- Date: March 20, 2018
Where: Asian Conference on Supercomputing Frontiers -
- Date: April 19, 2018
Where: Room 202 Stratton Hall Worcester Polytechnic Institute Brief - Andrew Knyazev, Distinguished Research Scientist of MERL, has accepted an invitation to speak at the Worcester Polytechnic Institute (WPI) chapter of the Society for Industrial and Applied Mathematics (SIAM) located in Worcester, MA, at a series of industry speakers about different career paths for applied mathematicians.
Andrew Knyazev studied at the Department of Computational Mathematics and Cybernetics of the Moscow State University in 1976-1981. He obtained PhD Degree in Numerical Mathematics at the Russian Academy of Sciences (RAS) in 1985. Knyazev worked at the Kurchatov Institute in 1981-1983 and at the Institute of Numerical Mathematics RAS in 1983-1992, where he collaborated with Academician Bakhvalov (Erdos number 3 via Kantorovich) on numerical methods for homogenization. In 1993-1994, Knyazev held a visiting position at the Courant Institute of Mathematical Sciences of New York University. From 1994 and until retirement in 2014, he was a Professor of Mathematics at the University of Colorado Denver (CU Denver), supported by many grants from the National Science Foundation and the United States Department of Energy. He was awarded the title of CU Denver Professor Emeritus and named the SIAM Fellow in 2016. During his 30 years in the academy, Knyazev supervised 7 PhD students. He is best known for his Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) eigenvalue solver. In 2012, Knyazev starts his industrial research career joining Mitsubishi Electric Research Laboratories (MERL) in Cambridge, MA, where he invents and develops algorithms for control, machine learning, data sciences, computer vision, coding, communications, material sciences, and signal processing, having 11 US patent applications filed (6 issued, 5 pending) and over 20 papers published.
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- Date: April 10, 2018
Research Area: Machine Learning
Brief - Andrew Knyazev, Distinguished Research Scientist of MERL, has accepted an invitation to speak about his work on Big Data and spectral graph partitioning at the Schlumberger-Tufts U. Computational and Applied Math Seminar. A primary focus of this seminar series is on mathematical and computational aspects of remote sensing. A partial list of the topics of interest includes: numerical solution of large scale PDEs (a.k.a. forward problems); theory and numerical methods of inverse and ill-posed problems; imaging; related problems in numerical linear algebra, approximation theory, optimization and model reduction. The seminar meets on average once a month, the location alternates between Schlumberger's office in Cambridge, MA and the Tufts Medford Campus.
Abstract: Data clustering via spectral graph partitioning requires constructing the graph Laplacian and solving the corresponding eigenvalue problem. We consider and motivate using negative edge weights in the graph Laplacian. Preconditioned iterative solvers for the Laplacian eigenvalue problem are discussed and preliminary numerical results are presented.
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- Date: June 2, 2018 - June 4, 2018
Where: Newton, Massachusetts (USA)
Research Areas: Control, Computer Vision, Dynamical Systems, Machine Learning, Data Analytics
Brief - Dr. Andrew Knyazev of MERL has accepted an invitation to participate at the 2018 MathWorks Research Summit. The objective of the Research Summit is to provide a forum for leading researchers in academia and industry to explore the latest research and technology results and directions in computation and its use in technology, engineering, and science. The event aims to foster discussion among scientists, engineers, and research faculty about challenges and research opportunities for the respective communities with a particular interest in exploring cross-disciplinary research avenues.
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- Date: August 4, 2017
Awarded to: David Zhuzhunashvili and Andrew Knyazev
Research Area: Machine Learning
Brief - David Zhuzhunashvili, an undergraduate student at UC Boulder, Colorado, and Andrew Knyazev, Distinguished Research Scientist at MERL, received the 2017 Graph Challenge Student Innovation Award. Their poster "Preconditioned Spectral Clustering for Stochastic Block Partition Streaming Graph Challenge" was accepted to the 2017 IEEE High Performance Extreme Computing Conference (HPEC '17), taking place 12-14 September 2017 (http://www.ieee-hpec.org/), and the paper was accepted to the IEEE Xplore HPEC proceedings.
