Resume

Naman Pesricha

✉️ pesricha@gmail.com
LinkedIn • GitHub • Website

🎓 Education

M.Tech, Computational and Data Science — Indian Institute of Science (IISc), Bangalore 2024 – 2026

B.Tech, Mechanical Engineering — Indian Institute of Technology (IIT), Roorkee 2019 – 2023

📚 Relevant Coursework

• Numerical Linear Algebra (A+)
• Intro to Scalable Systems (A+)
• Natural Language Processing (A+)
• Parallel Programming (A+)
• Machine Learning for Data Science (A+)
• Tensor Computations for Data Science (A+)

💼 Work Experience

Software Development Engineer — Truminds Software Systems Sep 2023 – Jul 2024

• Built and maintained Flask backend features and internal automation tools using Python.
• Refactored legacy codebase with generalized retry logic, reducing redundancy by 90%+.
• Developed robust Python scripts and Pytest test cases to automate workflows.

Research and Analytics Intern — Leap Wallet Jan 2023 – Feb 2023

• Benchmarked 10+ projects on the NEAR chain for integration evaluation.
• Built a token-wise blockchain data parser with 95% coverage using SQL.
• Automated 5+ business workflows and developed a product testing methodology.

🔬 Projects

Parallel Hybrid Optimal N-Way Tensor SVD — Argonne National Laboratory, USA Apr 2025 – Ongoing

• Designed cache-friendly parallel tensor compression algorithms using MPI, OpenMP, and GPU acceleration.
• Achieved up to 35× speedup on hybrid CPU-GPU systems; outperformed PCA in reconstruction quality.

Mixed Precision Two-Level Chebyshev Filter Based Eigensolver — IISc Bangalore Apr 2025 – Ongoing

• Developed mixed-precision techniques for exascale CPU+GPU systems.
• Conducted scalability studies on top-tier supercomputers: Frontier (#2), Aurora (#3), Fugaku (#7), Eos (#16).

Indian Name Generator & English-to-Hindi Name Translator Apr 2025

• Trained RNN and Neural N-gram models on Indian names dataset.
• Built Seq2Seq model with LSTM + attention and BPE tokenizer for name translation.

Unified ML Model for Diversity — IIT Roorkee Jun 2023 – Oct 2023

• Built ML-based methods to enhance diversity in multi-objective evolutionary algorithms (MOEAs).
• Integrated ML to accelerate convergence while maintaining diverse Pareto fronts.

Credit Card Fraud Detection Mar 2025

• Applied EDA, undersampling, and SMOTE to handle class imbalance.
• Compared performance of LogReg, KNN, SVM, Random Forest, XGBoost.

🛠 Technical Skills

• Languages: Python, C++
• Tools: PyTorch, LibTorch (C++), NumPy, Scikit-learn, CUDA, MPI, OpenMP, MKL
• Domains: Machine Learning, NLP, Parallel Programming, High-Performance Computing

🏅 Academic Accomplishments & Involvement

• Competitive Exams: AIR 43 (99.89%) in GATE DA 2024; AIR 2250 (99.82%) JEE Mains 2019; AIR 2718 JEE Advanced 2019.
• Teaching Assistant: DS284 (Numerical Linear Algebra)
• Leadership: Departmental Curriculum Committee Member, Department Rep in IISc Students’ Council.
• Contributed to updated teaching material for DS216 (ML for DS) on PCA & bias-variance tradeoff.