class PushpakSivaSai:
role = "AI/ML Engineer & Robotics Researcher"
education = "B.Tech CSE (AI) @ Amrita Vishwa Vidyapeetham"
cgpa = "8.25 / 10.0"
focus_areas = [
"Computer Vision & Markerless Motion Capture",
"Dual-Camera 3D Skeletal Triangulation & Kinematics",
"Edge-AI & Industrial Control Systems Security",
"Bioinformatics & Quantum Multi-Task Learning"
]
def core_philosophy(self):
return (
"Understand the mathematical foundations, "
"engineer robust pipelines from scratch, "
"and deploy real-time perception systems."
) |
|
Apr 2026 – Jul 2026 | Guided by Dr. Akhil V.M
- Developed MoCap Studio, a local-first markerless motion capture system for human movement analysis using MediaPipe Pose and Python.
- Built an offline video processing pipeline with FastAPI, enabling frame-wise pose tracking and calibration-aware 3D skeletal reconstruction using dual-camera DLT triangulation.
- Extracted biomechanical and kinematic metrics from human motion and exported results in CSV, JSON, annotated video, and 3D motion formats.
- Integrated real-time visualization and performance benchmarking to support AI and robotics research applications.
Jul 2026 | Iranian Journal of Science and Technology Transactions of Electrical Engineering
- Paper: "Secure Smart Grid Industrial Control Systems: Cryptographic Encryption, Anomaly Classification, and Zero-Day Attack Detection"
- Engineered a real-time Edge-AI cybersecurity platform for Industrial Control Systems (ICS) using Google Coral Edge TPU.
- Combined machine learning-based anomaly detection with hybrid cryptographic security (ECC + HKDF) for smart grid protection and zero-day intrusion detection.
| Project & Repository | Domain / Focus | Key Technical Approach & Achievements | Stack |
|---|---|---|---|
| 📷 MoCap Studio | Computer Vision & Robotics | Local-first multi-camera markerless 3D motion capture pipeline. Features frame-wise pose tracking with MediaPipe, calibration-aware dual-camera DLT triangulation, 3D skeletal reconstruction, kinematic metrics export, and 3D avatar visualization. | Python MediaPipe FastAPI OpenCV Panda3D Three.js |
| 🛡️ Smart-Grid ICS Security | Edge-AI & Cybersecurity | Journal publication implementation. Real-time Edge-AI cybersecurity platform integrating hybrid ECC + HKDF payload encryption with ML anomaly detection and zero-day attack classification on DNP3 Industrial Control System traffic. | Python TensorFlow Lite Cryptography Edge TPU scikit-learn |
| 🧬 MicroKPNN-MT | Bio-ML & Quantum Computing | Hybrid Quantum Knowledge-Primed Multi-Task Neural Network for microbiome-based disease prediction using biologically informed taxonomy-masked layers, multi-task auxiliary heads, and quantum residual fusion evaluated against SVM/RF/XGBoost. | PyTorch Quantum ML Bioinformatics XGBoost Captum |
| 👗 StyleSense FashionAI | Multimodal CV & LLMs | AI-powered personalized fashion recommendation platform. Extracts facial/body attributes (skin tone, eye color, hair color, body shape) via MediaPipe, drives XGBoost recommendation models, and features an interactive Groq LLM stylist chatbot. | Python MediaPipe XGBoost Groq LLM Streamlit |
| 🧠 EEG Signal Reconstruction | Biomedical Signal Processing | EEG signal recovery framework applying the Alternating Direction Method of Multipliers (ADMM) and sparse optimization techniques to reconstruct high-quality neural signals from noisy or incomplete channel measurements. | MATLAB ADMM SVD Signal Processing |
| 🧪 Cancer Driver Gene Classifier | Bioinformatics ML | Machine learning classification pipeline for cancer driver gene identification, incorporating genomic feature engineering, model benchmarking, and interpretable classification workflows. | Python Machine Learning Genomics scikit-learn |
| 🌾 KISSAN-BAZAAR | Full-Stack Web & OOP | Agriculture marketplace web application applying object-oriented design and core data structures. Features product catalog, shopping cart, authentication, and role-based management dashboards. | React TypeScript Supabase TailwindCSS Vite |
| 🎮 Double DQN Reinforcement Learning | Reinforcement Learning | Deep Reinforcement Learning implementation of a Double Deep Q-Network (Double DQN) agent trained on Tic-Tac-Toe using experience replay, target networks, and validation against a minimax baseline. | Python PyTorch RL NumPy |
| Category | Technologies & Tools |
|---|---|
| Programming Languages | Python · C/C++ · Java · JavaScript · TypeScript · SQL · R · MATLAB |
| AI / Machine Learning | PyTorch · TensorFlow · scikit-learn · Hugging Face · XGBoost · Captum · Quantum ML (QML) |
| Computer Vision & Perception | MediaPipe · OpenCV · Panda3D · Three.js · Human Pose Estimation · 3D Triangulation · Kinematics |
| Web & Backend | FastAPI · Flask · Next.js · React.js · Express.js · Node.js · REST APIs |
| Databases & Analytics | MySQL · MongoDB · Supabase · Pandas · NumPy · SciPy · Power BI · Matplotlib |
| Hardware & Systems | Google Coral Edge TPU · ESP32 · ZeroMQ · POSIX Syscalls · CUDA · ONNX |
| Developer Tools | Git · GitHub · Linux / Bash · VS Code · Conda |
I am actively seeking AI/ML & Robotics Internship opportunities, research collaborations, and engineering challenges in computer vision, spatial intelligence, and applied ML.
