Select a calendar:
Filter April Events by Event Type:
SUNMONTUEWEDTHUFRISAT
Events for the 5th week of April
-
Seminars in Biomedical Engineering
Mon, Apr 24, 2017 @ 12:30 PM - 01:50 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Arman Nadershahi, AMI, Sr. Director Corporate and Intellectual Property Counsel
Talk Title: Microbiology with Therapy
Biography: Mr. Nadershahi serves as Senior Director, Corporate and Intellectual Property Counsel. His expertise includes patent, trademark, and trade-secret protection and licensing; mergers and acquisitions; corporate law; raising capital for start-up companies; FDA regulatory affairs; quality assurance; competitive strategy and planning; product development; technology valuation and commercialization; and leadership.
Prior to joining AMI-USC, Mr. Nadershahi practiced intellectual property law at Knobbe, Martens, Olson & Bear, LLP, where he focused on patent prosecution, licensing, and intellectual property litigation in the fields of biotechnology, pharmaceuticals, and medical devices. Arman is also a co-founder and the CEO of Proa Medical, Inc., a spinout company from AMI-USC that commercializes medical devices for women's health.
Mr. Nadershahi is a member of the State Bar of California, is registered to practice before the United States Patent and Trademark Office, has earned US and EU Regulatory Affairs Certification (RAC-US & RAC-EU), is an ASQ Certified Biomedical Auditor (CBA), and is an ASQ Certified Manager of Quality/Organization Excellence (CMQ/OE).
Mr. Nadershahi graduated with distinction from the University of Wisconsin-Madison with a Bachelor of Arts degree in Zoology and English Literature. He received a Juris Doctor from the University of Minnesota Law School, a Master of Science degree in Biological Science from the University of Minnesota, a Master of Science degree in Regulatory Science from the University of Southern California (USC) School of Pharmacy, and a Master of Business Administration degree from the Marshall School of Business at USC.
Mr. Nadershahi may be contacted at nadersha AT usc DOT edu.
Host: Qifa Zhou
Location: Olin Hall of Engineering (OHE) - 122
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
-
Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute for Electrical Engineering Joint Seminar Series on Cyber-Physical Systems
Mon, Apr 24, 2017 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dennice F. Gayme, Assistant Professor, Johns Hopkins University
Talk Title: Quantifying efficiency and robustness in large-scale networks
Abstract: Dynamical systems coupled over graphs arise in a number of applications from power grids to vehicle networks. These systems are most often characterized in terms of their stability. However, the performance of these networks is also of great importance as it often corresponds to system efficiency and robustness. In this talk, we discuss a broad class of performance measures for first and second order systems whose outputs are defined so that particular performance metrics can be quantified through the input-output H2 norm of the system. We first present results for systems with the same physical interconnection and communication graph structures. We discuss the effect of graph size and interconnection structure for two applications; characterizing transient real power losses in power grids and evaluating long range disorder in vehicular platoons with both relative and absolute velocity feedback. We then extend our results to vehicular networks with arbitrary physical arrangements and communication structures to demonstrate that our proposed suite of performance measures can be adapted to determine the minimum disturbance energy that is required to cause a collision between any two vehicles. Finally, we further explore the effect of graph structure by considering systems with directed communication graphs.
Biography: Dennice F. Gayme is an Assistant Professor and the Carol Croft Linde Faculty Scholar in Mechanical Engineering at the Johns Hopkins University. She earned her B. Eng. & Society from McMaster University in 1997 and an M.S. from the University of California at Berkeley in 1998, both in Mechanical Engineering. She received her Ph.D. in Control and Dynamical Systems in 2010 from the California Institute of Technology, where she was a recipient of the P.E.O. scholar award in 2007 and the James Irvine Foundation Graduate Fellowship in 2003. Her research interests are in modeling, analysis and control for spatially distributed and large-scale networked systems in applications such as wall-bounded turbulent flows, wind farms, power grids and vehicular networks. She was a recipient of the JHU Catalyst Award in 2015, a 2017 ONR Young Investigator award, and an NSF CAREER award in 2017.
Host: Paul Bogdan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Estela Lopez
-
CS Colloquium Event: Facebook Tech Talk - Query Understanding and Semantic Search
Mon, Apr 24, 2017 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Haixun Wang, Facebook
Talk Title: Facebook Tech Talk - Query Understanding and Semantic Search
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Research Colloquium.
Understanding short texts is crucial to many applications, but challenges abound. First, queries do not always observe the syntax of a written language. As a result, traditional natural language processing methods cannot be easily applied. Second, queries usually do not contact in sufficient statistical signals to support many state-of-the-art approaches for text processing such as topic modeling. Third, queries are usually more ambiguous. We argue that knowledge is needed in order to better understand short texts. In this talk, I describe how to use lexical semantic knowledge provided by a well-known semantic network for short text understanding. Our knowledge-intensive approach disrupts traditional methods for tasks such as text segmentation, part-of-speech tagging, and concept labeling, in the sense that we focus on semantics in all the set tasks. We conduct a comprehensive performance evaluation on real-life data. The results show that knowledge is indispensable for short text understanding, and our knowledge-intensive approaches are effective in harvesting semantics of short texts.
