Palash Ahuja
Software Engineer at Google
San Francisco Bay Area United States
Summary
👤
Senior🎓
Top SchoolPalash Ahuja is a software engineer with 11 years of experience building full-stack and data-driven systems, currently at Google in the San Francisco Bay Area. He combines backend performance optimization (notably improving core probabilistic computations in the pgmpy Bayesian networks library) with front-end and cloud deployment expertise across React, AWS, and serverless stacks. Past roles span retail analytics tooling at Stackline, API and data pipeline work at NYU, and automation and integration projects at Apttus, demonstrating a track record of reducing operational friction and latency. He holds a master's in computer science from NYU and a BTech from NIT Surat, and has contributed space-efficient Dynamic Bayesian Network implementations during a Google Summer of Code internship. Comfortable across languages from Python and Go to JavaScript and C++, he favors pragmatic, performance-conscious solutions that bridge research-grade algorithms and production systems.
11 years of coding experience
2 years of employment as a software developer
Bachelor of Technology - BTech, Computer Engineering, Bachelor of Technology - BTech, Computer Engineering at Sardar Vallabhbhai National Institute of Technology, SuratMaster's degree, Computer Science, 3.93, Master's degree, Computer Science, 3.93 at New York University - Polytechnic School of Engineering
Stackoverflow
522reputation
37kreached
2answers
20questions
Github Skills (15)
algorithm10
data-structures10
algorithms10
factor10
python10
data-structure10
numpy10
inference9
causal-inference8
neural-network6
htaccess6
wait6
deep-learning6
loss-functions6
scikit-learn6
Programming languages (8)
Github contributions (5)
pgmpy/pgmpy
Feb 2015 - Sep 2015
Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:71 commits, 44 PRs, 115 comments in 6 months
Contributions summary:Palash primarily contributed to the `pgmpy` library by modifying and optimizing methods within the `Factor` class. They focused on improving the `marginalize` method, replacing loops with NumPy operations to enhance performance. These changes involved refactoring the code, removing redundant steps, and simplifying the logic for marginalizing variables. The user's work demonstrates a focus on improving the efficiency of core probabilistic calculations within the library.
causal-modelspythondagbayesian-inferencecausal
palashahuja/dotfiles
Aug 2018 - Mar 2025
Contributions:15 pushes, 1 branch in 6 years 8 months
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