Mukesh Paranthaman
Open to work1 years experienceSoftware Engineer / AI Researcher
Computer Science Student | AI Researcher | Software Engineer
Standing Rank
Rank Not Available
Developer Badges
Skills & Technologies
41 skillsGitHub Activity Evidence
GitHub Footprint Not Connected
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Work Experience
AI Intern
VDart Digital
- Built a Python-based static analysis parser for 200+ source files, constructing a structured knowledge base and reducing processing time by 80%.
- Developed an end-to-end RAG data pipeline for automated React code generation using FAISS, AST-aware code chunking, and a knowledge graph.
- Designed quantitative evaluation metrics using cyclomatic complexity, AST, and CFG analysis to assess generated code quality.
Software Engineering Intern
Asian Paints
- Built a mobile application with React Native Expo, reducing product image upload times by 50% and streamlining warehouse operations for improved productivity.
- Implemented backend data services with Firebase Firestore for real-time storage and retrieval, ensuring efficient data management, and added Firebase Authentication to maintain secure, role-based access control.
- Established a CI/CD pipeline with GitHub Actions as part of the software development lifecycle, reducing deployment time by 40%.
Undergraduate Researcher
Reasoning and Learning Research Group
- Wrote script to parse 1000+ C programs, insert tags and run experiments on a HPC node.
- Developed a static analysis tool to generate single-iteration symbolic execution harnesses with injected VERIFIER_assume bounds and assertions.
AI Researcher
Cherry Research Group
- Implemented Pytorch-based Physics informed neural net model combining Augmented Neural ODE and convolutional autoencoder to work on fibrillation of the 2D Aliev Panfilov Model over 2048 voxels.
- Applied multiple shooting with latent trajectories and patching for stable long-horizon training.
Projects
AI for Competitive Math - CS 4641 Project
- Built an end-to-end retrieval-augmented generation (RAG) pipeline for Lean 4 theorem proving, integrating FAISS-based vector retrieval with Gemini to generate compiler-valid formal proofs.
- Implemented tactic-level retrieval using sentence embeddings, enabling the model to condition proof generation on historically successful Lean tactics and local theorem context.
- Evaluated on a standard Lean 4 benchmark, achieving ~55% proof success rate, demonstrating measurable gains over vanilla generation without retrieval.
Cross-Sectional Equity Return Prediction
- Built a cross-sectional equity return prediction pipeline using XGBoost’s native C++ API.
- Engineered momentum and volatility-based alpha features with walk-forward validation to eliminate look-ahead bias.
- Designed and implemented a multithreaded C++ data pipeline for data ingestion and feature computation, reducing data ingestion time by ~50%.
GNNMapper
- Conceived and designed a GNN for qubit mapping in quantum circuits, integrating physics-informed constraints to reduce noise and improve gate placement.
- Formulated mapping as a MAXSAT optimization problem, developing the pipeline for simulated circuit evaluation and potential efficiency improvements.
Leaderboard Standings
Leaderboard Position Pending
Global test scores, peer standing percentiles, and algorithm leaderboard ranks are updated dynamically.
Assessment Highlights
Assessments Not Completed
Coding evaluations, system assessment results, and conceptual score badges will appear here after taking a test.
AI Collaboration Score
AI Collaboration Score Pending
Developer coding behavior, assistant cooperation, and AI pair-programming indicators are evaluated during live coding sessions.
Role Compatibility Profile
Role Compatibility Analysis Pending
Custom matching reports, candidate role compatibility percentiles, and core engineer strength profiles are processed once conceptual code screenings are complete.
Achievements
Academic Honors
Faculty Honors, Dean’s List
IMC Prosperity Trading Contest
Placed #102/12000+ participants globally in the IMC Prosperity Trading Contest.
About Details
Professional Bio
Computer Science student at Georgia Institute of Technology with experience in AI research, software engineering, and RAG pipelines. Skilled in Python, C++, React, and machine learning, with a focus on building efficient data pipelines and AI-driven tools.
BS in Computer Science
Georgia Institute of Technology ( - 2027)