Biography: Haixun Wang is a Research Scientist at Facebook and he manages the Query and Document Understanding team. Before Facebook, he was with Google Research, working on natural language processing. From 2009 to 2013, he led research in semantic search, graph data processing systems, and distributed query processing at Microsoft Research Asia. He had been a research staff member at IBM T. J. Watson Research Center from 2000 -“ 2009. He was Technical Assistant to Stuart Feldman (Vice President of Computer Science of IBM Research) from 2006 to 2007, and Technical Assistant to Mark Wegman (Head of Computer Science of IBM Research) from 2007 to 2009. He received the Ph.D. degree in Computer Science from the University of California, Los Angeles in 2000. He has published more than 150 research papers in referred international journals and conference proceedings. He served PC Chair of conferences such as CIKM'12, and he is on the editorial board of journals such as IEEE Transactions of Knowledge and Data Engineering (TKDE) and Journal of Computer Science and Technology (JCST). He won the best paper award in ICDE 2015, 10 year best paper award in ICDM 2013, and best paper award of ER 2009.
Host: CS Department
Location: Mark Taper Hall Of Humanities (THH) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
LavaLab Demo Night
Mon, Apr 24, 2017 @ 07:00 PM - 09:00 PM
Thomas Lord Department of Computer Science
Student Activity
LavaLab, the University of Southern California's product incubator, admits the top design, business, and engineering minds to create innovative products over the course of a semester.
On April 24, the ideas, innovation, and hard work of our seven teams will culminate into product demonstrations at the forefront of student development. Each team comprises of design, business, and engineering students focused on building productive solutions to problems of all kinds. Each has spent the entire semester working with industry experts and customers, refining their products from ideation, to wireframing and prototyping, to implementation.
Join an audience of tech experts, avid students, and bright minds at LavaLab Demo Night.
https://www.facebook.com/events/281058385680365/
Doors open at 6:30pm, pitches begin at 7pm. Light refreshments will be served. Demo time starts at 8pm.
We're located in the basement of TCC or SKS, in Tommy's Place
Location: Ronald Tutor Campus Center (TCC) - Tommy's Place
Audiences: Everyone Is Invited
Contact: Ryan Rozan
-
USC Stem Cell Seminar: Maurizio Pacifici, The Children's Hospital of Philadelphia
Tue, Apr 25, 2017 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Maurizio Pacifici, The Children's Hospital of Philadelphia
Talk Title: TBD
Series: Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research at USC Distinguished Speakers Series
Host: USC Stem Cell
More Info: http://stemcell.usc.edu/events
Webcast: http://keckmedia.usc.edu/stem-cell-seminarWebCast Link: http://keckmedia.usc.edu/stem-cell-seminar
Audiences: Everyone Is Invited
Contact: Cristy Lytal/USC Stem Cell
Event Link: http://stemcell.usc.edu/events
-
CommNetS seminar
Tue, Apr 25, 2017 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Christian Grussler, Lund University
Talk Title: Low-Rank Inducing Norms with Optimality Interpretations
Series: CommNetS
Abstract: This talk is on optimization problems which are convex apart from a sparsity/rank constraint. These problems are often found in the context of compressed sensing, linear regression, matrix completion, low-rank approximation and many more. Today, one of the most widely used methods for solving these problems is so-called nuclear norm regularization. Despite the nice probabilistic guarantees of this method, this approach often fails for problems with structural constraints.
In this talk, we will present an alternative by introducing the family of so-called low-rank inducing norms as convexifiers. Each norm is the convex envelope of a unitarily invariant norm plus a rank constraint. Therefore, they have several interesting properties, which will be discussed throughout the talk. They:
i) Give a simple deterministic test if the solution to the convexified problem is a solution to a specific non-convex problem.
ii) Often finds solutions where the nuclear norm fails to give low-rank solutions.
iii) Allow us to analyze the convergence of non-convex proximal splitting algorithms with convex analysis tools.
iv) Provide a more efficient regularization than the traditional scalar multiplication of the nuclear norm.
v) Leads to a different interpretation of the nuclear norm than the one that is traditionally presented.
vi) In particular, all the results can be generalized to so-called atomic norms.
Biography: Christian Grussler is a postdoc at the Department of Automatic Control at Lund University, Sweden. His current research interests include positive systems, model reduction, system identification and low-rank/sparse optimization. He received a Dipl.-Math. techn. degree (Industrial Mathematics) from TU Kaiserslautern, Germany and an M.Sc. degree (Engineering Mathematics) from Lund University in 2011. In 2017, he received a Ph.D. degree from Lund University under the guidance of Anders Rantzer and Pontus Giselsson.
Host: Prof. Mihailo Jovanovic
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Annie Yu
-
Epstein Seminar, ISE 651
Tue, Apr 25, 2017 @ 03:00 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Alper Atamturk, Professor, UC Berkeley
Talk Title: Combinatorial Optimization with a Probabilistic Objective
Host: Prof. Suvrajeet Sen
More Information: April 25, 2017_Atamturk.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - 206
Audiences: Everyone Is Invited
Contact: Grace Owh
-
MHI Emerging Trends Seminar Series
Wed, Apr 26, 2017 @ 10:00 AM - 11:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Kai Hwang, Professor, Ming Hsieh Department of Electrical Engineering
Talk Title: Big-Data Analytics for Cloud Computing in Cognitive Applications
Series: Emerging Trends
Abstract: In this talk, Dr. Hwang will address the effective use of big-data analytics on smart clouds, social networks, intelligent robots, and IoT platforms. He will assess machine/deep learning models and available software tools to advance the cognitive service industry represented by Google, Microsoft, Apple, Facebook, Baidu, IBM, Huawei, etc. The ultimate goal is to achieve enhanced agility, mobility, security, and scalability of public clouds, IoT platforms, and social-media networks.
