Abstract

In this talk we will discuss model compression in general and an algorithm to achieve it, and its specific case for neural network pruning. Pruning a neural net consists of removing weights with the goal of minimally degrading its performance. This is an old problem of renewed interest because of the need to compress ever larger nets so they can run on mobile devices. We formulate pruning as an optimization problem of finding the weights that minimize the loss while satisfying a pruning cost condition. We give a generic algorithm to solve this which alternates "learning" steps that optimize a regularized, data-dependent loss and "compression" steps that mark weights for pruning in a data-independent way. Using a single pruning-level user parameter, we achieve state-of-the-art pruning in LeNet-s and ResNet-s of various sizes.

Biography

Yerlan Idelbayev is a 3rd year PhD student of EECS department studying under the supervision of Prof. Miguel Carreira-Perpinan. Before UC Merced he studied Information Systems in International IT University in Almaty, Kazakhstan, and Computer Science in UC San Diego. His research interests consist of nonlinear optimization, neural networks and their compression.

Abstract

Recently, there has been a tremendous growth in the amount of big spatial data that are acquired by different sources such as satellites, IoT sensors, smartphones, autonomous cars, and others. For decades, end-users were familiar with an interactive exploratory interface that allows them to apply spatial operations and explore the results in real-time. However, the increasing volume of the data makes it unpractical to provide the desired exploratory and real-time interface. This talk presents a new system paradigm that overcomes the limitation of the existing system by providing an approximate and incremental query processing for big spatial data. The system consists of three modules, synoptic computation, incremental indexing, and interactive visualization. The synoptic computation module scales up the query processing by providing a real-time approximate answer over small-size synopses of the data such as samples and histograms. The incremental indexing module works in the background and incrementally organizes the data over a cluster of machines to speed up the query processing. Finally, the interactive visualization module presents the results in a visual format which allows the users to inspect the query answers. Preliminary results on the proposed system show that it can bridge the gap between the user requirements for interactivity and the increasing volume of big spatial data.

Biography

Ahmed Eldawy is an Assistant Professor in Computer Science at the University of California Riverside. His research interests lie in the broad area of databases with a focus on big data management and spatial data processing. Ahmed is the main inventor of SpatialHadoop, the most comprehensive open source system for big spatial data management. Ahmed has many collaborators in industrial research labs including Microsoft Research and IBM Watson. He was awarded the best poster award in SIGSPATIAL 2017, Quality Metrics Fellowship in 2016, Doctoral Dissertation Fellowship in 2015, and Best Poster Runner-up award in ICDE 2014.

Abstract

The number of everyday smart devices (such as smart TV, Samsung SmartThings, Nest, Google Home) is projected to grow to the billions in the coming decade. The Cyber-Physical Systems or Internet of Things systems that consist of these devices are used to obtain human information for various smart building applications. Different sensing approaches have been explored, including vision-, sound-, RF-, mobile-, and load-based methods, to obtain various indoor human information. From the system perspective, general problems faced by these existing technologies are their sensing requirements (e.g., line-of-sight, high deployment density, carrying a device) and intrusiveness (e.g., privacy concerns).
My research focuses on non-intrusive indoor human information acquisition through ambient structural vibration, which is referred to as "structures as sensors". People's interaction with structures in the ambient environment (e.g., floor, table, door) induces those structures to vibrate. By capturing and analyzing the vibration response of structures, we can indirectly infer information about the people and their actions that cause it. However, challenges remain. Due to the complexity of the physical world (in this case, both structures and people), sensing data distributions can change significantly under different sensing conditions. Therefore, from the data perspective, accurate information learning through a pure data-driven approach requires a large amount of labeled data, which is costly and difficult if not impossible to obtain in real-world sensing applications. My research addresses these challenges by combining physical knowledge and data-driven approaches. Specifically, my system can robustly learn human information from limited labeled data distributions by iteratively expanding the labeled dataset. With insights into the relationship between changes of sensing data distributions and measurable physical attributes, the iterative algorithm guides the expansion order by measured physical attributes to ensure a high learning accuracy in each iteration.

Biography

Dr. Shijia Pan is a postdoctoral researcher at Carnegie Mellon University. She received her Bachelor's degree in Computer Science and Technology from University of Science and Technology of China and her Ph.D. degree in Electrical and Computer Engineering at Carnegie Mellon University. Her research interests include cyber-physical systems, Internet-of-Things (IoT), and ubiquitous computing. She worked in multiple disciplines and focused on indoor human information acquisition through ambient sensing. She has published in both top-tier Computer Science ACM/IEEE conferences (IPSN, UbiComp) and high-impact Civil Engineering journals (Journal of Sound and Vibration, Frontiers Built Environment). She is the recipient of numerous awards and fellowships, including Rising Stars in EECS, Nick G. Vlahakis Graduate Fellowship, Google Anita Borg Scholarship, Best Poster Awards (SenSys, IPSN), Best Demo Award (Ubicomp), Best Presentation Award (SenSys Doctoral Colloquium), and Audience Choice Award (BuildSys) from ACM/IEEE conferences.

Abstract

Collaboration and communication gaps, ranging from difficulties in expressing oneself or understanding another person, to failure in coordinating actions in teamwork, are prevalent problems to individuals and organizations. While improving personal communication skills continues to be important, designing digital communication channels to afford what group collaboration needs, can offer solutions with scalability and cost-efficiency. In this talk, I will conceptualize social computing interaction design as a meta-solution to shape group behaviors toward more desirable processes and outcomes. I will demonstrate the approach with our recent work tackling different tasks and contexts, such as creative brainstorming, cross-lingual conversation, and generating and understanding referential expressions in remote collaborative work.

Biography

Hao-Chuan Wang is an Acting Associate Professor in the Department of Computer Science, University of California, Davis. Before joining UC Davis, he was an Associate Professor in National Tsing Hua University, Taiwan (NTHU), Taiwan from 2012 to 2018. He's also affiliated with National Taiwan University (NTU)'s IoX Research Center as a Principal Investigator. He has formed international collaborations with peer researchers in North America and Asia, as well as industrial collaborations with Intel Labs, Microsoft Research, and Google. Dr. Wang's main research interest lies in the collaborative and social aspects of Human-Computer Interaction (HCI). His work integrates system design and the behavioral sciences of social computing research for problem solving and value creation. His recent projects include system designs for supporting multilingual collaboration, motion sensing-based analytics for studying non-verbal behaviors in mediated conversations, and studies of interpersonal knowledge transfer for augmenting human work in the future. Dr. Wang is an active member of international and regional HCI communities, including ACM SIGCHI, CSCW and Chinese CHI. He also served as a member in the Steering Committees of CSCW and Chinese CHI, and was a Subcommittee Chair for ACM CHI 2017 and 2018.

Abstract

As HPC systems scale in size and power, the danger of silent errors, i.e., errors that can bypass hardware detection mechanisms and impact application state, grows dramatically. Consequently, applications running on HPC systems need to exhibit resilience to soft errors. Previous work has found that, for certain codes, this resilience can come for free, i.e., some applications are naturally resilient. However, we still lacks fundamental understanding on the program constructs that result in such natural error resilience. Understanding such nature resilience is critical for error detection and recovery to avoid overprotecting regions of code that are naturally resilient.

In this talk, we will present our research efforts to capture and characterize application natural resilience, based on which we can quantify and model application resilience. This talk has two parts. In the first part, will discuss FlipTracker, a framework designed to extract resilience code patterns using fine-grained tracking of error propagation and resilience properties. The framework and patterns enable a deeper understanding of resilience properties of applications. We also show how we can guide application design towards natural resilience using resilience code patterns.

