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Events for the 5th week of October
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Careers in Data Infosession
Mon, Oct 28, 2019 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science, USC Viterbi School of Engineering
Workshops & Infosessions
Hoa Nguyen, Director of Engineering at Insight, will discuss the growing demand for data scientists and data engineers at companies across the U.S. She'll also provide some tips and strategies for making the transition into the industry. Come learn about a variety of data careers. We will discuss what academic skills transfer to each career type and provide tips on how to bridge the gap between your current skills and your next dream job. All backgrounds are welcome!
RSVP HERELocation: James H. Zumberge Hall Of Science (ZHS) - 159
Audiences: Everyone Is Invited
Contact: Ryan Rozan
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CS Colloquium: Aditya Grover (Stanford University) - Mitigating Bias in Generative Modeling
Tue, Oct 29, 2019 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Aditya Grover, Stanford University
Talk Title: Mitigating Bias in Generative Modeling
Series: Computer Science Colloquium
Abstract: In the last few years, there has been remarkable progress in deep generative modeling. However, the learned models are noticeably inaccurate w.r.t. to the underlying data distribution, as evident from downstream metrics that compare statistics of interest across the true and generated data samples. This bias in downstream evaluation can be attributed to imperfections in learning ("model bias") or be propagated due to the bias in the training dataset itself ("dataset bias"). In this talk, I will present an importance weighting approach for mitigating both these kinds of biases of generative models. Our approach assumes only 'black-box' sample access to a generative model and is broadly applicable to both likelihood-based and likelihood-free generative models. Empirically, we find that our technique consistently improves standard goodness-of-fit metrics for evaluating the sample quality of state-of-the-art deep generative models, suggesting reduced bias. We demonstrate its utility on representative applications in a) data augmentation and b) model-based policy evaluation using off-policy data. Finally, I will present some recent work extending these ideas to fair data generation in the presence of biased training datasets.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Aditya Grover is a 5th-year Ph.D. candidate in Computer Science at Stanford University advised by Stefano Ermon. His research focuses on probabilistic machine learning, including topics in generative modeling, approximate inference, and deep learning as well as applications in sustainability. His research has been cited widely in academia, deployed into production at major technology companies, and recognized with a best paper award (StarAI), a Lieberman Fellowship, a Data Science Institute Scholarship, and a Microsoft Research Ph.D. Fellowship. He is also a Teaching Fellow at Stanford since 2018, where he co-designed and teaches a new class on Deep Generative Models. Previously, Aditya obtained his bachelors in Computer Science and Engineering from IIT Delhi in 2015, where he received a best undergraduate thesis award.
Host: If you would like to meet the speaker, please email the host Bistra Dilkina at dilkina@usc.edu
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
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CS Tech Talk: WiSE Presents: Walmart Tech Talk with Senior VP of Customer Technology, Fiona Tan
Tue, Oct 29, 2019 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Fiona Tan, Walmart
Talk Title: WiSE Presents: Walmart Tech Talk with Senior VP of Customer Technology, Fiona Tan
Series: Computer Science Colloquium
Abstract: Come hear more about the exciting opportunities available in tech at Walmart.
Fiona will also talk about her tech journey, as well as why she loves working for Walmart.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Fiona Tan joined Walmart in 2014 and is currently the Senior Vice President of Customer Technology. She is responsible for innovation and engineering execution on all customer-facing technology across Walmart's physical and digital footprint. Her team leverages data and machine learning to drive marketing and advertising campaigns, oversees all personalization capabilities, and delivers the desktop and mobile customer experience for Walmart's eCommerce as well as the technology across point-of-sale systems, pharmacy, specialty departments, and associate productivity apps in Walmart stores. Their goal is to deliver a seamless shopping experience for our customers -“ while empowering our millions of associates with technology.
Previously, Fiona was Walmart's Vice President of Engineering, responsible for product roadmap and engineering capabilities for Walmart's international eCommerce businesses as well as the Sam's Club business in the U.S. In addition, her team drove technology strategy and operational excellence across Walmart Labs.
Prior to Walmart, Fiona served in a number of leadership roles at Ariba and TIBCO Software. At Ariba, she led a global engineering organization responsible for the strategy, lifecycle, and delivery of the Ariba Commerce Network. At TIBCO Software, she was responsible for a major product line as well as the management of their offshore development centers.
Fiona has a master's degree in Computer Science from Stanford and a bachelor's degree in Computer Science and Engineering from Massachusetts Institute of Technology (MIT).
Host: WiSE
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
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Computer Science General Faculty Meeting
Wed, Oct 30, 2019 @ 12:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
Receptions & Special Events
Bi-Weekly regular faculty meeting for invited full-time Computer Science faculty only. Event details emailed directly to attendees.
Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Invited Faculty Only
Contact: Assistant to CS chair
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Theory Lunch
Thu, Oct 31, 2019 @ 12:15 PM - 02:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Federico Echenique, Professor of Economics at CalTech
Talk Title: The Edgeworth Conjecture with Small Coalitions and Approximate Equilibria in Large Economies
Abstract: A talk about the paper Federico Echenique worked on with Siddharth Barman entitled "The Edgeworth Conjecture with Small Coalitions and Approximate Equilibria in Large Economies." The paper shows that deciding if an allocation is approximately Walrasian can be done in polynomial time, even if finding the market equilibrium in intractable.
It will be a fun time, so make sure not to miss out!
