Solid-State Electron Gun Modulator Simulation
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Summary
Simulated a solid-state electron gun modulator.
About
AI/ML Technical Lead with over 7 years of experience specializing in computer vision, deep learning, and signal processing for semiconductor and defense applications. Proven track record of designing and deploying production-ready algorithms, achieving 90%+ accuracy in image registration and 100% detection rates in real-time intrusion systems. Eager to leverage deep expertise in Python, TensorFlow, and PyTorch to drive innovation in applied AI for cutting-edge industries.
Work
Applied Materials
|Algorithm Developer (Technical Lead)
Bengaluru, Karnataka, India
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Summary
Led the development and deployment of advanced AI/ML algorithms to optimize semiconductor inspection and manufacturing processes, driving innovation in materials engineering solutions.
Highlights
Developed Deep Reinforcement Learning (DRL) algorithms with PyTorch, automating CAD-to-SEM image registration and eliminating manual calibration in semiconductor inspection, significantly increasing throughput.
Automated simulation parameter tuning using Optuna, resulting in a 40% reduction in manual calibration time and substantial improvements in on-site operational efficiency.
Engineered an automated ROI detection algorithm for 3D NAND metrology, utilizing signal and image processing to identify nitride band centers with 3 µm precision, reducing manual effort and error.
Implemented an ML-based classifier with Scikit-Learn and Python, achieving over 80% accuracy in automatically selecting optimal registration scores and differentiating successful from poor cases.
Enhanced coarse edge extraction accuracy by over 90% using Elastix and ITK-based registration algorithms in Python, critical for high-precision manufacturing metrology.
Prototyped a GAN-based image inpainting solution using PyTorch, significantly improving defect-repair performance and accelerating R&D project timelines.
Collaborated with international cross-functional teams across Israel, USA, and India to successfully deploy advanced AI/ML solutions in global facilities.
Integrated complex algorithms into production environments via CI/CD pipelines, adhering to Low-Level System Design principles for scalable and robust software delivery.
Bhabha Atomic Research Centre (BARC)
|Scientific Officer
Mumbai, Maharashtra, India
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Summary
Served as a Scientific Officer, leading research and development in signal processing and machine learning for defense-grade security and atomic energy applications.
Highlights
Engineered DSP-FPGA-based algorithms using Wavelet and C++ for triboelectric sensor intrusion detection, achieving 100% detection and over 90% classification accuracy.
Developed phase demodulation algorithms for fiber-optic PIDS using advanced Signal Processing in MATLAB, improving intrusion localization accuracy to within ±50 meters.
Applied Teager-Kaiser Energy Operator (TKEO) and Discrete Wavelet Transform (DWT) for robust feature extraction, integrating with ML models in Python for real-time signal analysis under strict computational constraints.
Built and validated Simulink models in MATLAB for high-voltage energizer systems, streamlining prototyping cycles and accelerating development by 30%.
Optimized embedded signal-processing code in C++ for low-memory, real-time execution, enhancing performance in critical defense-grade security applications.
Education
Homi Bhabha National Institute
Mumbai, Maharashtra, India
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M.Tech
Electrical Engineering (Nuclear Engineering)
Jabalpur Engineering College
Jabalpur, Madhya Pradesh, India
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B.E. (Honors)
Electrical Engineering
Awards
Best Paper Award
Awarded By
5th IEEE International Conference on Computing, Communication and Security (ICCCS 2020, IIT Patna)
Awarded for the paper 'Event Detection and Classification Algorithm using Wavelet and Machine Learning Technique for Vibration Fence PIDS', published in IEEE ICCCS 2020 (DOI: 10.1109/ICCCS49678.2020.927745).
Skills
Programming
Python, C++, MATLAB, Data Structures & Algorithms, OOP, Low-Level System Design.
ML/AI
TensorFlow, PyTorch, Scikit-Learn, Deep Learning, Machine Learning, Transfer Learning, CNNs, GANs, Regression, Predictive Modelling.
Tools/Frameworks
OpenCV, Optuna, Elastix, ITK, Simulink, Git, MLOps, AWS, GCP, Azure.
Signal & Image Processing
Wavelets, TKEO, DWT, DSP, Image Registration, Feature Extraction, Object Detection, Image Segmentation.
Projects
Hybrid Energy Management System Development
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Summary
Developed an optimal hybrid energy management solution during B.E.
Electrical Maintenance Internship (NTPC Vindhayachal)
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Summary
Completed a 45-day internship in the Electrical Maintenance Department.