RESEARCH KEYNOTE SERIES
Johns Hopkins University, USA
Bio: Prof. Dr. Lanier Watkins holds a Ph.D. in Computer Science (May 2010) from Georgia State University where he was advised by Dr. Raheem Beyah, three M.S. degrees in the areas of Biotechnology (Johns Hopkins University-2017), Computer Science and Physics (Both from Clark Atlanta University-1999 & 1997), and a B.S. degree in Physics (Clark Atlanta University-1997). In May 2011, he joined the Asymmetric Operations Sector of the Johns Hopkins University Applied Physics Laboratory (JHU/APL) as a Senior Professional Staff II, in October of 2013 he was extended a dual appointment as a Lawrence R. Hafstad Fellow and an Associate Research Scientist with the JHU Information Security Institute, in October of 2015 he became a Lecturer for the JHU Engineering for Professional (EP) Master’s Program, and in February of 2019 he was appointed Chairman of the EP Computer Science and Cyber Security Master’s Program. He teaches 4 courses and has published more than 30 conference papers, journals, and book chapters. He also hold several patents and provisional patents. Further, he has mentored nearly 50 cyber security Master’s research students, and currently advises 3 doctoral students. Prior to joining APL, Lanier worked for over 10 years in industry. He first worked at the Ford Motor Company and then later at AT&T where he held roles such as systems engineer, network engineer, product development manager, and product manager. He is also a member of the IEEE and ACM.
The goal of his research is to develop innovative algorithms and frameworks to address the continuously changing needs of defending Critical Infrastructure (CI) networks and systems. His research efforts are concentrated in five areas: (1) network security – introduction of new covert channels, cloud paradigms, and network-based detectors to produce both offensive and defensive capabilities, (2) IoT security – focus on mobile, cyber physical, and wireless sensor/medical device security, (3) vulnerability monitoring & analysis – introduction of new risk management and security assessment frameworks for IoT devices, (4) malware monitoring & analysis – exploration of active malware defenses to contribute to the increasingly popular Hacking Back paradigm, and (5) data analytics & assured AI – investigating the use of autonomous decision making and methods of AI assurance and security to help data scientist and engineers defend CI against traditional threats and the inevitable threat of adversarial AI.
Title of the talk: A Red Team Evaluation of Commercial Off-The-Shelf Autonomous Drones
Abstract: The drone industry is projected to reach $84 billion by 2025, which indicates the prevalence of this technology in our society. Many of the advanced drone applications can only be realized via autonomous drones. One major issue regarding drones is security. Researchers have demonstrated and documented examples of the security issues with user-controlled drones;however, there are few security assessments or evaluation approaches put forth by the security community for autonomous drones. To address this gap, we: (1) offer a Red Team (external security assessment) evaluation for autonomous drones and (2) demonstrate how the use of this approach can lead to an external security assessment of the core aspects of autonomous drones (e.g., its sensors, autonomous code, and inherent embedded system interrupts that subvert autonomy). We experimentally apply our Red Team security assessment to the DJI Spark and Phantom 4 while in autonomous mode (i.e., Active Track) to demonstrate its practicality and usefulness. Finally, we discuss the initial vulnerabilities that were found and the methods for mitigation.
Google and UCSB, USA
Bio: Prof.John Martinis attended U.C. Berkeley from 1976 to 1987. His PhD thesis was a pioneering demonstration of quantum-bit states in superconductors. After postdoctoral research at CEA in France, he joined NIST Boulder where he developed electron counting devices and x-ray microcalorimeters. In 2004 he moved to U.C. Santa Barbara where he continued work on quantum computation. In 2014 he was awarded the London Prize for low-temperature physics research. In 2014 he joined the Google quantum-AI team to build a useful quantum computer.
Title of the talk: Quantum supremacy using a programmable superconducting processor
Abstract: The promise of quantum computers is that certain computational tasks might be executed exponentially faster on a quantum processor than on a classical processor. A fundamental challenge is to build a high-fidelity processor capable of running quantum algorithms in an exponentially large computational space. Here we report the use of a processor with programmable superconducting qubits to create quantum states on 53 qubits, corresponding to a computational state-space of dimension 2^53 (about 10^16). Measurements from repeated experiments sample the resulting probability distribution, which we verify using classical simulations. Our Sycamore processor takes about 200 seconds to sample one instance of a quantum circuit a million times—our benchmarks currently indicate that the equivalent task for a state-of-the-art classical supercomputer would take approximately 10,000 years. This dramatic increase in speed compared to all known classical algorithms is an experimental realization of quantum supremacy for this specific computational task, heralding a much-anticipated computing paradigm.
