- Date: September 30, 2020
Where: Rice University
Research Areas: Dynamical Systems, Optimization
Brief - MERL researcher Dr. S. Nabi was invited to give a talk on the state-of-the-art methods for airflow optimization and control at Rice University. Several industrial applications to buoyancy-driven flows in the built environment, atmospheric flows, and prevention of transmission of COVID-19 were discussed. Furthermore, some novel advances on data-driven fluid mechanics for industrial applications were covered.
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- Date: August 25, 2020
MERL Contact: Ankush Chakrabarty
Research Areas: Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Optimization, Robotics
Brief - Ankush Chakrabarty co-organized an invited session on “Data-Driven Control For Industrial Applications” at the IEEE Conference on Control Technology and Applications with Shahin Shahrampour (Asst. Prof., Texas A&M). Talks covered topics including reinforcement learning for aerospace systems, constrained reinforcement learning for motors, deep Q learning for traffic systems and participants included speakers from Stanford University, North Carolina State University, Texas A&M, Oklahoma State University, University of Science and Technology at Beijing, and TU Delft.
MERL presented research (Chakrabarty, Danielson, Wang) on constraint-enforcing output-tracking with approximate dynamic programming for servomotor systems.
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- Date: August 3, 2020
Where: Cambridge, MA
MERL Contact: Abraham P. Vinod
Research Areas: Artificial Intelligence, Control, Optimization, Robotics
Brief - Mitsubishi Electric Research Laboratories is excited to welcome Abraham P. Vinod as the newest member of its research staff, in the Control for Autonomy Team. Abraham joins MERL from the University of Texas, Austin, where he was a Postdoctoral Research Fellow. He obtained his Ph.D. from the University of New Mexico. His PhD research produced scalable algorithms for providing safety guarantees for stochastic, control-constrained, dynamical systems, with applications to motion planning. In his postdoctoral research, Abraham studied theory and algorithms for on-the-fly, data-driven control of unknown systems under severely limited data. His current research interests lie in the intersection of optimization, control, and learning. Abraham won the Best Student Paper Award at the 2017 ACM Hybrid Systems: Computation and Control Conference, was a finalist for the Best Paper Award in the 2018 ACM Hybrid Systems: Computation and Control Conference, and won the best undergraduate student research project award at the Indian Institute of Technology, Madras.
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- Date: July 12, 2020 - July 18, 2020
Where: Vienna, Austria (virtual this year)
MERL Contacts: Mouhacine Benosman; Anoop Cherian; Devesh K. Jha; Daniel N. Nikovski
Research Areas: Artificial Intelligence, Computer Vision, Data Analytics, Dynamical Systems, Machine Learning, Optimization, Robotics
Brief - MERL researchers are presenting three papers at the International Conference on Machine Learning (ICML 2020), which is virtually held this year from 12-18th July. ICML is one of the top-tier conferences in machine learning with an acceptance rate of 22%. The MERL papers are:
1) "Finite-time convergence in Continuous-Time Optimization" by Orlando Romero and Mouhacine Benosman.
2) "Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?" by Kei Ota, Tomoaki Oiki, Devesh Jha, Toshisada Mariyama, and Daniel Nikovski.
3) "Representation Learning Using Adversarially-Contrastive Optimal Transport" by Anoop Cherian and Shuchin Aeron.
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- Date: July 1, 2020 - July 3, 2020
Where: Denver, Colorado (virtual)
MERL Contacts: Mouhacine Benosman; Karl Berntorp; Ankush Chakrabarty; Stefano Di Cairano; Rien Quirynen; Yebin Wang; Avishai Weiss
Research Areas: Control, Machine Learning, Optimization
Brief - At the American Control Conference, MERL presented 10 papers on subjects including autonomous-vehicle decision making and motion planning, nonlinear estimation for thermal-fluid models and GNSS positioning, learning-based reference governors and reference governors for railway vehicles, and fail-safe rendezvous control.
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- Date: June 18, 2020
Awarded to: Tong Huang, Hongbo Sun, K.J. Kim, Daniel Nikovski, Le Xie
MERL Contacts: Daniel N. Nikovski; Hongbo Sun
Research Areas: Data Analytics, Electric Systems, Optimization
Brief - A paper on A Holistic Framework for Parameter Coordination of Interconnected Microgrids Against Natural Disasters, written by Tong Huang, a former MERL intern from Texas A&M University, has been selected as one of the Best Conference Papers at the 2020 Power and Energy Society General Meeting (PES-GM). IEEE PES-GM is the flagship conference for the IEEE Power and Energy Society. The work was done in collaboration with Hongbo Sun, K. J. Kim, and Daniel Nikovski from MERL, and Tong's advisor, Prof. Le Xie from Texas A&M University.