In the second part, we will discuss PARIS, a resilience prediction method that makes resilience predictions of fault manifestations using resilience code patterns and machine learning models. PARIS can predict the possibility of all fault manifestations, while the state-of-the-art resilience prediction model cannot. PARIS is also much faster (up to 450x speedup) than the traditional method (i.e., random fault injection).

Biography

Luanzheng Guo is a Ph.D. student of Computer Science at the University of California Merced. His study is under the supervision of Professor Dong Li. His research area is High Performance Computing System with a focus on fault tolerance in large-scale parallel systems. During his Ph.D. study, his poster was nominated as the best poster candidate in SC'16. He is a lead student volunteer in SC18. He is a reviewer of a couple of prestigious conferences and international journals. He was recognized as an outstanding reviewer by Elsevier in 2018. He was a summer intern at Lawrence Livermore National Laboratory in 2015-2018. Recently, his research is featured by HPCwire in its What's New in HPC Research. He is a student member of IEEE, ACM, and SIGHPC.

Abstract

Walnut is under constant attack by soil-borne plant pathogens including crown gall, root rots, and plant-parasitic nematodes. Because of the lifetime expectancy of walnut orchards of at least three to four decades, a high level of sustainable management and mitigation strategies for these soil-dwelling nematodes are paramount. Using rootstocks with elevated resistance and tolerance to all of these damaging organisms is an environmentally friendly and sustainable approach to reduce reliance on costly and possibly environment impacting management practices. Built on previous successes, a group of researchers from several UC campuses, USDA-ARS, the California State University of Fresno, and UCANR has formed to investigate the potential of walnut germplasm (Juglans spp.) to generate such rootstocks. Interspecific crosses within Juglans have been made, taken into tissue culture by embryo rescue, and regenerated into clonal plants. Recent efforts have focused on two breeding populations with ca. 300 genotypes of clonal offspring. These are characterized for responses against different soil-borne pathogens including Crown gall, Phytophthora root rots, and plant-parasitic nematodes. In parallel, each genotype is sequenced to create a genotypic map. As soon as phenotypic maps become available, these will be overlaid on the genotypic map to identify quantitative trait loci (QTL). Depending on the time necessary for the pathogen testing, progress varies among pathogen systems. Goal of these efforts are to improve breeding strategies, release new superior rootstocks, and to convey information on plant utility and economics to the walnut stakeholders.

Biography

Andreas Westphal is a native to Germany. He completed his College and early University training in Germany before pursuing his Ph.D. in the US. For two decades, he has been working in several nematode-host plant systems. His research endeavors encompass nematode management in several annual crops including maize, potato, small grains, soybean, sugar beet, watermelon and others. After a scientist role at the German resort research institute "Julius Kühn-Institut", he focused his research emphasis on host plant resistance and tolerance research in perennial crops. Since his employment with UC Riverside in 2015, he directs selection efforts for nematode resistance and tolerance in Walnut, Prunus, Pistachio, and grape. He also conducts management research in these crops.

Abstract

Recent advances in new memory technologies and packaging options has focused attention on computer memory system design and evaluation. Examples include high bandwidth memories such as Hybrid Memory Cube and HBM, 3DXpoint non-volatile memory, STT-MRAM, and ReRAM. Emerging memories display a wide range of bandwidths, latencies, and capacities, making it challenging for the computer architect to navigate the design space of potential memory configurations, and for the application developer to assess performance impact of complex memory systems.

The Logic in Memory Emulator (LiME) is an FPGA-based hardware/software tool specially designed for memory system evaluation and experiment. LiME uses the Xilinx Zynq UltraScale+ Multi Processor System on Chip (MPSoC) to capture any/all memory access, either from the CPU (Processing System or PS) or the FPGA (Programmable Logic or PL). LiME employs novel loopback circuitry in conjunction with address map relocation to pass memory references from the PS into the PL side. The memory request is looped back into the PS DRAM memory controller and concurrently processed by LiME.

We have demonstrated three high value use cases: non-intrusive memory access logging, emulation of multiple memory systems by passing the memory request through delay registers before entering the PS memory subsystem, and emulation of acceleration engines that can independently access memory. In this talk, I will describe this novel application of state-of-the-art FPGA embodied by the LiME framework and highlight its uses.

Biography

Maya Gokhale is Distinguished Member of Technical Staff at the Lawrence Livermore National Laboratory, USA. Her career spans research conducted in academia, industry, and National Laboratories. Maya received a Ph.D. in Computer Science from University of Pennsylvania. Her current research interests include data intensive architectures and reconfigurable computing. Maya's Streams-C programming language and compiler was adoptd by Impulse Accelerated Technologies as the basis for Impulse C. Maya is co-recipient of an R&D 100 award for the Trident C-to-FPGA compiler, co-recipient of four patents related to memory architectures for embedded processors, reconfigurable computing architectures, and cybersecurity, and co-author of more than one hundred technical publications. Maya is a member of Phi Beta Kappa and a Fellow of the IEEE for contributions to reconfigurable computing technology.

Abstract

Deep learning algorithms often require solving a highly non-linear and nonconvex unconstrained optimization problem. Generally, methods for solving the optimization problems in deep learning are restricted to the class of first-order algorithms, like stochastic gradient descent (SGD). SGD methods has several drawbacks such as undesirable effect of not escaping saddle-points and requirement for tuning so many hyper parameters. Using the second-order curvature information to find the search direction can help with more robust convergence for the non-convex optimization problem. However, computing the Hessian matrix for the large-scale problems in not computationally practical. Alternatively, quasi-Newton methods construct an approximate of Hessian matrix to build a quadratic model of the objective function. Quasi-Newton methods, like SGD, require only first-order gradient information, but they can result in superlinear convergence, which makes them attractive alternatives for solving the non-convex optimization problem in deep learning. In this talk, I will introduce limited-memory quasi-Newton optimization methods that are efficient for deep learning problems such as classification and regression of big data.

Biography

Jacob Rafati is a Ph.D. candidate in the Electrical Engineering and Computer Science program at the University of California, Merced. He is also a member of Dr. David C. Noelle’s Computational Cognitive Neuroscience Lab. His research focus is on Optimization, Machine Learning and Reinforcement Learning. This talk is based on his recent collaborative research work that involves investigating and implementing alternative optimization methods for large-scale machine learning problems, such as deep learning and deep reinforcement learning. For more details about this project visit his website at http://rafati.net.

Abstract

Artificial Intelligence (AI) technologies are often included as product features (e.g., facial and voice recognition, autonomous driving) that drive product and service innovation. However, such innovations increase software complexity, leading to security and privacy issues. Customer reactions to security or privacy failures may affect product demand; customer demand reaction to the security or privacy implications of new features, such as AI-driven technology, plays a role in regulating the rate of innovation. This work examines the trade-off between product innovation and the increased risk of security breaches in AI-enabled products and services.

Biography

Dr. Lisa Yeo is an Assistant Professor in the Ernest & Julio Management program at UC Merced who works to help organizations understand how to safely govern the data and information they need to compete. By focusing on people and process, Lisa believes that organizations can design and build information systems that make it easy to protect privacy and prevent security breaches without requiring extensive investments in security layers after the fact. Lisa has worked in information security for 15 years as both a technical specialist and a business advisor. During this time, she wrote the book Personal Firewalls, protected the infrastructure for the Alberta Legislature, and guided the secure connection of all public libraries in Alberta as part of the Alberta SuperNet project. Lisa holds a B. Math in Applied Math from the University of Waterloo and an MBA and PhD (Operations & Information Systems) from the University of Alberta.

Abstract

Extracting information of interest from remote sensing images, such as taken from satellites or aircraft, is a long-standing problem, yet one which has benefited significantly over the past few years from advances in deep learning. In this talk, we cover some of the advances in remote sensing image analysis based on deep learning and present our recent research in the field. In particular, we describe our work on cloud and cloud shadow detection in Landsat multi-spectral imagery, and our work on extracting geo-objects of interest from high resolution satellite and aerial imagery.

