Murray Wiki - User contributions [en]

Sarah Dean, 11-12 Feb 2020

2020-02-05T07:33:59Z

Adames: /* Wednesday (12 Feb) */

Sarah Dean, a PhD student working with Ben Recht, will visit Caltech on 11-12 Feb 2020. If you would like to meet with her, please sign up for a slot below (using your IMSS credentials to log in). Please make sure to put the location where she should meet you.

=== Schedule ===

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==== Tuesday (11 Feb) ====
* 11:40 am arrival in BUR
* ~12:30 pm: arrival on campus
* 12:30 pm: lunch with John Doyle (if available)
* 1:30 pm: Richard Murray (109 Steele Lab)
* 2:00 pm: Open
* 2:45 pm: Open
* 3:30 pm: Open (30 min)
* 4:00 pm: Seminar
* 5:00 pm: Open (30 min)
* 6:00 pm: Dinner with Richard + grad students, postdocs
| width=50% |

==== Wednesday (12 Feb) ====
* 8:45 am: Open
* 9:30 am: Open
* 10:15 am: Open
* 11:00 am: Ludwig Schmidt seminar
* 12:00 pm: Lunch with faculty or grad students
* 1:15 pm: Yisong Yue (303 Annenberg)
* 2:00 pm: Aaron Ames (266 Gates-Thomas)
* 2:30 pm: Meet with Ames' Students (121 Gates Thomas)
* 3:00 pm: CDS tea
* 3:30 pm: Open (30 min)
* 4:00 pm: Open (30 min)
* 4:30 pm: Wrap up meeting with Richard (109 Steele Lab)
* 4:45 pm: Depart campus
* 6:40 pm departure from BUR
|}

=== Seminar ===

Safe and Robust Perception-Based Control 
Sarah Dean, UC Berkeley

Tue, 11 February, 4 pm 
105 Annenberg

Machine learning provides a promising path to distill information from high dimensional sensors like cameras -- a fact that often serves as motivation for merging learning with control. This talk aims to provide rigorous guarantees for systems with such learned perception components in closed-loop. Our approach is comprised of characterizing uncertainty in perception and then designing a robust controller to account for these errors. We use a framework which handles uncertainties in an explicit way, allowing us to provide performance guarantees and illustrate how trade-offs arise from limitations of the training data. Throughout, I will motivate this work with the example of autonomous vehicles, including both simulated experiments and an implementation on a 1/10 scale autonomous car. Joint work with Aurelia Guy, Nikolai Matni, Ben Recht, Rohan Sinha, and Vickie Ye.

Paul Van den Hof, Dec 2018

2018-12-07T01:23:01Z

Adames: /* Schedule */

Paul van den Hof is Full Professor and Chair of the Control Systems (CS) Group at the Department of Electrical Engineering. He is interested in data-driven modeling, control and optimization of dynamic systems in several technological fields: industrial process control, oil reservoir engineering, high-tech mechatronic and cyber-physical systems, etc. His focus is the development of fundamental techniques, such as data-driven modeling, closed-loop and control-oriented identification and data analytics, experimental design and performance monitoring, and model-based control, monitoring and optimization.

=== Schedule ===

* 10 am: Richard Murray, 107 Steele Lab
* 10:30 am: Petter Nilsson
* 11:00 am: Yuxiao Chen, Thomas Gurriet
* 11:45 am: Lunch with Aaron Ames, Petter Nilsson, Thomas Gurriet, Yuxiao Chen, meet in 266 Gates-Thomas
* 1:00 pm: Seminar, 106 Annenberg
* 2:00 pm: Soon-Jo Chung, 106 Annenberg
* 2:45 pm: Open
* 3:30 pm: Richard Murray, 107 Steele Lab
* 4:00 pm: Chelsea and Ayush, 2nd floor lounge, Annenberg
* 4:45 pm: Open (if nothing else available)
* 5:30 pm: Depart

=== Seminar ===

'''Data-driven modeling in linear dynamic networks''' 
Friday, December 7th at 1pm, 106 Annenberg

In many areas of science and technology, the complexity of dynamic systems that are being considered, grows beyond the level of single systems into interconnected networks of dynamic systems. In control and optimization this has led to the development of decentralized and distributed algorithms for control/optimization, as e.g. in multi-agent systems.
From the modelling perspective, data-driven modelling tools are typically developed for relatively simple open-loop and closed-loop structures, while the opportunities for big data handling in the current data science era, are becoming abundant. As a result there is a strong need for the development of data-driven modelling tools for large-scale interconnected dynamic networks.
In this seminar we will highlight the main developments and challenges in this area. Besides setting up a modelling framework, we will address problems of local identification of a particular part of the network, including the selection of the appropriate signals to be measured. The concept of network identifiability is highlighted and the role of structural properties of the network, in terms of its topology/graph, is given strong attention. It is also shown how classical closed-loop identification methods need to be generalized to be able to cope with the new situations.