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- Date & Time: Thursday, May 7, 2020; 12:00 PM
Speaker: Christopher Rackauckas, MIT
MERL Host: Christopher R. Laughman
Research Areas: Machine Learning, Multi-Physical Modeling, Optimization
Abstract - In the context of science, the well-known adage "a picture is worth a thousand words" might well be "a model is worth a thousand datasets." Scientific models, such as Newtonian physics or biological gene regulatory networks, are human-driven simplifications of complex phenomena that serve as surrogates for the countless experiments that validated the models. Recently, machine learning has been able to overcome the inaccuracies of approximate modeling by directly learning the entire set of nonlinear interactions from data. However, without any predetermined structure from the scientific basis behind the problem, machine learning approaches are flexible but data-expensive, requiring large databases of homogeneous labeled training data. A central challenge is reco nciling data that is at odds with simplified models without requiring "big data". In this talk we discuss a new methodology, universal differential equations (UDEs), which augment scientific models with machine-learnable structures for scientifically-based learning. We show how UDEs can be utilized to discover previously unknown governing equations, accurately extrapolate beyond the original data, and accelerate model simulation, all in a time and data-efficient manner. This advance is coupled with open-source software that allows for training UDEs which incorporate physical constraints, delayed interactions, implicitly-defined events, and intrinsic stochasticity in the model. Our examples show how a diverse set of computationally-difficult modeling issues across scientific disciplines, from automatically discovering biological mechanisms to accelerating climate simulations by 15,000x, can be handled by training UDEs.
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- Date: July 7, 2021 - July 14, 2021
Where: Bratislava, Slovakia
MERL Contact: Stefano Di Cairano
Research Areas: Control, Machine Learning, Optimization
Brief - MERL researcher Stefano Di Cairano has been appointed as Vice-Chair for Industry of the International Program Committee of the 7th IFAC Symposium on Nonlinear Model Predictive Control, which will be held in Bratislava, Slovakia, in July 2021.
IFAC NMPC is the main symposium focused on model predictive control, theory, methods and applications, includes contributions on control, optimization, and machine learning research, and is held every 3 years.
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- Date: April 29, 2020
Where: N/A
Research Areas: Communications, Optimization, Signal Processing, Information Security
Brief - Kyeong Jin Kim, a Senior Principal Research Scientist in the Signal Processing Group, will serve as lead guest editor for the upcoming JSTSP issue on, "Advanced Signal Processing for Local and Private 5G Networks." The issue is also being organized with the help of other researchers and investigators from leading organizations such as Memorial University, Nokia Bell Laboratories, Princeton University, Aalborg University, Jinan University, and South China University of Technology. This special issue aims to capture the latest research activities in local and private 5G networks from the signal processing perspective and is targeted for publication January 2022.
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- Date: December 8, 2020 - December 11, 2020
Where: IEEE Conference on Decision and Control (CDC)
MERL Contact: Ankush Chakrabarty
Research Areas: Control, Optimization
Brief - Ankush Chakrabarty, a Research Scientist in MERL's Multi-Physical Systems, will be serving as an Associate Editor at the 2020 IEEE Conference on Decision and Control (CDC).
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- Date: May 26, 2020
Where: 2020 SIAM Conference on Optimization, Hong Kong
MERL Contact: Arvind Raghunathan
Research Area: Optimization
Brief - Arvind Raghunathan, Data Analytics, has been invited to serve on the SIAM Activity Group on Optimization Early Career Prize (SIAG/OPT Early Career Prize) committee. Instituted in 2018, the SIAG/OPT Early Career Prize is awarded every three years to an outstanding early career researcher in the field of optimization for distinguished contributions to the field in the six calendar years prior to the award year. The 2020 SIAG/OPT Early Career Prize will be awarded during the 2020 SIAM Conference on Optimization to be held in Hong Kong.
Arvind Raghunathan will also host a mini-symposium on global optimization titled "Global Optimization of MINLP: Recent Advances". The mini-symposium will feature talks related to theoretical and algorithmic aspects of global optimization.
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- Date: December 11, 2019 - December 13, 2019
Where: Nice, France
MERL Contacts: Mouhacine Benosman; Karl Berntorp; Scott A. Bortoff; Ankush Chakrabarty; Stefano Di Cairano; Jing Zhang
Research Areas: Control, Machine Learning, Optimization
Brief - At the Conference on Decision and Control, MERL presented 8 papers on subjects including estimation for thermal-fluid models and transportation networks, analysis of HVAC systems, extremum seeking for multi-agent systems, reinforcement learning for vehicle platoons, and learning with applications to autonomous vehicles.
