MERL turned 25. Thank you to all who joined us to celebrate on Thursday, June 2, 2016, at the Norton's Woods Conference Center at the American Academy of Arts & Sciences in Cambridge, MA. The event was a great success, with inspiring keynote talks, insightful panel sessions, an exciting research showcase of MERL's latest breakthroughs, and many occasions to socialize.
Photos of each session are now available via links in the schedule below.
Video presentations of many of the works presented in the research showcase are also available.
A commemorative booklet that highlights past and current research has also been prepared.
- Date: Thursday, June 2, 2016
- Time: 8:30 AM - 8:30 PM
- Venue: Norton's Woods Conference Center at the American Academy of Arts & Sciences, Cambridge, MA, USA
ScheduleClick on the talk or panel session title to jump to the corresponding abstract and bios.
Boeing Professor of Operations Research at the Sloan School of Management Management and co-director Operations Research Center
Massachusetts Institute of Technology, Cambridge, MA
"Machine Learning and Statistics via a modern optimization lens"
|11:15-12:30||Morning Panel Sessions
Mitsubishi Electric Research Labs, Cambridge, MA
"What can we scientists do to nudge the world in the right direction?"
|16:15-17:30||Afternoon Panel Sessions
The workshop was hosted at the Norton's Woods Conference Center at the American Academy of Arts & Sciences, in Cambridge, MA.
Machine Learning and Statistics via a modern optimization lens
Boeing Professor of Operations Research at the Sloan School of Management and co-director Operations Research Center
The field of Statistics has historically been linked with Probability Theory. However, some of the central problems of classification, regression and estimation can naturally be written as optimization problems. While continuous optimization approaches has had a significant impact in Statistics, mixed integer optimization (MIO) has played a very limited role, primarily based on the belief that MIO models are computationally intractable. The period 1991—2015 has witnessed a) algorithmic advances in mixed integer optimization (MIO), which coupled with hardware improvements have resulted in an astonishing 450 billion factor speedup in solving MIO problems, b) significant advances in our ability to model and solve very high dimensional robust and convex optimization models. In this talk, we demonstrate that modern convex, robust and especially mixed integer optimization methods, when applied to a variety of classical Machine Learning (ML) /Statistics (S) problems can lead to certifiable optimal solutions for large scale instances that have often significantly improved out of sample accuracy compared to heuristic methods used in ML/S. Specifically, we report results on
- The classical variable selection problem in regression currently solved by Lasso heuristically.
- We show that robustness and not sparsity is the major reason of the success of Lasso in contrast to widely held beliefs in ML/S.
- A systematic approach to design linear and logistic regression models based on MIO.
- Optimal trees for classification solved by CART heuristically.
- Robust classification including robust Logistic regression, robust optimal trees and robust support vector machines.
- Sparse matrix estimation problems: Principal Component Analysis, Factor Analysis and Covariance matrix estimation.
In all cases we demonstrate that optimal solutions to large scale instances (a) can be found in seconds, (b) can be certified to be optimal in minutes and (c) outperform classical approaches. Most importantly, this body of work suggests that linking ML/S to modern optimization will lead to significant advantages.
Dimitris Bertsimas is currently the Boeing Professor of Operations Research and the co-director of the Operations Research Center at the Massachusetts Institute of Technology. He has received a BS in Electrical Engineering and Computer Science at the National Technical University of Athens, Greece in 1985, a MS in Operations Research at MIT in 1987, and a Ph.D in Applied Mathematics and Operations Research at MIT in 1988. Since 1988, he has been with the MIT faculty. Since the 1990s he has started several successful companies in the areas of financial services, asset management, health care, publishing, analytics and aviation. His research interests include analytics, optimization and their applications in a variety of industries. He has co-authored more than 170 scientific papers and four textbooks, including the book "The Analytics Edge" published in 2016. He is former area editor in Operations Research in Financial Engineering and in Management Science in Optimization. He has supervised 57 doctoral students and he is currently supervising 16 others. He is a member of the US National Academy of Engineering, and an INFORMS fellow. He has received several research awards including the Philip Morse lectureship award (2013), the William Pierskalla award for best paper in health care (2013), the best paper award in Transportation Science (2013), the Farkas prize (2008), the Erlang prize (1996), the SIAM prize in optimization (1996), the Bodossaki prize (1998) and the Presidential Young Investigator award (1991-1996).