University of Nevada, Las Vegas, USA
Bio: Prof. Dr. Shahram Latifi,an IEEE Fellow, received the Master of Science and the PhD degrees both in Electrical and Computer Engineering from Louisiana State University, Baton Rouge, in 1986 and 1989, respectively. He is currently a Professor of Electrical Engineering at the University of Nevada, Las Vegas. He has authored over 250 technical articles in the areas of networking, cybersecurity, image processing, biosurveillance, and biometrics. His research has been funded by NSF, NASA, DOE, DoD, Boeing, Lockheed and Cray Inc. He has served as the editor of IEEE Transactions on Computers and as a distinguished speaker of IEEE Computer Society . He is the founder of two major international conferences (IEEE-ITCC and ITNG) and has chaired several other conferences. Dr. Latifi has given keynotes in areas of imaging, security and machine learning in several international forums.
Title of the talk: Advanced Biometrics
Abstract: Biometrics have long been used to secure lives and investments. Most of the biometrics techniques are image-based and have their own merits and demerits offering a tradeoff among various factors such as ase of use, resilience, reliability and cost-effectiveness. Inspired by recent advances in technology such as high processing power and high resolution cameras, current research focuses on moving biometrics techniques from an overt mode to covert, touch-based to stand-off capture-based, and
single-mode to multibiometrics. In this talk, an overview of advanced biometrics techniques is presented. We also present our research results on partial iris recognition to be employed in a covert or stand-off mode. We also address a multimodal biometrics technique obtained by fusing iris and retina images which gives a more reliable and accurate result than each of the unimodals
University of North Texas, USA
Bio: Prof. Dr. Renee Bryce,is a Professor of Computer Science & Engineering at University of North Texas. She earned her Ph.D. in Computer Science from Arizona State University in May 2006. She earned her B.S. (1999) and M.S. (2000) degrees from Rensselaer Polytechnic Institute. Her research areas interests include Software Engineering, with emphasis on combinatorial testing and test suite prioritization for emerging technologies. She has served as Primary Investigator on funding from the National Science Foundation, National Institute of Standards and Technology, U.S. Forest Service, Lawrence Livermore National Lab, and more. Professor Bryce is the recipient of the 2018 Dallas/Fort Worth Tech Titans Award Recipient for the University Level, 2015 NCWIT Undergraduate Research Mentor Award for the category of Junior Faculty (Assistant/Associate Professor) at a Research University, and 2006 Arizona State Commission on the Status of Women award for her "achievements and contributions towards advancing the status of women".
Title of the talk: Systematic Software Testing Techniques for Emerging Technologies
Abstract: Software systems can be large and exhaustive testing is usually not feasible. Products released with inadequate testing can cause bodily harm, result in large economic losses, and affect the quality of daily life. The National Institute for Standards and Technology (NIST) last reported that software defects cost the U.S. economy close to $60 billion a year. Novel cost-effective software testing techniques have the potential to make a large economic impact. Software testers often intuitively test for defects that they anticipate while less foreseen defects are overlooked. My research applies combinatorial testing strategies that are systematic to offset human bias, yet are more practical than exhaustive testing.
In this talk, I review my work in the areas of combinatorial testing and combinatorial-based test suite prioritization. I present my current and ongoing work that explores techniques to systematically and cost-effectively test context-aware systems. Such context-aware systems involve combinatorial explosions of event and context interactions, creating further testing challenges. For instance, mobile applications not only respond to user events, but also to context events such as changes to network connectivity, battery level, screen orientation, and more. This talk discusses novel testing techniques for context-sensitive systems, such as mobile applications, Internet of Things, and autonomous vehicles, where streams of context changes from their environments pose challenges.
