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DTSTART;TZID=America/New_York:20190219T104500
DTEND;TZID=America/New_York:20190219T114500
DTSTAMP:20260409T023650
CREATED:20190215T211443Z
LAST-MODIFIED:20190215T211443Z
UID:10006168-1550573100-1550576700@seasevents.nmsdev7.com
SUMMARY:MSE Faculty Candidate Seminar: “Tuning Nanoscale Materials Using the Local Environment”
DESCRIPTION:
URL:https://seasevents.nmsdev7.com/event/mse-faculty-candidate-seminar-tuning-nanoscale-materials-using-the-local-environment/
LOCATION:Auditorium\, LRSM Building\, 3231 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190219T110000
DTEND;TZID=America/New_York:20190219T120000
DTSTAMP:20260409T023650
CREATED:20190128T164807Z
LAST-MODIFIED:20190128T164807Z
UID:10006143-1550574000-1550577600@seasevents.nmsdev7.com
SUMMARY:ESE Seminar: "Hybrid Quantum Networks: Interfacing Photons\, Phonons\, and Superconducting Qubits"
DESCRIPTION:Quantum information science strives to utilize the fundamental laws of physics to achieve revolutionary improvement in computation\, communication\, and sensing. Existing quantum protocols rely on a wide variety of physical platforms for storing\, transferring\, and processing of quantum information. Optical photons are the ideal carriers of information because of their low loss\, large bandwidth of transmission\, and resilience to thermal noise. However\, the task of processing quantum information is exceedingly difficult to achieve in the optical domain because of the weakness of optical nonlinearities. Alternatively\, superconducting quantum circuits provide a scalable means of storing and processing quantum information in the microwave regime but lack a mechanism for long-range information transfer. \nHybrid quantum networks promise to combine such essential functionalities in a system where superconducting processing nodes are joined via optical communication links. An integral element in this architecture is a quantum interconnect capable of interfacing the electrical and optical components across an immense frequency gap. I provide a summary of my past and current research on optical and microwave quantum systems and outline my future research directions\, which aim to develop nano-engineered devices for entangling superconducting qubits with telecom-band optical photons and long-lived phonons.
URL:https://seasevents.nmsdev7.com/event/ese-seminar-hybrid-quantum-networks-interfacing-photons-phonons-and-superconducting-qubits/
LOCATION:Room 337\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190220T150000
DTEND;TZID=America/New_York:20190220T160000
DTSTAMP:20260409T023650
CREATED:20190204T143801Z
LAST-MODIFIED:20190204T143801Z
UID:10006147-1550674800-1550678400@seasevents.nmsdev7.com
SUMMARY:CBE Seminar: "C4E – Computational Chemistry of Compounds for Catalysis and Energy"
DESCRIPTION:
URL:https://seasevents.nmsdev7.com/event/cbe-seminar-tba/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190221T104500
DTEND;TZID=America/New_York:20190221T114500
DTSTAMP:20260409T023650
CREATED:20190215T211713Z
LAST-MODIFIED:20190215T211713Z
UID:10006169-1550745900-1550749500@seasevents.nmsdev7.com
SUMMARY:MSE Faculty Candidate Seminar: "Atom-Scale Engineering using 2D Materials"
DESCRIPTION:
URL:https://seasevents.nmsdev7.com/event/mse-faculty-candidate-seminar-atom-scale-engineering-using-2d-materials/
LOCATION:Auditorium\, LRSM Building\, 3231 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190221T110000
DTEND;TZID=America/New_York:20190221T120000
DTSTAMP:20260409T023650
CREATED:20190208T230317Z
LAST-MODIFIED:20190208T230317Z
UID:10006155-1550746800-1550750400@seasevents.nmsdev7.com
SUMMARY:ESE Seminar: "Physics-Driven Sensing and Processing: From Computational Periscopy to Particle Beam Microscopy"
DESCRIPTION:In many areas of science and engineering\, novel signal acquisition methods allow unprecedented access to physical measurements. From digital cameras to microscopes and nano-scale biosensors\, the data generated are shaped by both the underlying physics of the phenomena and characteristics of the acquisition device. Meanwhile\, in many practical scenarios\, the useful signals are remarkably weak\, the measurements sparse\, or even the acquisition process itself may damage the observed sample. These realities therefore necessitate the development of techniques that combine signal processing with physics-driven modelling to transcend current capabilities and enable\, for instance: imaging of hidden scenes (or computational periscopy)\, the algebraic inversion of physical fields\, and the reduction of sample damage in particle beam microscopy.\nThis concept of combining physics with signal processing will be the main theme of my talk. First\, I will show that computational periscopy with ordinary digital cameras can be made possible by judiciously exploiting the physics of light transport to analyze subtle shadows\, in a photograph of a visible surface. Second\, I will briefly present an algebraic inversion method for fields constrained by partial differential equations and highlight its application to load-balancing in processors. Finally\, by developing a detailed model and analysis for particle beam microscopy\, I will show how introducing time-resolution into the acquisition process can significantly reduce beam dose\, and sample damage\, without compromising on imaging quality.
