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SUNMONTUEWEDTHUFRISAT
Events for the 3rd week of June
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Internship & CPT Success Panel - MS & PhD
Mon, Jun 15, 2020 @ 10:00 AM - 11:00 AM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Join panelists from VCC, OIS, and VASE Student Affairs to get tips on having a successful internship and answer your academic and immigration questions.
You are invited to a Zoom webinar.
When: Jun 15, 2020 10:00 AM Pacific Time (US and Canada)
Topic: Internship & CPT Success Panel Session
Register in advance for this webinar through Viterbi Career Gateway
https://shibboleth-viterbi-usc-csm.symplicity.com/sso/
After registering, you will receive a confirmation email containing information about joining the webinar.Location: ZOOM
Audiences: Viterbi PhD and Masters Students
Contact: RTH 218 Viterbi Career Connections
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Virtual First-Year Admission Information SessionTue, Jun 16, 2020 @ 04:00 PM - 05:00 PM
Viterbi School of Engineering Undergraduate Admission
Workshops & Infosessions
Our virtual information session is a live presentation from a USC Viterbi admission counselor designed for high school students and their family members to learn more about the USC Viterbi undergraduate experience.Our session will cover an overview of our undergraduate engineering programs, the application process, and more on student life.Guests will be able to ask questions and engage in further discussion toward the end of the session.
Please register here!Audiences: Everyone Is Invited
Contact: Viterbi Admission
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Internship Course Enrollment & Success- (ENGR 395) Undergrads
Wed, Jun 17, 2020 @ 10:00 AM - 11:00 AM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Are you currently working at an internship & want to receive course credit for your work?
Did you just receive an internship offer and need to enroll in a course for credit to obtain CPT?
It is not too late! Join Viterbi Career Connections online next week, Wednesday, June 17th at 10:00 am (PST) to discuss ENGR 395, the undergraduate internship course for credit. We will review Enrollment, How to Have a Successful Internship, Understanding Course Requirements, and Internships & Academic Progress.
Register in advance for this webinar through Viterbi Career Gateway
https://shibboleth-viterbi-usc-csm.symplicity.com/sso/
After registering, you will receive a confirmation email containing information about joining the webinar.
*This presentation will be recorded in case you cannot make itLocation: ZOOM
Audiences: Viterbi BS
Contact: RTH 218 Viterbi Career Connections
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PhD Defense - Liron Cohen
Wed, Jun 17, 2020 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
Ph.D. Defense - Liron Cohen 6/17 10:00am "Efficient Bounded-Suboptimal Multi-Agent Path Finding and Motion Planning via Improvements to Focal Search"
Ph.D. Candidate: Liron Cohen
Date: Wednesday, June 17, 2020
Time: 10:00 AM - 12:00 PM
Committee: Sven Koenig (Chair), Bistra Dilkina, Peng Shi, Maxim Likhachev
Location: Online due to COVID-19
Zoom: https://usc.zoom.us/j/4421939378
Google Meet (only if there are issues with Zoom): meet.google.com/ova-uozj-vej
Title: Efficient Bounded-Suboptimal Multi-Agent Path Finding and Motion Planning via Improvements to Focal Search
Abstract:
Cooperative autonomous agents navigating in different environments can be useful in real-world application domains such as warehouse automation, search-and-rescue, fighting forest fires, traffic control, computer games and mining. Agents are required to navigate in such complex environments while avoiding obstacles and each other. In Artificial Intelligence (AI), a simplified model of this problem is called the Multi-Agent Path Finding (MAPF) problem. In MAPF, time is discretized into timesteps and the environment is discretized into cells that individual agents can occupy exclusively at any given timestep. Along with the environment, the MAPF problem also specifies unique start and goal cells for each agent. A solution to the MAPF problem is a set of paths, one for each agent, that take the agents from their respective start cells to their respective goal cells without collisions. A path for an agent is a sequence of move (between adjacent cells) or wait (at a cell) actions, each with some duration in timesteps.
Different measures for the cost of a solution to the MAPF problem, such as sum-of-costs or makespan, are commonly used in AI. Unfortunately, finding an optimal solution to a MAPF problem according to any of these cost measures is NP-hard, thus explaining why optimal MAPF solvers are inefficient in many real-world application domains. On the other end of the spectrum, suboptimal MAPF solvers are efficient but can be ineffective or even incomplete. One framework that balances the trade-off between efficiency and effectiveness is that of bounded-suboptimality. A bounded-suboptimal solver takes a user-specified constant w>=1 and returns a solution with a cost guaranteed to be at most w times the cost of an optimal solution. On the one hand, the freedom to explore suboptimal solutions allows these solvers to be more efficient than optimal ones. On the other hand, and as opposed to suboptimal solvers, these solvers ensure that their solutions are effective.