His talk will assess current AI programs and brain projects pursued by high-tech companies, including Google X-Lab, TensorFlow, DeepMind AlphaGo, Nvidia Digits 5 for using GPU in deep learning, IBM neuromorphic computer, and CAS/ICT Camericon project, etc. Some hidden R/D opportunities are revealed for building smart machinesï¼delivery drones, self-driving cars, blockchains, AR/VR gears, etc. Extended cognitive applications will be discussed for 5G health-care, desease detection, emotion control, and social media community services.
Biography: Kai Hwang is a Professor of EE/CS at the Univ. of Southern California. He received the Ph.D. from UC Berkeley. He has published extensively in computer architecture, parallel processing, cloud computing, and network security. His latest two books are entitled: Cloud Computing for Machine Learning and Cognitive Applications (The MIT Press, April 2017) and Big Data Analytics for Cloud/IoT and Cognitive Computing (Wiley, U.K, May 2017).
An IEEE Life Fellow, he received the very-first CFC Outstanding Achievement Award in 2004 and the Lifetime Achievement Award from IEEE Cloud2012 for his pioneering work in parallel computing and distributed systems. Four of his graduated Ph.D. students were elected as IEEE Fellows and one an IBM Fellow. He has delivered four dozens of keynote or distinguished lectures in international Conferences or Research Centers. Dr. Hwang has performed consulting work with IBM, MIT Lincoln Lab, Chinese Academy of Sciences, and INRIA in France. He can be reached via his Email at USC: kaihwang@usc.edu.
Host: Shri Narayanan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Cathy Huang
-
MHI Emerging Trends Seminar Series
Wed, Apr 26, 2017 @ 10:00 AM - 11:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Kai Hwang, Professor, Ming Hsieh Department of Electrical Engineering
Talk Title: Big-Data Analytics for Cloud Computing in Cognitive Applications
Series: Emerging Trends
Abstract: In this talk, Dr. Hwang will address the effective use of big-data analytics on smart clouds, social networks, intelligent robots, and IoT platforms. He will assess machine/deep learning models and available software tools to advance the cognitive service industry represented by Google, Microsoft, Apple, Facebook, Baidu, IBM, Huawei, etc. The ultimate goal is to achieve enhanced agility, mobility, security, and scalability of public clouds, IoT platforms, and social-media networks.
His talk will assess current AI programs and brain projects pursued by high-tech companies, including Google X-Lab, TensorFlow, DeepMind AlphaGo, Nvidia Digits 5 for using GPU in deep learning, IBM neuromorphic computer, and CAS/ICT Camericon project, etc. Some hidden R/D opportunities are revealed for building smart machines, delivery drones, self-driving cars, blockchains, AR/VR gears, etc. Extended cognitive applications will be discussed for 5G health-care, disease detection, emotion control, and social media community services.
Biography: Kai Hwang is a Professor of EE/CS at the Univ. of Southern California. He received his Ph.D. from UC Berkeley. He has published extensively in computer architecture, parallel processing, cloud computing, and network security. His latest two books are entitled: Cloud Computing for Machine Learning and Cognitive Applications (The MIT Press, April 2017) and Big Data Analytics for Cloud/IoT and Cognitive Computing (Wiley, U.K, May 2017).
An IEEE Life Fellow, he received the very first CFC Outstanding Achievement Award in 2004 and the Lifetime Achievement Award from IEEE Cloud2012 for his pioneering work in parallel computing and distributed systems. Four of his graduated Ph.D. students were elected as IEEE Fellows and one an IBM Fellow. He has delivered dozens of keynote or distinguished lectures in international Conferences or Research Centers. Dr. Hwang has performed consulting work with IBM, MIT Lincoln Lab, the Chinese Academy of Sciences, and INRIA in France. He can be reached via his Email at USC: kaihwang@usc.edu
Host: Shri Narayanan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Cathy Huang
-
Computer Science General Faculty Meeting
Wed, Apr 26, 2017 @ 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
-
Aerospace & Mechanical Engineering Laufer Lecture
Wed, Apr 26, 2017 @ 12:00 PM - 02:00 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Clarence W. Rowley, Professor, Department of Mechanical and Aerospace Engineering, Princeton University
Talk Title: Structure, Stability, and Simplicity in Complex Fluid Flows
Series: John Laufer Keynote Lecture Series
Abstract: Fluid flows can be extraordinarily complex, and even turbulent, yet often there is structure lying within the apparent complexity. Understanding this structure can help explain observed physical phenomena, and can help with the design of control strategies in situations where one would like to change the natural state of a flow. This talk addresses techniques for obtaining simple, approximate models for fluid flows, using data from simulations or experiments. We discuss a number of methods, including balanced truncation, linear stability theory, and dynamic mode decomposition, and apply them to several flows with complex behavior, including a transitional channel flow, a jet in crossflow, and a T-junction in a pipe.