San Diego State University, USA
Bio: Dr. Sarkar is a Professor in the Department of Electrical and Computer Engineering at San Diego State University (SDSU). She received her doctorate degree in January 2006 from University of California, San Diego. After a brief stint as a senior scientist in SPAWAR Lab, Point Loma, Dr. Sarkar joined SDSU as a tenure-track faculty member in August 2006. Her research interest lies in the area of wireless data networks. She has published over 80 research articles in technical journals, conference proceedings and book chapters. Her research work has been primarily funded by National Science Foundation (NSF). She is the Director of the NSF funded Wireless Networks Research Group at SDSU where she leads a team of Ph.D. and Masters students along with post-doctoral scholars, visiting faculty and PhD students from various countries. She is the recipient of the President’s Leadership award at SDSU in 2010 for her excellence in research and the Outstanding Faculty Award in 2014 for her excellence in teaching. In her role as the Co- Director of Education in the NSF funded Engineering Research Center (Center for Neurotechnology), she conducts extensive outreach programs in building the next generation of women scientists and those from underrepresented communities. She is currently the Chair of the Diversity, Equity and Inclusion committee in the College of Engineering at SDSU.
Title of the talk: The Future of Wireless Health
Abstract: The Wireless in Healthcare Industry has sustained significant growth in the last decade and is likely to continue to expand in the future because of the increasing demand for patient care and an expansion of wireless electronic devices and networks in hospitals and other clinical settings. Additionally, economic growth, coupled with greater access to health care in countries such as China, India and Brazil, has helped to foster an increase in the use of medical products worldwide. Moreover, within the wireless medical device industry, there has been continuous innovation in the technologies of sensors, implants and wireless communication, which is increasing the scope of wireless medical technology applications. This talk is an overview of the technological advances that are being made in the domain of healthcare using wireless technologies and machine learning. Advances in wireless networks and communication, sensor development and machine learning techniques have moved the frontiers of Brain Computer Interfaces (BCI) – both its invasive and non-invasive applications. In addition, the need for patients and their families to have access and monitoring capabilities of medical record and physical whereabouts have led to innovations in a wide area of other applications of wireless health like fall detection in elderly adults, restoring mobility in human paralyzed limbs and robotic surgeries, to name a few. This talk addresses current research trends in the aforementioned areas and discusses the challenges of putting healthcare in the hands of technology and the lay person.
Rice University, USA
Bio: Dr. Akane Sano is an Assistant Professor at Rice University, Department of Electrical Computer Engineering and Computer Science. She directs Computational Wellbeing Group. Her research focuses on affective computing and mobile health: human sensing, data analysis and application development for health, wellbeing and cognitive performance.
She has worked on measuring and predicting stress, mental health, sleep and performance and designing systems to help people to reduce their stress and improve their mental health, sleep and performance including SNAPSHOT study project, Eureka project (symptom prediction and digital phenotyping in schizopherenia using phone data) and IARPA mPerf project (using mobile sensors to support productivity and employee well-being).
She obtained her PhD at MIT Media Lab, and her MEng and BEng at Keio University, Japan. Before she joined Rice University, she was a Research Scientist in Affective Computing Group at MIT Media Lab, and a visiting scientist/lecturer at People-Aware Computing Lab, Cornell University.
Before she came to the US, she was a researcher/engineer at Sony Corporation and worked on affective/wearable computing, intelligent systems and human computer interaction from 2005-2010.
Recent awards include the Best Paper Award at IEEE BHI 2019 conference, the Best Paper Award at the NIPS 2016 Workshop on Machine Learning for Health and the AAAI Spring Symposium Best Presentation Award.
Title of the talk: Toward data-driven personalized wellbeing and performance assistant
Abstract: Imagine 24/7 rich human physiological and behavioral data could identify your wellbeing and performance such as your stress level and cognitive ability and provide personalized early warnings to help you more resilient to stress or be alert and focus on your work. We have developed tools and algorithms for measuring, predicting and supporting individual, organizational and community wellbeing and performance. Our studies targeted patients with mental illness as well as people at high risk of mental health and wellbeing due to adverse events such as job/academic related demands and extended/shift work schedules. This talk will highlight a series of studies and technologies we have developed to investigate how to leverage multi-modal human data to measure, understand and improve wellbeing and performance, challenges and learned lessons.
|Full Paper Submission:||20th November 2019|
|Acceptance Notification:||12th December 2019|
|Final Paper Submission:||22nd December 2019|
|Early Bird Registration:||20th December 2019|
|Presentation Submission:||31st December 2019|
|Conference:||6 - 8 January 2020|
• Conference Proceedings will be submitted for publication at IEEE Xplore Digital Library.
• Best Paper Award will be given for each track
• There will be one workshop on IoT on Jan 8, 2020
• Conference Record No- 47524