Category: Events

Faculty Candidate Tao Li to Present Lecture February 27

The Colleges of Computing and Engineering invite the campus community to a lecture by faculty candidate Tao Li on Thursday, February 27, 2020, at 3:00 p.m. in. Fisher 325. His talk is titled, “Security and Privacy in the Era of Artificial Intelligence of Things.”

Tao Li is a Ph.D. candidate in computer engineering in the School of Electrical, Computer and Energy Engineering at Arizona State University. He received an M.S. in somputer science and technology from Xi’an Jiaotong University in 2015, and a B.E. in software engineering from Hangzhou Dianzi University in 2012. His research focuses on cybersecurity and privacy, indoor navigation systems for visually impaired people, and mobile computing. 

AIoT—Artificial Intelligence of Things (AIoT)—combines artificial intelligence (AI) technologies with the Internet of Things (IoT) infrastructure. By 2025, the number of IoT devices in use is estimated to reach 75 billion.

And as AIoT plays an incrreasingly significant role in our everyday lives, the security and privacy of AIoT has become a critical concern for the research community and the public and private sectors. 

In his talk, Li will introduce his recent research focused on the protection of AIoT devices. A novel system that can automatically lock mobile devices against data theft will be introduced, and a touchscreen key stroke attack (based on a video capturing the victim’s eye movements) will be discussed. Li will briefly introduce additional projects of interest.

Li has served as a reviewer for journals and conferences including IEEE TMC, IEEE TWC, ACM MobiHoc, and IEEE INFOCOM.


Faculty Candidate Chensheng Wu to Present Lecture March 4

The Colleges of Computing and Engineering invite the campus community to a lecture by faculty candidate Chensheng Wu on Wednesday, March 4, 2020, at 3:00 p.m. in Chem Sci 101. (In the original announcement, the date of the talk was incorrect.) Wu’s talk is titled, “Design and implementation of computational optics: perception, control, and processing of light-field information and future challenges.”

Dr. Wu is an assistant research scientist in the Department of Electrical and Computer Engineering at the University of Maryland College Park, where he received a Ph.D. degree in ECE.  His doctoral thesis, “the plenoptic sensor,” was was awarded distinguished dissertation honors. Wu also has a B.E. degree in micro-electronics and B.S. in economy, both from Tsinghua University, Beijing, China. 

The emerging field of computational optics is growing rapidly, and it constantly requires newer sensors and computational architectures to satisfy the exploding needs in data collection and processing. Many other research disciplines, such as machine learning, the internet of things, data privacy, and security have also added great challenges to the means of collecting, processing and transmitting data.

The concept of using special optical structures or coded lenses to perform the computation along with data collection, encryption or transmission is becoming a favorable solution in countless applications.

In his talk, Wu will discuss his recent work on the use of new computational optics hardware in solving difficult problems in wavefront sensing, adaptive laser beam formation and correction, imaging through turbulence, detecting hidden objects through scattering media, and space optics. He will discuss how these recent discoveries reveal the potential of specially designed optical structures for computing, and share examples of how future computational optics will take part in sensing, communication, and computation. Wu will conclude his talk with a monologue on predicting the future of computational optics. 

Wu is an advocator for computational sensing using optical and photonics approaches. He is a leading scientist on multiple projects funded by the Office of Naval Research (ONR) and the Directed Energy Joint Technology Office (DE-JTO) Wu’s innovations of the plenoptic sensor, multi-aperture laser transmissometer, computational beam shaping with two deformable mirrors, and lossy sensing-based adaptive optics correction have become well-known. 

Wu has also worked with the Naval Air Warfare Center Aircraft Division (NAWCAD) to configure a new approach to identify and profile hidden objects in murky water environments. He is recognized as a key contributor to NASA’s next generation lunar reflector (NGLR) task to put three new retro-reflectors on the Moon for lunar laser ranging experiments in the 21st Century. Wu is also a team member in the joint collaboration of the Lunar Geophysical Network.

Faculty Candidate Mohammad Khalili to Present Lecture February 24

The Colleges of Computing and Engineering invite the campus community to a lecture by faculty candidate Mohammad Khalili on February 24, 2020, at 3:00 p.m., in Chem. Sci. 102. Khalili’s talk is titled, “Security and Privacy Management: The Role of Cyber Insurance and Randomized Algorithms.”

Mohammad Mahdi Khalilgarekani is a postdoctoral research associate in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley. His research interests are in security economics, algorithmic fairness, data privacy, optimization, and machine learning.