Biography: Clancy Rowley is a Professor in the Mechanical and Aerospace Engineering department at Princeton University. He received his undergraduate degree from Princeton in 1995, and his doctoral degree from Caltech in 2001, both in Mechanical Engineering. He returned to Princeton in 2001 as an Assistant Professor and was appointed Associate Professor in 2007, and Full Professor in 2012. He has received several awards, including an NSF CAREER Award and an AFOSR Young Investigator Award. His research interests lie at the intersection of dynamical systems, control theory, and fluid mechanics, and focus on reduced-order models suitable for analysis and control design.
Host: Department of Aerospace and Mechanical Engineering
More Info: https://ame.usc.edu/about/seminars/
Location: Ronald Tutor Campus Center (TCC) - Trojan Ballroom A
Audiences: Everyone Is Invited
Contact: Ashleen Knutsen
Event Link: https://ame.usc.edu/about/seminars/
-
MHI CommNetS seminar
Wed, Apr 26, 2017 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Adam Wierman, Caltech
Talk Title: Platforms & Networked Markets: Transparency & Market Power
Series: CommNetS
Abstract: Platforms have emerged as a powerful economic force, driving both traditional markets, like the electricity market, and emerging markets, like the sharing economy. The power of platforms comes from their ability to tame the complexities of networked marketplaces -- marketplaces where there is not a single centralized market, but instead a network of interconnected markets loosely defined by a graph of feasible exchanges. Despite the power and prominence of platforms, the workings of platforms are often guarded secrets, e.g., we know little about how amazon matches buyers and seller and how uber matches drivers and riders. Further, many competing platforms make very different design choices, but little is understood about the impact of these differing choices. In this talk, I will overview recent work that focuses on reverse engineering the design of platforms and understanding the consequences of design choices underlying modern platforms. I will use electricity markets and ridesharing services as motivating examples throughout the talk.
Biography: Adam Wierman is a Professor in the Department of Computing and Mathematical Sciences at the California Institute of Technology, where he currently serves as Executive Officer. He is also the director of the Information Science and Technology (IST) initiative at Caltech. He is the founding director of the Rigorous Systems Research Group (RSRG) and co-Director of the Social and Information Sciences Laboratory (SISL). His research interests center around resource allocation and scheduling decisions in computer systems and services. He received the 2011 ACM SIGMETRICS Rising Star award, the 2014 IEEE Communications Society William R. Bennett Prize, and has been coauthor on papers that received of best paper awards at ACM SIGMETRICS, IEEE INFOCOM, IFIP Performance (twice), IEEE Green Computing Conference, IEEE Power & Energy Society General Meeting, and ACM GREENMETRICS. Additionally, he maintains a popular blog called Rigor + Relevance.
Host: Prof. Insoon Yang
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Annie Yu
-
PhD Defense - Chien-Chun Hung
Thu, Apr 27, 2017 @ 02:15 AM - 04:15 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate:
Chien-Chun Hung
Title:
Resource Scheduling in Geo-distributed Computing
Date & Time:
April 27th, Thursday; 2:15-4:15pm
Room:
SAL 322
Committee:
Professor Leana Golubchik (advisor)
Professor Bhaskar Krishnamachari (external member)
Professor Wyatt Lloyd
Professor Minlan Yu
Doctor Ganesh Ananthanarayanan (Microsoft Research)
Abstract:
Due to the growing needs in computing and the increasing volume of data, cloud service providers deploy multiple datacenters around the world in order to provide fast computing response. Many applications utilizing such geo-distributed deployment include web search, user behavior analysis, machine learning applications and live camera feeds processing. Depending on the characteristics of the applications, their data may be generated, stored, and processed across the geo-distributed sites. Hence, how to efficiently process the data across the geo-distributed sites has become critical for the applications' performance.
Existing solutions first aggregate all the required data to one location and execute the computation within the site. Such solutions incur a large amount of data transfer across the WAN and lead to prolonged response time for the applications due to the significant network delay. An emerging trend is to instead distribute the computation across the sites based on data distribution, and aggregate only the results afterward. Recent works have shown such new approach results in an improvement of 3-19X in response time, or 250X in the reduction of WAN bandwidth usage.
Despite the preliminary gains, the performance of the geo-distributed jobs highly depends on how the resources are scheduled, which raises new challenges as the trivial extensions of state-of-the-art scheduling solutions lead to sub-optimal performance.
In this thesis, we first take an initiative step for improving the performance of geo-distributed jobs from the perspective of computation resource. We provide the insights into how conventional Shortest Remaining Processing Time (SRPT) falls short due to the lack of scheduling coordination among the sites, and propose a light-weight heuristic that significantly improves the jobs' response time. We also design a new job scheduling heuristic that coordinates the workload demands and the resource availability among the sites, and greedily schedule for the job that can quickly finish.
The trace-driven simulation studies show that our proposed scheduling heuristics effectively reduce the response time for the geo-distributed jobs by up to 50%.
Next, we take a step further by addressing the geo-distributed jobs' performance from the perspectives of both the computation and the network resources. Specifically, we address the scheduling challenge of the heterogeneity of the resources availability across the sites and the mismatch of the data distribution across the geo-distributed sites. We formulate the task placement decisions into Linear Programming optimization, and allocate the resources to the job that can finish quickly. In addition to the response time, our design can also nicely incorporate other performance goals, e.g., fairness and WAN usage, with simple control knobs. The EC2-based deployment of our prototype and the large-scale trace-driven simulations showed that our solutions can improve the response time of the baseline in-place scheduling approach by up to 77%, and improve the state-of-the-art geo-distributed analytics solution by up to 55%.