What can we scientists do to nudge the world in the right direction?
Technological advances in the last thirty years have radically democratized access and opportunity world-wide, allowing all of humanity to share in new kinds of cultural wealth. Economically, the benefits are less universal: Some advances, notably in data compression and communication, have helped to level the playing field for all members of society, while others, particularly in logistics and data aggregation/mining, seem to be swiftly tilting it. The impacts of soon-to-be-widespread technologies are hotly contested: who will benefit from machine learning, agile robotics, algorithmic commerce? Surprisingly, that is partly up to us: I will highlight some research directions and practices that might give us a chance to tip the scales in favor of increased opportunity and happiness when the world belongs to our children.
Matt Brand is a computer scientist in MERL's Algorithms group. His current interests are parallel solutions of convex programs and applications to optics, optimal control, radiation therapy, and social infrastructure problems. He invented some widely used technologies, won some prizes, has some titles, has stuff in museums. Prior to MERL, he taught at MIT and the University of Chicago, and ran a restaurant.
Artificial intelligence is arguably one of the last grand frontiers of science of our times, and advances in it are expected to have very critical impact on technology and society. Although the nature of human thought has fascinated and perplexed philosophers and scientists for millennia, it has been only in the last 60 years that advances in computing technology made it feasible to seriously attempt to recreate human-level intelligence in a thinking machine. After multiple ups and downs, the past few years have witnessed spectacular advances in the ability of machines to perform highly complicated tasks at a superhuman level, such as image classification, game play, and vehicle driving. In this panel, we will explore the technological and societal significance of the latest developments in this field, and attempt to chart its likely future course with the help of some of its foremost experts.
Moderator: Dr. Daniel Nikovski, MERLPanelists:
- Prof. Shih-Fu Chang, Columbia University
- Prof. Sadaoki Furui, Toyota Technological Institute at Chicago
- Prof. Honglak Lee, University of Michigan
- Prof. Nicholas Roy, MIT
Shih-Fu Chang is a leading researcher in multimedia information retrieval, computer vision, signal processing, and machine learning. His work set trends in research of content-based image search, video recognition, image authentication, multimodal analysis, compact hashing for large-scale search, and novel applications of visual search in brain machine interface and mobile systems. Impact of his work can be seen in more than 350 peer-reviewed publications, a large number of best paper awards, 34 issued patents, and technologies licensed to companies. He has been recognized with the IEEE Signal Processing Society Technical Achievement Award, ACM Multimedia Special Interest Group Technical Achievement Award, IEEE Kiyo Tomiyasu Award, ONY Young Investigator Award, IBM Faculty Award, NSF CAREER Award, and the Great Teacher Award from the Society of Columbia Graduates. He served as the Editor-in-Chief of the IEEE Signal Processing Magazine during 2006-8. In his current capacity as Senior Executive Vice Dean of Columbia Engineering School, he plays a key role in strategic planning, special research initiatives, and faculty development. He is a Fellow of IEEE and the American Association for the Advancement of Science.