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- Date: October 21, 2019 - October 23, 2019
MERL Contact: Arvind Raghunathan
Research Area: Optimization
Brief - Arvind Raghunathan, of MERL's Data Analytics group, and collaborators will present 4 invited talks at 2019 Institute for Operations Research and Management Science (INFORMS) Annual Meeting. The talks cover a broad range of topics including decision diagrams, algorithms for mixed integer quadratic, applications in transportation and integration of prescriptive and predictive analytics.
INFORMS is the world’s largest professional association dedicated to and promoting best practices and advances in operations research, management science, and analytics to improve operational processes, decision-making, and outcomes. INFORMS Annual Meeting is a premier annual conference bringing together researchers and practitioners in operations research and management science.
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- Date: September 22, 2019 - September 26, 2019
MERL Contacts: Devesh K. Jha; Toshiaki Koike-Akino; Kieran Parsons; Ye Wang
Research Areas: Artificial Intelligence, Communications, Electronic and Photonic Devices, Optimization, Signal Processing
Brief - MERL Optical Team scientists will be presenting 5 papers including 2 invited talks at the 45th European Conference on Optical Communication (ECOC) 2019, which is being held in Dublin from September 22-26, 2019. Topics to be presented include recent advances in sophisticated constellation shaping schemes, lattice coding, and deep learning-based turbo equalization to mitigate fiber nonlinearity. Dr. Kojima is giving an invited workshop talk on deep learning-based nano-photonic device optimization. Dr. Tobias Fehenberger, a former Visiting Scientist is giving an invited talk related to our joint paper "Mapping Strategies for Short-Length Probabilistic Shaping"
ECOC is the largest optical communications event in Europe and a key meeting place for more than 1,500 scientists and researchers from institutions and companies across the world. The conference features more than 400 oral and poster presentations from various major telecoms industries and universities. As well as being one of the largest scientific conferences globally, ECOC also features Europe’s largest optical communications exhibition.
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- Date: July 10, 2019 - July 12, 2019
Where: Philadelphia
MERL Contacts: Mouhacine Benosman; Karl Berntorp; Ankush Chakrabarty; Stefano Di Cairano; Devesh K. Jha; Rien Quirynen; Yebin Wang; Avishai Weiss
Research Areas: Control, Machine Learning, Optimization
Brief - At the American Control Conference, MERL presented 8 papers on subjects including model predictive control applications, estimation and motion planning for vehicles, modular control architectures, and adaptation and learning.
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- Date & Time: Tuesday, July 16, 2019; 12:00 PM
Speaker: Prof. Jeff Linderoth, University of Wisconsin-Madison
MERL Host: Arvind Raghunathan
Research Areas: Machine Learning, Optimization
Abstract 
Algorithms to solve mixed integer linear programs have made incredible progress in the past 20 years. Key to these advances has been a mathematical analysis of the structure of the set of feasible solutions. We argue that a similar analysis is required in the case of mixed integer quadratic programs, like those that arise in sparse optimization in machine learning. One such analysis leads to the so-called perspective relaxation, which significantly improves solution performance on separable instances. Extensions of the perspective reformulation can lead to algorithms that are equivalent to some of the most popular, modern, sparsity-inducing non-convex regularizations in variable selection. Based on joint work with Hongbo Dong (Washington State Univ. ), Oktay Gunluk (IBM), and Kun Chen (Univ. Connecticut).
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- Date: June 25, 2019 - June 28, 2019
Where: Naples, Italy
MERL Contacts: Karl Berntorp; Scott A. Bortoff; Ankush Chakrabarty; Stefano Di Cairano; Devesh K. Jha; Christopher R. Laughman; Daniel N. Nikovski; Rien Quirynen; Diego Romeres; William S. Yerazunis
Research Areas: Control, Machine Learning, Optimization
Brief - The European Control Conference is the premier control conference in Europe. This year MERL was well represented with papers on control for HVAC, machine learning for estimation and control, robot assembly, and optimization methods for control.
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- Date: July 4, 2019
Where: University of Edinburgh
MERL Contact: Arvind Raghunathan
Research Area: Optimization
Brief - Arvind Raghunathan, of MERL's Data Analytics group, will deliver a keynote titled "Embedding Perfect Structures in Process Systems" in the School of Engineering at University of Edinburgh. Abstract of the talk can be found in the link below.
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- Date: July 2, 2019
Where: Imperial College London
MERL Contact: Arvind Raghunathan
Research Area: Optimization
Brief - Arvind Raghunathan, of MERL's Data Analytics group, will deliver a seminar titled "Chordal Completions – Semidefinite Programming and Minimum Completions" in the Computational Optimisation Group at Imperial College London. Abstract of the talk can be found in the link below.