Biography

Dr. Dengfeng Chai is an associate professor at the Institute of Spatial Information Technique, Zhejiang University, China. He received his PhD from the State Key Lab of CAD&CG at Zhejiang University. Before that, he received his Master’s degree from the State Key Lab of Information Engineering in Surveying, Mapping and Remote Sensing at Wuhan University and his Bachelor’s degree from Wuhan University. He was a postdoctoral fellow at Department of Photogrammetry, University of Bonn, Germany. He is currently a visiting scholar at the University of California, Merced where he is being hosted by Prof. Shawn Newsam. His research interests include photogrammetry and remote sensing, computer vision and pattern recognition, mainly focusing on statistical approaches and spatial models for object recognition and extraction from remote sensing images, especially deep learning based approaches. He is a principle investigator of two projects supported by National Natural Science Foundation of China and one project supported by Zhejiang Provincial Natural Science Foundation of China. He has published at ICCV, CVPR and Pattern Recognition among other venues.

Abstract

Social communication is one key component to success and happiness. Our ability to express our needs and wants as well as understand others is central to our connection to one another and our availability to teach and learn. Challenges with social communication puts learning on hold and youth at risk for bullying, social isolation, and potentially serious mental-health concerns. Thus, supporting social skills of people with autism could have a positive impact on both the social and mental wellbeing of individuals with autism. Although much researched has focused on supporting social skills broadly, little attention has been paid to developing effective nonverbal behaviors-which are necessary to initiate, maintain, and gracefully terminate a social interaction. The talk describes the design and evaluate the effect of realtime visualizations of prosody and proximity through three lab-based experiments as well as interviewing the participants and family members about their experience with these novel AR and VR technologies. The results from the interviews with participants and parents about their experiences highlight issues of usability, learnability, and comfortability of the systems culminate in an assistive technology design concept-Sensory Accommodation Framework-which provides four technical mechanisms for supporting sensory perception differences through computation.

Biography

Dr. LouAnne Boyd is an Assistant Professor of Software Engineering and Computer Science Department in Chapman's Schmid College of Science and Technology. Her current research interests in Human-Computer Interaction include designing, developing, and evaluating novel assistive and accessible technologies for neurodiverse users. LouAnne holds a B.A. in psychology from Washington University in St. Louis, a M.A. in psychology from Towson University, and a Ph.D. in Informatics from UC Irvine. She also is a Board Certified Behavior Analyst with over 20 years of professional clinical experience working with neurodiverse people in hospital, school, home, and community settings. Her overarching goal is to promoting diversity and inclusion. To that end, her current HCI research explores technical mechanisms to support sensory accommodation for assistive technology users.

Abstract

Deep learning became the core algorithm of many applications recently. Deep learning enables computing devices to automatically recognize individuals in photos, cars to navigate by themselves, and medical devices to diagnose cancer. With deep learning, software developers do not need to design sophisticated algorithms to extract important features from the input data. Under this computing paradigm transition, researchers need to understand the types of applications that can be accelerated by deep learning and the performance bottlenecks of deep learning applications. In this talk, Dr. Jeon will first introduce a few example deep learning applications that her research group has developed for smart city, secure computing, and medical image processing. In the second part of the talk, she will introduce a new deep learning benchmark suite, Tango, that her research group has recently released. She will show a few in-depth architectural characterization results measured by Tango from various accelerators, which will be helpful for developing a new accelerator design.

Biography

Hyeran Jeon is an Assistant Professor at San Jose State University. Her research interests lie in energy-efficient high-throughput processor and systems design. Recently, she is leading several research projects mainly on the efficient acceleration of deep learning applications and development of new deep learning applications. Her research group is sponsored by the California Energy Commission, Lam Research, NVIDIA, and Xilinx. Before joining San Jose State University, she earned her Ph.D. at the University of Southern California in 2015 and worked for Samsung, AMD, and IBM T.J. Watson Research Center as a systems software engineer and a research intern.

Abstract

This work investigates social multimedia for geographic knowledge discovery. Specifically, community-contributed ground-level images and videos are used to map what-is-where on the surface of the Earth in much the same way that overhead images taken from air- or space-borne platforms have been used for decades in the traditional field of remote sensing. The overarching premise is that georeferenced social multimedia data can be considered a form of volunteered geographic information. Further, it can enable geographic discovery not possible through traditional means. The framework, termed proximate sensing, is applied to a range of geographic discovery problems including land cover and land use mapping, mapping public sentiment, mapping pet ownership, and mapping human activities. The image and video analysis is performed using state-of-the-art computer vision techniques based on deep learning.

Biography

Dr. Shawn Newsam is an associate professor and founding faculty in Electrical Engineering and Computer Science at the University of California, Merced. He has degrees from UC Berkeley, UC Davis, and UC Santa Barbara and did a postdoc at Lawrence Livermore National Laboratory before joining UC Merced. He is the recipient of a DOE Early Career Scientist and Engineer Award, an NSF Faculty Early Career Development (CAREER) Award, and a Presidential Early Career Award for Scientists and Engineers (PECASE). His research interests include image processing, computer vision, and machine learning particularly as applied to spatial data.

Abstract

It is estimated that the global Internet of Things (IoT) system will connect about 30 billion objects and the global market value of IoT will reach $7.1 trillion by 2020. The deployed IoT systems are changing our lives and how we interact with the surrounding world. In this talk, I will introduce my research on building IoT systems via networked sensing and mobile computing innovations. In an interdisciplinary project, we develop a networked sensing system that measures the water quality of urban reservoirs and the spatial wind distribution over the water surface, which in turn enables real-time monitoring and analysis of water quality for smart cities. Three fundamental research problems have been solved. I worked with my colleagues and first found the best locations for wind sensors by studying the correlation of the wind stress at different locations. 10 wind sensors have been deployed in an urban reservoir of Singapore. To collect data from the deployed sensors, we further developed a sparse wireless networking system that provided adaptive communications over long-distance low-power wireless links and efficient data collection over multi-hop paths. To remotely update the software of the deploy sensors or diffuse a bulk of data to them, we designed a fast data dissemination protocol which significantly improved the data dissemination efficiency by transmitting rateless-encoded packets over constructive interference and pipelining. Besides the above academic achievements, the networked sensing system has been providing essential information for Public Utility Board of Singapore to conduct smart reservoir management, which makes the project socially responsible as well. Finally, I will also introduce two mobile computing systems we have developed to enable some interesting IoT applications.

Biography

Dr. Wan Du is currently an Assistant Professor in Electrical Engineering and Computer Science at the University of California, Merced. He had worked as a Research Fellow in the School of Computer Science and Engineering, Nanyang Technological University, Singapore, from 2011 to 2017. Dr. Du has been doing active research on IoT system development, especially networked sensing and mobile computing. His representative research projects include the deployment of a water quality monitoring system in urban reservoirs, visible light communication based on smartphones, smartphone-based activity profiling system, etc. He is also working on two data analytics projects for urban computing. A number of high quality research papers have been published in reputed conferences including ACM MobiCom, ACM SenSys, ACM MobiHoc; ACM/IEEE IPSN; IEEE INFOCOM, IEEE ICDCS; and journals including IEEE/ACM Transactions on Networking, IEEE Transactions on Mobile Computing, IEEE Transactions on Wireless Communications, ACM Transactions on Sensor Networks, etc. His research of water quality monitoring system has received the best paper award in ACM SenSys 2015 and the best demo award in IEEE SECON 2014. He has also received the Distinguished Technical Program Committee (TPC) member award of IEEE INFOCOM 2018.