Sadaoki Furui received the B.S., M.S., and Ph.D. degrees from the University of Tokyo, Japan in 1968, 1970, and 1978, respectively. After joining the Nippon Telegraph and Telephone Corporation (NTT) Labs in 1970, he has worked on speech analysis, speech recognition, speaker recognition, speech synthesis, speech perception, and multimodal human-computer interaction. From 1978 to 1979, he was a visiting researcher at AT&T Bell Laboratories, Murray Hill, New Jersey. He was a Research Fellow and the Director of Furui Research Laboratory at NTT Labs. He became a Professor at Tokyo Institute of Technology in 1997. He was Dean of Graduate School of Information Science and Engineering, and Director of University Library. He was given the title of Professor Emeritus and became Professor at Academy for Global Leadership in 2011. He is now serving as President of Toyota Technological Institute at Chicago (TTI-C). He has authored or coauthored around 1,000 published papers and books. He was elected a Fellow of the IEEE, the Acoustical Society of America (ASA), the Institute of Electronics, Information and Communication Engineers of Japan (IEICE) and the International Speech Communication Association (ISCA). He received the Paper Award and the Achievement Award from the IEEE SP Society, the IEICE, and the Acoustical Society of Japan (ASJ). He received the ISCA Medal for Scientific Achievement, and the IEEE James L. Flanagan Speech and Audio Processing Award. He received the NHK (Japan Broadcasting Corporation) Broadcast Cultural Award and the Okawa Prize. He also received the Achievement Award from the Minister of Science and Technology and the Minister of Education, Japan, and the Purple Ribbon Medal from Japanese Emperor.
Honglak Lee is an Assistant Professor of Computer Science and Engineering at the University of Michigan, Ann Arbor. He received his Ph.D. from Computer Science Department at Stanford University in 2010, advised by Prof. Andrew Ng. His research focuses on deep learning and representation learning, which spans over unsupervised and semi-supervised learning, supervised learning, transfer learning, reinforcement learning, structured prediction, graphical models, and optimization. His methods have been successfully applied to computer vision and other perception problems. He received best paper awards at ICML and CEAS. He has served as a guest editor of IEEE TPAMI Special Issue on Learning Deep Architectures, as well as area chairs of ICML, NIPS, ICCV, ECCV, AAAI, IJCAI, and ICLR. He received the Google Faculty Research Award (2011), NSF CAREER Award (2015), and was selected as one of AI's 10 to Watch by IEEE Intelligent Systems (2013) and a research fellow by Alfred P. Sloan Foundation (2016).
Nicholas Roy is an Associate Professor in the Department of Aeronautics & Astronautics at the Massachusetts Institute of Technology and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. He received his Ph. D. in Robotics from Carnegie Mellon University in 2003. He directs the Robust Robotics Group in CSAIL, which studies the problems of planning, inference and learning for unmanned aerial and ground vehicles in populated environments. He is the winner of the RAS Early Career Award and the MAV '08 and AUVSI IARC '09 competitions. He spent two years at Google [x] as the founder of Project Wing.
Daniel Nikovski is the Group Manager of the Data Analytics Group at Mitsubishi Electric Research Labs. Daniel's research is focused on algorithms for reasoning, planning, and learning with probabilistic models. After receiving his Ph.D. in Robotics from Carnegie Mellon University in 2002, he has been working at MERL on hard industrial problems such as group elevator control, energy optimization for power systems and air conditioners, and traffic, driver, and thermal comfort prediction. He has also supervised multiple projects in the areas of machine learning and optimization, and has co-authored a textbook on Artificial Intelligence.
Internet of Things & Connected Societies
Advances in communication and computation technologies that reduce cost, improve reliability and mobility, are fueling the adoption of industrial and consumer IoT products, platforms and services. In 2016, nearly one hundred new digital industry platforms will launch and 6.4-billion connected devices will be in use worldwide. Some important challenges for IoT include standardization of IoT communication technologies for interoperability, development of cyber-security solutions, regulations for data privacy and designing innovative business models. This panel brings experts together to discuss these challenges, emerging technologies and business model trends in the world of IoT.
Moderator: Dr. Zafer Sahinoglu, MERLPanelists:
- Prof. Stark Draper, University of Toronto
- Mr. Mark Rakoski, Mitsubishi Electric Automotive America, Inc.
