Jiachen has been actively bringing data science skills into sustainability with three years of experience in the academic and nonprofit sectors. He has a Master of Data Science from the University of British Columbia and B.Env. in Geomatics from the University of Waterloo. His project proposes to develop a 5G-enabled management system for campus building energy and space use. The proposed system will help optimize energy consumption and space allocation, inform building investments to meet UBC’s sustainability goals, and advance research and innovations among UBC faculty, operational staff, and entrepreneurs.

Shahab Bahrami received his PhD degree in Electrical and Computer Engineering (ECE) from The University of British Columbia (UBC) in August 2017. Shahab received various prestigious scholarships at UBC, including the distinguished and highly competitive UBC’s Four Year Fellowship (2013-2017) as well as the Graduate Support Initiative Award from the Faculty of Applied Science at UBC (2014-2017). Currently, Shahab is working as a postdoctoral fellow under the co-supervision of Professors Vincent Wong and Christine Chen. As a part of 5G-enabled Energy and Space Management project for Rogers Communications Inc., Shahab is designing data-driven algorithms for energy management in UBC campus buildings. Shahab’s research interests include energy management, 5G communication networks, power markets, demand side management, with applications to smart grid.

Originally from Tehran, Iran, as his B.Sc., Iman have studied mechanical engineering. Graduated ranking in the top 3 among more than 80 mechanical engineering students. So, he was honored with the title of exceptionally talented student by the Amirkabir University of Technology (AUT) admission office, which allowed him to get admitted directly to AUT (which ranks among the 3 best Iranian universities) to start studying Mechatronics as his M.Sc. During his M.Sc. studies, he selected “Structural Health Monitoring of oil Pipelines” as the theme of his research, which introduced him to the world of IoT and Machine Learning. Most recently, Iman started utilizing various sensors and IoT devices in small projects through different online courses in the field of Machine Learning and also was introduced to Digital Twin concept. That’s why, he chose to start a PhD in the field of Industry 4.0 and Advanced Manufacturing at University of British Columbia, which he hopes he can gain more experience specially in Data Science, since he will be collaborating with both research groups at Composites Research Network (CRN) and Data Analytics and Intelligent Systems (DAIS) Labs. During his free time, Iman enjoys mountain climbing and fishing and also snow sports, which he loves to experience them all in BC.

Jingyi Wang is a Ph.D. student studying Chemical and Biological Engineering (CHBE) at UBC. She received her bachelor's degree and master's degree in Chemical Engineering/Process Control from the University of Alberta in Canada. Her previous research includes sensor fusion, soft sensor, and artificial neural networks. Her current research interest includes combining traditional control methods with machine learning techniques to improve algorithm performance.

Yixiu Wang is a PhD student studying Chemical and Biological Engineering (CHBE) at UBC. He received his bachelor's degree and master's degree in Vehicle Engineering/Automotive Electronics from Tongji University in China, where he worked on life equalization strategy for series battery pack to maximum discharge energy. His current research interests include applying machine learning techniques to develop lithium-ion battery degradation models to accurately estimate and predict power and capacity loss, and optimizing power system operation incorporating battery degradation.

Liang Cao is a Ph.D. student studying Chemical and Biological Engineering (CHBE) at UBC. He received his BASc and MASc in Automation/Control Engineering from Beijing University of Chemical and Technology in China. He also is a joint master student at the University of Duisburg-Essen (Germany) and a research assistant at Tsinghua University (China). His previous research includes diagnosis and compensation of multiple channel faults in one-dimension systems and two-dimension systems. His current Ph.D. research interests focus on applying machine learning techniques to historical refinery process data to build inferential sensors, extract causality, and mine sequence patterns.

Nathan is a PhD student in applied mathematics at UBC. Prior to moving to Vancouver, he earned his Bachelor's and Master's degrees in mathematics at Portland State University. He is interested in the interplay between reinforcement learning and control. More specifically, his work aims to develop actionable methods based on deep reinforcement learning for maintenance-free PID control and MPC of industrial processes. Outside of research, he enjoys boardgames and ice skating.

Originally from Iran, Ali received his bachelor's degree in petroleum engineering from the Petroleum University of Technology (PUT). He then moved to the Memorial University of Newfoundland (MUN), St. Johns, to start his MASc. His current research is in applying data analytics techniques in T cell manufacturing, design of experiment, and machine learning. During his free time, he loves spending time with his family and reading/learning philosophy and mysticism.

Lee Rippon is a PhD student studying Chemical and Biologial Engineering (CHBE) at UBC. He also holds BASc and MASc degrees from UBC in CHBE where his research experience includes applications of compressive sensing, adaptive control, system identification and process monitoring on sheet and film processes. His current research interests include applying process analytics and machine learning techniques to historical process data to perform fault detection, isolation, and diagnosis in a kraft pulping process.

Yiting has obtained both his BASc and MASc degrees at CHBE. His current PhD research is focused on feature extraction, the identification of combined raw input variables which contribute to various process outcomes. This is achieved using Machine Learning algorithms such as Deep Learning, Variational Autoencoders, and Generative Adversarial Networks. Yiting is also working as a part-time data analyst in a consulting company, where he uses his fluency in Python to design predictive models to forecast and diagnose anomalies for real-time process data.

Originally from Calgary, Daniel moved to Vancouver to pursue a bachelor's degree in engineering at the University of British Columbia. After being allured by the scenic hikes and beaches around the city as well as the interesting research being done by the DAIS Lab, he chose to stay for his master's degree. He is now studying reinforcement learning for use in control systems. Outside of his studies, Daniel enjoys skiing at Cypress Mountain in the winter and hiking around Vancouver in the summer.