Abstract

We have a complicated relationship with tech. Throughout history, technological advancements have helped us address some of our most pressing challenges, but its application has also created new ones. "ATech + Human Love Story" will share examples of how tech--from AI and digital identity systems to social media platforms--can be applied to change our world for good, but also provides caution on how tech must be designed and applied in ways that are inclusive, fair and just.

Biography

Dr. Brandie Nonnecke is the Research & Development Manager for CITRIS, UC Berkeley and Program Director for CITRIS, UC Davis. She is a Fellow at the World Economic Forum where she serves on the Council on the Futureof the Digital Economy and Society. Brandie researches human rights at the intersection of law, policy, and emerging technologies. Her current research is focused on the benefits and risks of AI-enabled decision-making, including issues of fairness, accountability, and appropriate governance structures. She has published research on algorithmic-based decision-making for public service provision in the urban context and outlined recommendations for how to better ensure application of AI to support equity and fairness. She is also researching ethics of biometric-based digital identity systems and recently published a piece highlighting the risks of digital ID systems for refugees.

Abstract

Text entry has become an essential part of our daily life. Nowadays, we input text and on/with various devices, in both stationary and mobile settings. Since the process of text entry involves both cognitive and motor skills and requires a close cooperation between the system and the user, an understanding of both factors is necessary to develop more efficient input techniques. In this talk, I will discuss the development of a model that accounts for the most important human and system factors to predict text entry performance. I will demonstrate how this model was used to identify and address bottlenecks in text entry performance by making subtle changes in the user interfaces. I will then shift focus to interaction with text end numbers. Data exploration is an integral part of uncovering the secrets and structure of scientific datasets. However, this process is challenging, especially for non-experts who are coming into an expert domain. I will discuss how the common coding theory can be exploited in user interfaces to facilitate collaborative learning, conceptual understanding, and exploration and discovery in different datasets, including gene expressions and metabolic pathways. Finally, I will conclude reflecting on future directions of my research.

Biography

Ahmed Sabbir Arif is an Assistant Professor of Electrical Engineering and Computer Science at UC Merced. As a researcher, his goal is to make computer technologies accessible to everyone by developing intuitive input and interaction techniques. A major thread of his work focuses on smarter solutions for text entry. His other interests include tangible user interfaces, mobile interaction, child-computer interaction, usable security, and data visualization. His research has contributed towards the development of more reliable interactive systems and influenced practices. He has received many prestigious awards for his research, including the Michael A. J. Sweeney Award and the CHISIG Gitte Lindgaard Award. Before joining UC Merced, he was a Postdoctoral Fellow at Ryerson University. He was also an NSERC ENGAGE Postdoctoral Fellow at Flowton Tech and a Research Intern at Microsoft Research, Redmond.

Abstract

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Biography

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Abstract

Lawns, also known as turf, cover an estimated 128,000 square kilometers in North America alone, with landscape requirements representing 30% of freshwater consumed in the residential domain. With this consumption comes a large amount of environmental, economic, and social incentive to make turf irrigation systems as efficient as possible. Recent work introduced the concept of distributed control in irrigation systems, but existing control strategies either do not take advantage of the distributed control, or don’t revise the strategy over time in response to collected data. In this work, we introduce PICS, a data-driven control strategy that self-improves over time, adapts to the local specific conditions and weather changes, and requires virtually no human input in both setup and maintenance providing a plug-and-play system that requires minimal pre-deployment efforts. In addition to substantial improvements in ease-of-use, we find across 4 weeks of large-scale irrigation system deployment that PICS improves irrigation system efficiency by 12.0% in comparison to industry best and 3.3% in comparison to academic state-of-the-art. Despite using less water, PICS also was found to improve quality of service by a factor of 4.0x compared to industry best and 2.5x compared to academic state of the art.

Biography

Daniel Winkler received his BS in Computer Science Engineering with honors from UC Merced in 2013. An ACM member, he since has been pursuing his PhD under advisement of Dr. Alberto Cerpa in UC Merced's ANDES Lab. Although his current research focuses on intelligent design and management of turf irrigation systems through the use of embedded devices, Daniel maintains a diverse interest in general resource management applications.

Abstract

The movement of large numbers of people is important in many situations, such as the evacuation of a building in an emergency, urban planning, and visual effects. Since laboratory experiments are not readily available, most research is conducted by means of computer simulations of crowds. Graphics researchers and others have proposed many simulation models. However, most of these models are tailored for specific scenarios, and are computationally expensive. One of the main challenge stems from the difficulty in leveraging all these into a unified model that scales and works well for both sparse and dense crowds. In this talk, I focus on my recent work in developing a position-based framework for crowd simulation. I demonstrate the framework's strengths by simulating large crowd masses in interactive rates for hundreds of thousands of agents, which was previously unachievable. This new method is suitable for use in interactive games, and was recently presented in the ACM SIGGRAPH conference on Motion in Games 2017, where it received the best paper award.

Biography

Tomer Weiss is a PhD candidate at the University of California Los Angeles, scheduled to defend this thesis in this year. He received the best paper award from the ACM SIGGRAPH conference on motion in games, for his work on virtual crowd simulation. He received his BSc degree in computer science from Tel Aviv University in 2013, and MS in Computer Science from UCLA in 2016. His research interests include computer graphics and optimization methods. He is a member of the UCLA Computer Graphics & Vision Laboratory, directed by Professor Demetri Terzopoulos.

Abstract

People with mobility challenges, for example, the elderly, blind, and disabled, face a multitude of challenges every day that can prevent them from getting where they want to go. Despite the technical success of existing assistive technologies, for example, electric wheelchairs and scooters, they are still far from effective enough in helping those in need navigate to their destinations in a hassle-free manner. Riders often face challenges operating scooters in certain indoor and crowded places, especially on sidewalks with numerous obstacles and pedestrians. People with certain disabilities, such as the blind, are often unable to drive their scooters. In this talk, we will discuss our ongoing work in designing a cutting-edge autonomous scooter. We focus on indoor navigation scenarios for the autonomous scooter where the current location, maps, and nearby obstacles are unknown. To solve the discrepancies of system complexity, sensor coverage, and resolution, we propose solutions for object mapping and recognition under various spatial and lighting conditions. Solving these challenges will enable the scooter to both travel within buildings and perform tight maneuvers through densely crowded areas automatically. We hope our system will allow people with mobility challenges to ambulate independently and safely in possibly unfamiliar surroundings.

Biography

Kaikai Liu is an assistant professor in the Department of Computer Engineering since August 2015. His research interests include Mobile and Cyber-Physical Systems (CPS), Smart and Intelligent Systems, Internet-of-Things (IoT), Software-Defined Computing and Networking. He has published over 20 peer-reviewed papers in journals and conference proceedings, 1 book, and holds 4 patents (licensed by three companies). He developed several prototype systems from scratch, for example, emergency communication systems for the smart city, Ultra-wideband system for search and rescue victims, indoor localization and navigation. He is a recipient of the Outstanding Achievement Award at UF (four times), the Apple WWDC Scholarship (2013 and 2014), the Innovator Award from the Office of Technology Licensing at UF (2014), the Top Team Award at NSF I-Corps Winter Cohort (Bay area, 2015), the 2015 Gator Engineering Attribute Award for Creativity at UF, IEEE SWC 2017 Best Paper Award, IEEE SECON 2016 Best Paper Award, ACM SenSys 2016 Best Demo - Runner up, 2016 CoE Kordestani Endowed Research Professor, 2017 and 2018 CoE Research Professor Award.