Paul Van den Hof, Dec 2018

2018-12-07T01:16:49Z

Adames: /* Schedule */

Paul van den Hof is Full Professor and Chair of the Control Systems (CS) Group at the Department of Electrical Engineering. He is interested in data-driven modeling, control and optimization of dynamic systems in several technological fields: industrial process control, oil reservoir engineering, high-tech mechatronic and cyber-physical systems, etc. His focus is the development of fundamental techniques, such as data-driven modeling, closed-loop and control-oriented identification and data analytics, experimental design and performance monitoring, and model-based control, monitoring and optimization.

=== Schedule ===

* 10 am: Richard Murray, 107 Steele Lab
* 10:30 am: Petter Nilsson
* 11:00 am: Yuxiao Chen
* 11:45 am: Lunch with Aaron Ames, Petter Nilsson, Thomas Gurriet, Yuxiao Chen, meet in 266 Gates-Thomas
* 1:00 pm: Seminar, 106 Annenberg
* 2:00 pm: Soon-Jo Chung, 106 Annenberg
* 2:45 pm: Open
* 3:30 pm: Richard Murray, 107 Steele Lab
* 4:00 pm: Chelsea and Ayush, 2nd floor lounge, Annenberg
* 4:45 pm: Open (if nothing else available)
* 5:30 pm: Depart

=== Seminar ===

'''Data-driven modeling in linear dynamic networks''' 
Friday, December 7th at 1pm, 106 Annenberg

In many areas of science and technology, the complexity of dynamic systems that are being considered, grows beyond the level of single systems into interconnected networks of dynamic systems. In control and optimization this has led to the development of decentralized and distributed algorithms for control/optimization, as e.g. in multi-agent systems.
From the modelling perspective, data-driven modelling tools are typically developed for relatively simple open-loop and closed-loop structures, while the opportunities for big data handling in the current data science era, are becoming abundant. As a result there is a strong need for the development of data-driven modelling tools for large-scale interconnected dynamic networks.
In this seminar we will highlight the main developments and challenges in this area. Besides setting up a modelling framework, we will address problems of local identification of a particular part of the network, including the selection of the appropriate signals to be measured. The concept of network identifiability is highlighted and the role of structural properties of the network, in terms of its topology/graph, is given strong attention. It is also shown how classical closed-loop identification methods need to be generalized to be able to cope with the new situations.

Paul Van den Hof, Dec 2018

2018-12-06T23:36:27Z

Adames: /* Schedule */

Paul van den Hof is Full Professor and Chair of the Control Systems (CS) Group at the Department of Electrical Engineering. He is interested in data-driven modeling, control and optimization of dynamic systems in several technological fields: industrial process control, oil reservoir engineering, high-tech mechatronic and cyber-physical systems, etc. His focus is the development of fundamental techniques, such as data-driven modeling, closed-loop and control-oriented identification and data analytics, experimental design and performance monitoring, and model-based control, monitoring and optimization.

=== Schedule ===

* 10 am: Richard Murray, 107 Steele Lab
* 10:30 am: Petter Nilsson
* 11:00 am: open
* 11:45 am: Lunch with Aaron Ames, Petter Nilsson, Thomas Gurriet, Yuxiao Chen
* 1:00 pm: Seminar, 106 Annenberg
* 2:00 pm: Soon-Jo Chung, 106 Annenberg
* 2:45 pm: Open
* 3:30 pm: Richard Murray, 107 Steele Lab
* 4:00 pm: Chelsea and Ayush, 2nd floor lounge, Annenberg
* 4:45 pm: Open (if nothing else available)
* 5:30 pm: Depart

=== Seminar ===

'''Data-driven modeling in linear dynamic networks''' 
Friday, December 7th at 1pm, 106 Annenberg

In many areas of science and technology, the complexity of dynamic systems that are being considered, grows beyond the level of single systems into interconnected networks of dynamic systems. In control and optimization this has led to the development of decentralized and distributed algorithms for control/optimization, as e.g. in multi-agent systems.
From the modelling perspective, data-driven modelling tools are typically developed for relatively simple open-loop and closed-loop structures, while the opportunities for big data handling in the current data science era, are becoming abundant. As a result there is a strong need for the development of data-driven modelling tools for large-scale interconnected dynamic networks.
In this seminar we will highlight the main developments and challenges in this area. Besides setting up a modelling framework, we will address problems of local identification of a particular part of the network, including the selection of the appropriate signals to be measured. The concept of network identifiability is highlighted and the role of structural properties of the network, in terms of its topology/graph, is given strong attention. It is also shown how classical closed-loop identification methods need to be generalized to be able to cope with the new situations.

