CIS Seminar: “Equilibrium Complexity and Deep Learning”

Deep Learning has recently made significant progress in learning challenges such as speech and image recognition, automatic translation, and text generation, much of that progress being fueled by the success of […]

ESE/CIS Joint Seminar: “Future Heterogeneous Systems Need More First-Class Citizens”

For those that are not able to attend please join on Zoom:
https://upenn.zoom.us/j/93664182228?pwd=NVBHT0wzaERxaWxRUERWYjV2eXorZz09
Meeting ID: 936 6418 2228
Passcode: 096853

CIS Seminar: ” Rich Babies, Poor Robots: towards rich sensing, continuous data and multiple environments”

For those that may not be able to attend the talk please sue this zoom link:
https://upenn.zoom.us/j/92928358554?pwd=MWdDU0lJRmE3U0hDWUdmU284UmNGZz09
Meeting ID: 929 2835 8554
Passcode: 488035

Theory Seminar- Rachel Cummings (Columbia University)

Theory Seminar- Recent Developments in Combinatorial Auctions, Matt Weinberg (Princeton University)

Abstract: In a combinatorial auction there are m items, and each of n players has a valuation function v_i which maps sets of items to non-negative reals. A designer wishes […]

Theory Seminar- Aaron Roth (University of Pennsylvania)

Abstract: Dawid gives two conceptualizations for models of individual probabilities: “Group to Individual” and “Individual to Group”. A classical concern about the “Group to Individual” view of probability is the […]

CIS Seminar: “Live Programming and Programming by Example: Better Together”

For those that can not attend in person here is a zoom link for viewing purposes:
https://upenn.zoom.us/j/92251688978?pwd=aEpTc2h6U3pOQWJFc2svT2hBMXlpZz09
Meeting ID: 922 5168 8978
Passcode: 289824

ASSET Seminar: Statistical and Machine Learning for Electronic Health Records: Challenges and Opportunities, Qi Long (University of Pennsylvania)

ABSTRACT: Electronic health records (EHRs) offer great promises in advancing clinical research and transforming learning health systems. However, complex, temporal EHRs are fraught with biases and present daunting analytical challenges […]

ASSET Seminar: ML for Causal Inference, Konrad Kording, University of Pennsylvania

ABSTRACT Machine learning traditionally does not get at causality and causality research traditionally treats machine learning as a dangerous set of highly biased estimators. In my talk I will talk […]

ASSET Seminar: AI and Medicine: One Possible Future for Augmented Care, Kevin B Johnson (University of Pennsylvania)

Abstract: Scientific discoveries, fueled by data collected during the course of care, are promising to radically change how we think about health, disease, prevention and treatment. However, the very systems […]