URL:https://seasevents.nmsdev7.com/event/ese-seminar-physics-driven-sensing-and-processing-from-computational-periscopy-to-particle-beam-microscopy/
LOCATION:Room 337\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190221T150000
DTEND;TZID=America/New_York:20190221T160000
DTSTAMP:20260409T023650
CREATED:20190212T142113Z
LAST-MODIFIED:20190212T142113Z
UID:10006157-1550761200-1550764800@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: "Cataloging the Visible Universe through Bayesian Inference at Petascale"
DESCRIPTION:Abstract: \nA key task in astronomy is to locate astronomical objects in images and to characterize them according to physical parameters such as brightness\, color\, and morphology. This task\, known as cataloging\, is challenging for several reasons: many astronomical objects are much dimmer than the sky background\, labeled data is generally unavailable\, overlapping astronomical objects must be resolved collectively\, and the datasets are enormous — terabytes now\, petabytes soon. Existing approaches to cataloging are largely based on algorithmic software pipelines. In this talk\, I present a new approach to cataloging based on inference in a fully specified probabilistic model. I consider two inference procedures: one based on variational inference (VI) and another based on MCMC. A distributed implementation of VI\, written in Julia and run on a supercomputer\, achieves petascale performance — a first for any high-productivity programming language. The run is the largest-scale application of Bayesian inference reported to date. In an extension\, using new ideas from variational autoencoders and deep learning\, I avoid many of the traditional disadvantages of VI relative to MCMC\, and improve model fit
URL:https://seasevents.nmsdev7.com/event/cis-seminar-cataloging-the-visible-universe-through-bayesian-inference-at-petascale/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190222T110000
DTEND;TZID=America/New_York:20190222T120000
DTSTAMP:20260409T023650
CREATED:20190206T211719Z
LAST-MODIFIED:20190206T211719Z
UID:10006153-1550833200-1550836800@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: "Towards Generalization and Efficiency in Reinforcement Learning"
DESCRIPTION:Abstract: \n\nIn classic supervised machine learning\, a learning agent behaves as a passive observer: it receives examples from some external environment which it has no control over and then makes predictions. Reinforcement Learning (RL)\, on the other hand\, is fundamentally interactive : an autonomous agent must learn how to behave in an unknown and possibly hostile environment\, by actively interacting with the environment to collect useful feedback. One central challenge in RL is how to explore an unknown environment and collect useful feedback efficiently. In recent practical RL success stories\, we notice that most of them rely on random exploration which requires large a number of interactions with the environment before it can learn anything useful.  The theoretical RL literature has developed more sophisticated algorithms for efficient learning\, however\, the sample complexity of these algorithms has to scale exponentially with respect to key parameters of underlying systems such as the dimensionality of state vector\, which prohibits a direct application of these theoretically elegant RL algorithms to large-scale applications. Without any further assumptions\, RL is hard\, both in practice and in theory.\n  \nIn this work\, we improve generalization and efficiency on RL problems by introducing  extra sources of help and additional assumptions. The first contribution of this work comes from improving RL sample efficiency via Imitation Learning (IL). Imitation Learning reduces policy improvement to classic supervised learning. We study in both theory and in practice how one can imitate experts to reduce sample complexity compared to RL approaches. The second contribution of this work comes from exploiting the underlying structures of the RL problems via model-based learning approaches.  While there exist efficient model-based RL approaches specialized for specific RL problems (e.g.\, tabular MDPs\, Linear Quadratic Systems)\, we develop a unified model-based algorithm that generalizes a large number of RL problems that were often studied independently in the literature. We also revisit the long standing debate on whether model-based RL is more efficient than model-free RL from a theoretical perspective\, and demonstrate that model-based RL can be exponentially more sample efficient than model-free ones\, which to the best of our knowledge\, is the first that separates model-based and model-free general approaches.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-towards-generalization-and-efficiency-in-reinforcement-learning/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190222T150000
DTEND;TZID=America/New_York:20190222T160000
DTSTAMP:20260409T023650
CREATED:20190219T195254Z
LAST-MODIFIED:20190219T195254Z
UID:10006171-1550847600-1550851200@seasevents.nmsdev7.com
SUMMARY:PRiML Seminar: "Optimizing probability distributions for learning: sampling meets optimization"
DESCRIPTION:Optimization and sampling are both of central importance in large-scale machine learning problems\, but they are typically viewed as very different problems. This talk presents recent results that exploit the interplay between them. Viewing Markov chain Monte Carlo sampling algorithms as performing an optimization over the space of probability distributions\, we demonstrate analogs of Nesterov’s acceleration approach in the sampling domain\, in the form of a discretization of an underdamped Langevin diffusion. In the other direction\, we view stochastic gradient optimization methods\, such as those that are common in deep learning\, as sampling algorithms\, and study the finite-time convergence of their iterates to an invariant distribution.
URL:https://seasevents.nmsdev7.com/event/priml-seminar-optimizing-probability-distributions-for-learning-sampling-meets-optimization/
LOCATION:Room 401B\, 3401 Walnut\, 3401 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
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