Unfortunately, state-of-the-art bounded-suboptimal MAPF solvers, all of which rely heavily on a bounded-suboptimal heuristic search method called Focal Search (FS), have a few shortcomings. First, small changes in w can significantly affect these solvers' runtime. Thus, it is often difficult to determine a w such that an effective solution is found efficiently. Second, these solvers are inefficient when agents are huddled together and the environment has some structural components that create bottlenecks. One such example is a typical warehouse domain. Here, the environment has long narrow corridors connecting open spaces or other corridors, and agents have to move between different regions of the warehouse through the corridors. Third, these solvers are inefficient when move actions have different durations. One such example is actions that model kinodynamically feasible motions. These shortcomings make bounded-suboptimal MAPF solvers inefficient in many real-world application domains, thus making suboptimal MAPF solvers the only viable option. This inefficiency is problematic because a low solution cost is often important for performing the task at hand sufficiently well.
In this dissertation, we improve FS in ways that resolve the above shortcomings of bounded-suboptimal MAPF solvers. Specifically, we develop an anytime framework for FS, which alleviates the need to choose w carefully. An anytime bounded-suboptimal MAPF solver based on this framework finds a ``good'' solution quickly and refines it to better and better solutions if time allows. We also show that FS provides bounded-suboptimality guarantees even when it is used with inflated heuristics. We develop an inflated heuristic, called the highway heuristic, which improves the efficiency of bounded-suboptimal MAPF solvers when the domain's environment has structural components that create bottlenecks. The highway heuristic alters the paths that agents choose by biasing the agents away from their individual optimal paths and towards paths on shared ``highways'' (directional lanes) in the environment. This results in implicit coordination of the agents, which, in turn, increases the efficiency of bounded-suboptimal MAPF solvers. Finally, we develop a version of FS that reasons about wait durations ``in bulk'' by using timeintervals instead of timesteps. A bounded-suboptimal MAPF solver that uses this version is efficient even when move actions have different durations, thus making it suitable for solving MAPF problems with actions that model kinodynamically feasible motions.
On the theoretical side, we formally prove that our improvements are bounded-suboptimal. On the experimental side, we show that our improvements result in increased efficiency in domains inspired by real-world applications. The overall impact of this dissertation is twofold. The first impact is in opening up the possibility of using bounded-suboptimal MAPF solvers for new application domains. New application domains include ones in which existing bounded-suboptimal MAPF solvers are not applicable, such as when the agents are kinodynamically constrained (for example, forklifts), as well as ones in which existing bounded-suboptimal MAPF solvers are too inefficient, such as automated warehouses. Different benefits of using bounded-suboptimal MAPF solvers in such application domains include safety (that is, the solutions are guaranteed to be collision-free), completeness and effectiveness (since the cost of the solution is guaranteed to be at most w times the cost of an optimal solution). The second impact is in revitalizing FS. Compared to other heuristic search methods, such as A* and wA*, FS is less restricted in the states it can choose to expand and can take advantage of a broader family of heuristics. Nevertheless, A* has received significantly more attention than FS from the scientific community.\footnote{According to Google Scholar, as of May 15, 2020, the first paper to introduce A* has been cited 9,522 times while the first paper to introduce FS has been cited 83 times.} Our improvements to FS make it more applicable and call for revisiting its importance.
WebCast Link: https://usc.zoom.us/j/4421939378
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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Science Impact of Sustained Infrastructure
Wed, Jun 17, 2020 @ 11:00 AM - 12:00 PM
Viterbi School of Engineering Alumni
Conferences, Lectures, & Seminars
Speaker: Dr. Ewa Deelman, Research Professor; Research Director, Scientific Computation Technologies; Principal Scientist
Talk Title: Viterbi Live
Abstract: Dr. Ewa Deelman describes the challenges of developing and sustaining cyberinfrastructure capabilities that have impact on scientific discovery and that innovate in the changing cyberinfrastructure landscape. She will deep dive into The Pegasus project in an example of a cyberinfrastructure effort that enables LIGO and other communities to accomplish their scientific goals. Join us on June 17 at 11:00 am PDT for a presentation and live Q&A.
