Spring 2018
Jan. 26
Geographic Knowledge Discovery Using Ground-Level Images and Videos, Professor Shawn Newsam, EECS, UC Merced
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.
Feb. 2
Building Internet of Things Systems via Networked Sensing and Mobile Computing Innovations, Professor Wan Du, EECS, UC Merced
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.
Feb. 9
For Better or Worse, Richer or Poorer: The Future of Tech for Good, Dr. Brandie Nonnecke, UC Berkeley CITRIS Director of Tech for Social Good
This talk is part of the EECS | CITRIS Frontiers in Technology Series.
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.
Feb. 16
The Psychology of Input and Interaction of/with Text and Numbers,Professor Ahmed Sabbir Arif, EECS, UC Merced
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.
Feb. 23
No seminar
Abstract
No Seminar
Biography
No Seminar.
March 2
Plug-and-play Irrigation Control at Scale, Daniel Winkler, EECS, UC Merced
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.
March 9
Simulating virtual crowds with 100,000 agents in real-time on your laptop,Tomer Weiss, CS, UCLA
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.
March 16
Autonomous Scooter Design for People with Mobility Challenges,Professor Kaikai Liu, San Jose State University
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.
March 23
A Robot Character for Every Home, Mark Palatucci Co-Founder/Head of Cloud AI and Machine Learning at Anki
This talk is part of the EECS | CITRIS Frontiers in Technology Series.
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.
April 6
An Exciting Future: At the crossroads of people, profit, planet and petabytes of data, Chandrakant Patel Chief Engineering and Senior Fellow, Hewlett-Packard.
This talk is part of the EECS | CITRIS Frontiers in Technology Series.
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.
April 13
Computational Approaches toward Better Drugs and Better Health Care, Professor Xia Ning Indiana University - Purdue University Indianapolis
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.
April 20
Hidden Two-Stream Convolutional Networks for Action Recognition, Yi Zhu, EECS, UC Merced
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.
April 27
Online Partial Throughput Maximization for Multidimensional Coflow, Maryam Shadloo, EECS, UC Merced
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.
May 4
Optimizing Thread Management on GPUs, Dr. Guoyang Chen, Alibaba Research
Fall 2018
EECS 290 (Fall 2018) Webpage: https://www.asarif.com/courses/eecs290/fall2018.html.
"Learning-Compression" Algorithm and Its Application for Neural Network Pruning, Yerlan Idelbayev, EECS, UC Merced
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.
Interactive Exploratory Analytics of Big Spatial Data, Dr. Ahmed Eldawy, Assistant Professor, University of California, Riverside
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.
Indoor Human Information Acquisition from Physical Vibrations, Dr. Shijia Pan, Postdoctoral Fellow, Carnegie Mellon University
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.
Augmenting Collaborations with Social Computing Interaction Designs of Communication Channels, Dr. Hao-Chuan Wang, Associate Professor, University of California, Davis
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.
Characterization and Modeling of Error Resilience in HPC Applications Luanzheng Guo, EECS, UC Merced
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.
Walnut Rootstock Development for Sustainable Nut Production: What Things Are, What They Look Like and Why Big Data, Dr. Andreas Westphal, Assistant Cooperative Extension Specialist, Assistant Nematologist, Kearney Agricultural Research and Extension Center
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.
Microscope on Memory: FPGA Acceleration of Computer Memory System Assessments, Dr. Maya B. Gokhale, Distinguished Member of Technical Staff, Lawrence Livermore National Laboratory
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.
Limited-memory Quasi-Newton Optimization Methods for Deep Learning Jacob Rafati Heravi EECS, UC Merced
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.
Artificial Intelligence: How Customer Reactions Impact Innovation, Dr. Lisa Yeo, Assistant Professor, University of California, Merced
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.
Remote Sensing Image Analysis Based on Deep Learning, Dr. Dengfeng Chai, Associate Professor, Institute of Spatial Information Technique, Zhejiang University
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.
Designing Alternative Sensory Channels: Visualizing Nonverbal Communication through AR and VR Systems for People with Autism, Dr. LouAnne Boyd, Assistant Professor, Chapman University
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.
Architectural Study for Deep Learning Era Dr. Hyeran Jeon Assistant Professor, San Jose State University
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.