IIT Faculty Candidate Seminar Sponsored by Department of Integrated Information Technology Yoris Au Department of Information Systems and Cyber Security College of Business The University of Texas at San Antonio Abstract: This study investigates the effect of observational learning in the crowdsourcing market as an attempt to identify appropriate mechanism(s) for sustaining this increasingly popular business model. Observational learning occurs when crowdsourcing participating agents obtain knowledge from signals they observe in the marketplace and incorporate such knowledge into subsequent actions to improve their participation outcomes. This form of learning is examined in the context of the two-sided crowdsourcing platform in which participating customers’ and professionals’ decisions interact with and influence each other. Two structural models are constructed to capture customer and professional’s probability of success in the presence of various constantly changing market signals. A third model is developed to capture factors that influence market outcomes such as level of participation by professionals. These models will be estimated using the Bayesian approach on a longitudinal dataset that consists of seven years of transaction data in four product categories from a leading crowdsourcing site. We expect to observe learning effect in this crowdsourcing market and to identify various factors that influence the probability of a professional (agent) submitting a bid to a crowdsourcing project and the probability of a customer (principal) selecting a winner through observational learning.

IIT Faculty Candidate Seminar Sponsored by Department of Integrated Information Technology Martin Stytz Chief Research & Technology Officer Calculated Insight Abstract: Healthcare decision-support is a new, crucial, vibrant area of research motivated by the rapid pace of advances in medical information technology and the vast amount of data that a healthcare provider will have to comprehend and prioritize when technology allows a complete patient medical record to be made available. We believe that the amount of data that the healthcare provider will confront will be overwhelming. In a large-scale medical data environment, preventing data overload and protecting the integrity of the data will be important. Clearly, the healthcare provider requires decision-support tools that will aid in retrieving, identifying, displaying, and analyzing the relevant medical data. Our goal is to develop simulation technologies that can be used to build advanced medical decision-support tools that can exploit the large-scale amounts of medical data that will be available. However, as noted the data must be trustworthy. A cyber-attack upon medical data can disrupt information, sow confusion, thwart situational awareness, increase decision time, and delay reaction to events. Because of the seriousness of the consequences of a cyber-attack, we contend that medical decision-makers must be prepared to operate within environments where information is compromised. A safe method for preparing for the cyber-attacks is to acclimate medical decision-makers to information compromised environments using simulation systems. The cyber-attack simulation environments can cause the information uncertainty and confusion that cyber-attacks produce. These same cyber simulation environments can be used to develop intelligent cyber defense systems that react to preserve the medical information environment. Additionally, the cyber-attack simulation environment can be used to develop and test cyber defense strategies and technologies. In the talk, we will discuss simulation to improve healthcare delivery through the development of better decision-support tools and the use of simulation to improve cyber security, and medical infrastructure cyber resilience. Biography: Dr. Martin Stytz received his PhD form University of Michigan in 1989. His research interests encompass secure systems, secure software development, cybersecurity, high-confidence data analysis, and cyber situational awareness.. Dr. Stytz has published 28 journal articles, over 300 technical articles and holds two patents. Dr. Stytz has conducted $12.8 Million ($8 Million as PI) in research for the US Government.