Finally, we expand to a more general setting in which each job has multiple configuration options, and its quality depends on the configuration it utilizes. We motivate this problem by the scenario of processing live camera feeds across hierarchical clusters. In this setting, we focus on the scheduling problem of jointly deciding job configuration and placement for concurrent jobs, and design efficient heuristic to maximize the overall quality with available resources across the geo-distributed sites. Our evaluation based on the Azure deployment of our prototype showed that the proposed solution outperforms the stat-of-the-art video analytics scheduler by up to $2.3X$, and outperforms the widely deployed Fair Scheduler by up to $15.7X$, in terms of the average quality of the concurrent jobs.
Location: Henry Salvatori Computer Science Center (SAL) - 322
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
-
CS Colloquium and RASC seminar: Steven Waslander (University of Waterloo) - Gimballed multi-camera localization and mapping for aerial vehicles
Thu, Apr 27, 2017 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Steven Waslander, University of Waterloo
Talk Title: Gimballed multi-camera localization and mapping for aerial vehicles
Series: RASC Seminar Series
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
Multi-camera clusters used for visual SLAM assume a fixed calibration between the cameras, which places many limitations on its performance, and directly excludes all configurations where a camera in the cluster is mounted to a moving component. We present a calibration method and SLAM solution for dynamic multi-camera clusters, where one or more of the cluster cameras is mounted to an actuated mechanism, such as a gimbal or robotic manipulator. Our approach parametrizes the actuated mechanism using the Denavit-Hartenberg convention, then determines the calibration parameters which allow for the estimation of the time varying extrinsic transformations between camera frames. We rely on joint encoder data or camera-attached IMU to identify the extrinsic transformations during operation, and are developing active calibration methods to automate the process in the field. We validate our calibration approach using a dynamic camera cluster consisting of a static camera and a camera mounted to a pan-tilt unit as well as on a four-camera system with a single three-axis gimballed unit on a hexacopter aerial vehicle, and demonstrate that dynamic camera clusters can be provide accurate pose tracking when used to perform SLAM.
Biography: Prof. Steven Waslander is an Associate Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo in Waterloo, Ontario, Canada and director of the Waterloo Autonomous Vehicles Laboratory (WAVELab, http://wavelab.uwaterloo.ca). He received his B.Sc.E.in 1998 from Queen's University, his M.S. in 2002 and his Ph.D. in 2007, both from Stanford University in Aeronautics and Astronautics. He is the Program Co-Chair for the CIPPRS Computer and Robot Vision Conference, the Competition Chair for the IEEE/RSJ International Conference on Intelligent Robots and Systems and the former General Chair of the International Autonomous Robot Racing competition. His research interests lie in the areas of autonomous aerial and ground vehicles, autonomous driving, simultaneous localization and mapping, quadrotor vehicles, and machine learning. Prof. Waslander currently collaborates with numerous industrial partners, including Aeryon Labs, Clearpath Robotics, Nuvation Engineering, Denso, Renesas Electronics Corp, Qnx, and Applanix, and is a member of the NSERC Canadian Field Robotics Network. He also acts as the academic advisor to the University of Waterloo Robotics Team, which compete in multiple competitions, including the NASA Sample Return Robot Challenge, the Intelligent Ground Vehicle Competition and the University Rover Challenge.
Host: Gaurav Sukhatme
Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
Measurement and Analysis of Mobile and Social Networks
Thu, Apr 27, 2017 @ 11:00 AM - 12:15 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Athina Markopoulou, Professor/UC Irvine
Talk Title: Measurement and Analysis of Mobile and Social Networks
Abstract: The majority of Internet traffic today is through mobile devices and social media. Large-scale measurement and analysis of these systems is necessary in order to understand underlying patterns and enable engineering optimizations and new applications. In this talk, I will present highlights of our research in this area.
First, I will discuss online social networks. I will present our "2K+" framework for generating synthetic graphs that resemble online social networks, in terms of joint degree distribution and additional characteristics, such as clustering and node attributes [INFOCOM'13, INFOCOM'15]. This problem was motivated by our prior work on graph sampling [JSAC'11, SIGMETRICS'11, INFOCOM'10] and by popular demand to make the Facebook datasets we collected publicly available.
Second, I will discuss cellular networks. I will present our work on analyzing Call Detail Records (CDRs) in order to characterize human activity in urban environments, with applications to urban ecology [MOBIHOC'15] and ride-sharing [UBICOMP'14, SIGSPATIAL'15-16].
Third, I will present our ongoing work on AntMonitor - a system for monitoring network traffic on mobile devices [SIGCOMM C2BID'15], with applications to privacy leaks detection [MOBICOM Demo'15], crowdsourcing of network performance measurements, and improved wireless access.
