Summary:
My academic background bridges AI and Quantum Physics, with advanced degrees in Digital Signal Processing and Applied Physics. My expertise spans Generative AI, Diffusion Models, Large Language Models (LLMs), speech and image synthesis, Quantum Machine Learning (QML), Computer Vision, and Numerical Simulations. I have authored a 400-page AI monograph, recognized by over 4,400 GitHub users, and have led AI research teams to achieve groundbreaking innovations with practical applications.
Generative AI:
I possess in-depth knowledge and hands-on experience in generative AI, including denoising diffusion probabilistic models, stable-diffusion, and score-based diffusion models. My understanding extends to the underlying mathematical concepts such as stochastic differential equations, Langevin dynamics, stochastic calculus, and variational inference.
LLMs:
I have extensive experience with LLMs and Vision-Language Models (VLMs), including training with LLMFlow, mlx-ml, serving using llama.cpp, vLLM, and LMDeploy. Proficient in fine-tuning using LoRA. Advanced ChatGPT prompt engineering techniques.
Quantum Computing:
My interests in AI and Quantum Computing include hybrid algorithms, Variational Quantum Eigensolvers (VQEs), Quantum Machine Learning (QML) and Quantum Convolutional Neural Networks (Quantum CNNs). I am familiar with QC frameworks such as Yao.jl, PaddleQuantum, and PennyLane.
Software Engineering:
I have a robust technical foundation in Python, PyTorch, MATLAB, Julia, C++11 standards, CMake, Docker, CUDA, ONNX, and TensorRT. I excel in leading research that integrates theoretical concepts, training across multiple GPUs, and real-time deployment.