Khalili received a Ph.D. in electrical and computer engineering from the University of Michigan in 2019, an M.Sc. degree in applied mathematics from the University of Michigan in 2018, and a M.Sc. and B.Sc. in electrical engineering from Sharif University of Technology, Iran, in 2015 and 2013, respectively.

Lecture Abstract: Cyber technologies have brought enormous benefits to our society over the past decades and people and communities are more connected than ever before. On the other hand, these technologies provide opportunities for damaging data breaches and large-scale cyber attacks–incidents that can compromise sensitive data, cause business interruptions, and ruin a company’s reputation.

In his talk, Khalili will discuss the role of cyber insurance in addressing security issues, focusing on two features of cybersecurity that differentiate cyber insurance from other types of insurance: the interdependent nature of cyber risks and our ability to perform an accurate quantitative assessment of a firm’s security posture. These features can serve as incentive for profit-maximizing cyber insurers to design insurance contracts that encourage higher security investment, thus improving network security.

Kahlili will also discuss some privacy issues raised in a distributed optimization problem where multiple entities collaboratively work toward a common optimization objective (e.g., training a classifier). Using an interactive process of local computation (over local, private data) and message passing, this information exchange leads to privacy leakage and attackers can infer a user’s personal information from the exchanged computations. A randomized algorithm is introduced to protect an individual’s privacy such that the privacy leakage only happens in half of the iterations, improving the privacy-accuracy tradeoff in comparison to existing algorithms.

Khalili was a recipient of the University of Michigan Rackham Fellowship and the outstanding master’s thesis award from Sharif University of Technology, both in 2015, and in 2018 he was a finalist for the Eleanor Towner dissertation award.


Faculty Candidate Hongyu An to Present Lecture February 12

The Colleges of Computing and Engineering invite the campus community to a lecture by faculty candidate Hongyu An on Wednesday, February 12, at 3:00 p.m. in Chem Sci 101. Hongyu’s talk is titled, “Brain on a Chip: Designing Self-learning and Low-power Neuromorphic Systems.”

Hongyu is a doctoral candidate in the Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University (Virginia Tech). He received B.S. and M.S. degrees in electrical engineering at Shenyang University of Technology, Shenyang, China, and the Missouri University of Science and Technology, Rolla, Mo., respectively.

In 2019, Hongyu was awarded the Paul E. Torgersen Research Excellence Award and a fellowship from the Advanced Short-Term Research Opportunity Program at Oak Ridge National Laboratory. In 2017, he was awarded an NSF Student Travel Fellowship Award, and a paper authored by Hongyu was nominated as best paper in the IEEE International Symposium on Quality Electronic Design (ISQED).

Hongyu’s research interests include neuromorphic and brain-inspired computing, energy-efficient neuromorphic electronic circuit design for Artificial Intelligence, three-dimensional integrated circuit (3D-IC) design, and emerging nanoscale device design. 

His research aims to build a self-learning, low-power neuromorphic system. Inspired by the learning mechanism of the human brain, Hongyu proposed and realized an Associative Memory Learning through neuromorphic circuits and memristors. The proposed learning method correlates two concurrent visual and auditory information together through Artificial Neural Networks. 

Lecture Abstract: How can a silicon brain in a chip be built with self-learning capability? What are the challenges for neural network-based artificial intelligence in the next decade, and how can those challenges be solved? 

In order to answer these questions, Hongyu introduces a cutting-edge research topic: Brain-inspired Computing. Also called neuromorphic computing, Brain-inspired Computing aims to physically reproduce the brain’s structure in a silicon chip to resolve critical challenges in deep learning deployment.

In his talk, Hongyu will explore the underlying biological mechanism of associative memory learning, novel non-von Neumann computer architectures, and circuit implementations with transistors and memristors. 

A widespread self-learning method in animals, associative memory enables the nervous system to remember the relationship between two concurrent events. Rebuilding associative memory is significant, both to reveal a way of designing a brain-like self-learning neuromorphic system, and to explore a method of comprehending the function of the human brain.

Hingyu is a reviewer for several top-tier conferences and journals, including IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Transactions on Circuits and System I: Regular Papers (TCAS-1),Design Automation Conference (DAC). Design, Automation and Test in Europe Conference and Exhibition (DATE), International Symposium on Circuits and Systems (ISCAS).

Visit Hongyu An’s personal website.