Biography: Athina Markopoulou is an Associate Professor in EECS at the University of California, Irvine. She received the Diploma degree in Electrical and Computer Engineering from the National Technical University of Athens, Greece, in 1996, and the Master's and Ph.D. degrees in Electrical Engineering from Stanford University, in 1998 and 2003, respectively. She has held short-term/visiting appointments at SprintLabs (2003), Arista Networks (2005), IT University of Copenhagen (2012-2013), and she co-founded Shoelace Wireless (2012). She has received the NSF CAREER Award (2008), the Henry Samueli School of Engineering Faculty Midcareer Award for Research (2014), and the OCEC Educator Award (2017). She has been an Associate Editor for IEEE/ACM Transactions on Networking (2013-2015), an Associate Editor for ACM CCR (2016), the General Co-Chair for ACM CoNEXT 2016, and the Director of the Networked Systems program at UCI. Her research interests are in the area of networking including mobile systems and mobile data analytics, network measurement, online social networks, network security and privacy, network coding, and multimedia traffic.
Host: Professor Konstantinos Psounis, kpsounis@usc.edu
More Information: Seminar Announcement - Markopoulou 042717.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
-
Le Val Lund Lecture with Student Symposium
Thu, Apr 27, 2017 @ 01:00 PM - 09:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Craig Davis, Technical Speaker and Recipient of the 2016 ASCE Le Val Lund Award for Practicing Lifeline Risk Reduction
Talk Title: Operationalizing Resilience for Lifeline Systems
Host: ASCE
More Information: Final_LeVal Lund_Lecture_save_the_date_v4_27April17.pdf
Location: California Institute of Technology
Audiences: Everyone Is Invited
Contact: Kaela Berry
-
PhD Defense
Thu, Apr 27, 2017 @ 01:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Akshay Gadde, University of Southern California
Talk Title: Sampling and Filtering of Signals on Graphs with Applications to Active Learning and Image Processing
Abstract: Processing of signals defined over the nodes of a graph has generated a lot of interest recently. This is due to the emergence of modern application domains such as social networks, web information analysis, sensor networks and machine learning, in which graphs provide a natural representation for the data. Traditional data such as images and videos can also be represented as signals on graphs. A frequency domain representation for graph signals can be obtained using the eigenvectors and eigenvalues of operators which measure the variation in signals taking into account the underlying connectivity in the graph. Spectral filtering can then be defined in this frequency domain. Based on this, we develop a sampling theory for graph signals by answering the following questions: 1. When can we uniquely and stably reconstruct a bandlimited graph signal from its samples on a subset of the nodes? 2. What is the best subset of nodes for sampling a signal so that the resulting bandlimited reconstruction is most stable? 3. How to compute a bandlimited reconstruction efficiently from a subset of samples? The algorithms developed for sampling set selection and reconstruction do not require explicit eigenvalue decomposition of the variation operator and admit efficient, localized implementation. Using graph sampling theory, we propose effective graph based active semi-supervised learning techniques. We also give a probabilistic interpretation for the proposed techniques. Based on this interpretation, we generalize the framework of active learning on graphs using Bayesian methods to give an adaptive sampling method. Additionally, we study the application graph spectral filtering in image processing by representing the image as a graph, where the nodes correspond to the pixels and edge weights capture the similarity between them given by the coefficients of the bilateral filter. We show that the bilateral filter is a low pass graph spectral filter with linearly decaying spectral response. We then generalize the bilateral filter by defining filters on the above graph with different spectral responses depending on the application. We also consider the problem of constructing a sparse graph from the given data efficiently, which can be used in graph based learning and fast image adaptive filtering.
Biography: Akshay Gadde received his Bachelor of Technology degree in Electrical Engineering from Indian Institute of Technology (IIT), Kharagpur, India in 2011. He has been working towards a Ph.D. in Electrical Engineering at the University of Southern California (USC), Los Angeles since 2011. His work (with Prof. Antonio Ortega and Aamir Anis) won the Best Student Paper Award at ICASSP 2014. His research interests include graph signal processing and machine learning with applications to multimedia data processing and compression.
Host: Dr. Antonio Ortega
More Information: Gadde Seminar Announcement.png
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Gloria Halfacre
-
Computer Architectures for Deep Learning Applications
Thu, Apr 27, 2017 @ 03:30 PM - 05:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: David Brooks, Harvard University
Talk Title: Computer Architectures for Deep Learning Applications
Abstract: Deep learning has been popularized by its recent successes on challenging artificial intelligence problems. One of the reasons for its dominance is also an ongoing challenge: the need for immense amounts of computational power. Hardware architects have responded by proposing a wide array of promising ideas, but to date, the majority of the work has focused on specific algorithms in somewhat narrow application domains. While their specificity does not diminish these approaches, there is a clear need for more flexible solutions. We believe the first step is to examine the characteristics of cutting edge models from across the deep learning community. Consequently, we have assembled Fathom: a collection of eight archetypal deep learning workloads for study. Each of these models comes from a seminal work in the deep learning community, ranging from the familiar deep convolutional neural network of Krizhevsky et al., to the more exotic memory networks from Facebook's AI research group. Fathom has been released online, and this talk describes the fundamental performance characteristics of each model. We use a set of application-level modeling tools built around the TensorFlow deep learning framework in order to analyze the behavior of the Fathom workloads. We present a breakdown of where time is spent, the similarities between the performance profiles of our models, an analysis of behavior in inference and training, and the effects of parallelism on scaling. The talk will then consider novel computer architectures that can improve the performance and efficiency of deep learning workloads.
