I am at Google Research working on deep learning. Previously I was a Research Scientist at Salesforce Research (MetaMind) where I worked on deep learning for multimodal data, zero-shot learning, recommendation systems, and automated speech recognition. My research interests cover inclusion of prior knowledge and expert knowledge, and deep learning for specialized domains such as medicine and engineering. I also conducted research at the intersection of applied mathematics and energy, by combining statistics, optimization, and machine learning to improve energy generation and efficiency.
I earned my PhD and MS from the University of California, Berkeley in Mechanical Engineering. I completed my BASci in Engineering Science from the University of Toronto.
- Courses Studied
- Google Scholar
- Email: lhu [at] berkeley [dot] edu
Publications
Journals
Machine Learning for Automated Sensor Selection for Energy Fault Detection
Hu, RL, Granderson, JG, Auslander, DM, Agogino, AM.
Applied Energy, 2019
Diagnosing and Predicting Wind Turbine Faults Using Machine Learning Techniques Applied to SCADA Data
Leahy, K, Hu, RL, Konstantakopoulos, IC, Spanos, C, Agogino, AM, O'Sullivan, DTJ.
International Journal of Prognostics and Health Management, 2018
A Mathematical Programming Formulation for Optimal Load Shifting of Electricity Demand for the Smart Grid
Hu, RL, Skorupski, R, Entriken, R, Ye, Y.
IEEE Transactions on Big Data: Big Data for Cyber-Physical Systems, 2017
Peer-Reviewed Conference and Workshop Papers
Zero-Shot Image Classification Guided by Natural Language Descriptions of Classes
Hu, RL, Xiong, C, Socher, R.
Neural Information Processing Systems Visually Grounded Interaction and Language Workshop, 2018
Image Segmentation to Distinguish Between Overlapping Human Chromosomes
Hu, RL, Karnowski, J, Fadely, R, Pommier, JP.
Neural Information Processing Systems Machine Learning for Health Workshop, 2017
Application Domain Knowledge Features for Diagnostics in Wind Turbines
Hu, RL, Leahy, K, Konstantakopoulos, IC, Auslander, DM, Spanos, C, Agogino, AM.
IEEE International Conference on Machine Learning and Applications, 2016.
Diagnosing Wind Turbine Faults Using Machine Learning Techniques Applied to Operational Data
Leahy, K, Hu, RL, Konstantakopoulos, IC, Spanos, C, Agogino, AM.
IEEE Reliability Society: IEEE International Conference on Prognostics and Health Management, 2016.
Can We Practically Bring Physics-based Modeling Into Operational Analytics Tools?
Granderson, J, Bonvini, M, Piette, MA, Lin, G, Hu, RL.
American Council on an Energy-Efficient Economy: Summer Study on Energy Efficiency in Buildings, 2016.
Detection of a Chiller Energy Efficiency Fault Using Expectation Maximization
Hu, RL, Granderson, J, Agogino, A.
ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2015.
A Benefit-Cost Framework for Optimal Load Shifting
Skorupski, R, Hu, RL, Entriken, R, Ye, Y.
Advanced Workshop in Regulation and Competition 28th Annual Western Conference, 2014.
A Benefit-Cost Framework for Optimal Load Shifting
Skorupski, R, Hu, RL, Entriken, R, Ye, Y.
CIGRÉ (The Council on Large Electric Systems) US National Committee: Grid of the Future Symposium, 2014.
Zero Emissions Medical Shelter
Ing, A, Hu, L, Hyun, S, Kuang, A.
Meeting of the International Society for the Systems Sciences, 2012.
Fundamental analysis methods for heating and cooling systems, from the Energy Information Handbook
Granderson, J, Piette MA, Rosenblum, B, Hu, L.
HPAC Engineering, 2012.
Making the most of energy data: A handbook for facility managers, owners and operators
Granderson, J, Hu, RL, Piette MA, Rosenblum, B.
American Council on an Energy-Efficient Economy: Summer Study on Energy Efficiency in Buildings, 2012.
Influence of geographic location and local demand on the attractiveness of airports to transfer traffic
de Barros, AG, Hu, L, Tay, R.
Air Transport Research Society World Conference, 2011.
Book
Energy Information Handbook: Applications for efficient building operations
Printed and published by the US Department of Energy, Energy Efficiency and Renewable Energy, Building Technologies Program
Available on Amazon and online.