COLLOQUIUM William Harrison Abstract There is no such thing as high assurance without high assurance hardware. High assurance hardware is essential, because any and all high assurance systems ultimately depend on hardware that conforms to, and does not undermine, critical system properties and invariants. And yet, high assurance hardware development is stymied by the conceptual gap between formal methods and hardware description languages used by engineers. This talk presents ReWire, a functional programming language providing a suitable foundation for formal verification of hardware designs, and a compiler for that language that translates high-level designs directly into working hardware. ReWire is a subset of the Haskell language (i.e., every ReWire program is a Haskell program) that can be translated automatically to synthesizable VHDL. Furthermore, ReWire programs can be verified as one would any functional program ? e.g., with equational reasoning in Coq ? but they may also be rendered as efficient circuitry by the ReWire compiler. We describe the design and implementation of ReWire as well as its application to the construction and verification of secure hardware artifacts. Dr. William Harrison received his BA in Mathematics from Berkeley in 1986 and his doctorate from the University of Illinois at Urbana-Champaign in 2001 in Computer Science. From 2000-2003, he was a post-doctoral research associate at the Oregon Graduate Institute in Portland, Oregon where he was a member of the Programatica project. Dr. Harrison is an associate professor in the Computer Science department at the University of Missouri, where he has been since 2003. In December 2007, he received the CAREER award from the National Science Foundation's CyberTrust program. In 2013, Dr Harrison spent a sabbatical year at the National Security Agency's research directorate. His interests include all aspects of programming languages research (e.g., language-based computer security, semantics, design and implementation), reconfigurable computing, formal methods and malware analysis.

COLLOQUIUM Department of Computer Science and Engineering University of South Carolina Chien-Ming Huang Abstract Robots hold promise in assisting people in a variety of domains including healthcare services, household chores, collaborative manufacturing, and educational learning. In supporting these activities, robots need to engage with humans in socially cooperative interactions in which they work together toward a common goal in a socially intuitive manner. Such interactions require robots to coordinate actions, predict task intent, direct attention, and convey relevant information to human partners. In this talk, I will present how techniques in human-computer interaction, artificial intelligence, and robotics can be applied in a principled manner to create and study socially cooperative interactions between humans and robots. I will demonstrate social, cognitive, and task benefits of effective human-robot teams in various application contexts. I will also describe my current research that focuses on building socially cooperative robots to facilitate behavioral intervention for children with autism spectrum disorders (ASD). I will discuss broader impacts of my research, as well as future directions of my research program to develop personalized social technologies. Chien-Ming Huang is a Postdoctoral Associate in the Department of Computer Science at Yale University, leading the NSF Expedition project on Socially Assistive Robotics. Dr. Huang received his Ph.D. in Computer Science at the University of Wisconsin?Madison in 2015, his M.S. in Computer Science at the Georgia Institute of Technology in 2010, and his B.S. in Computer Science at National Chiao Tung University in Taiwan in 2006. Dr. Huang?s research has been published at selective conferences such as HRI (Human-Robot Interaction) and RSS (Robotics: Science and Systems). His research has also been awarded a Best Paper Runner-Up at RSS 2013 and has received media coverage from MIT Technology Review, Tech Insider, and Science Nation. In 2016, Dr. Huang was invited to give an RSS early career spotlight talk at AAAI.