Abstract

For the past several decades, consumer applications of robotics have been more science fiction than reality. However, recent developments in deep-learning, cloud AI, and plummeting prices of both computation and sensing have created the necessary components for a rapidly growing consumer robotics industry to finally emerge. In this talk, I’ll discuss the evolution of Anki from 3 Ph.Ds and a kitchen table prototype, to a global company that has quickly become the 2nd largest producer of consumer robots in the world. I’ll share many of the successes and challenges of producing robots at million+ unit scale, and the important trends that will impact both academia and industry. I’ll talk about the importance of emotion and character for building a great user experience, and some surprising findings about human-robot interaction. I’ll also discuss Anki’s unique “bottom’s up approach" to robotics, and show how with an increasingly complicated series of low-cost mass-market robots, we’ve created a virtuous cycle that’s driving growth in the industry and moving to a future with intelligent, emotive, robot characters for every home.

Biography

Mark Palatucci is the Co-founder and Head of Cloud AI and Machine Learning at Anki. While at Anki, he led the software teams that developed award winning products including Anki Overdrive and Cozmo. He is an inventor on multiple US Patents, and was awarded Ph.D fellowships from the National Science Foundation and Intel Corporation for his research on machine learning. Mark earned a bachelor’s degree in computer science from the University of Pennsylvania and a M.S and Ph.D in Robotics from Carnegie Mellon University.

Abstract

Humanity will face more change over the next 15 years than in all of human history to date. The world will be deeply affected by population increase, shifting resource constraints, rapid urbanization, changing demographics, hyper globalization and sustainability challenges. Moreover, externalities such as environmental pollution, natural disasters and military conflicts will increasingly become a burden to society. In this talk, I will outline the megatrends, and examine the role of future cyber physical systems in addressing these 21st century megatrends. I will seek to drive a vigorous conversation on the role of physical fundamentals and information technologies in instantiating systemic innovations that make life better for everyone. I will close with a perspective on an idea-to-value framework that builds on lessons I have learnt in my career in Silicon Valley.

Biography

Chandrakant is currently the Chief Engineer and Senior Fellow of HP Inc. Chandrakant has led HP Labs in delivering innovations in chips, systems, data centers, storage, networking, print engines and software platforms. He is a pioneer in thermal and energy management in data centers, and in the application of the information technology for available energy management at city scales. Chandrakant is an ASME and an IEEE Fellow, and has been granted 151 patents and published more than150 papers. An advocate of return to fundamentals, he has served as an adjunct faculty in engineering at Chabot College, U.C. Berkeley Extension, San Jose State University and Santa Clara University. In 2014, Chandrakant was elected to the Silicon Valley Engineering Hall of Fame.

Abstract

Drug development and responsible drug use represent critical issues for health care. Drug development has been extremely costly and of extremely low success rate. Even after successful development and FDA approval, many marketed drugs do not introduce equal efficacy on different patients. In this talk, we will present how computational approaches can help accelerate drug development and facilitate precision drug selection. In specific, we will discuss a new ranking framework and ranking methods to prioritize drug candidates when multiple criteria are considered (e.g., drug bioactivity and selectivity). We will also discuss a new ranking-based approach to selecting effective cancer drugs for different patients.

Biography

Xia Ning is an Assistant Professor in the Department of Computer and Information Science (CIS) at the Indiana University – Purdue University Indianapolis (IUPUI). She received her Ph.D. from University of Minnesota, Twin cities, in 2012. From 2012 to 2014, she worked as a research staff member at NEC Labs, America. In Fall 2014, she joined IUPUI. She is also affiliated with the Center for Computational Biology and Bioinformatics (CCBB), Indiana University, and Regenstrief Institute. Ning’s research is on Data Mining, Machine Learning and Big Data analysis with applications on Chemical Informatics, Bioinformatics, Health Informatics and e-commerce, etc., and has been highly interdisciplinary. In specific, Ning’s research focuses on developing scalable models and computational methods to derive knowledge from heterogeneous Big Data, conduct modeling, ranking, classification and prediction, etc., and ultimately solve critical and real high-impact problems. Specific research topics include drug candidate prioritization for drug discovery, cancer drug selection for precision medicine, and information retrieval from electronic medical records.

Abstract

Analyzing videos of human actions involves understanding the temporal relationships among video frames. Convolutional Neural Networks (CNNs) are the current state-of-the-art methods for action recognition in videos. However, the CNN architectures currently being used have difficulty in capturing these relationships. State-of-the-art action recognition approaches rely on traditional local optical flow estimation methods to pre-compute motion information for CNNs. Such a two-stage approach is computationally expensive, storage demanding and not end-to-end trainable.

In this talk, I will first describe the literature and challenges of video classification, and then introduce the motivation of our work. Then I will present a novel CNN architecture that implicitly captures motion information between adjacent frames. This new module can be plugged into any state-of-the-art action recognition framework. We name our approach hidden two-stream CNNs because it takes raw video frames as input and directly predicts action classes without explicitly computing optical flow. We show that our end-to-end approach is 10x faster than a two-stage one, and requires significantly less storage since optical flow does not need to be saved. We present experimental results on four challenging action recognition datasets: UCF101, HMDB51, THUMOS14 and ActivityNet v1.2. Our approach is shown to significantly outperform the previous best real-time approaches.

Biography

Yi Zhu is a PhD student in the EECS program at UC Merced. Since 2014, he has been working with Professor Shawn Newsam towards his PhD degree on computer vision. His research is focused on video action recognition/detection, optical flow/depth estimation and geospatial knowledge discovery.

Abstract

Coflow has recently been introduced to capture communication patterns that are widely observed in the cloud and massively parallel computing. Coflow consists of a number of flows that each represents data communication from one machine to another. A coflow is completed when all of its flows are completed. Due to its elegant abstraction of the complicated communication processes found in various parallel computing platforms, it has received significant attention. In this talk, we optimize coflow for the objective of maximizing partial throughput. This objective seeks to measure the progress made for partially completed coflows before their deadline. Partially processed coflows still could be useful when their flows send out useful data that can be used for the next round computation. In our measure, a coflow is processed by a certain fraction when all of its flows are processed by the same fraction or more. We consider a natural class of greedy algorithms, which we call myopic concurrent. The algorithms seek to maximize the marginal increase of the partial throughput objective at each time. We analyze the performance of our algorithm against the optimal scheduler. In fact, our result is more general as a flow could be extended to demand various heterogeneous resources. Our experiment demonstrates our algorithm’s superior performance.

Biography

Maryam Shadloo is a PhD student in the EECS program at UC Merced. Since 2014, she has been working in the area of theoretical computer science under the supervision of Prof. Sungjin Im. Specifically, she is interested in designing approximation and online algorithms for algorithmic problems arising in scheduling and resource allocations.

Abstract

Heterogeneous architectures, especially Graphic Processing Units (GPU), has been embraced in modern computing for its tremendous computing power. Two factors critical for the performance of heterogeneous computing are memory performance and threads management. The former is essential for maximizing effective memory bandwidth of heterogeneous computing, while the latter is important for leveraging architectures’ concurrency and enabling flexible scheduling policies while minimizing runtime overhead. In this talk, I will focus on introducing two techniques for optimizing thread managements on GPUs. One is called Free Launch, which is designed for overcoming the shortcomings of hardware-based dynamic parallelism and the other one is named Effisha, which is a framework to enable block-level preemptive scheduling on GPU with little runtime overhead.

Biography

Dr. Guoyang Chen is currently a senior engineer (researcher) working on accelerating emerging machine learning algorithms on heterogeneous platforms at Alibaba Group US Inc. He received his Ph.D. degree of computer science from North Carolina State University (NCSU) in 2016 and BS degree of computer science from University of Science and Technology of China in 2012. His research interests span the areas of high performance computing, compiler optimizations and system architecture, with a focus on enabling both efficient software supports and program optimizations for heterogeneous computing and data intensive applications (e.g, data analytics and machine learning applications). Along those directions, he has developed novel solutions for data placement problems on GPU, enabling preemptive scheduling on GPU, eliminating the large overhead in dynamic parallelism on GPU, and systematic treatment to position independence on Non-Volatile Memory (NVM). His works have been published in 10+ top conferences and journals in both computer system and data engineering areas, such as MICRO, PPoPP, ICS, and ICDE.