HPEC is the premier conference in the world on the convergence of High Performance and Embedded Computing. DARPA/Amazon/IEEE Graph Challenge is a special HPEC event. Graph Challenge encourages community approaches to developing new solutions for analyzing graphs derived from social media, sensor feeds, and scientific data to enable relationships between events to be discovered as they unfold in the field. The 2017 Streaming Graph Challenge is Stochastic Block Partition. This challenge seeks to identify optimal blocks (or clusters) in a larger graph with known ground-truth clusters, while performance is evaluated compared to baseline Python and C codes, provided by the Graph Challenge organizers.
The proposed approach is spectral clustering that performs block partition of graphs using eigenvectors of a matrix representing the graph. Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) method iteratively approximates a few leading eigenvectors of the symmetric graph Laplacian for multi-way graph partitioning. Preliminary tests for all static cases for the Graph Challenge demonstrate 100% correctness of partition using any of the IEEE HPEC Graph Challenge metrics, while at the same time also being approximately 500-1000 times faster compared to the provided baseline code, e.g., 2M static graph is 100% correctly partitioned in ~2,100 sec. Warm-starts of LOBPCG further cut the execution time 2-3x for the streaming graphs.
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- Date: May 24, 2017 - May 26, 2017
MERL Contacts: Mouhacine Benosman; Stefano Di Cairano; Abraham Goldsmith; Daniel N. Nikovski; Arvind Raghunathan; Yebin Wang
Research Areas: Control, Dynamical Systems, Machine Learning
Brief - Talks were presented by members of several groups at MERL and covered a wide range of topics:
- Similarity-Based Vehicle-Motion Prediction
- Transfer Operator Based Approach for Optimal Stabilization of Stochastic Systems
- Extended command governors for constraint enforcement in dual stage processing machines
- Cooperative Optimal Output Regulation of Multi-Agent Systems Using Adaptive Dynamic Programming
- Deep Reinforcement Learning for Partial Differential Equation Control
- Indirect Adaptive MPC for Output Tracking of Uncertain Linear Polytopic Systems
- Constraint Satisfaction for Switched Linear Systems with Restricted Dwell-Time
- Path Planning and Integrated Collision Avoidance for Autonomous Vehicles
- Least Squares Dynamics in Newton-Krylov Model Predictive Control
- A Neuro-Adaptive Architecture for Extremum Seeking Control Using Hybrid Learning Dynamics
- Robust POD Model Stabilization for the 3D Boussinesq Equations Based on Lyapunov Theory and Extremum Seeking.
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- Date & Time: Monday, July 10, 2017; 6:15 PM - 7:15 PM
Location: David Lawrence Convention Center, Pittsburgh PA
Speaker: Andrew Knyazev and other panelists, MERL Brief - Andrew Knyazev accepted an invitation to represent MERL at the panel on Student Careers in Business, Industry and Government at the annual meeting of the Society for Industrial and Applied Mathematics (SIAM).
The format consists of a five minute introduction by each of the panelists covering their background and an overview of the mathematical and computational challenges at their organization. The introductions will be followed by questions from the students.
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- Date & Time: Monday, December 12, 2016; 12:00 PM
Speaker: Yanlai Chen, Department of Mathematics at the University of Massachusetts Dartmouth
Research Areas: Control, Dynamical Systems
Abstract - Models of reduced computational complexity is indispensable in scenarios where a large number of numerical solutions to a parametrized problem are desired in a fast/real-time fashion. These include simulation-based design, parameter optimization, optimal control, multi-model/scale analysis, uncertainty quantification. Thanks to an offline-online procedure and the recognition that the parameter-induced solution manifolds can be well approximated by finite-dimensional spaces, reduced basis method (RBM) and reduced collocation method (RCM) can improve efficiency by several orders of magnitudes. The accuracy of the RBM solution is maintained through a rigorous a posteriori error estimator whose efficient development is critical and involves fast eigensolves.
In this talk, I will give a brief introduction of the RBM/RCM, and explain how they can be used for data compression, face recognition, and significantly delaying the curse of dimensionality for uncertainty quantification.
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- Date & Time: Wednesday, August 17, 2016; 1 PM
Speaker: Gilles Zerah, Centre Francais en Calcul Atomique et Moleculaire-Ile-de-France (CFCAM-IdF)
Research Areas: Applied Physics, Electronic and Photonic Devices
Abstract - The first part of the talk is a high-level review of modern technologies for atomic-level modelling of materials. The second part discusses band gap calculations and MERL results for semi-conductors.