COLLOQUIUM Department of Computer Science and Engineering University of South Carolina Yang Zhou Abstract With continued advances in science and technology, digital data have grown at an astonishing rate in various domains and forms, such as business, geography, health, multimedia, network, text, and web data. Network data are also known as graph data, such as academic collaboration, biological, communication, electrical, social, and transportation networks. Such big graph data have huge potential to reveal hidden insights and promote innovation in many business, science, and engineering domains. The reality is that people are often overwhelmed with the flood of big graph data in terms of size, type, and complexity. In order to help people quickly discover interesting knowledge and make good decisions when faced with big graph data, my research is dedicated to developing a wide spectrum of comprehensive solutions that span algorithms, systems, and applications: (1) big graph data mining and learning algorithms; (2) big graph data processing systems; and (3) domain-specific graph analytics applications. In this talk, I will introduce problems, challenges, and solutions for collecting, processing, understanding, and learning big graph data with billions of vertices and edges. I will also discuss recent work for how to leverage algorithmic and systemic techniques to alleviate challenging bottlenecks in the development of advanced big graph data analytics tools in terms of both quality and scalability. I will conclude the talk by sketching interesting future directions for big data computing. More details can be found at: http://www.cc.gatech.edu/~yzhou86/ Dr. Yang Zhou received his Ph.D. degree in computer science at the Georgia Institute of Technology in December 2016. His primary research bridges several areas of big data algorithms and systems, including data mining, parallel and distributed computing, machine learning, database systems, and cloud computing, with a focus on the development of effective and scalable algorithms, systems, and applications that address the challenges of big data. He has also worked with researchers from diverse research fields, such as software engineering, storage systems, web services, and trust management, to build and deploy domain-driven knowledge discovery solutions that improve domain-specific system design, data management, and data analytics in real-world settings. His research efforts have led to 30 publications with 850 citations in top venues of data mining (SIGKDD, ICDM, TKDD, DMKD), database systems (VLDB), high performance computing (HPDC, SC), networking (JSAC), and software engineering (ISSTA). Some of his research results have been included in reading lists and taught in courses at universities worldwide. He has been selected among the 20 rising stars of the KDD community by Microsoft Academic Search and Microsoft Research Asia in 2016. He has been serving as the reviewer of DMKD, JPDC, Machine Learning, TDSC, TKDD, TOIT, TSC, TWEB, and WWWJ.

COLLOQUIUM Department of Computer Science and Engineering University of South Carolina Lannan (Lisa) Luo Abstract As the digital brainpower of the IT revolution, software has become an important driving force of today?s economy as well as an indispensable element of personal life. Hence, the security of the software and systems becomes increasingly important. In this talk, I will present my work on analyzing and enhancing software and systems security, which applies rich and powerful software analysis methodologies. A particular emphasis is placed on two problems: automatically detecting software plagiarism and automatically discovering vulnerabilities in Android Framework. First, I will present CoP, a technique that can be applied to detect software plagiarism. Identifying similar code segments among programs is faced with a notorious challenge caused by code obfuscation and is even more difficult when the source code is unavailable. I will present how CoP addresses them. Then, I will present Centaur, a technique that applies symbolic execution to Android Framework aiming at discovering vulnerabilities and generating proof-of-concept exploits automatically. Android Framework is an integral and foundational part of the Android system, containing multiple million lines of code. Despite extensive work on Android, most of the existing tools are only capable of analyzing Android applications. There is a severe lack of techniques and tools for insecurity analysis of the underlying framework code in Android. Due to unique characteristics of Android Framework, many challenges are raised when conducting such program analysis as symbolic execution and taint analysis. I will show how we overcame these challenges and implemented the system for insecurity analysis of Android Framework. Finally, I will conclude the talk with a brief discussion on future research directions. Lannan (Lisa) Luo is a Ph.D. candidate in the College of Information Sciences and Technology at The Pennsylvania State University, under the supervision of Prof. Peng Liu. She received her B.S. in Telecommunications Engineering from Xidian University, Xi?an, China in 2009, and M.S. in Communications and Information Systems from The University of Electronic Science and Technology of China in 2012. Her research interests are software and systems security. During her PhD study, she mainly works on the software piracy problem and mobile computing security. Her research work has been published in FSE (Best Paper Award nomination), ICSE, DSN, and TSE. She did an internship at Microsoft Research Asia in 2015. Find more about her here: http://www.personal.psu.edu/lzl144/.

COLLOQUIUM Department of Computer Science and Engineering University of South Carolina Yonghong Yan Date: March 2, 2017 Time: 3:00-4:15pm Place: 300 Main B101 Abstract In this era of multicore, manycore and heterogeneous architectures with deep memory systems, portable parallel programming has become much more challenging than ever for both computation-intensive scientific and engineering applications, and applications that involve large-scale data processing such as computer vision or machine learning. It requires applications to expose significantly more concurrency at multiple levels including intra-node and inter-node, and to optimize local and shared data access with regard to the memory hierarchy of SRAM, DRAM, HBM, and storage. In this talk, the speaker will highlight the latest development of node-level parallel programming models for extreme scale performance, and discuss challenges and ongoing work in his research team for compiler and runtime systems to realize those models for many-/multi-core CPUs and GPUs. The talks will conclude with the discussion of memory-centric architecture and programming for future computer systems. Dr. Yonghong Yan is an Assistant Professor from Oakland University, Rochester MI, and a member of OpenMP Architectural Review Board and OpenMP Language Committee. Dr. Yan is an expert in parallel computing, compiler technology and high performance computer architecture and systems. He is an NSF CAREER awardee. His research team develop intra-/inter-node programming models, compiler, runtime systems and performance tools based on OpenMP, MPI and LLVM compiler, explore conventional and advanced computer architectures including CPU, vector, GPU, MIC, FPGA, and dataflow system, and support applications ranging from classical HPC, to big data analysis and machine learning, and to computer imaging. The ongoing development can be found from https://github.com/passlab. Dr. Yan received his PhD degree in computer science from University of Houston, has a bachelor degree in mechanical engineering, and loves physics and electric engineering as well. Apart from all those, he enjoys playing sports, fishing, writing science fictions, and playing with kids.

