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DTSTART;TZID=America/New_York:20200317T103000
DTEND;TZID=America/New_York:20200317T120000
DTSTAMP:20260408T030650
CREATED:20200225T212258Z
LAST-MODIFIED:20200225T212258Z
UID:10006419-1584441000-1584446400@seasevents.nmsdev7.com
SUMMARY:CANCELLED: MEAM Seminar: "Wind Farm Dynamics and Power Optimization in Realistic Atmospheric Boundary Layer Conditions"
DESCRIPTION:The study of wind farms within realistic atmospheric boundary layer conditions is critical to understand the governing physics of the system and to design optimal operational protocols. Historically\, control protocols have optimized performance of individual wind turbines resulting in aerodynamic wakes which typically reduce total wind farm power production 10-20% and increase the cost of electricity for this resource. Considering the wind farm as a collective\, we designed a physics- and data-driven wake steering control method to increase the power production of wind farms. The method was tested in a multi-turbine array at a utility-scale operational wind farm\, where it statistically significantly increased the power production over standard operation. The analytic gradient-based wind farm power optimization methodology we developed can optimize the yaw misalignment angles for large wind farms on the order of seconds\, enabling online real-time control. In order to rapidly design and improve dynamic closed-loop wind farm controllers\, we developed wind farm large eddy simulation capabilities that incorporate Coriolis and stratification effects. The traditional approximation made in typical simulations assumes that the horizontal component of Earth’s rotation is negligible in the atmospheric boundary layer. When including the horizontal component of Earth’s rotation\, the boundary layer and wind farm physics are a function of the geostrophic wind direction. The influence of the geostrophic wind direction on a wind farm atmospheric boundary layer was characterized using conventionally neutral and stable boundary layer simulations. Dynamic wake steering controllers are tested in simulations and\, altogether\, the results indicate that closed-loop wake steering control can significantly increase wind farm power production over greedy operation provided that site-specific wind farm data is assimilated into the optimal control model.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-wind-farm-dynamics-and-power-optimization-in-realistic-atmospheric-boundary-layer-conditions/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200318T013000
DTEND;TZID=America/New_York:20200318T143000
DTSTAMP:20260408T030650
CREATED:20200220T192156Z
LAST-MODIFIED:20200220T192156Z
UID:10006414-1584495000-1584541800@seasevents.nmsdev7.com
SUMMARY:Cancelled:  CIS Seminar:  "The Value Alignment Problem in Artificial Intelligence"
DESCRIPTION:Abstract: \nAbstract: Much of our success in artificial intelligence stems from the adoption of a simple paradigm: specify an objective or goal\, and then use optimization algorithms to identify a behavior (or predictor) that optimally achieves this goal. This has been true since the early days of AI (e.g.\, search algorithms such as A* that aim to find the optimal path to a goal state)\, and this paradigm is common to AI\, statistics\, control theory\, operations research\, and economics. Loosely speaking\, the field has evaluated the intelligence of an AI system by how efficiently and effectively it optimizes for its objective. This talk will provide an overview of my thesis work\, which proposes and explores the consequences of a simple\, but consequential\, shift in perspective: we should measure the intelligence of an AI system by its ability to optimize for our objectives. \n  \nIn an ideal world\, these measurements would be the same — all we have to do is write down the correct objective! This is easier said than done: misalignment between the behavior a system designer actually wants and the behavior incentivized by the reward or loss functions they specify is routine\, it is commonly observed in a wide variety of practical applications\, and fundamental\, as a consequence of limited human cognitive capacity. This talk will build up a formal model of this value alignment problem as a cooperative human-robot interaction: an assistance game of partial information between a human principal and an autonomous agent. It will begin with a discussion of a simple instantiation of this game where the human designer takes one action\, write down a proxy objective\, and the robot attempts to optimize for the true objective by treating the observed proxy as evidence about the intended goal. Next\, I will generalize this model to introduce Cooperative Inverse Reinforcement Learning\, a general and formal model of this assistance game\, and discuss the design of efficient algorithms to solve it. The talk will conclude with a discussion of directions for further research including applications to content recommendation and home robotics\, the development of reliable and robust design environments for AI objectives\, and the theoretical study of AI regulation by society as a value alignment problem with multiple human principals.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-the-value-alignment-problem-in-artificial-intelligence/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Colloquium
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200319T150000
DTEND;TZID=America/New_York:20200319T160000
DTSTAMP:20260408T030650
CREATED:20200220T192914Z
LAST-MODIFIED:20200220T192914Z
UID:10006415-1584630000-1584633600@seasevents.nmsdev7.com
SUMMARY:Cancelled: CIS Seminar: "Deep Probabilistic Graphical Modeling"
DESCRIPTION:Abstract: \nDeep learning (DL) is a powerful approach to modeling complex and large scale data. However\, DL models lack interpretable quantities and calibrated uncertainty. In contrast\, probabilistic graphical modeling (PGM) provides a framework for formulating an interpretable generative process of data and a way to express uncertainty about what we do not know. How can we develop machine learning methods that bring together the expressivity of DL with the interpretability and calibration of PGM to build flexible models endowed with an interpretable latent structure that can be fit efficiently? I call this line of research deep probabilistic graphical modeling (DPGM). In this talk\, I will discuss my work on developing DPGM for text data. In particular\, I will show how DPGM enables flexible and interpretable topic modeling at large scale\, unlocking several known challenges. Furthermore\, I will describe how we can account for both local and long-range context\, under the DPGM framework\, to build a flexible sequential document model that leads to state-of-the-art performance on a downstream document classification task.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-deep-probabilistic-graphical-modeling/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Colloquium
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200320T140000
DTEND;TZID=America/New_York:20200320T150000
DTSTAMP:20260408T030650
CREATED:20191118T172045Z
LAST-MODIFIED:20191118T172045Z
UID:10006338-1584712800-1584716400@seasevents.nmsdev7.com
SUMMARY:Cancelled: PICS Seminar - Dr. Ivan Bermejo-Moreno of the University of Southern California\, Viterbi School of Engineering
DESCRIPTION:
URL:https://seasevents.nmsdev7.com/event/pics-seminar-with-dr-ivan-bermejo-moreno-of-usc-viterbi-school-of-engineering/
LOCATION:PICS Conference Room 534 – A Wing \, 5th Floor\, 3401 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Colloquium
ORGANIZER;CN="Penn Institute for Computational Science (PICS)":MAILTO:dkparks@seas.upenn.edu
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