- Prof. Henk Wymeersch, Chalmers University
Stark Draper received the M.S. and Ph.D. degrees in electrical engineering and computer science from the Massachusetts Institute of Technology (MIT), and the B.S. and B.A. degrees in electrical engineering and history, respectively, from Stanford University. He is an associate professor in the Department of Electrical and Computer Engineering at the University of Toronto. Previously he was an Associate Professor at the University of Wisconsin, Madison. Before moving to Wisconsin, Stark worked at the Mitsubishi Electric Research Laboratory (MERL) in Cambridge, Massachusetts. Stark has received the NSF CAREER grant in 2009, the MERL 2010 President's Award, the UW ECE Gerald Holdridge Teaching Award, the MIT Carlton E. Tucker Teaching Award, an Intel Graduate Fellowship, Stanford's Frederick E. Terman Engineering Scholastic Award, and a Fulbright Fellowship. research is centered on data communications, large-scale probabilistic inference, and security, with an emphasis on conceptual understanding of how to architect networks and services, and how to improve algorithmic solutions to provide those services more efficiently, quickly, and reliably.
Mark Rakoski is an executive director of Sales and Engineering at Mitsubishi Electric. He oversees global business development for powertrain, body and chassis products. As the Executive Director, he is responsible for business functions including strategic planning, sales, marketing, application engineering and quality engineering. He currently oversees product development, testing development, marketing and sales for start-stop applications. Prior to becoming Executive Director, Rakoski served as the senior manager responsible for business development for the former Daimler Chrysler Automotive account. He began his career with Mitsubishi Electric Automotive America in 1996 as a technical liaison to Chrysler Electrical Engineering after earning a Bachelor's of Science in Mechanical Engineering from Michigan Technological University. Rakoski is a member of the Society of Automotive Engineers and the Society of Automotive Analysts, and has served on the SAE Convergence Technical Council since 2006.
Henk Wymeersch is an Associate Professor with the Department of Signals and Systems at Chalmers University of Technology, Sweden. Prior to joining Chalmers, he was a Postdoctoral Associate during 2006-2009 with the Laboratory for Information and Decision Systems (LIDS) at the Massachusetts Institute of Technology (MIT). Henk Wymeersch obtained the Ph.D. degree in Electrical Engineering/Applied sciences in 2005 from Ghent University, Belgium. He served as Associate Editor for IEEE Transactions on Communications (2016-present), IEEE Transactions on Wireless Communications (2013-present), and IEEE Communication Letters (2009-2013). In 2015, he was the General Chair of the International Conference on Localization and GNSS. He has co-authored over 150 contributions in journals and international conferences, and is the author of Iterative Receiver Design (Cambridge University Press, August 2007).
Dr. Sahinoglu earned his PhD degree in Electrical Engineering from New Jersey Institute of Technology in 2001 and joined MERL the same year as a research scientist. He also earned an MBA degree from Sloan School of Management, MIT, in 2013. He worked at Mitsubishi Electric Corporate R&D Center in Tokyo for 6 months in 2014 to help product-centric business units expand into service and platform-based new businesses. He has been leading innovative business model design and technology strategy development within Mitsubishi Electric US since 2014. He currently enjoys conducting market and industry research to identify new innovation opportunities for corporate expansion and entrepreneurship in the US and transforming disruptive ideas into innovation with emphasis on IoT, automotive, smart buildings, factory automation and healthcare. He has numerous technical contributions to international standards in the areas of sensor networks, wireless communications and indoor localization including ZigBee, IEEE 802.15.4a and IEEE 802.154e. He has written two books published by Cambridge University Press; and co-authored about 100 peer-reviewed papers in international journals and conferences.
The introduction of autonomous vehicles onto roadways world-wide faces enormous technical and social problems. What are the key problems that will be hard to solve? How will different regions shape local solutions? Where will inroads happen first and what will be the last barriers to fall? How will the envisioned autonomous vehicles change society? Our panel is composed of technical experts in various aspects of the autonomous vehicles—sensing, planning, control, communication, deployment. They will give their perspectives on the changes coming down the road.