Biography: David Brooks is the Haley Family Professor of Computer Science in the School of Engineering and Applied Sciences at Harvard University. Prior to joining Harvard, he was a research staff member at IBM T.J. Watson Research Center. Prof. Brooks received his BS in Electrical Engineering at the University of Southern California and MA and PhD degrees in Electrical Engineering at Princeton University. His research interests include resilient and power-efficient computer hardware and software design for high-performance and embedded systems. Prof. Brooks is a Fellow of the IEEE and has received several honors and awards including the ACM Maurice Wilkes Award, ISCA Influential Paper Award, NSF CAREER award, IBM Faculty Partnership Award, and DARPA Young Faculty Award.
Host: Xuehai Qian, x04459, xuehai.qian@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
-
CS & ML Colloquium: Matus Telgarsky (UIUC) - Representation power of neural networks
Thu, Apr 27, 2017 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Matus Telgarsky, UIUC
Talk Title: Representation power of neural networks
Series: Yahoo! Labs Machine Learning Seminar Series
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
This talk will present a series of mathematical vignettes on the representation power of neural networks. Amongst old results, the classical universal approximation theorem will be presented, along with Kolmogorov's superposition theorem. Recent results will include depth hierarchies (for any choice of depth, there exists functions which can only be approximated by slightly less deep networks when they have exponential size), connections to polynomials (namely, rational functions and neural networks well-approximate each other), and the power of recurrent networks. Open problems will be sprinkled throughout.
Biography: Matus Telgarsky is an assistant professor at UIUC. He received his PhD in 2013 at UCSD under Sanjoy Dasgupta. He works in machine learning theory; his current interests are non-convex optimization and neural network representation.
Host: CS Department
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
AI Seminar
Fri, Apr 28, 2017 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Avi Pfeffer, Charles River Analytics
Talk Title: PROGRAMMING: PAST, PRESENT, AND FUTURE
Abstract: Probabilistic reasoning lets you predict the future, infer past causes of current observations, and learn from experience. It can be hard to implement a probabilistic application because you have to implement the representation, inference, and learning algorithms. Probabilistic programming makes this much easier by providing an expressive language to represent models as well as inference and learning algorithms that automatically apply to models written in the language. In this talk, I will present the past, present, and future of probabilistic programming and our Figaro probabilistic programming system. I will start with the motivation for probabilistic programming and Figaro. After presenting some basic Figaro concepts, I will introduce several applications we have been developing at Charles River Analytics using Figaro. Finally, I will describe our future vision of providing a probabilistic programming tool that domain experts with no machine learning knowledge can use. In particular, I will present a new inference method that is designed to work well on a wide variety of problems with no user configuration. Prior knowledge of machine learning is not required to follow the talk.
Biography: Dr. Avi Pfeffer is Chief Scientist at Charles River Analytics. Dr. Pfeffer is a leading researcher on a variety of computational intelligence techniques including probabilistic reasoning, machine learning, and computational game theory. Dr. Pfeffer has developed numerous innovative probabilistic representation and reasoning frameworks, such as probabilistic programming, which enables the development of probabilistic models using the full power of programming languages, and statistical relational learning, which provides the ability to combine probabilistic and relational reasoning. He is the lead developer of Charles River Analytics Figaro probabilistic programming language. As an Associate Professor at Harvard, he developed IBAL, the first general-purpose probabilistic programming language. While at Harvard, he also produced systems for representing, reasoning about, and learning the beliefs, preferences, and decision making strategies of people in strategic situations. Prior to joining Harvard, he invented object-oriented Bayesian networks and probabilistic relational models, which form the foundation of the field of statistical relational learning. Dr. Pfeffer serves as Action Editor of the Journal of Machine Learning Research and served as Associate Editor of Artificial Intelligence Journal and as Program Chair of the Conference on Uncertainty in Artificial Intelligence. He has published many journals and conference articles and is the author of a text on probabilistic programming. Dr. Pfeffer received his Ph.D. in computer science from Stanford University and his B.A. in computer science from the University of California, Berkeley.
Host: Craig Knoblock
More Info: http://webcastermshd.isi.edu/Mediasite/Play/9b1644b4150f48cabdccf208f55773a51d
Location: 11th floor large conference room
Audiences: Everyone Is Invited
Contact: Kary LAU
Event Link: http://webcastermshd.isi.edu/Mediasite/Play/9b1644b4150f48cabdccf208f55773a51d
-
Seminars in Biomedical Engineering
Fri, Apr 28, 2017 @ 02:00 PM - 04:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Samir Mitragotri , Mellichamp Chair Professor in the Department of Chemical Engineering at UC Santa Barbara
Talk Title: TBA
Series: Systems Cellular-Molecular Bioengineering Distinguished Speaker Series
Abstract: TBA
Biography: Professor Mitragotri is the Founding Director of Center for BioEngineering (CBE). CBE is a hub for research and teaching at the interface of biology, engineering and physical sciences and enables transition of fundamental scientific discoveries to applications in medicine and biotechnology. Research at the CBE is yielding important advances in the understanding, diagnosis and treatment of common and devastating diseases such as cancer, diabetes, Alzheimer's and macular degeneration.