Please register here: https://usc.zoom.us/webinar/register/WN_0JlVcmiaR5iUiyPtFPymFw
Host: Viterbi Advancement
Audiences: Everyone Is Invited
Contact: Kristy Ly
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Core Power Yoga
Wed, Jun 17, 2020 @ 12:00 PM - 01:00 PM
Viterbi School of Engineering Student Affairs
Student Activity
Take a break from your summer activities and join is in Wellness Wednesday for an all Viterbi yoga session. Come strengthen your mind and body!
Zoom Meeting ID: 962 2579 0057
Password: 596892Location: Zoom
Audiences: Everyone Is Invited
Contact: Viterbi Undergraduate Programs
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PhD Defense - Max Pflueger
Wed, Jun 17, 2020 @ 04:00 PM - 05:30 PM
Thomas Lord Department of Computer Science
University Calendar
Ph.D. Defense - Max Pflueger 6/17 4:00 pm "Learning from Planners to Enable New Robot Capabilities"
Ph.D. Candidate: Max Pflueger
Date: Wednesday, June 17, 2020
Time: 4:00 pm - 5:30 pm
Committee: Gaurav S. Sukhatme (chair), Joseph Lim, Sandeep Gupta, Ali Agha
Title: Learning from Planners to EnableNew Robot Capabilities
Abstract:
Solving complex robotic problems often involves reasoning about behavior multiple steps in the future, yet many robot learning algorithms do not incorporate planning structures into their architecture. In this dissertation we show how we can harness the capabilities of planning algorithms to learn from the structure of the robotic problems we wish to solve, thus expanding beyond what was available from baseline planners. We consider problems in multi-arm manipulation planning, path planning for planetary rovers, and reinforcement learning for torque controlled robots, and show how in each case it is possible to learn from the behavior of planning algorithms that are limited and unable to solve the full generalized problem. Despite not being full solutions these planners provide useful tools and insights that can be leveraged in larger solutions.
In multi-step planning for manipulation we develop a high level planner that can find solutions in difficult spaces by solving sub-problems in sub-spaces of the main planning space. For planetary rovers show how to use inverse reinforcement learning to learn a new planning algorithm that can function on different (and generally cheaper) input data. Reinforcement learning algorithms often suffer from unstable or unreliable training, we show how this can be mitigated by augmenting the robot state with a state embedding space learned from planner demonstrations.
Planning and control algorithms often rely on rigid and prescribed assumptions about the nature of robot problems, which may not be suitable for the generalized and versatile robot systems we wish to build. However, as this dissertation argues, those structures are still useful in informing the behavior of more flexible families of algorithms.
Meeting Links:
Zoom Meeting:
https://usc.zoom.us/j/93816840971
Meeting ID: 938 1684 0971
Google Meet (ONLY A BACKUP - IF WE EXPERIENCE PROBLEMS WITH ZOOM):
Meeting ID:
https://meet.google.com/iuj-gygb-utw
WebCast Link: https://usc.zoom.us/j/93816840971
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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Virtual First-Year Admission Information Session
Thu, Jun 18, 2020 @ 04:00 PM - 05:00 PM
Viterbi School of Engineering Undergraduate Admission
Workshops & Infosessions
Our virtual information session is a live presentation from a USC Viterbi admission counselor designed for high school students and their family members to learn more about the USC Viterbi undergraduate experience.Our session will cover an overview of our undergraduate engineering programs, the application process, and more on student life.Guests will be able to ask questions and engage in further discussion toward the end of the session.
Please register here!Audiences: Everyone Is Invited
Contact: Viterbi Admission
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WEBINAR SERIES: Digital Technologies for COVID-19
Fri, Jun 19, 2020 @ 11:00 AM - 12:00 PM
USC Viterbi School of Engineering
University Calendar
This week our webinar will feature a talk by Prof. Dave Conti and Dr. Abigail Horn. Prof. Conti is in Division of Biostatistics in the Department of Preventive Medicine and the Norris Comprehensive Cancer Center (NCCC) at USC, and Dr. Horn is a Postdoctoral Fellow in the Department of Preventive Medicine at USC.