Moderator: Dr. Jay Thornton, MERLPanelists:
- Prof. Sertac Karaman, MIT
- Prof. Ilya Kolmanovsky, University of Michigan
- Prof. Ümit Özgüner, The Ohio State University
- Prof. Ed Olson, University of Michigan
Sertac Karaman is the Charles Stark Draper Assistant Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology (since Fall 2012). He has obtained B.S. degrees in mechanical engineering and and in computer engineering from the Istanbul Technical University, Turkey, in 2007, an S.M. degree in mechanical engineering from MIT in 2009, and a Ph.D. degree in electrical engineering and computer science also from MIT in 2012. His research interests lie in the broad areas of robotics and control theory. In particular, he studies the applications of probability theory, stochastic processes, stochastic geometry, formal methods, and optimization for the design and analysis of high-performance cyber-physical systems. The application areas include driverless cars, unmanned aerial vehicles, distributed aerial surveillance systems, air traffic control, certification and verification of control systems software, among many others.
Ilya Kolmanovsky is a full professor in the Department of Aerospace Engineering at the University of Michigan. His research interests include applications of control, estimation and diagnostics to aerospace and automotive systems, and to spacecraft control, propulsion systems and optimal energy management, in particular. He has also been conducting research on control algorithms for enforcing pointwise-in-time state and control constraints (such as imposed by actuator amplitude/rate limits obstacle/debris avoidance requirements and safety limits), and on control/estimation based on nonlinear, stochastic and distributed parameter system models. Professor Kolmanovsky has coauthored over 300 journal and conference publications on these topics. He is a former graduate of the Department of Aerospace Engineering, and has spent close to 15 years at Ford Motor Company Research and Advanced Engineering before re-joining the department. Professor Kolmanovsky is named as an inventor on over 90 United States patents.
Ümit Özgüner is the TRC Inc. Chair in ITS, Electrical & Computer Engineering at Ohio State University. He received his PhD degree from University of Illinois and has worked at IBM Research (Yorktown Heights), Istanbul Technical University and The Ohio State University. He has spent sabbaticals at Ohio Aerospace Institute and Ford Motor Company. He is a Professor of Electrical and Computer Engineering at OSU and holds the title of TRC Inc. Chair on ITS. Starting in October 2013, he is the Director of the OSU Crash Imminent Safety University Transportation Center. Professor Ozguner's research interests are in large scale systems, decentralized control, intelligent transportation systems and autonomous vehicles. He has over 400 publications and has advised over 25 students on their PhD studies. Professor Ozguner has been the General or Program Chair of many International Conferences in control and ITS. He is a IEEE fellow and was the founding President of the IEEE ITS Council which is now the IEEE ITS Society. Teams that he led have participated successfully in various autonomous driving challenges including Demo'97 and all the DARPA Grand and Urban Challenges.
Edwin Olson is an Associate Professor of Computer Science and Engineering at the University of Michigan. He is the director of the APRIL robotics lab, which studies Autonomy, Perception, Robotics, Interfaces, and Learning. His active research projects include applications to explosive ordinance disposal, search and rescue, multi-robot communication, railway safety, and automobile autonomy and safety. He received a PhD from the Massachusetts Institute of Technology in 2008 for his work in robust robot mapping. During his time as a PhD student, he was a core member of their DARPA Urban Challenge Team which finished the race in 4th place. His work on autonomous cars continues in cooperation with Ford Motor Company on the Next Generation Vehicle project. In 2010, he led the winning team in the MAGIC 2010 competition by developing a team of 14 robots that semi-autonomously explored and mapped a large-scale urban environment. For winning, the U.S. Department of Defense awarded him $750,000. He was named one of Popular Science's "Brilliant Ten" in September, 2012. In 2013, he was awarded a DARPA Young Faculty Award. He is active in the open source software community as one of the original developers of the message-passing system LCM, and the creator of the OrcBoard robotics controller. Much of his current software is available under open source licenses.
