institute of Engineering & Management (IEM)
English, Bengali, Hindi
Stackoverflow
633reputation
126kreached
4answers
3questions
neural-network
top-5%
deep-learning
top-5%
Github Skills (19)
algorithm10
algorithms10
distance-matrix10
python10
nltk10
data-structure10
natural-language-processing10
data-structures10
metric10
machine-learning9
neural-network9
deep-learning9
nlp6
keras6
keras-layer6
Programming languages (2)
Github contributions (5)
nltk/nltk
Jun 2018 - Jul 2018
NLTK Source
Role in this project:
Back-end Developer
Back-end DeveloperContributions:13 commits, 7 PRs, 70 comments in 22 days
Contributions summary:Pranay contributed to the development of distance metrics for natural language processing tasks within the NLTK library. Their work included implementing the Jaro-Winkler distance, optimizing the algorithm, and resolving PEP-8 style issues. The user also added and refined doc tests to ensure the correctness and reliability of the Jaro-Winkler implementation. These contributions enhance the functionality and quality of the NLTK library for NLP applications.
nlppythonmachine-learningnltknatural-language-processing
53X/53X.github.io
Jul 2018 - Apr 2019
Contributions:14 commits, 12 pushes, 1 branch in 9 months
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