Paul Van den Hof, Dec 2018

2018-12-06T23:36:11Z

Adames: /* Schedule */

Paul van den Hof is Full Professor and Chair of the Control Systems (CS) Group at the Department of Electrical Engineering. He is interested in data-driven modeling, control and optimization of dynamic systems in several technological fields: industrial process control, oil reservoir engineering, high-tech mechatronic and cyber-physical systems, etc. His focus is the development of fundamental techniques, such as data-driven modeling, closed-loop and control-oriented identification and data analytics, experimental design and performance monitoring, and model-based control, monitoring and optimization.

=== Schedule ===

* 10 am: Richard Murray, 107 Steele Lab
* 10:30 am: Petter Nilsson
* 11:00 am: open
* 11:45 am: Lunch Aaron Ames, Petter Nilsson, Thomas Gurriet, Yuxiao Chen
* 1:00 pm: Seminar, 106 Annenberg
* 2:00 pm: Soon-Jo Chung, 106 Annenberg
* 2:45 pm: Open
* 3:30 pm: Richard Murray, 107 Steele Lab
* 4:00 pm: Chelsea and Ayush, 2nd floor lounge, Annenberg
* 4:45 pm: Open (if nothing else available)
* 5:30 pm: Depart

=== Seminar ===

'''Data-driven modeling in linear dynamic networks''' 
Friday, December 7th at 1pm, 106 Annenberg

In many areas of science and technology, the complexity of dynamic systems that are being considered, grows beyond the level of single systems into interconnected networks of dynamic systems. In control and optimization this has led to the development of decentralized and distributed algorithms for control/optimization, as e.g. in multi-agent systems.
From the modelling perspective, data-driven modelling tools are typically developed for relatively simple open-loop and closed-loop structures, while the opportunities for big data handling in the current data science era, are becoming abundant. As a result there is a strong need for the development of data-driven modelling tools for large-scale interconnected dynamic networks.
In this seminar we will highlight the main developments and challenges in this area. Besides setting up a modelling framework, we will address problems of local identification of a particular part of the network, including the selection of the appropriate signals to be measured. The concept of network identifiability is highlighted and the role of structural properties of the network, in terms of its topology/graph, is given strong attention. It is also shown how classical closed-loop identification methods need to be generalized to be able to cope with the new situations.

Paul Van den Hof, Dec 2018

2018-12-06T05:24:31Z

Adames: /* Schedule */

Paul van den Hof is Full Professor and Chair of the Control Systems (CS) Group at the Department of Electrical Engineering. He is interested in data-driven modeling, control and optimization of dynamic systems in several technological fields: industrial process control, oil reservoir engineering, high-tech mechatronic and cyber-physical systems, etc. His focus is the development of fundamental techniques, such as data-driven modeling, closed-loop and control-oriented identification and data analytics, experimental design and performance monitoring, and model-based control, monitoring and optimization.

=== Schedule ===

* 10 am: Richard Murray, 107 Steele Lab
* 10:30 am: open
* 11:00 am: open
* 11:45 am: Lunch Aaron Ames
* 1:00 pm: Seminar, 106 Annenberg
* 2:00 pm: Soon-Jo Chung, 106 Annenberg
* 2:45 pm: Open
* 3:30 pm: Richard Murray, 107 Steele Lab
* 4:00 pm: Chelsea and Ayush, 2nd floor lounge, Annenberg
* 4:45 pm: Open (if nothing else available)
* 5:30 pm: Depart

=== Seminar ===

'''Data-driven modeling in linear dynamic networks''' 
Friday, December 7th at 1pm, 106 Annenberg