Abstract

Scientific applications are generating an ever-increasing volume of multi-dimensional data that are largely processed inside distributed array databases and frameworks. Similarity join is a fundamental operation across scientific workloads that requires complex processing over an unbounded number of pairs of multi-dimensional points. In this talk, we introduce a novel distributed similarity join operator for multi-dimensional arrays. Unlike immediate extensions to array join and relational similarity join, the proposed operator minimizes the overall data transfer and network congestion while providing load-balancing, without completely repartitioning and replicating the input arrays. We define formally array similarity join and present the design, optimization strategies, and evaluation of the first array similarity join operator. Meanwhile, if the data are rapidly updated, the join result can be considered as a view, which is defined by the similarity join. We model the process as incremental view maintenance with batch updates and give a three-stage heuristic that finds effective update plans. Moreover, the heuristic repartitions the array and the view continuously based on a window of past updates as a side-effect of view maintenance. We design an analytical cost model for integrating materialized array views in queries. A thorough experimental evaluation confirms that the proposed techniques are able to incrementally maintain a real astronomical data product in a production pipeline.

Biography

Weijie Zhao is a PhD student in the EECS graduate group at UC Merced, working with Prof. Florin Rusu. He received his BS from East China Normal University in Shanghai. His research interests include databases and scientific data management. Weijie is an avid computer programming contestant.

Abstract

In the first part of this talk, we present a sematic-aware image editing algorithm for automatic sky replacement. The key idea of our algorithm is to utilize visual semantics to guide the entire process including sky segmentation, search and replacement. First, we train a deep convolutional neural network for semantic scene parsing, which is used as visual prior to segment sky regions in a coarse-to-fine manner. Second, in order to find proper skies for replacement, we propose a data-driven scheme based on semantic layout of the input image. Finally, to re-compose the stylized sky with the original foreground naturally, an appearance transfer method is developed to match statistics locally and semantically. We show that the proposed algorithm can automatically generate a set of visually pleasing and realistic results. In the second part, a work on learning image filters for low-level vision is presented (e.g., edge-preserving filtering and denoising), in which a unified hybrid neural network is proposed. The network contains several spatially variant recurrent neural networks (RNN) as equivalents of a group of distinct recursive filters for each pixel, and a deep convolutional neural network (CNN) that learns the weights of RNNs. The proposed model does not need a large number of convolutional channels nor big kernels to learn features for low-level vision filters. Experimental results show that many low-level vision tasks can be effectively learned and carried out in real-time by the proposed algorithm.

Biography

Yi-Hsuan Tsai (https://sites.google.com/site/yihsuantsai/) received the B.S. in Electronics Engineering from National Chiao-Tung University, Hsinchu, Taiwan and the M.S. in Electrical Engineering and Computer Science from University of Michigan, Ann Arbor. He is currently working toward the PhD advised by Prof. Ming-Hsuan Yang at UC Merced and is the recipient of the Graduate Dean's Dissertation Fellowship in 2016. He was invited to attend the doctoral consortium at the IEEE Conference on Computer Vision and Pattern Recognition in 2016. His research interests include computer vision, computational photography and machine learning with the focus on visual object recognition and image editing. He also did research internships at Qualcomm Research, Max Planck Institute and Adobe Research.
Sifei Liu (http://www.sifeiliu.net/) is a Ph.D candidate in Electrical Engineering and Computer Science with Prof. Ming-Hsuan Yang. Her research interests are in computer vision and machine learning. She completed her M.C.S. at University of Science and Technology of China (USTC) under Stan.Z Li and Bin Li, and received the B.S. in control science and technology from North China Electric Power University. She received the Baidu fellowship in 2013. In 2013 and 2014, she was a intern at Baidu Deep Learning Institute. In addition, she was a visiting student at the Chinese University of Hong Kong in 2015. She was invited to attend the doctoral consortium at the IEEE Conference on Computer Vision and Pattern Recognition in 2016. Her research interests include computer vision, machine learning and computational photography.

Abstract

This talk is about cache, and more specifically, solid-state storage based cache for large-scale computing systems such as cloud computing and big data systems. With the increasing workload data intensity and increasing level of consolidation in such systems, storage is becoming a serous bottleneck. Emerging solid-state storage devices such as flash memory and 3D Xpoint have the potential to address this scalability issue by providing a new caching layer between main memory and hard drives in the storage hierarchy. However, solid-state storage has limited capacity and endurance, and needs to be managed carefully when used for caching. This talk will present several recent works done by the ASU VISA Research Lab for addressing these limitations and making effective use of solid-state caching.
First, the talk will introduce CloudCache, an on-demand cache allocation solution for understanding the cache demands of workloads and allocating the shared cache capacity efficiently. It is able to reduce a workload’s cache usage by 78% and the amount of writes sent to cache device by 40%, compared to traditional working-set-based approach. Second, the talk will present CacheDedup, an in-line cache deduplication solution that integrates caching and deduplication with duplication-aware cache replacement to improve the performance and endurance of solid-state caches. It can reduce a workload’s I/O latency by 51% and the amount of writes sent to cache device by 89%, compared to traditional cache management approaches. Finally, the talk will be concluded with a brief overview of the systems research at the VISA lab.

Biography

Ming Zhao is an associate professor of the Arizona State University (ASU) School of Computing, Informatics, and Decision Systems Engineering (CIDSE), where he directs the research laboratory for Virtualized Infrastructures, Systems, and Applications (VISA, http://visa.lab.asu.edu). His research is in the areas of experimental computer systems, including distributed/cloud, big-data, and high-performance systems as well as operating systems and storage in general. He is also interested in the interdisciplinary studies that bridge computer systems research with other domains. His work has been funded by the National Science Foundation (NSF), Department of Homeland Security, Department of Defense, Department of Energy, and industry companies, and his research outcomes have been adopted by several production systems in industry. Dr. Zhao has received the NSF Faculty Early Career Development (CAREER) award, the Air Force Summer Faculty Fellowship, the VMware Faculty Award, and the Best Paper Award of the IEEE International Conference on Autonomic Computing. He received his bachelor’s and master’s degrees from Tsinghua University, and his PhD from University of Florida.

Abstract

In the first part of this talk, we present a work for face parsing via conditional random field with unary and pairwise classifiers. We develop a novel multi-objective learning method that optimizes a single unified deep convolutional network with two distinct non-structured loss functions: one encoding the unary label likelihoods and the other encoding the pairwise label dependencies. Moreover, we regularize the network by using a nonparametric prior as new input channels in addition to the RGB image, and show that significant performance improvements can be achieved with a much smaller network size. Experiments show state-of-the-art and accurate labeling results on challenging images for real-world applications. In the second part, a work on video object segmentation is presented. It is a challenging problem due to fast moving objects, deformed shapes, and cluttered backgrounds. To obtain accurate segmentation across time, we propose an efficient algorithm that considers video segmentation and optical flow estimation simultaneously. For video segmentation, we formulate a principled, multi-scale, spatio-temporal objective function that uses optical flow to propagate information between frames. For optical flow estimation, particularly at object boundaries, we compute the flow independently in the segmented regions and recompose the results. We call the process "object flow" and demonstrate the effectiveness of jointly optimizing optical flow and video segmentation using an iterative scheme.