Incorporation of risk factors in a stochastic epidemiological COVID-19 model for Los Angeles County -- by Dave Conti & Abigail Horn
Abstract: We have developed an epidemic compartmental model to address the key questions of (1) When and how the epidemic dynamics will impact health care capacity? (2) What happens to the dynamics of the epidemic when social distancing changes? and (3) How will the epidemic affect different at-risk groups? Our model uses stochastic differential equations and approximate Bayes calculation techniques for parameter estimation. We incorporate external information to inform prior distributions for parameter specification. This includes previous studies on risk factors for COVID-19 to inform differences in illness severity (e.g. advanced age, existing health conditions) and the prevalence of these risk factors in Los Angeles County; mobility data to inform time-varying changes in contact rate; and (3) seroprevalence data to estimate the fraction of unobserved illnesses.
Our modeling framework enables modifying parameters at different time points, enabling the specification of interventions, e.g. social distancing scenarios. A key contribution of our model over existing models estimating hospital resource demand is that it accounts for combinations of risk factors (age, unhealthy behaviors, existing comorbidities, and combinations of comorbidities), including area-level differences in prevalence of these risk factors, in model dynamics.
Accounting for differential risk for specific populations allows us to estimate the impact of COVID-19 on these populations in Los Angeles, including analyzing findings by race/ethnicity groups, to inform the prioritization of these populations for protection. For example, this allows us to compare our projections to the observed disparities in death rates across race/ethnicity groups and answer the question of whether these differences can be explained by prevalence of risk factors alone, or if other factors (e.g. disparities in exposure, differences in contact rates) must be involved. We find that death rates are higher for these populations than can be explained by the risk factors alone.
Model-estimated parameters, projections, and probabilities of severe illness for different combinations of risk factors are provided online on our project website: uscbiostats.github.io/COVID19
Bios:
Dave Conti, PhD
Dr. Conti is Professor in the Division of Biostatistics in the Department of Preventive Medicine and the Norris Comprehensive Cancer Center (NCCC) at USC. He is Associate Director for Data Science Integration for the NCCC at USC and the Kenneth T. Norris, Jr. Chair in Cancer Prevention. His research focuses on study design and statistical methods for genetic and environmental epidemiology. His methodological research aims to integrate biological knowledge in statistical modeling and he has several past R01s to develop and investigate the use of Bayesian hierarchical models for genomic studies and to develop statistical methods to integrate genetic and omics data. He has developed statistical and bioinformatics software for analysis, most recently this includes LUCIDus (https://cran.r-project.org/web/packages/LUCIDus/index.html) and hJAM (https://cran.r-project.org/web/packages/hJAM/index.html).
Dr. Conti is currently Director of the Data Science Core for a U19 project investigating aggressive prostate cancer in African-American men integrating the built environment, germline and somatic genetic profiles, gene expression and tumor microenvironment data. He is also Co-Investigator on a P01 focusing on the development of statistical methods for integrated genomics analysis, as well as numerous applied epidemiology studies.
Abigail Horn, PhD
Dr. Horn is a Postdoctoral Fellow in the Department of Preventive Medicine at the University of Southern California and a member of the Center for Applied Network Analysis (CANA). Her research interests involve network epidemiology, probabilistic modeling and data science in the context of public health, with a focus on foodborne diseases and diseases of diet. She received a Ph.D. from the Institute for Data, Systems and Society at MIT and a Bachelor's in Physics from the College of Creative Studies at UCSB.
Prior to joining USC, she led a research project at the German federal-level food protection agency to develop, implement, and evaluate algorithms and decision support systems for modeling food supply networks to identify the source of large-scale outbreaks of foodborne disease. Her work has been funded by the NIH, the Robert Wood Johnson Foundation, the Bayer Foundation, the German Research Foundation, and the Santa Fe Institute.
Co-hosted by:
Craig Knoblock, Executive Director, USC Information Sciences Institute
Bhaskar Krishnamachari, Director, USC Viterbi Center for CPS and IoTWebCast Link: https://usc.zoom.us/webinar/register/WN_bS2IGZDMTw2aymiLaBAzIw
Audiences: Everyone Is Invited
Contact: Bhaskar Krishnamachari
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Virtual Transfer Information Session
Fri, Jun 19, 2020 @ 02:00 PM - 03:00 PM
Viterbi School of Engineering Undergraduate Admission
Workshops & Infosessions
Our virtual information session is a live presentation from a USC Viterbi admission counselor designed for prospective transfer students and their family members to learn more about the USC Viterbi undergraduate experience. Our session will cover an overview of our undergraduate engineering programs, the application process, and more on student life. Guests will be able to ask questions and engage in further discussion toward the end of the session.
Register HereAudiences: Everyone Is Invited
Contact: Viterbi Admission