COLLOQUIUM Department of Computer Science and Engineering University of South Carolina Mahanth Gowda Abstract Motion tracking is a broad and classical problem that dates back many decades. While significant advances have come from the areas of robotics, control systems, and signal processing, the emergence of mobile and IoT devices is ushering a new age of embedded, human-centric applications. Fitbit is a simple example that has rapidly mobilized proactive healthcare; medical rehabilitation centers are utilizing wearable devices towards injury diagnosis and prediction. In this talk, I will discuss a variety of (new and old) IoT applications that present unique challenges at the intersection of mobility, multi-modal sensing, and indirect inference. For instance, I will discuss how inertial sensors embedded in balls, racquets, and shoes can be harnessed to deliver real-time sports analytics on your phone. In a separate application, I will show how GPS signals can be utilized to track the 3D orientation of an aggressively flying drone, ultimately delivering the much needed reliability against crashes. I will also show how injecting controlled mobility into conventional wireless infrastructure can open new opportunities in indoor WiFi and outdoor cellular networks. I will end with how arm motions of an individual can be inferred from smartwatch sensors alone, even when her arm and body are moving simultaneously (e.g., dancing). In general, I hope to show that information fusion across wireless signals, sensors, and physical models can together deliver motion-related insights, useful to a range of applications in IoT, healthcare, and cyber physical systems. Mahanth Gowda is a PhD candidate in the Computer Science department at the University of Illinois, Urbana Champaign (UIUC). His research interests include wireless networking, mobile sensing, and wearable computing, with applications to IoT, cyber physical systems, and human gesture recognition. He has published across diverse research forums, including NSDI, Mobicom, WWW, Infocom, Hotnets, ASPLOS, etc. Prior to joining UIUC, Mahanth obtained his M.S. from Duke University, and a B.Tech from Indian Institute of Technology, Varanasi. He has interned at Microsoft Research, IBM Labs, and recently at the wearable computing group at Intel.

COLLOQUIUM Department of Computer Science and Engineering University of South Carolina Rong Su Abstract We are at the dawn of the 4th industrial revolution - the era of the ICT backed Smart Manufacturing (or Industry 4.0). Among all challenges, the problem of how to model and manage efficiently the low volume high mixed (LVHM) manufacturing processes has been gaining more and more attentions from both academia and industry, owing to the rise of the maker/Do-It-Yourself (DIY) culture around the world. The major challenges in both modelling and operation planning are due to the complexity resulted from the scale and heterogeneity of the system, and the sophistication of relevant operations. In this talk I will first briefly mention one novel event-based modelling framework for cyber physical material handling, which, by separating operations and the corresponding materials, can significantly improve reusability of pre-developed models, making it potentially feasible to support a “drag and play” strategy, when constructing or reconfiguring a material handling system without a need of starting from scratch. After that, I will focus on a novel task routing and scheduling approach within a distributed synthesis framework based on time weighted discrete-event models. By going through an example of operation planning for linear cluster tools, I will show the potential advantage of this supervisor synthesis approach. In addition, I will show that the same modelling and synthesis framework can be applied to robot motion planning problems, accompanied by large–scale case studies in a simulated environment. Dr. Rong Su obtained his Bachelor of Engineering degree from University of Science and Technology of China in 1997, and Master of Applied Science and PhD degrees from University of Toronto in 2000 and 2004, respectively. After being affiliated with University of Waterloo and Technical University of Eindhoven, he joined Nanyang Technological University in 2010. Dr Su’s research interests include discrete event system theory, model-based fault diagnosis, operation planning and scheduling and control of multi-agent systems, with applications in smart manufacturing, intelligent transportation, human-robot interface, power management and smart buildings. He has more than 110 publications and 2 patents in the aforementioned areas. So far he has been involved in several projects funded by Singapore National Research Foundation (NRF), Singapore Agency of Science, Technology and Research (A*STAR), Singapore Ministry of Education (MoE), Singapore Civil Aviation Authority (CAAS) and Singapore Economic Development Board (EDB). Dr Su is a senior member of IEEE, and an associate editor for Journal of Discrete Event Dynamic Systems: Theory and Applications, Transactions of the Institute of Measurement and Control, and Journal of Control and Decision. He is also the Chair of the Technical Committee on Smart Cities in the IEEE Control Systems Society.