Jay Thornton has managed a group at MERL doing research on machine learning, vision for robotics, medical imaging, computational photography, and processing of the 3D world since 2002. Prior to joining MERL, he worked at Polaroid Corporation for many years on human vision and image science problems concerning color reproduction, image quality, half toning, and image processing. He received a PhD in Mathematical Psychology from the University of Michigan, is a senior member of the IEEE, and holds more than 30 US patents.
Energy and Environment
Electric energy generation, distribution and consumption ("the grid"), considered one of the greatest engineering achievements of the 20th century, and is undergoing rapid change globally in the 21st. Much of this change is driven by the need to address climate change, arguably one of the biggest challenges facing the planet. Clean, distributed generation (solar, wind) is supplying an increasing amount of energy globally, while conventional nuclear and coal powered plants are being decommissioned. Recent breakthroughs in fusion technology may finally offer a clean alternative to large-scale centralized generation. On the load side, energy efficiency is driving significant innovations in products ranging from computers to air conditioners. This panel will explore the business implications and research and development opportunities associated with these trends. What are the biggest technical risks associated with the current trends? What are and will be the key research and development challenges in the next 25 years? How should our research institutions, both industry and academic, evolve to meet these changes? In 100 years, will the "grid" still be considered one of the greatest engineering achievements?
Moderator: Dr. Scott Bortoff, MERLPanelists:
- Prof. Francesco Borrelli, UC Berkeley
- Dr. Sonja Glavaski, Department of Energy
- Mr. Brian Heery, Mitsubishi Electric Power Products, Inc.
- Prof. Dennis Whyte, MIT
Francesco Borrelli received the 'Laurea' degree in computer science engineering in 1998 from the University of Naples 'Federico II', Italy. In 2002 he received the PhD from the Automatic Control Laboratory at ETH-Zurich, Switzerland. He is currently a Professor at the Department of Mechanical Engineering of the University of California at Berkeley, USA. He is the author of more than one hundred publications in the field of predictive control. He is author of the book Constrained Optimal Control of Linear and Hybrid Systems published by Springer Verlag, the winner of the 2009 NSF CAREER Award and the winner of the 2012 IEEE Control System Technology Award. In 2008 he was appointed the chair of the IEEE Technical Committee on Automotive Control. In 2016 he was nominated IEEE Fellow. He is the founder and CTO of BrightBox Technology, a start up focused on cloud-based optimization for energy systems. His research interests include constrained optimal control, model predictive control and its application to advanced automotive control and energy efficient building operation.
Sonja Glavaski currently serves as a Program Director at the Advanced Research Projects Agency-Energy (ARPA-E). Her focus includes data analytics, and distributed control and optimization in complex, cyber-physical, and networked systems with applications to control, monitoring, and security of energy systems.During her 20-plus-year career, Dr. Glavaski has contributed significantly to technical advancements in numerous product areas, including propulsion systems, hybrid vehicles, energy efficient building HVAC/R systems, and aircraft systems. Prior to joining ARPA-E, Dr. Glavaski served as Control Systems Group Leader at United Technologies Research Center, where she made significant technical contributions to UTC's world-class product portfolio, advancing new knowledge and technology in the area of control & intelligent systems. Her group specialized in advanced control & optimization; physics based & data driven modeling and diagnostics; and distributed system design. Prior to being at UTRC, Dr. Glavaski led key programs at Eaton Innovation Center and Honeywell Labs. She received the Honeywell Aerospace Technical Achievement Award. Dr. Glavaski has leveraged her research expertise to provide significant leadership in professional societies. A Senior Member of IEEE, she served as the IEEE Control Systems Society Women in Control Chair. Her research findings have appeared in more than 35 publications. Dr. Glavaski received her M.S. and Ph.D. in Electrical Engineering at the California Institute of Technology.