Biography

Sifei Liu (http://www.sifeiliu.net/) is a Ph.D candidate in Electrical Engineering and Computer Science with Prof. Ming-Hsuan Yang. Her research interests are in computer vision and machine learning. She completed her M.C.S. at University of Science and Technology of China (USTC) under Stan.Z Li and Bin Li, and received the B.S. in control science and technology from North China Electric Power University. She received the Baidu fellowship in 2013. In 2013 and 2014, she was a intern at Baidu Deep Learning Institute. In addition, she was a visiting student at the Chinese University of Hong Kong in 2015. She was invited to attend the doctoral consortium at the IEEE Conference on Computer Vision and Pattern Recognition in 2016. Her research interests include computer vision, machine learning and computational photography.
Yi-Hsuan Tsai (https://sites.google.com/site/yihsuantsai/) received the B.S. in Electronics Engineering from National Chiao-Tung University, Hsinchu, Taiwan and the M.S. in Electrical Engineering and Computer Science from University of Michigan, Ann Arbor. He is currently working toward the PhD advised by Prof. Ming-Hsuan Yang at UC Merced and is the recipient of the Graduate Dean's Dissertation Fellowship in 2016. He was invited to attend the doctoral consortium at the IEEE Conference on Computer Vision and Pattern Recognition in 2016. His research interests include computer vision, computational photography and machine learning with the focus on visual object recognition and image editing. He also did research internships at Qualcomm Research, Max Planck Institute and Adobe Research.

Abstract

Imagine computer displays become a space to augment human thinking. Essential human activities such as seeing, gesturing, and exploring can couple with powerful computational solutions using natural interfaces and accurate visualizations. In this talk, I will present research effort to quantify visualization techniques of all kinds. Our ongoing work includes research in: (1) perceptually accurate visualization – constructing a visualization language to study how to depict spatially complex fields in quantum-physics simulations and brain-imaging datasets; (2) using space to compensate for limited human memory – developing new computing and interactive capabilities for bat-flight motion analysis in a new metaphorical interface; and (3) extending exploratory metaphors to biological pathways to make possible integrated analysis of multifaceted datasets. During the talk, I will point to a number of other projects being carried out by my team. I will close with some thoughts on automating the evaluation of visualizations and venture that a science of visualization and metaphors now has the potential to be developed in full, and that its success will be crucial in understanding data-to-knowledge techniques in traditional desktop and immersive settings.

Biography

Jian Chen is an Assistant Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC), where she leads the Interactive Visual Computing Lab (http:// ivcl.umbc.edu) and UMBC’s Immersive Hybrid Reality Lab (http://tinyurl.com/ ztnvdmf). She maintains general research interests in the design and evaluation of visualizations (encoding of spatially complex brain imaging, integrating spatial and non-spatial data, perceptually accurate visualization, and event analysis) and interaction (exploring large biological pathways, immersive modeling, embodiment, and gesture input). She has garnered best-paper awards at international conferences, and her work is funded by NSF, NIST, and DoD. She is also an UMBC innovation fellow and a co-chair of the first international workshop on the emerging field of Immersive Analytics. Chen did her post-doctoral research at Brown University jointly with the Departments of Computer Science (with Dr. David H. Laidlaw) and Ecology and Evolutionary Biology. She received her Ph.D. in Computer Science from Virginia Tech with Dr. Doug A. Bowman. To learn about Jian Chen and her work, please visit http:// www.csee.umbc.edu/~jichen.

Abstract

Communication is an intricate dance, an ensemble of coordinated individual actions. Imagine a future where machines interact with us like humans, waking us up in the morning, navigating us to work, or discussing our daily schedules in a coordinated and natural manner. Current interactive systems being developed by Apple, Google, Microsoft, and Amazon attempt to reach this goal by combining a large set of single-task systems. But products like Siri, Google Now, Cortana and Echo still follow pre-specified agendas that cannot transition between tasks smoothly and track and adapt to different users naturally. My research draws on recent developments in speech and natural language processing, human-computer interaction, and machine learning to work towards the goal of developing situated intelligent interactive systems. These systems can coordinate with users to achieve effective and natural interactions. I have successfully applied the proposed concepts to various tasks, such as social conversation, job interview training and movie promotion. My team's proposal on engaging social conversation systems was selected to receive $100,000 from Amazon Inc. to compete in the Amazon Alexa Prize Challenge.

Biography

Zhou Yu is a graduating PhD student at the Language Technology Institute under School of Computer Science, Carnegie Mellon University, working with Prof. Alan W Black and Prof. Alexander I. Rudnicky. She interned with Prof. David Suendermann-Oeft in ETS San Francisco Office on cloud based mulitmodal dialog systems in 2015 summer and 2016 summer. She also interned with Dan Bohus and Eric Horvitz in Microsoft Research on human-robot interaction in 2014 Fall. Prior to CMU, she received a B.S. in Computer Science and a B.A. in Linguistics from Zhejiang University in 2011. She worked with Prof. Xiaofei He and Prof. Deng Cai on Machine Learning and Computer Vision, and Prof. Yunhua Qu on Machine Translation.

Abstract

In this talk, I will first present the results of my first research project on autonomous driving as a PhD student in UC Merced. This work was conducted in collaboration with Berkeley Deep Drive (http://bdd.berkeley.edu/project/implicit-communication-through-motion). With progress in enabling autonomous cars to drive safely on the road, it is time to start asking how they should be driving. From the days of Alvinn to the latest autonomous driving technologies like NVidia’s Drive PX, researchers have used Learning from Demonstration to teach autonomous cars how to drive. Therefore, when it comes to customization of autonomous driving, a common answer is that the car should adopt the user’s style. In this project, we questioned this assumption and conducted user research in driving simulator to test our hypothesis. We found that users tend to prefer a significantly more defensive driving style than their own. Interestingly, they prefer the style they think is their own, even though their actual driving style tends to be more aggressive. The results show that conventional Learning from Demonstration algorithms will be inadequate for personalizing autonomous driving. In the second part of the talk I will discuss the implications of this result in greater detail and present some of the potential algorithms that we can use to augment user demonstrations.

Biography

Chandrayee Basu (http://chandrayee-basu.squarespace.com) is a 2nd year Ph.D. student in UC Merced advised by Prof. Mukesh Singhal, UC Merced and Prof. Anca Dragan, UC Berkeley. She is applying Human-Robot Interaction Algorithms to integrate human interaction into motion planning of autonomous cars. Chandrayee has multi-disciplinary research experience in design, applied machine learning, smart environment and human-robot interaction that she acquired as a Graduate student at UC Berkeley and Carnegie Mellon University.

Abstract

In this talk I will compare and contrast research vs startup innovation, based on my experiences at Stanford, the MIT Media Lab and Bay Area startups. I’ll discuss how the desired outcomes of each context encourages different kinds of risk and exploration, takeaways from my research experiences, and how we structure the early ideation process at Lemnos Labs where I am an Entrepreneur In Residence.

Biography

David Merrill is a technology executive and hardware startup founder with a computer science and human computer interaction background. His tactile learning system startup Sifteo - based on his Ph.D. from MIT - was acquired by drone-maker 3D Robotics in 2014 to become the kernel of a new consumer product group. At 3D Robotics he took various roles on the team that launched Solo: the Smart Drone in 2015, then he led R&D and IP. Alumnus of MIT, Stanford Computer Science and Symbolic Systems. TED speaker. Human-computer interaction expert. Drone builder. His work has been featured by the Discovery Channel, Popular Science, Wired, and the New York Times. Merrill is currently Entrepreneur in Residence at Lemnos Labs, an early-stage VC firm in San Francisco, working on the next project.