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- Date & Time: Wednesday, July 13, 2016; 2:30 PM - 3:30
Speaker: Richard Lehoucq, Sandia National Laboratories
Research Areas: Computer Vision, Digital Video, Machine Learning
Abstract 
My presentation considers the research question of whether existing algorithms and software for the large-scale sparse eigenvalue problem can be applied to problems in spectral graph theory. I first provide an introduction to several problems involving spectral graph theory. I then provide a review of several different algorithms for the large-scale eigenvalue problem and briefly introduce the Anasazi package of eigensolvers.
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- Date & Time: Monday, July 11, 2016; 10 AM - 9:15 PM
Location: Westin Boston Waterfront Pavilion, Boston, Massachusetts
MERL Contacts: Matthew Brand; Arvind Raghunathan Brief - MERL researchers participate in SIAM Job fair to showcase MERL's research and highlight employment and intern opportunities at MERL. The Career Fair emphasizes careers in business, industry, and government, and takes place during the SIAM Annual Meeting.
The SIAM Applied Mathematics and Computational Science Career Fair is an informational and interactive event at which employers and prospective employees can discuss careers. It is a great opportunity for prospective employees to meet government and industry representatives and discuss what they are looking for and what each employer has to offer.
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- Date: July 12, 2016
Where: Westin Boston Waterfront Brief - MERL researcher Andrew Knyazev is to be honored for his recent selection as a SIAM Fellow at the 2016 SIAM Annual Meeting, during the Business Meeting on Tuesday, July 12, 6:15-7:15 PM in Grand Ballroom AB on the concourse level of the Westin Boston Waterfront, 425 Summer Street, Boston, MA (open to all conference participants). The Business Meeting is followed by a short reception for the new Fellows.
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- Date: July 6, 2016 - July 8, 2016
Where: American Control Conference (ACC)
MERL Contacts: Mouhacine Benosman; Karl Berntorp; Scott A. Bortoff; Petros T. Boufounos; Stefano Di Cairano; Abraham Goldsmith; Christopher R. Laughman; Daniel N. Nikovski; Arvind Raghunathan; Yebin Wang; Avishai Weiss
Research Areas: Control, Dynamical Systems, Machine Learning
Brief - The premier American Control Conference (ACC) takes place in Boston July 6-8. This year MERL researchers will present a record 20 papers(!) at ACC, with several contributions, especially in autonomous vehicle path planning and in Model Predictive Control (MPC) theory and applications, including manufacturing machines, electric motors, satellite station keeping, and HVAC. Other important themes developed in MERL's presentations concern adaptation, learning, and optimization in control systems.
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- Date & Time: Friday, May 13, 2016; 12:00 PM
Speaker: Oleg Iliev, Fraunhofer Institute for Industrial Mathematics, ITWM
Research Area: Dynamical Systems
Abstract 
Li-ion batteries are widely used in automotive industry, in electronic devices, etc. In this talk we will discuss challenges related to the multiscale nature of batteries, mainly the understanding of processes in the porous electrodes at pore scale and at macroscale. A software tool for simulation of isothermal and non-isothermal electrochemical processes in porous electrodes will be presented. The pore scale simulations are done on 3D images of porous electrodes, or on computer generated 3D microstructures, which have the same characterization as real porous electrodes. Finite Volume and Finite Element algorithms for the highly nonlinear problems describing processes at pore level will be shortly presented. Model order reduction, MOR, empirical interpolation method, EIM-MOR algorithms for acceleration of the computations will be discussed, as well as the reduced basis method for studying parameters dependent problems. Next, homogenization of the equations describing the electrochemical processes at the pore scale will be presented, and the results will be compared to the engineering approach based on Newman's 1D+1D model. Simulations at battery cell level will also be addressed. Finally, the challenges in modeling and simulation of degradation processes in the battery will be discussed and our first simulation results in this area will be presented.
This is joint work with A.Latz (DLR), M.Taralov, V.Taralova, J.Zausch, S.Zhang from Fraunhofer ITWM, Y.Maday from LJLL, Paris 6 and Y.Efendiev from Texas A&M.
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- Date: March 31, 2016
Awarded to: Andrew Knyazev
Research Areas: Control, Optimization, Dynamical Systems, Machine Learning, Data Analytics, Communications, Signal Processing
Brief - Andrew Knyazev selected as a Fellow of the Society for Industrial and Applied Mathematics (SIAM) for contributions to computational mathematics and development of numerical methods for eigenvalue problems.