Brian Heery is President and Chief Executive Officer for MEPPI. The company has two business groups, each with several operating divisions. The Power Systems Group primarily serves the electric utility markets with generation and substation products. The Public Utilities Group serves diverse markets with heavy electrical infrastructure products. Mr. Heery joined MEPPI in Houston TX in 1989 as a Region Manager for the Central United States. He moved to Warrendale in 1994 as the Manufacturing Department Manager. He was named General Manager of the GCB Division in 1998 as MEPPI reorganized in response to continued growth. Mr. Heery became COO in 2006, President in 2008 and CEO, effective January 1, 2011. Mr. Heery began his career with Westinghouse Electric in assignments at their power transformer and circuit breaker factories. He was later assigned to the South Texas Project nuclear plant near Bay City TX. Mr. Heery received a BSME from the University of Connecticut. He is active in the community, serving as Chairman of the Board of Life'sWork of Western PA and is a member of the Allegheny Conference on Community Development. He also serves as the MEPPI executive representative for several industry associations.
Dennis Whyte is Director of MIT's Plasma Science & Fusion Center and Professor and Head of the department of Nuclear Science & Engineering. A recognized leader in the field of fusion research using the magnetic confinement of plasmas for energy production, Professor Whyte's work in magnetic fusion specializes on the interface between the plasma and materials. Professor Whyte received his Ph.D. from the Université du Québec, INRS in 1993. Professor Whyte has over 300 publications and is a two time winner of the Ruth & Joel Spira Award for Distinguished Teaching. As an educator he is heavily involved in student design activities through courses. Recently he has been working with students to advance surface and material measurement techniques of fusion and reactive power plant designs for pilot plants. He has served as leader of the Boundary-Plasma Interface Topical Group of the US Burning Plasma Organization and is a Fellow of the American Physical Society. Professor Whyte was awarded the Department of Energy's Plasma Physics Junior Faculty Award in 2003 and in 2013 won the International Atomic Energy Agency's Nuclear Fusion Prize. Among his many lectures on fusion energy research, In 2015 Professor Whyte was an invited speaker at CERAWeek, the world's largest energy conference and the National Science Foundation's Engineering Distinguished Lecture.
Scott A. Bortoff is a Distinguished Member of Research Staff and Strategic Project Leader at Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA. From 2009 — 2016 he was Group Manager of Mechatronics at MERL, establishing the group, growing its membership and defining its control-oriented strategy. Prior to his tenure at MERL, he was Group Leader of Control System Technology at United Technologies Research Center, where he led efforts in system level dynamic modeling and control of fuel cell power plants, vapor compression systems, and aerospace power generation and distribution systems. He was previously Associate Professor in the Department of Electrical and Computer Engineering at the University of Toronto, where he conducted research into nonlinear control systems and taught undergraduate and graduate courses in control. His research interests include system level dynamic modeling and control of building energy systems and mechatronic systems.
This session will feature some of the latest and greatest achievements by MERL researchers.
- "Indoor Localization using Wireless and Acoustic Signals"
Milutin Pajovic, Phil Orlik
- "Modeling Waves with Neural Networks"
Ulugbek Kamilov, Dehong Liu, Hassan Mansour, Petros Boufounos
- "Truncated Approximate Dynamic Programming"
Amir-massoud Farahmand, Daniel N. Nikovski
- "1000Hz Visual Odometry"
- "Detection of a 1 Tb/s Superchannel with a Single Optical Receiver"
Kieran Parsons, David S. Millar, Milutin Pajovic, Toshiaki Koike-Akino, Keisuke Kojima
- "Cracking the Cocktail Party Problem: Deep Clustering for Speech Separation"
John Hershey, Jonathan Le Roux, Shinji Watanabe
- "Global Optimization of Large Optimal Power Flow Problem"
Arvind U. Raghunathan, Daniel N. Nikovski
- "Semantic Segmentation and Labeling of Objects in Images"
- "Control of Ion-Thrust Satellites"
Avishai Weiss, Uros Kalabic, Stefano Di Cairano
- "Energy-Optimizing Control of Air Conditioners"
Daniel J. Burns, Christopher R. Laughman
- "Dose Optimization for Particle Beam Therapy Systems"
Alan Sullivan, Matthew Brand