USC Cyber Security Club is hosting a public Town Hall featuring USC's new Chief Information Officer, Doug Foster. We will hear about his vision for USC and learn about what it takes to become a CIO. If you've ever had a problem with a USC system, you can discuss your issue with the CIO himself. The event will be held Friday February 24 at 5:30pm in Swearingen 1C01 (Amoco Hall). Everyone is welcome. Thanks, Ronni Wilkinson Information Technology Services College of Engineering and Computing University of South Carolina

COLLOQUIUM Department of Computer Science and Engineering University of South Carolina Shiyi Wei Abstract Software is ubiquitous. As its importance grows, the mistakes made by programmers have an increasingly negative effect, leading to critical failures and security exploits. As software complexity and diversity grows, such negative effects become even more likely. Automated program analysis has the potential to help. A program analysis tool approximates possible executions of a program, and thereby can discover otherwise hard-to-find errors. However, significant challenges must still be overcome to make program analysis tools practical for real-world software. I have gained substantial experience in building novel program analysis tools whose aim is to produce more secure and reliable software. Recently, I have focused on the challenge of building analysis tools that perform well (i.e., can analyze realistic code in a reasonable amount of time) and are precise (i.e., do not produce too many "false alarms"). To this end, I have developed an approach that systematically uncovers sources of imprecision and performance bottlenecks in program analysis. The goal is to significantly reduce the time-consuming manual effort otherwise required during analysis design process. In addition, I have designed an adaptive analysis, in which appropriate techniques are selected based on the coding styles of the target programs. Selection is based on heuristics derived from a machine learning algorithm. The idea is that precise techniques can be deployed only as where and when they are needed, leading to a better balance overall. Shiyi Wei is a post-doctoral associate at University of Maryland, College Park. He obtained his Ph.D. in Computer Science from Virginia Tech in 2015, and B.E. in Software Engineering from Shanghai Jiao Tong University in 2009. His research interests span the areas of Programming Languages, Software Engineering and Security. The goal of his research is to make program analysis practical for improving the security and reliability of real-world software. He has published articles at top venues in his areas of interest, such as PLDI, FSE, ECOOP, and ISSTA. He has interned at IBM T. J. Watson Research Center.

COLLOQUIUM Department of Computer Science and Engineering University of South Carolina Zhichuan Huang Abstract The White House announced Smart Cities Initiative with $160 million investment to address emerging challenges in this inevitable urbanization. Under the scope of this initiative, my work addresses emerging problems in the smart energy systems in connected communities with a data-driven approach, including sensing hardware design, streaming data collection to data analytics and privacy, system modeling and control, application design and deployments. In this talk, I will focus on an example of data driven solutions for data and energy management in smart grids. I will first show how to collect the energy data from large-scale deployed low cost smart meters and minimize the communication and storage overhead. Then I will show how we can conduct energy data analytics with the collected energy data and utilize data analytics results for real-time energy management in a microgrid to minimize the operational cost. Finally, I will present real-world impact of my research and some future work about CPS in smart cities. Zhichuan Huang is a Ph.D. candidate in Department of Computer Science and Electrical Engineering at University of Maryland, Baltimore County. He is interested in incorporating big data analytics in Cyber-Physical Systems (also known as Internet of Things under some contexts) for data driven applications in Smart Connected Communities. His current focus is on data driven solutions for smart energy systems including from sensing hardware design, streaming data collection to data analytics and privacy, system modeling and control, application design and deployments. His technical contributions have led to more than 20 papers, featuring 14 first-author papers in premier venues, e.g., IEEE BigData, ICCPS, IPSN, RTSS and best paper runner-up in BuildSys 2014.