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SUNMONTUEWEDTHUFRISAT
Events for March 26, 2018
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Meet USC: Admission Presentation, Campus Tour, and Engineering Talk
Mon, Mar 26, 2018
Viterbi School of Engineering Undergraduate Admission
University Calendar
This half day program is designed for prospective freshmen (HS juniors and younger) and family members. Meet USC includes an information session on the University and the Admission process, a student led walking tour of campus, and a meeting with us in the Viterbi School. During the engineering session we will discuss the curriculum, research opportunities, hands-on projects, entrepreneurial support programs, and other aspects of the engineering school. Meet USC is designed to answer all of your questions about USC, the application process, and financial aid.
Reservations are required for Meet USC. This program occurs twice, once at 8:30 a.m. and again at 12:30 p.m.
Please make sure to check availability and register online for the session you wish to attend. Also, remember to list an Engineering major as your "intended major" on the webform!
RSVPLocation: Ronald Tutor Campus Center (TCC) - USC Admission Office
Audiences: Prospective Freshmen (HS Juniors and Younger) & Family Members
Contact: Viterbi Admission
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CS Colloquium: Himabindu Lakkaraju (Stanford University) Human-Centric Machine Learning: Enabling Machine Learning for High-Stakes Decision-Making
Mon, Mar 26, 2018 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Himabindu Lakkaraju, Stanford University
Talk Title: Human-Centric Machine Learning: Enabling Machine Learning for High-Stakes Decision-Making
Series: CS Colloquium
Abstract: Domains such as law, healthcare, and public policy often involve highly consequential decisions which are predominantly made by human decision-makers. The growing availability of data pertaining to such decisions offers an unprecedented opportunity to develop machine learning models which can aid human decision-makers in making better decisions. However, the applicability of machine learning to the aforementioned domains is limited by certain fundamental challenges:
1) The data is selectively labeled i.e., we only observe the outcomes of the decisions made by human decision-makers and not the counterfactuals.
2) The data is prone to a variety of selection biases and confounding effects.
3) The successful adoption of the models that we develop depends on how well decision-makers can understand and trust their functionality, however, most of the existing machine learning models are primarily optimized for predictive accuracy and are not very interpretable.
In this talk, I will describe novel computational frameworks which address the aforementioned challenges, thus, paving the way for large-scale deployment of machine learning models to address problems of significant societal impact. First, I will discuss how to build interpretable predictive models and explanations of complex black box models which can be readily understood and consequently trusted by human decision-makers. I will then outline efficient and provably near-optimal approximation algorithms to solve these problems. Next, I will present a novel evaluation framework which allows us to reliably compare the quality of decisions made by human decision-makers and machine learning models amidst challenges such as missing counterfactuals and presence of unmeasured confounders (unobservables). Lastly, I will provide a brief overview of my research on diagnosing and characterizing biases (systematic errors) in human decisions and predictions of machine learning models.
I will conclude the talk by sketching future directions which enable effective and efficient collaboration between humans and machine learning models to address problems of societal impact.
This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity, seats will be first come first serve.
Biography: Hima Lakkaraju is a Ph.D. candidate in Computer Science at Stanford University. Her research focuses on enabling machine learning models to complement human decision making in high-stakes settings such as law, healthcare, public policy, and education. At the core of her research lie rigorous computational techniques leading to algorithmic contributions in machine learning, data mining, and econometrics. Hima has received several fellowships and awards including the Robert Bosch Stanford graduate fellowship, Microsoft research dissertation grant, Google Anita Borg scholarship, IBM eminence and excellence award, and best paper awards at SIAM International Conference on Data Mining (SDM) and INFORMS. Her research has been covered by various media outlets such as the New York Times, MIT Tech Review, Harvard Business Review, TIME, Forbes, Business Insider, and Bloomberg.
Host: Aleksandra Korolova
Location: Ronald Tutor Hall of Engineering (RTH) - 115
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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EE-EP Faculty Candidate, Wei Bao, Monday, March 26th @12pm in EEB 132
Mon, Mar 26, 2018 @ 12:00 PM - 01:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Wei Bao, University of California, Berkeley
Talk Title: Interacting Light with Semiconductor at the Nanoscale
Abstract: The ability to probe and control light-matter interaction at the nanometer scale not only advances frontiers of fundamental science, but also is a critical prerequisite to device applications in electronics, sensing, catalysis, energy harvesting, and more. Exploiting and enhancing the originally weak light-matter interactions via nanofabricated photonic structures; we will be able to sense chemical species at single molecule levels, to devise better imaging and manufacturing tools, to transfer data more efficiently at higher speed.