COLLOQUIUM Selcuk Uluagac Abstract Cyber space is expanding fast with the introduction of new Internet of Things (IoT) and CPS devices. Wearables, smart watches, glasses, fitness trackers, medical devices, Internet-connected house appliances and vehicles have grown exponentially in a short period of time. Our everyday lives will be dominated by billions of connected smart devices by the end of this decade. Similarly, our nation's critical infrastructure (e.g., Smart Grid) also deploys a myriad of CPS and IoT equipment. Given the increasingly critical nature of the cyberspace of these CPS and IoT devices and the CPS infrastructure, it is imperative that they are secured against malicious activities. In this talk, I will briefly introduce three different current research topics related to the security of CPS and IoT devices and the CPS infrastructure: (1) The first topic will introduce the sensory channel threats to CPS and IoT systems. I will discuss how using sensory channels (e.g., light, temperature, infrared), an adversary can successfully attack IoT/CPS applications and devices. (2) The second topic will introduce the design of a novel IoT device fingerprinting and identification framework (IFF) to complement existing security solutions (e.g., authentication and access control) in identifying CPS and IoT devices (i.e., ensuring the devices are actually who they are). Finally, (3) The third topic will focus on the threat of counterfeit smart grid devices (e.g., PMUs, IEDs). Such devices with corrupted hardware components may exist in the deployment region without a priori knowledge and may leak important information to malicious entities. Dr. Selcuk Uluagac is currently an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) at Florida International University (FIU). Before joining FIU, he was a Senior Research Engineer in the School of Electrical and Computer Engineering (ECE) at Georgia Institute of Technology. Prior to Georgia Tech, he was a Senior Research Engineer at Symantec. He earned his Ph.D. with a concentration in information security and networking from the School of ECE, Georgia Tech in 2010. He also received an M.Sc. in Information Security from the School of Computer Science, Georgia Tech and an M.Sc. in ECE from Carnegie Mellon University in 2009 and 2002, respectively. He obtained his BS in Computer Science and Engineering and BA in Naval Science from the Turkish Naval Academy in 1997. The focus of his research is on cyber security topics with an emphasis on its practical and applied aspects. He is interested in and currently working on problems pertinent to the security of Internet of Things and Cyber-Physical Systems. In 2015, he received a Faculty Early Career Development (CAREER) Award from the US National Science Foundation (NSF). In 2015, he was also selected to receive fellowship from the US Air Force Office of Sponsored Research (AFOSR)?s 2015 Summer Faculty Fellowship Program. In 2016, he received the Summer Faculty Fellowship from the University of Padova, Italy. In 2007, he received the ?Outstanding ECE Graduate Teaching Assistant Award? from the School of ECE, Georgia Tech. He is an active member of IEEE (senior grade), ACM, USENIX, and ASEE and a regular contributor to national panels and leading journals and conferences in the field. Currently, he is the area editor of Elsevier Journal of Network and Computer Applications and serves on the editorial board of the IEEE Communication Surveys and Tutorials. More information can be obtained from: http://web.eng.fiu.edu/selcuk.

COLLOQUIUM Xueqing Huang Mobile and wireless systems are bracing for a massive penetration of Internet of Things (IoT) devices and experiencing an exponential growth in wireless applications. To achieve the expected service requirements, the networking resources are being pushed to the edge, such that each edge node can function as a standalone local unit, which has its own green energy harvester, cache and computing resources. Since resources available at the edge nodes are limited and dynamic, network cooperation is critical to guarantee the smooth operation and security of the wireless access networks. In this talk, I will present the network cooperation framework that allows the ?connected? edge nodes to share their networking resources. The first part of this talk concentrates on secure cooperative data transmission. By exploring the broadcasting nature of wireless links, the radio resources in terms of energy can be shared by allowing base stations to transmit data to the same user. I will present cooperative data transmission schemes to improve the network performance in terms of energy efficiency and data confidentiality. The second part of this talk focuses on secure data crowdsourcing. By leveraging multiple data paths at the mobile edge, distributed storage resources can be used to facilitate data sharing among a crowd of users. Data privacy is paramount in assuring users in crowdsourcing; I will present a multi-party data transmission scheme to improve the data sharing latency and data privacy. Xueqing Huang received the B.E. degree from the Hefei University of Technology in 2009, and the M.E. degree from the Beijing University of Posts and Telecommunications in 2012. She is currently a Ph.D. candidate from the New Jersey Institute of Technology. Her research interests are in internet of things, physical layer and network security, and cooperative mobile edge.

COLLOQUIUM Department of Computer Science and Engineering University of South Carolina Damindra Bandara Abstract Safety objectives of Positive Train Control (PTC) are to avoid train to train collisions, train derailments and ensure railroad worker safety. Under published specifications of Interoperable Electronic Train Management System (I-ETMS), the on-board PTC controller communicates with two networks; the Signaling Network and the Wayside Interface Unit (WIU) network to gather navigational information such as the positions of other trains, the status of critical infrastructure and any hazardous conditions along the train path. PTC systems are predicated on having a reliable radio communication network. Secure Intelligent Radio for Trains (SIRT) is an intelligent radio that is customized for train operations with the aim of improving the reliability and security of the radio communication network. SIRT system can (1) operate in areas with high train congestion, different noise levels and interference conditions, (2) withstand jamming attacks, (3) improve data throughput and (4) detect threats and improve communication security. My work includes (1) Analyzing the PTC system to identify communication constraints and vulnerabilities, (2) Designing SIRT to overcome them, (3) Developing a prototype of SIRT using Software Defined Radios and (4) Testing it under varying channel conditions, noise levels and attackers. My experiments show that SIRT dynamically chooses the best modulation schemes based on the channel noise level and switches channels in response to channel jamming. Also, it changes cryptographic key values using a scheme like Lamport scheme and detects replay and forgery attacks with an accuracy more than 93%. Damindra Bandara is a Ph.D. candidate at George Mason University, Fairfax, Virginia. She will defend her Ph.D. by the end of March 2017. She received her Bachelor's degree in Electrical and Electronic Engineering from University of Peradeniya, Sri Lanka and Master degree in Information Security and Assurance from George Mason University. Previously she has worked as a Wireless Quality Assurance intern at Time Warner Cable, Herndon Virginia and as a lecturer in Department of Electrical and Electronic Engineering, University of Peradeniya, Sri Lanka. Her research interests are network security, wireless controlled trains and Software Defined Radio.