Host: Eun Ji Chung, PhD
Location: Corwin D. Denney Research Center (DRB) - 146
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
-
Senior Design Expo
Fri, Apr 28, 2017 @ 02:30 PM - 04:30 PM
Viterbi School of Engineering Student Affairs
Receptions & Special Events
The 9th annual Viterbi Senior Design Expo showcases the design projects that are completed in senior capstone engineering courses. The projects are often presented within the class, but rarely to other students, staff, faculty and industry partners. The Senior Design Expo is an opportunity to celebrate the accomplishments of Viterbi graduating seniors!
Location: VHE Breezeway
Audiences: Everyone Is Invited
Contact: Jenny Vazquez-Akim
-
Astani Civil and Environmental Engineering Ph.D. Seminar
Fri, Apr 28, 2017 @ 03:00 PM - 04:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Calogero Benedetto Rizzo and Mohammed Abdelbarr, Astani CEE Graduate Students
Talk Title: A Systematic Investigation of Hydraulic Connectivity in Heterogeneous Porous Media and its Impact on Transport Dynamics
Abstract: Defining the level of connectivity of heterogeneous porous media is of key importance to understand subsurface flow and transport dynamics. Several studies show that non-Fickian behavior observed in solute spreading in heterogeneous subsurface formations are strictly related to the presence of preferential channels. The presence of these channels control the trajectory of the solute front leading edge of the plume and it is highly correlated to early arrival times. Nevertheless there are multiple metric and frameworks that allow to determine preferential channels and connectivity properties of a complex heterogeneous permeability field. The aim of this work is to understand connectivity properties by using the concept of least resistance path through the use of an efficient algorithm. We explore differences among a range of fields and analyze the factors that significantly affect the connectivity of the field and its impact on transport. The results help to further understand the impact of hydraulic connectivity on solute transport and to establish a criteria for which heterogeneity and preferential channels cannot be neglected.
Talk by Mohamed Abdelbarr
Title: Inexpensive and Contactless Color and Depth Data Fusion for Dynamic Displacement-Field Measurement
Abstract:
Quantitative and accurate measurements concerning the time history of the multi-component deformation field of a distributed system undergoing dynamic response is an important and challenging problem in the broad field of structural dynamics. There are only very limited, and relatively quite expensive, methodologies for obtaining multi component deformations of a displacement of a dynamically deformation field. This study presents an extensive analytical and experimental study to assess, implement, and evaluate the feasibility and performance of a class of inexpensive vision based sensors RGB-D sensors to acquire dynamic measurements of the displacement field of a test structure.
Location: John Stauffer Science Lecture Hall (SLH) - 102
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
-
NL Seminar-Modeling Dialog using Probabilistic Programs
Fri, Apr 28, 2017 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Andreas Stuhlmuller , Stanford Univ.
Talk Title: Modeling Dialog Using Probabilistic Programs
Series: Natural Language Seminar
Abstract: How can we effectively explore the space of automated dialog systems? In this talk, I introduce WebPPL, a probabilistic programming language that provides a wide range of inference and optimization algorithms out of the box. This language makes it easy to express and combine probabilistic models, including regression and categorization models, highly structured cognitive models, models of agents that make sequential plans, and deep neural nets. I show that this also includes recent sequence to sequence architectures for dialog. I then use this framework to implement *dialog automation using workspaces, a variation on these architectures that is aimed at dialogs that require sufficiently deep reasoning between utterances that it is difficult to learn how to automate them from transcripts alone.
Biography: Andreas Stuhlmüller is a post-doctoral researcher at Stanford, working in Prof. Noah Goodman's Computation & Cognition lab, and founder of Ought Inc. Previously, he received his Ph.D. in Brain and Cognitive Sciences from MIT, where he was part of Prof. Josh Tenenbaum's Computational Cognitive Science group. He has worked on the design and implementation of probabilistic programming languages, on their application to cognitive modeling, and recently on dialog systems. He is broadly interested in leveraging machine learning to help people think.
Host: Marjan Ghazvininejad and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Location: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
-
Chemical Engineering Senior Dinner
Fri, Apr 28, 2017 @ 06:30 PM - 09:00 PM
Mork Family Department of Chemical Engineering and Materials Science
Receptions & Special Events
Come join us for a night of food and fun with your fellow ChemE family!
(Open to MFD Faculty and MFD Graduating Seniors only)
Friday, April 28th
6:30 - 9 pm
El Cholo
1037 S Flower St
Los Angeles, CA 90015Location: El Cholo Restaurant
Audiences: MFD Faculty and Graduating Seniors
Contact: Aleessa Atienza
-
Annual Viterbi Robotics Invitational
Sat, Apr 29, 2017 @ 11:00 AM - 12:00 PM
Viterbi School of Engineering K-12 STEM Center
Receptions & Special Events
Robotics are increasingly important in our everyday lives. Mathematicians, Engineers and Scientists are developing new technologies to solve many of our current environmental issues. The purpose of the competition is to encourage gracious professionalism that leaves everyone involved feeling valued with a sense of integrity and teamwork. The goal is not just to win, but to participate fairly and to extend gracious professionalism and respect to all teams and students involved.
Prior year competitions were based on the 'wicked problems' facing our times such as water security, sustainability and climate change, and terrorism; as well as the Grand Engineering Challenges of advanced health informatics, restoring and improving urban infrastructure, providing access to clean water, preventing nuclear terror and engineering the tools of scientific discovery.Audiences: Everyone Is Invited
Contact: Darin Gray/Viterbi STEM Educational Outreach Programs