In this talk, I will first describe a simple and general nano-optical device developed during my Ph.D., called campanile probe, which lay groundwork for generally-applicable nano-optical studies. Two examples will be discussed, where we cross the boundary from insufficient to sufficient resolution beyond optical diffraction limit and perform optical hyperspectral imaging of luminescence heterogeneity along InP nanowires and synthetic monolayer MoS2, providing spectral information distinct from diffraction limited micro-PL spectral imaging. Following this, I will discuss the recent works using cavities to further enhance the strength of light-matter interaction into the strong coupling regime. The formation of coherently coupled cavity exciton-polariton in two-dimensional monolayer WS2 and the inorganic perovskite CsPbBr3 as well as the ultralow threshold optically pumped polariton lasing in perovskite cavities will be shown. Finally, I will conclude by presenting my vision of how these devices can enable a wide range of capabilities with relevance to multidimensional spectroscopy imaging, efficient solid-state lighting and even beyond.
Biography: Dr. Wei Bao is a postdoctoral researcher in Prof. Xiang Zhang's lab at the University of California, Berkeley. Previously he earned his B.A. in Physics (minor in Chemistry) at Peking University in 2009, and his M.S. in Mechanical Engineering (minor in Electrical Engineering) at UCLA in 2010. Wei then received his Ph.D. in Materials Science and Engineering (minor in Electrical Engineering) at University of California, Berkeley under the supervision of Prof. Miquel Salmeron and Prof. P. James Schuck in 2015. His Ph.D. work in nanoscale spectroscopic investigations of optoelectronic has led to several awards including: MRS Graduate Student Gold Award, Dorothy M. and Earl S. Hoffman Scholarships, Ross N. Tucker Memorial Award, as well as a R&D 100 Award 2013. His postdoc research currently focuses on polaritonics lasing devices, a scientific direction at the interface between low-dimensional semiconductor nanophotonics and quantum physics.
Host: EE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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Biomedical Engineering Seminars
Mon, Mar 26, 2018 @ 12:30 PM - 01:50 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Talk Title: TBA
Host: Professor Qifa Zhou
Location: Olin Hall of Engineering (OHE) - 122
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
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Center for Systems and Control (CSC@USC) and Ming Hsieh Institute for Electrical Engineering
Mon, Mar 26, 2018 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: B. Ross Barmish, University of Wisconsin, Madison
Talk Title: From the Kelly-Shannon Collaboration to Stock Trading Based on Feedback Control
Series: Joint CSC@USC/CommNetS-MHI Seminar Series
Abstract: This talk begins with a description of some ideas related to gambling which originated at Bell Labs in the 1950s by John Kelly and Claude Shannon. With their work serving as motivation for this talk, I will provide an overview of my research on the development of new stock-trading algorithms. The most salient feature of my approach is that no model of any sort is used for the underlying stock-price dynamics. Instead, in the spirit of technical analysis, the size of the time-varying stock position is determined using some simple ideas involving the adaptive power of feedback control loops. This approach is said to be "reactive" rather than predictive and amounts to assigning high priority to sound money management. After the key ideas driving this research are explained, the back-testing of the trading algorithms using historical data will be addressed with attention paid to practical considerations such as transaction costs, leverage and margin. It is interesting to note that sometimes the simulations lead to unexpected results which were not contemplated during the course of the research.
Biography: B. Ross Barmish is Professor of Electrical and Computer Engineering at the University of Wisconsin, Madison. Prior to joining UW in 1984, he held faculty positions at Yale University and the University of Rochester. From 2001-2003, he served as Chair of the EECS Department at Case Western Reserve while holding the Nord endowed professorship. He received his Bachelor's degree in EE from McGill University and the M.S. and Ph.D. degrees, also in EE, from Cornell University.
Throughout his career, he has served the IEEE Control Systems Society in many capacities and has been a consultant for a number of companies. Professor Barmish is the author of the textbook ``New Tools for Robustness of Linear Systems'' and is a Fellow of both the IEEE and IFAC for his contributions to robust control. He received two Best Journal Publication awards, each covering a three-year period, from the International Federation of Automatic Control and has given a number of keynotes and plenary lectures at major conferences. In~2013, he received the IEEE Control Systems Society Bode Prize.
While his earlier work concentrated on robustness of dynamical systems, his current university research involves building a bridge between feedback control theory and trading in complex financial markets. In addition to this academic pursuit, in his capacity as CEO of Robust Trading Solutions, his work involves transition of stock-trading algorithms from theory to practice and government sponsored research on the NASDAQ Limit Order Book.