In many areas of science and technology, the complexity of dynamic systems that are being considered, grows beyond the level of single systems into interconnected networks of dynamic systems. In control and optimization this has led to the development of decentralized and distributed algorithms for control/optimization, as e.g. in multi-agent systems.
From the modelling perspective, data-driven modelling tools are typically developed for relatively simple open-loop and closed-loop structures, while the opportunities for big data handling in the current data science era, are becoming abundant. As a result there is a strong need for the development of data-driven modelling tools for large-scale interconnected dynamic networks.
In this seminar we will highlight the main developments and challenges in this area. Besides setting up a modelling framework, we will address problems of local identification of a particular part of the network, including the selection of the appropriate signals to be measured. The concept of network identifiability is highlighted and the role of structural properties of the network, in terms of its topology/graph, is given strong attention. It is also shown how classical closed-loop identification methods need to be generalized to be able to cope with the new situations.

Karan Kalsi, Oct 2017

2017-09-29T05:48:08Z

Adames: /* 3 Oct (Tue) */

__NOTOC__
[[Image:Karan_Kalsi.jpg||right]]
Dr. Karan Kalsi is currently a power systems research engineer at the Pacific Northwest National Lab, Richland, WA, USA. He is the lead investigator on Department of Energy funded research to develop efficient, reliable and secure control strategies for Smart Grid assets. He is the principle investigator on the topic of developing future power grid control paradigms as part of the laboratory’s Future Power Grid Initiative. He is the Co-principle investigator on EED SEED project related to modeling and controls for distributed energy resources in microgrids. Karanjit is also actively involved with IEEE including being the 2011 Chair of the IEEE Richland Section Graduates of the Last Decade (GOLD) Affinity Group and was recently elected as the 2012 Vice-chair of Technical Activities and Outreach of the IEEE Richland Section Power and Energy Society.

More: http://energyenvironment.pnnl.gov/staff/staff_info.asp?staff_num=2178

== Schedule for Karan Kalsi visit, 2-5 Oct 2017 ==

=== 2 Oct (Mon) ===
* Morning: HR check-in, office setup
* Lunch: Richard
* 2 pm: Open
* 3:30 pm: Colloqium reception (location?)
* 4 pm: CMS colloqium - George Papanicoulou (Stanford), 105 Annenberg

=== 3 Oct (Tue) ===
* 10 am: Open
* 11 am: Aaron Ames (266 Gates-Thomas)
* IST lunch bunch
* Afternoon off

=== 4 Oct (Wed) ===
* Morning off
* Lunch open
* 3 pm: CDS tea
* 4 pm: Open

=== 5 Oct (Thu) ===
* 10 am: Soon-Jo Chung (235 Guggenheim) CAST and aerospace robotics research
* 11 am: Open
* Lunch: Open
* 2 pm: head for airport

Karan Kalsi, Oct 2017

2017-09-29T05:47:53Z

Adames: /* 2 Oct (Mon) */

__NOTOC__
[[Image:Karan_Kalsi.jpg||right]]
Dr. Karan Kalsi is currently a power systems research engineer at the Pacific Northwest National Lab, Richland, WA, USA. He is the lead investigator on Department of Energy funded research to develop efficient, reliable and secure control strategies for Smart Grid assets. He is the principle investigator on the topic of developing future power grid control paradigms as part of the laboratory’s Future Power Grid Initiative. He is the Co-principle investigator on EED SEED project related to modeling and controls for distributed energy resources in microgrids. Karanjit is also actively involved with IEEE including being the 2011 Chair of the IEEE Richland Section Graduates of the Last Decade (GOLD) Affinity Group and was recently elected as the 2012 Vice-chair of Technical Activities and Outreach of the IEEE Richland Section Power and Energy Society.

More: http://energyenvironment.pnnl.gov/staff/staff_info.asp?staff_num=2178

== Schedule for Karan Kalsi visit, 2-5 Oct 2017 ==

=== 2 Oct (Mon) ===
* Morning: HR check-in, office setup
* Lunch: Richard
* 2 pm: Open
* 3:30 pm: Colloqium reception (location?)
* 4 pm: CMS colloqium - George Papanicoulou (Stanford), 105 Annenberg

=== 3 Oct (Tue) ===
* 10 am: Open
* 11 am: Open
* IST lunch bunch
* Afternoon off

=== 4 Oct (Wed) ===
* Morning off
* Lunch open
* 3 pm: CDS tea
* 4 pm: Open

=== 5 Oct (Thu) ===
* 10 am: Soon-Jo Chung (235 Guggenheim) CAST and aerospace robotics research
* 11 am: Open
* Lunch: Open
* 2 pm: head for airport