Abstract

In this talk I will present new approaches for achieving full-body motion coordination for humanoid virtual characters. I will first present a parametric data-based mobility controller with known coverage and validity characteristics, achieving flexible real-time deformations for locomotion control. I will then present a method for switching between different types of locomotion in order to navigate cluttered environments. The proposed method incorporates the locomotion capabilities of the character in the path planning stage, producing paths that address the trade-off between path length and locomotion behavior choice for handling narrow passages. In the last part, I will introduce a new approach for the coordination of locomotion with manipulation. The approach is based on a coordination model built from motion capture data in order to represent proximity relationships between the action and the environment. The result is a real-time controller that can autonomously produce environment-dependent full-body motion strategies. The obtained coordination model is successfully applied on top of generic walking controllers, achieving controllable characters which are able to perform complete full-body interactions with the environment.

Short Bio

Alain Juarez-Perez is a Ph.D. candidate in the Electrical Engineering and Computer Science graduate group of the University of California, Merced. His work is being developed at the Computer Graphics Lab under the advice of Prof. Marcelo Kallmann and has been supported by a UC-Mexus Doctoral Fellowship. He received his B.S. in Computer Science in 2012 from the University of Guanajuato, and in 2014 he was a visiting research assistant at the USC Institute for Creative Technologies. His research interests include Computer Animation, Data-Driven Algorithms, Motion Capture, Computational Geometry, Machine Learning, Computer Graphics and Motion Planning.

Abstract

In this talk, I will introduce my recent research projects on Internet of Things (IoT) security. First, I will introduce a physical layer authentication method for RFID tags. Second, I will talk about a fast and reliable protocol for authentication and key agreement among multiple IoT devices based on wireless signal information. Third, I will introduce an IoT data communication framework that guarantees data authenticity and integrity.

Biography

Chen Qian is an Assistant Professor in Department of Computer Engineering at University of California Santa Cruz. He was on the faculty of Computer Science at University of Kentucky 2013-2016. He got his PhD degree from The University of Texas at Austin, where he worked with Simon Lam. His research interests include computer networking and distributed systems, Internet of Things, network security, and cloud computing. He has published more than 60 papers, most which appeared in top journals and conferences including ToN, TPDS, ICNP, INFOCOM, ICDCS, SIGMETRICS, CoNEXT, CCS, NDSS, Ubicomp, etc.

Abstract

This seminar presents a brief and selected survey on the advances on stochastic distribution control, where the purpose of the controller design is to control the shape of output probability density functions (pdf) of non-Gaussian and general stochastic systems. This research was motivated through the requirement of distribution shape control of a number of practical systems. In recent years much research has been performed internationally and journal special issues and invited session at major conferences have been seen since 2001. It is expected that this seminar will provide with some up-to-date information on this new area.

Biography

Dr. Hong Wang joined PNNL in February 2016 as a Laboratory Fellow. He is based in the Controls team within the Electricity Infrastructure and Buildings Division of the Energy and Environment Directorate. Prior to joining PNNL, he was a full (chair) professor in process control at the University of Manchester in the U.K. Dr. Wang 's research interests are in advanced modelling and control for complex industrial processes, and fault diagnosis and tolerant control. He originated the research on stochastic distribution control where the main purpose of control input design is to make the shape of the output probability density functions to follow a targeted function. This area alone has found a wide spectrum of potential applications in modelling, data-mining, signal processing, optimization and distributed control systems design. Dr. Wang is the lead author of five books and has published over 300 papers in international journals and conferences. He is a member of three International Federation of Automatic Control Technical Committees and associate editor for IEEE Transactions on Control Systems Technology, IEEE Transactions on Automation Science and Engineering, and seven other international journals. He has been the associate editor of IEEE Transactions on Automatic Control, and has served as IPC member and conference chairman for many international conferences. Dr. Wang has also received several best paper awards from International Conferences including the best paper awards at Int. Conf. Control 2006, the Jasbar Memorial Prize for his outstanding contribution in the Science and Technology Development for paper industries in 2006, best theory paper award at World Congress on Intelligent Control and Automation in 2014 and one of the five finalists for the best application paper prize at 2014 IFAC World Congress. Dr. Wang holds a Ph.D. degree from Huazhong University of Science and Technology (HUST) in P R China.

Abstract

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Biography

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Abstract

Robot grasp planning has been extensively studied in the last decades and it often consists of two different stages, i.e., where to grasp an object and how to measure the quality of a tentative grasp. Because these two processes are computationally demanding, form closure grasps are more widely used in practice instead of force closure grasps, even though the latter is in many cases preferable. In this talk, we introduce our framework to improve grasp quality evaluation. We accelerate the computation of the grasp wrench space, used to measure the grasp quality, by exploiting some geometric insights in the computation of convex hull. In particular, we identify a cutoff sequence to terminate the convex hull calculation with guaranteed convergence to the quality measure. Furthermore, we study how noise at each joint of the manipulator affects grasp quality. Different arm configurations will generate different noise distributions at the end-effector which have a huge impact in the robustness of grasping. In the last part of the talk, I will introduce a grasp planner taking into account the local geometry of the object to be grasped. In particular, for concave objects we exploit the fact that grasping at the concave region can make the grasp more robust. These insights are studied in theory and validated on an experimental platform.

Biography

Shuo Liu received his B.Sc. degree in computer engineer from University of Minnesota in 2012 and his B.Sc. degree in mathematics from Beijing Jiaotong University in 2012. In his Junior year of college, he participated in 2011 Robocup and won the champion title in the Middle Size League. He also participated in IROS 2016 Grasping and Manipulation Challenge and won 2nd place in automation track. Since August 2012 he is pursuing a Ph.D. degree in electrical engineering and computer science at the University of California, Merced, working with Dr.Stefano Carpin. His interests include manipulation, grasping, and computational geometry.

Abstract

Flying robots, such as multicopters, are increasingly becoming part of our everyday lives, with current and future applications including personal transportation, delivery services, entertainment, and aerial sensing. These systems are expected to be safe and to have a high degree of autonomy. This talk will discuss the dynamics and control of multicopters, including some research results on trajectory generation for multicopters and fail-safe algorithms. Finally, we will present the application of a failsafe algorithm to a fleet of drones performing as part of a live theatre performance on New York's Broadway.

Biography

Mark W. Mueller joined the mechanical engineering department at UC Berkeley in September 2016. He completed his PhD studies, advised by Prof. Raffaello D'Andrea, at the Institute for Dynamic Systems and Control at the ETH Zurich at the end of 2015. He received a bachelors degree from the University of Pretoria, and a masters from the ETH Zurich in 2011, both in Mechanical Engineering. http://www.me.berkeley.edu/people/faculty/mark-mueller

Abstract

Coping with hostile environments, the inevitability of failure, and being truly alone.

Biography

James Gosling received a BSc in Computer Science from the University of Calgary, Canada in 1977. He received a PhD in Computer Science from Carnegie-Mellon University in 1983. The title of his thesis was "The Algebraic Manipulation of Constraints". He spent many years as a VP & Fellow at Sun Microsystems. He has built satellite data acquisition systems, a multiprocessor version of Unix, several compilers, mail systems and window managers. He has also built a WYSIWYG text editor, a constraint based drawing editor and a text editor called `Emacs' for Unix systems. At Sun his early activity was as lead engineer of the NeWS window system. He did the original design of the Java programming language and implemented its original compiler and virtual machine. He has been a contributor to the Real-Time Specification for Java, and a researcher at Sun labs where his primary interest was software development tools. He then was the Chief Technology Officer of Sun's Developer Products Group and the CTO of Sun's Client Software Group. He briefly worked for Oracle after the acquisition of Sun. After a year off, he spent some time at Google and is now the chief software architect at Liquid Robotics where he spends his time writing software for the Waveglider, an autonomous ocean-going robot.