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- Date: June 10, 2019 - June 14, 2019
Where: Paris
MERL Contact: Stefano Di Cairano
Research Areas: Control, Dynamical Systems, Optimization
Brief - MERL researcher Stefano Di Cairano and Prof. Ilya Kolmanovsky, Dept. Aerospace Engineering, the University of Michigan, were invited to teach a class on "Predictive and Optimization Based Control for Automotive and Aerospace Application" at the 2019 International Graduate School in Control, of the European Embedded Control Institute (EECI). Every year EECI invites world renown experts to teach 21-hours class modules, mostly for PhD students but also for professionals, on selected control subjects. Stefano and Ilya's class was attended by 30 "students" from both academia and industry, from all around the world, interested in automotive and aerospace control. The module described the fundamentals of modeling and control design in automotive and aerospace through lectures, real world examples and exercises, and placed particular emphasis on techniques such as MPC, reference governors, and optimal control.
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- Date: April 28, 2019
Where: 3rd IAVSD Workshop on Dynamics of Road Vehicles: Connected and Automated Vehicles
MERL Contact: Stefano Di Cairano
Research Areas: Control, Optimization, Robotics
Brief - Stefano Di Cairano, Distinguished Scientist and Senior Team Leader in the Control and Dynamical Systems Group, will give an invited talk entitled: "Modularity, integration and synergy in architectures for autonomous driving" that covers recent work in the lab concerning building a modular, robust control framework for autonomous driving.
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- Date: March 3, 2019 - March 7, 2019
Where: San Diego, CA
MERL Contacts: Devesh K. Jha; Toshiaki Koike-Akino; Chungwei Lin; Kieran Parsons; Bingnan Wang; Ye Wang
Research Areas: Communications, Machine Learning, Optimization, Signal Processing
Brief - MERL researchers are presenting 4 papers at the OSA Optical Fiber Conference (OFC), which is being held in San Diego from March 3-7, 2019. Topics to be presented include recent advances in nonbinary polar codes, joint polar-coded shaping, and deep learning-based photonics circuit design. Additionally, recent work on multiset-partition distribution matching is presented as an invited talk.
OFC is the flagship conference of the OSA, and the world's most comprehensive technical conference focused on the research advances and latest technological development in optics and photonics. The event attracts more than 10000 participants each year.
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- Date & Time: Thursday, November 29, 2018; 4-6pm
Location: 201 Broadway, 8th floor, Cambridge, MA
MERL Contacts: Elizabeth Phillips; Anthony Vetro
Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio
Brief - Snacks, demos, science: On Thursday 11/29, Mitsubishi Electric Research Labs (MERL) will host an open house for graduate+ students interested in internships, post-docs, and research scientist positions. The event will be held from 4-6pm and will feature demos & short presentations in our main areas of research including artificial intelligence, robotics, computer vision, speech processing, optimization, machine learning, data analytics, signal processing, communications, sensing, control and dynamical systems, as well as multi-physyical modeling and electronic devices. MERL is a high impact publication-oriented research lab with very extensive internship and university collaboration programs. Most internships lead to publication; many of our interns and staff have gone on to notable careers at MERL and in academia. Come mix with our researchers, see our state of the art technologies, and learn about our research opportunities. Dress code: casual, with resumes.
Pre-registration for the event is strongly encouraged:
merlopenhouse.eventbrite.com
Current internship and employment openings:
www.merl.com/internship/openings
www.merl.com/employment/employment
Information about working at MERL:
www.merl.com/employment.
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- Date: June 4, 2018
Where: Pittsburgh, Pennsylvania
MERL Contact: Arvind Raghunathan
Research Area: Optimization
Brief - Thiago Serra, currently a Visiting Research Scientist in the Data Analytics group, has been awarded the Gerald L. Thompson Doctoral Dissertation Award in Management Science from the Tepper School of Business, Carnegie Mellon University. This is awarded each year to honor an outstanding doctoral dissertation involving theoretical, computational and applied contributions in the area of Management Science. One of the thesis chapters, "The Integrated Last-Mile Transportation Problem" was work performed at MERL in conjunction with Arvind Raghunathan during a summer internship. This work resulted in a patent application and will be presented at the 2018 International Conference on Automated Planning and Scheduling (ICAPS).
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- Date: February 14, 2018
Where: Tokyo, Japan
MERL Contacts: Devesh K. Jha; Daniel N. Nikovski; Diego Romeres; William S. Yerazunis
Research Areas: Optimization, Computer Vision
Brief - New technology for model-based AI learning for equipment control was demonstrated by MERL researchers at a recent press release event in Tokyo. The AI learning method constructs predictive models of the equipment through repeated trial and error, and then learns control rules based on these models. The new technology is expected to significantly reduce the cost and time needed to develop control programs in the future. Please see the link below for the full text of the Mitsubishi Electric press release.
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