Yiwei Fu Email & Phone Number

Albany, Ny, Us

Albany, Ny, Us

Niskayuna, New York, United States

Channeling my enthusiasm towards AI for the energy transition, I've directed my focus to power systems and energy domains, gaining insights into their unique requirements. Beyond contributing to academic papers in machine learning, I'm actively spearheading innovation through AI and ML in these fields. My contributions extend tangibly, marked by IP creations, product integrations in the electrical grid and renewable energy sectors, and a pivotal role in driving the swift implementation of emerging technologies such as Large Language Models and Graph Neural Networks.

Niskayuna, Ny

As a key contributor to multiple projects funded by DARPA, DOE, and GE Businesses, I drive both fundamental and applied machine learning research in my role as a Research Scientist at GE Research for both the energy industry and the aerospace industry. My research encompasses diverse areas, including deep learning for time series forecasting, anomaly detection, and representation learning in complex engineering systems. I also delve into reinforcement learning's applications in robotics and numerical methods, as well as exploring robustness and interpretability in deep neural networks.

University Park, Pa

I specialize in designing and applying machine learning to engineering systems and robotics. Notably, I've utilized deep learning to analyze time series data, implemented deep reinforcement learning for autonomous ground robots, and explored Bayesian Nonparametrics modeling for engineering systems.

ME 370 - Vibration of Mechanical SystemsME 450 - Modeling of Dynamic SystemsME 355 - Dynamic Systems Lab

Niskayuna, Ny

In this role, I conducted cutting-edge research at the intersection of robotics, autonomy, and deep learning, spearheading the development of an innovative Generative Adversarial Network (GAN) tailored to address intricate challenges posed by spatiotemporal data in real-world robotics scenarios. Leveraging a low-dimensional latent space, this architecture efficiently extracted and manipulated information, resulting in the generation of precise control signals pivotal for guiding high-level decision-making processes in diverse robotics applications. Concurrently, I contributed to refining action-conditioned state prediction techniques and pioneering few-shot imitation learning methods, collectively advancing the frontiers of intelligent and adaptable robotics systems.

Singapore

Summer Intern at Autonomous Center of Excellence.

Doctor Of Philosophy (Ph.D.), Mechanical Engineering

Advisor: Dr. Asok Ray, Distinguished Professor of Mechanical Engineering and Mathematics

Master'S Degree, Electrical Engineering

Advisor: Dr. Minghui Zhu, Associate Professor Specialization Area: Control Systems

Bachelor Of Science (Bs), Mechanical Engineering And Automation, 3.8/4.0