COLLOQUIUM Sherif Abdelwahed Abstract Modern computing systems support a range of mission-critical information technology applications crucial to commerce and banking, transportation, and command and control systems, to name just a few. Consequently, their reliable design and operation have significant economic and social impact. To operate such systems effectively while maintaining their availability and security multiple operational data and parameters must be analyzed in real-time and dynamically tuned to adapt to abnormal conditions such as failures or cyber-attacks. As system and application scales increase, ad hoc heuristic-based approaches to system adaptation and management quickly become ineffective. Model-based technologies help address this problem by enabling design-time and run-time analysis, and providing means to automate the development, verification, deployment and real-time adaptation of computing systems. This presentation introduces recent work on developing model-based approaches for systematic design of reliable and secure self-managing computing systems. The developed approaches use mathematical models to represent the system reaction to both control and environment inputs. In these approaches, the system management problems of interest are posed as a sequential and discrete optimization under uncertainty. Results of this work show that model-based techniques can be effectively applied to maintain the security and reliability of complex modern computing systems. The presentation introduces several implementations of this model-based technology and discusses future related research directions. Sherif Abdelwahed is an Associate Director of the Distributed Analytics and Security Institute (DASI) and an Associate Professor in the Electrical and Computer Engineering Department at Mississippi State University (MSU) where he teaches and conducts research in the area of computer engineering, with specific interests in cyber-security, autonomic computing, real-time systems, modeling and analysis of discrete-event and hybrid systems, model-integrated computing, and formal verification. He received his Ph.D in 2002 from the Department of Electrical and Computer Engineering at the University of Toronto. Prior to joining Mississippi State University, he was a research assistant professor at the Department of Electrical Engineering and Computer Science and senior research scientist at the Institute for Software Integrated Systems, Vanderbilt University, from 2001-2007. From 2000-2001 he worked as a research scientist with Rockwell Scientific Company. He established, collaboratively, the first NSF I/UCRC center at Mississippi State University, the Center for Autonomic Computing. He is currently the co-director of this center. He co-chaired several international conferences and conference tracks, and has served as technical committee member at various national and international conferences. He received the StatePride Faculty award for 2010 and 2011, the Bagley College of Engineering Hearin Faculty Excellence award in 2010, and recently the 2016 Faculty Research Award from the Bagley College of Engineering at MSU. Dr. Abdelwahed has more than 140 publications and is a senior member of the IEEE.

COLLOQUIUM Department of Computer Science and Engineering University of South Carolina Shuai Li Abstract With the advances of mechanics, electronics, computer engineering, using autonomous robots, or a collection of them, to perform various tasks is becoming increasingly popular in both industry and our daily lives. Control plays an important role for stable and accurate task execution while learning is outstanding in dealing with unknowns or uncertainties. Recent advances in machine learning provide us with an opportunity to employ innovative learning structures for efficient adaptation. However, it remains challenging on how to efficiently integrate learning with control efficiently to reach provable and guaranteed stability even in the worst case. This talk will present our recent results along this research direction. Shuai Li received the B.E. degree in electrical engineering from the Hefei University of Technology, Hefei, China, in 2005, the M.E. degree in control engineering from the University of Science and Technology of China, Hefei, in 2008, and the Ph.D. degree in electrical and computer engineering from the Stevens Institute of Technology, Hoboken, NJ, USA, in 2014. He joined Hong Kong Polytechnic University after graduation and directed his group to do research in robotics, cyber physical systems, intelligent control, etc. Dr. Li is an associate editor of the International Journal of Advanced Robotic Systems, Frontiers in Neurorobotics, and Neural Processing Letters.

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.