Host: Shaddin Dughmi
Location: Henry Salvatori Computer Science Center (SAL) - 213
Audiences: Everyone Is Invited
Contact: Cherie Carter
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CS Colloquium: Masahiro Ono (NASA JPL) - Robots in Space: How AI and Machine Learning are Revolutionizing Space Exploration
Thu, Oct 31, 2019 @ 02:00 PM - 03:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Masahiro Ono, NASA JPL
Talk Title: Robots in Space: How AI and Machine Learning are Revolutionizing Space Exploration
Series: Computer Science Colloquium
Abstract: On the third planet of the Solar System, there is an ongoing revolution caused by a group of technologies collectively called as "AI," including, but not limited to, machine learning, optimal decision making, situation awareness, and autonomous navigation. The same revolution is taking off elsewhere in the Solar System, triggered by an advent of HPSC (high-performance spacecraft computing). This talk introduces ongoing research efforts to adapt and enhance latest AI technologies for future exploration missions to Mars, Europa, and Enceladus, such as resource-aware autonomous rover driving, on-board science interpretation, and autonomous descent into Enceladus's vents, which are believed to be connected to the subsurface ocean that may harbor extraterrestrial life.
If you would like to take a peek of the talk, take a look at the following YouTube movies:
https://www.youtube.com/watch?v=-AYvgTlDKQM&t=5s
https://www.youtube.com/watch?v=LJXQ0-a9IJE
https://www.youtube.com/watch?v=7bdS_xpYz7A
This lecture satisfies requirements for CSCI 591: Research Colloquium. *Note: Rescheduled to 10/31/19, 2:00-3:20PM*
Biography: Dr. Masahiro (Hiro) Ono is a Research Technologist at NASA Jet Propulsion Laboratory, California Institute of Technology. His broad interest is centered around the application of robotic autonomy to space exploration, with an emphasis on machine learning applications to perception, data interpretation, and decision making. Before joining JPL in 2013, he was an assistant professor at Keio University in Japan. He graduated from MIT with PhD in Aeronautics and Astronautics in 2012. Since 2017 he is the PI of a JPL-funded Strategic Research and Development task on the machine learning-based analytics for automated rover systems (MAARS). From 2015 he has led the development of machine learning- based Martian terrain classifier, which is used by MSL and Mars 2020 Rover missions. He was also the PI of a JPL-Caltech joint project on imitation learning-based planning from 2016 to 2018. He was awarded two NIAC Phase I studies: Comet Hitchhiker (2014) and Journey to the Center of Icy Moon (2016). Since 2019, he has been the Autonomy Lead of the EELS (Exobiology Extant Life Surveyor) project.
Host: Stefanos Nikolaidis
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
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MASCLE Machine Learning Seminar: Joan Bruna (NYU) - On (Provably) Learning with Large Neural Networks
Thu, Oct 31, 2019 @ 03:30 PM - 04:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Joan Bruna, New York University
Talk Title: On (Provably) Learning with Large Neural Networks
Series: Machine Learning Seminar Series hosted by USC Machine Learning Center
Abstract: Virtually all modern deep learning systems are trained with some form of local descent algorithm over a high-dimensional parameter space. Despite its apparent simplicity, the mathematical picture of the resulting setup contains several mysteries that combine statistics, approximation theory and optimization, all intertwined in a curse of dimensionality.
In order to make progress, authors have focused in the so-called 'overparametrised' regime, which studies asymptotic properties of the algorithm as the number of neurons grows. In particular, neural networks with a large number of parameters admit a mean-field description, which has recently served as a theoretical explanation for its favorable training properties. In this regime, gradient descent obeys a deterministic partial differential equation (PDE) that converges to a globally optimal solution for networks with a single hidden layer under appropriate assumptions.
In this talk, we will review recent progress on this problem, and argue that such framework might provide crucial robustness against the curse of dimensionality. First, we will describe a non-local mass transport dynamics that leads to a modified PDE with the same minimizer, that can be implemented as a stochastic neuronal birth-death process, and such that it provably accelerates the rate of convergence in the mean-field limit. Next, such dynamics fit naturally within the framework of total-variation regularization, which following [Bach'17] have fundamental advantages in the high-dimensional regime. We will discuss a unified framework that controls both optimization, approximation and generalisation errors using large deviation principles, and discuss current open problems in this research direction.
Joint work with G. Rotskoff (NYU), Z. Chen (NYU), S. Jelassi (NYU) and E. Vanden-Eijnden (NYU).
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Joan Bruna is an Assistant Professor at Courant Institute, New York University (NYU), in the Department of Computer Science, Department of Mathematics (affiliated) and the Center for Data Science, since Fall 2016. He belongs to the CILVR group and to the Math and Data groups. From 2015 to 2016, he was Assistant Professor of Statistics at UC Berkeley and part of BAIR (Berkeley AI Research). Before that, he worked at FAIR (Facebook AI Research) in New York. Prior to that, he was a postdoctoral researcher at Courant Institute, NYU. He completed his PhD in 2013 at Ecole Polytechnique, France. Before his PhD he was a Research Engineer at a semi-conductor company, developing real-time video processing algorithms. Even before that, he did a MsC at Ecole Normale Superieure de Cachan in Applied Mathematics (MVA) and a BA and MS at UPC (Universitat Politecnica de Catalunya, Barcelona) in both Mathematics and Telecommunication Engineering. For his research contributions, he has been awarded a Sloan Research Fellowship (2018), a NSF CAREER Award (2019) and a best paper award at ICMLA (2018).
Host: Yan Liu
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