Host: Petros Ioannou, ioannou@usc.edu
More Information: barmish.jpg (JPEG Image, 411 × 568 pixels).pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
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EE Seminar: Enabling Optical Methods for Next-Generation Neural Prostheses
Mon, Mar 26, 2018 @ 03:00 PM - 04:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Andrea Giovannucci, Research Scientist, Flatiron Institute, Simons Foundation
Talk Title: Enabling Optical Methods for Next-Generation Neural Prostheses
Abstract: Optical methods present interesting new opportunities for brain computer interfaces (BCIs) and closed-loop experiments because of their capability to densely monitor and stimulate in-vivo large neural populations across weeks with single cell resolution. For instance, combining optical methods for recording (two-photon imaging of calcium indicators) and perturbing (optogenetics) neural ensembles opens the door to exciting closed-loop experiments, where the stimulation pattern can be determined based on the recorded activity and/or the behavioral state. However, the adoption of such tools for BCIs is currently hindered by the lack of algorithms that track neural activity in real-time. In a typical closed-loop experiment, the monitored/perturbed regions of interest (ROIs) have been preselected by analyzing offline a previous dataset from the same field of view. Monitoring the activity of a ROI, which usually corresponds to a soma, typically entails averaging the fluorescence over the corresponding ROI, resulting in a signal that is only a proxy for the actual neural activity and which can be sensitive to motion artifacts and drifts, as well as spatially overlapping sources, background/neuropil contamination, and noise. Furthermore, by preselecting the ROIs, the experimenter is unable to detect and incorporate new sources that become active later during the experiment or track changes in neuronal morphology, which prevents the execution of truly closed-loop experiments.
In the first portion of this talk I will present an Online, single-pass, algorithmic framework for the Analysis of Calcium Imaging Data (OnACID). The framework is highly scalable with minimal memory requirements, as it processes the data in a streaming fashion one frame at a time, while keeping in memory a set of low dimensional sufficient statistics and a small minibatch of the last data frames. Every frame is processed in four sequential steps: i) The frame is registered against the previous denoised (and registered) frame to correct for motion artifacts. ii) The fluorescence activity of the already detected sources is tracked. iii) Newly appearing neurons are detected and incorporated to the set of existing sources. iv) The fluorescence trace of each source is denoised and deconvolved to provide an estimate of the underlying spiking activity. I will present the results of applying OnACID to several large-scale (90-350GB) mouse and zebrafish larvae in-vivo datasets. OnAcid can find and track tens of thousands of neurons faster than real-time, and outperforms state of the art algorithms benchmarked on multiple manual annotations using a precision-recall framework.
In the second portion of the talk, I will present an application of brain optical imaging to unveil coding properties and feedback mechanisms implemented by neurons in the cerebellum, a brain area implied in motor control and in the production of agile movement sequences. By monitoring across days the same neuronal populations of mice undergoing associative learning I will show that a predictive signal about the upcoming movement is widely available at the input stage of the cerebellar cortex, as required by forward models of cerebellar control.
In the last section of the talk, I will discuss my plans to develop all-optical neural prostheses interfacing with the cerebellum to recover lost motor function in the central nervous system because of injury or disease.
Biography: Andrea Giovannucci has a Ph.D. in computer science from Universitat Autònoma de Barcelona in Spain and a B.S. in electrical engineering from Politecnico di Milano in Italy. From 2008 to 2010 he was a postdoctoral fellow at Pompeu Fabra University (Barcelona), where he developed signal processing algorithms and circuit models for neuroprosthetic applications. From 2010 to 2015 he completed a postdoctoral fellowship at the Princeton Neuroscience Institute (PNI), Princeton University. At PNI, he pioneered the use of genetically encoded calcium indicators to image neurons in the cerebellum of awake learning mice, and applied them to investigate coding properties of cerebellar neurons during motor learning. Since 2015 Andrea Giovannucci is a research scientist at the Flatiron Institute, Simons Foundation, where he develops algorithms for the analysis of calcium imaging data, general-purpose neural networks and data-intensive computing projects. Dr. Giovannucci was the recipient of the First Prize for the Best Agent Service or Application in the Agent Technology Competition (IST Agentcities.net) in 2003, was shortlisted for the best Ph.D. thesis in artificial intelligence (ECCAI), and was the recipient of the prestigious Juan de La Cierva (Spain) and New Jersey Commission on Brain Injury Research (USA) fellowships. Andrea Giovannucci is the leader developer of the CaImAn open source software platform for calcium imaging analysis, currently used by hundreds of research laboratories worldwide.
Host: Maryam Shanechi, shanechi@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
