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GRASP/ESE Special Seminar: Andrzej Banaszuk, Tong Sun, and Hala Mostafa, United Technologies Research Center, “Machine Learning Research at United Technologies Research Center”

March 13, 2018 @ 2:00 pm - 3:00 pm


We will present a broad overview of UTRC’s Systems Department research in the area of machine learning to automate and optimize decision process for aerospace engineering, smart building and digital manufacturing. The presentation will show how we apply the state-of- art deep learning techniques in relevant industrial domains and then dive into our research in transfer learning with domain knowledge. At UTRC, we work with our businesses to build predictive models from historical data. Despite the hype around Big Data, obtaining more data in industrial settings is often expensive or time-consuming. Transfer learning (TL) reduces data requirements by leveraging data-rich source tasks to improve learning on different but related target tasks. Building domain knowledge (e.g., from engineers) into models can also reduce data requirements. We discuss different types of domain knowledge and how we can incorporate them into existing TL frameworks. We also touch on our domain adaptation work to address more general TL settings. We will conclude with research problems of interest to UTRC and discuss career and internship opportunities in the broad area of machine learning.


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Dr. Andrzej Banaszuk is a Senior Director of Systems Department at the United Technologies Research Center. Before his current appointment he was a Program Leader of Autonomous and Intelligent Systems and Sikorsky Program Offices at UTRC. Since joining UTRC in 1997, he has conducted research in analysis, design, and control of dynamical systems applied to jet engines, rotorcraft, electric power networks, and buildings. Since 2000 he has led collaborative multi-university research teams in the area of flow control, control of combustion instabilities, robust design of large uncertain dynamic networks, and autonomy. He is an author of 44 journal papers, 72 conference papers, and 11 patents. For his work on active and passive control of flow instabilities in jet engines he received IEEE Controls Systems Technology Award in 2007. He became an IEEE Fellow in 2011. He was elected to the Connecticut Academy of Science and Engineering in 2015. He holds Ph.D. in EE from Warsaw University of Technology and Ph.D. in Mathematics from Georgia Institute of Technology.

Dr. Tong Sun is an Associate Director of Decision Support & Machine Intelligence at the United Technologies Research Center. Her research portfolio includes using large-scale text and graph analytics to understand user behavior and social influence, applying deep neural network, active learning and online stream analytics to uncover actionable insights from heterogeneous data in distributed computing environments. Tong held 22 US Patents and co-authored over 35 peer-reviewed publications. She earned her Ph.D. in Distributed and Parallel Computing from University of Rhode Island in 1996. She is also an accomplished research thought-leader and technology innovator with 10+ years proven track of leadership in incubating new concepts through state-of- art machine learning methods/tools, developing advanced rapid prototypes that lead to impactful outcomes.

Dr. Hala Mostafa is a Staff Research Scientist at United Technology Research Center. She uses machine learning to solve a variety of problems for UTC business partners like Otis, Pratt and Whitney and United Technologies Aerospace Systems. She is also the Principal Investigator on multiple corporate- and government-funded research projects in the areas of sequential decision-making, transfer learning, PHM and graph analytics. Before joining UTRC, Hala was a Research Scientist in the Distributed Systems Group at BBN Technologies where she worked on graphical models, distributed constraint optimization and decision making under uncertainty. Hala got her PhD in Computer Science from the University of Massachusetts, Amherst in 2011. She has published in, and reviewed for, top AI conferences, including AAAI, IJCAI and UAI.


March 13, 2018
2:00 pm - 3:00 pm
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