Faculty Candidate Lan Zhang to Present Lecture February 5

The Colleges of Computing and Engineering invite the campus community to a lecture by faculty candidate Lan Zhang on Wednesday, February 5, 2020, at 3:00 p.m., in Chem. Sci. 101. Zhang’s lecture is titled, “Machine Learning Enabled Better Cyber-Physical Systems: A Case Study on Better Networking for Connected Vehicles.”

Bio: Lan Zhang is a Ph.D. candidate in the Department of Electrical and Computer Engineering at the University of Florida. She received the B.Eng. and M.S. degrees in telecommunication engineering from the University of Electronic Science and Technology of China, in 2013 and 2016, respectively. Zhang’s research interest spans across the fields of big data, cyber-physical systems, machine learning, wireless communications, and cybersecurity. She has published 15 technical papers in top-tier journals and conference venues, such as IEEE Transactions on Vehicular Technology, Proceedings of the IEEE, and IEEE Transactions on Wireless Communication.

Zhang has served as a technical program committee (TPC) member for several high-quality conferences, such as the 2020 IEEE INFOCOM poster/demo section and the 2018 International Conference on Computing, Networking and Communications. She also serves as reviewer for several leading journals, such as IEEE Transactions on Communications, IEEE Transactions on Vehicular Technology, IEEE Transactions on Mobile Computing, and IEEE Transactions on Wireless Computing. Zhang was the speaker at several flagship celebrations and conferences, such as IEEE Global Communications Conference 19, Grace Hopper Celebration 19, and the IEEE International Conference on Communications 19.

Lecture Abstract: With the recent success of big data analytics, machine learning is being used in various Cyber-Physical Systems (CPS) applications, such as smart transportation, smart healthcare, and industrial automation. As a highly interdisciplinary field, the CPS applications require the machine learning-enabled wireless communication strategies to facilitate information exchanges, and meanwhile call for secure and private learning pipelines to manage information exchanges.

In her talk, Zhang focuses on connected vehicles, aiming at supporting the demand for multi-Gbps sensory data exchanges through millimeter-wave bands for enhancing (semi)-autonomous driving. Unlike most traditional networking analysis that manipulates end devices to adapt to the transmission environments, i.e., fight against any transmission obstacles, we propose an innovative idea to proactively manipulate, reconfigure, and augment the transmission environments for better communications.

Without damaging the aesthetic nature of environments, we deploy multiple small-piece controllable reflecting surfaces, and adaptively manipulate the angle of the used reflecting surfaces to address the vulnerability of blockages in mmWave vehicular communications by creating alternative indirect line-of-sight connections. To autonomously and efficiently augment the highly dynamic vehicular environments in real-time, deep reinforcement learning techniques are implemented. Effectiveness of our proposal is showcased on the traffic at the City of Luxembourg using a traffic simulation toolkit, Simulation of Urban MObility (SUMO). 


Faculty Candidate Fan Chen to Present Lecture February 10

The Colleges of Computing and Engineering invite the campus community to a lecture by faculty candidate Fan Chen, Monday, February 10, 2020, at 3:00 p.m., in Chem. Sci. 102. Chen’s talk is titled, “Efficient Hardware Acceleration of Unsupervised Deep Learning.”

Chen is a Ph.D. candidate in the Department of Electrical and Computer Engineering, Duke University, where she is advised by Professor Yiran Chen and Professor Hai “Helen” Li. Her research interests include computer architecture, emerging nonvolatile memory technologies, and hardware accelerators for machine learning. Fan won the Best Paper Award and the Ph.D. forum Best Poster Award at ASP-DAC 2018. She is a recipient of the 2019 Cadence Women in Technology Scholarship.

Abstract: Recent advances in deep learning are at the core of the latest revolution in various artificial intelligence (AI) applications including computer vision, autonomous systems, medicine, and other key aspects of human life. The current mainstream supervised learning relies heavily on the availability of labeled training data, which is often prohibitively expensive to collect and accessible to only a few industry giants. The unsupervised learning algorithm represented by Generative Adversarial Networks (GAN) is seen as an effective technique to obtain a learning representation from unlabeled data. However, the effective execution of GANs poses a major challenge to the underlying computing platform.

In her talk, Chen will discuss her work that devises a comprehensive full-stack solution for enabling GAN training in emerging resistive memory based main memory. A zero-free dataflow and pipelined/parallel training method is proposed to improve resource utilization and computation efficiency. Hao will also introduce an inference accelerator that enables developed deep learning models to run on edge devices with limited resources. Finally, Hao’s lecture will discuss her vision of incorporating hardware acceleration for emerging compact deep learning models, large-scale decentralized training models, and other application areas.