Fellowship honors SIAM members who have made outstanding contributions to the fields served by the SIAM. Andrew Knyazev was among a distinguished group of members nominated by peers and selected for the 2016 Class of Fellows.
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- Date: January 6, 2016
Awarded to: Andrew Knyazev Brief - Andrew Knyazev is awarded the title of Professor Emeritus at the University of Colorado Denver effective 1/31/2016. The award letter from the Chancellor of the University of Colorado Denver provides examples of the record of excellence over 20 years of contributions to the university such as 2008 CU Denver Excellence in Research Award, 2000 Teaching Excellence Award for the college, supervision of Ph.D. students, and two decades of uninterrupted external research funding from the US National Science Foundation and Department of Energy.
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- Date: December 15, 2015
Where: 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
MERL Contact: Hassan Mansour
Research Area: Machine Learning
Brief - MERL researcher Andrew Knyazev gave 3 talks at the 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP). The papers were published in IEEE conference proceedings.
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- Date & Time: Monday, November 23, 2015; 12:00 PM
Speaker: Manuchehr Aminian, University of North Carolina, Chapel Hill Abstract 
The classic work by G.I. Taylor describes the enhanced longitudinal diffusivity of a passive tracer subjected to laminar pipe flow. Much work since then has gone into extending this result particularly in calculating the evolution of the scalar variance. However, less work has been done to describe the evolution of asymmetry in the distribution. We present the results from a modeling effort to understand how the higher moments of the tracer distribution depend on geometry based off of explicit results in the circular pipe. We do this via analysis of "channel-limiting" geometries (rectangular ducts and elliptical pipes parameterized by their aspect ratio), using both new analytical tools and Monte Carlo simulation, which have revealed a wealth of nontrivial behavior of the distributions at short and intermediate time.
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- Date: September 17, 2015
MERL Contacts: Stefano Di Cairano; Scott A. Bortoff; Abraham Goldsmith Brief - MERL researchers presented 3 papers at the 5th IFAC Nonlinear Model Predictive Control Conference. Approximately 150 attendees. Conference topics range from theory (existence, stability), to algorithms (optimization, design), to applications (process control, mechatronics, energy, automotive, aerospace). MERL was an industry sponsor for the conference. MERL researcher co-chaired the Industry Session on Industry perspective on Model Predictive Control. MERL researcher acted as Program Co-chair, organizing the conference program.
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- Date: October 6, 2015
Where: IFAC workshop on control applications of optimization 2015
Research Area: Control
Brief - MERL researchers Andrew Knyazev and Alexander Malyshev gave two talks at the IFAC workshop on control applications of optimization, 2015. The papers were published by Elsevier B.V. in the conference proceedings.
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- Date: September 18, 2015
Where: IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2015
Research Area: Machine Learning
Brief - MERL researchers A. Knyazev and A. Malyshev gave a talk at the IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2015. The paper was published at the IEEE Xplore conference proceedings.
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- Date: July 13, 2015
Where: SIAM Conference on Control and Its Applications 2015
Research Area: Control
Brief - MERL and Mitsubishi Electric researchers presented talks at the SIAM Conference on Control and Its Applications 2015. The papers were published by SIAM in the conference proceedings.
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- Date: June 1, 2015
Where: International Conference on Computational Science (ICCS) Brief - Andrew Knyazev gave a talk at the International Conference on Computational Science (ICCS), 2015 on Nonsymmetric preconditioning for conjugate gradient and steepest descent methods. The paper was published by Elsevier B.V. in the conference proceedings.
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- Date & Time: Thursday, May 2, 2013; 12:00 PM
Speaker: Ben Miller, MIT Abstract 
Graph theory provides an intuitive mathematical foundation for dealing with relational data, but there are numerous computational challenges in the detection of interesting behavior within small subsets of vertices, especially as the graphs grow larger and the behavior becomes more subtle. This presentation discusses computational considerations of a residuals-based subgraph detection framework, including the implications on inference with recent statistical models. We also present scaling properties, demonstrating analysis of a billion-vertex graph using commodity hardware.
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- Date: March 14, 2013
Where: SIAM Journal of Scientific Computing Brief - The article "Absolute Value Preconditioning for Symmetric Indefinite Linear Systems" by Vecharynski, E. and Knyazev, A.V. was published in SIAM Journal of Scientific Computing.
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