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X-WR-CALDESC:Events for 
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DTSTART:20230101T000000
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20241004
DTEND;VALUE=DATE:20241005
DTSTAMP:20241003T074425Z
CREATED:20240620T115527Z
LAST-MODIFIED:20241003T074425Z
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SUMMARY:Guest Lecture by Bernard Coetzee and Lidia Auret from Stone three.
DESCRIPTION:From computer vision to concentrator value: Robust and actionable froth and pulp analytics \nBernard Coetzee\, Lidia Auret \nFlotation froth image analysis has been a topic of academic research and industrial implementation since the early 1990s\, while online pulp image analysis is a more recent phenomenon. Algorithmic advances (such as deep learning) have led to improved accuracy in bubble segmentation\, while also allowing additional inferences to be made on the operational state of the observed flotation cell. In this talk\, we will discuss various practical considerations which ensure that commercial froth and pulp image analysis yields robust and valuable measurements. These considerations include sensor sampling frequency\, sensor integrity analytics\, operational state estimation\, and the implications of these considerations in automated control.
URL:https://sacac.org.za/event/guest-lecture-by-lidia-auret-from-stone-three/
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20241008
DTEND;VALUE=DATE:20241009
DTSTAMP:20240814T102518Z
CREATED:20240620T074355Z
LAST-MODIFIED:20240814T102518Z
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SUMMARY:Distinguished Guest Lecture – Prof Na Li – Representation-based Learning and Control for Dynamical Systems
DESCRIPTION:REGISTER HERE NOW \nAbstract: \nThe explosive growth of machine learning and data-driven methodologies have revolutionized numerous fields. Yet\, adapting these successes to the domain of dynamical physical systems remains a significant challenge. Closing the loop from data to actions in these systems faces many difficulties\, stemming from the need for sample efficiency and computational feasibility\, along with many other requirements such as verifiability\, robustness\, and safety. In this talk\, we bridge this gap by introducing innovative representations to develop nonlinear stochastic control and reinforcement learning methods. The key in the representation is to represent the stochastic\, nonlinear dynamics linearly onto a nonlinear feature space. We present a comprehensive framework to develop control and learning strategies that achieve efficiency\, safety\, robustness\, and scalability with provable performance. We also show how the representation could be used to close the sim-to-real gap. Lastly\, we will briefly present some concrete real-world applications\, discussing how domain knowledge is applied in practice to further close the loop from data to actions. \nBiography: \nNa Li is a Winokur Family Professor of Electrical Engineering and Applied Mathematics at Harvard University.  She received her Bachelor’s degree in Mathematics from Zhejiang University in 2007 and Ph.D. degree in Control and Dynamical systems from California Institute of Technology in 2013. She was a postdoctoral associate at the Massachusetts Institute of Technology 2013-2014.  She has held a variety of short-term visiting appointments including the Simons Institute for the Theory of Computing\, MIT\, and Google Brain. Her research lies in the control\, learning\, and optimization of networked systems\, including theory development\, algorithm design\, and applications to real-world cyber-physical societal system.  She has been an associate editor for IEEE Transactions on Automatic Control\, Systems & Control Letters\, IEEE Control Systems Letters\, and served on the organizing committee for a few conferences.  She received the NSF career award\, AFSOR Young Investigator Award\, ONR Young Investigator Award\,  Donald P. Eckman Award\, McDonald Mentoring Award\, IFAC Distinguished Lecture\, and IFAC Manfred Thoma Medal\, along with other awards.
URL:https://sacac.org.za/event/distinguished-guest-lecture-prof-na-li-representation-based-learning-and-control-for-dynamical-systems/
LOCATION:University of Pretoria
ORGANIZER;CN="SACAC":MAILTO:secretariat@sacac.org.za
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20241009
DTEND;VALUE=DATE:20241010
DTSTAMP:20240814T102621Z
CREATED:20240620T074443Z
LAST-MODIFIED:20240814T102621Z
UID:1196-1728432000-1728518399@sacac.org.za
SUMMARY:Distinguished Guest Lecture – Prof Na Li – Representation-based Learning and Control for Dynamical Systems
DESCRIPTION:REGISTER HERE NOW \nAbstract: \nThe explosive growth of machine learning and data-driven methodologies have revolutionized numerous fields. Yet\, adapting these successes to the domain of dynamical physical systems remains a significant challenge. Closing the loop from data to actions in these systems faces many difficulties\, stemming from the need for sample efficiency and computational feasibility\, along with many other requirements such as verifiability\, robustness\, and safety. In this talk\, we bridge this gap by introducing innovative representations to develop nonlinear stochastic control and reinforcement learning methods. The key in the representation is to represent the stochastic\, nonlinear dynamics linearly onto a nonlinear feature space. We present a comprehensive framework to develop control and learning strategies that achieve efficiency\, safety\, robustness\, and scalability with provable performance. We also show how the representation could be used to close the sim-to-real gap. Lastly\, we will briefly present some concrete real-world applications\, discussing how domain knowledge is applied in practice to further close the loop from data to actions. \nBiography: \nNa Li is a Winokur Family Professor of Electrical Engineering and Applied Mathematics at Harvard University.  She received her Bachelor’s degree in Mathematics from Zhejiang University in 2007 and Ph.D. degree in Control and Dynamical systems from California Institute of Technology in 2013. She was a postdoctoral associate at the Massachusetts Institute of Technology 2013-2014.  She has held a variety of short-term visiting appointments including the Simons Institute for the Theory of Computing\, MIT\, and Google Brain. Her research lies in the control\, learning\, and optimization of networked systems\, including theory development\, algorithm design\, and applications to real-world cyber-physical societal system.  She has been an associate editor for IEEE Transactions on Automatic Control\, Systems & Control Letters\, IEEE Control Systems Letters\, and served on the organizing committee for a few conferences.  She received the NSF career award\, AFSOR Young Investigator Award\, ONR Young Investigator Award\,  Donald P. Eckman Award\, McDonald Mentoring Award\, IFAC Distinguished Lecture\, and IFAC Manfred Thoma Medal\, along with other awards.
URL:https://sacac.org.za/event/distinguished-guest-lecture-prof-na-li-representation-based-learning-and-control-for-dynamical-systems-2/
LOCATION:Stellenbosch University\, Department of Process Engineering
ORGANIZER;CN="SACAC":MAILTO:secretariat@sacac.org.za
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