Dr. Sur Receives NSF Career Award
We are proud to announce that Dr. Sanjib Sur has received an NSF Career Award for his project titled "Vision and Learning Augmented D-Band Networking and Imaging".
This project aims to enable the practical adoption of D-band mmWave networks and applications by solving the fundamental challenges in deployment, link adaptation, coordination, and unified networking-imaging. Specifically, the project explores an optical vision and deep learning augmented paradigm by thoroughly understanding the physical properties of the D-band channel, building measurement-driven empirical and learning models, and designing practical, real-time systems. Successful execution of this project would enable the following. (1) A framework for optimal deployment and a “what-if” analysis tool to help optimize the cost and benefits of D-band deployment in both indoor and outdoor environments. (2) Link adaptation and coordination protocols that significantly minimize latency and maximize throughput and efficiency for scalable D-band networking. (3) A unified networking-imaging protocol that reduces disruptions to the throughput and latency and overcomes challenges with the channel specularity to enable high-resolution D-band images. The project will design, build, and empirically validate the proposed systems in a D-band testbed, and th
Dr. Zeng Receives NSF Career Award
We are proud to announce that Dr. Qiang Zeng has received an NSF Career Award for his project titled "Towards Secure and Usable IoT Authentication Under Constraints".
The project seeks to improve IoT authentication and deliver novel approaches, algorithms, techniques, and systems through the following thrusts. Thrust 1: Authentication for UI-Constrained Devices. A protocol that supports mutual authentication, over an insecure wireless channel, to establish trust between a UI-constrained device and the user to support authentication for heterogeneous IoT devices. Thrust 2: Authentication for Distance-Constrained Devices. A highly usable approach enables secure authentication between an IoT device and the user even when they are multiple meters apart, which has applications ranging from drone delivery to ride sharing. Thrust 3: Authentication for Operation-Constrained Devices. For traditional objects retrofitted with zero-UI sensor nodes, AI-assisted implicit authentication enables recognizing a user without requiring any explicit authentication operations. In sum, the research seeks to substantially advance IoT authentication and foster a variety of IoT applications.
McDowell and Bulusu Receive Best Poster Runner-Up Award at HotMobile'22
Dr. Sanjib Sur is excited to share that their MilliDrone poster by Ian C. McDowell and Rahul Bulusu, both CSE undergraduates, has won the Best Poster Runner-Up Award at the ACM HotMobile'22. Ian and Rahul did a fantastic job pitching the poster!
Alumni Spotlight: De'Aira Bryant
CSE alumnus De’Aira Bryant is featured in the following Amazon Science article "How De’Aira Bryant found her path into robotics":
The computer scientist recently finished her second internship at Amazon, where she worked on a new way to estimate the human expression on faces in images.
New Faculty: Vignesh Narayanan
We would like to welcome Dr. Vignesh Narayanan as a new Assistant Professor to our Computer Science and Engineering department. He received his Ph.D. in Electrical Engineering from Missouri University of Science and Technology (2017), a M.Tech.in Control Systems from the National Institute of Technology, Kurukshetra, India (2014) and a B.Tech. Electrical and Electronics Engineering, SASTRA University, India, (2012).
His research is in the areas of dynamical systems and networks, data science and learning theory, and computational neuroscience. Visit his homepage to learn more.
New Faculty: Christian O’Reilly
We welcome Dr. Christian O’Reilly as a new Assistant Professor to our Computer Science and Engineering department. Dr. O'Reilly received his B.Ing (electrical eng.; 2007), his M.Sc.A. (biomedical eng.; 2011), and his Ph.D. (biomedical eng.; 2012) from the École Polytechnique de Montréal.
His main interests are related to better understanding the brain across spatial and temporal scales in order to address complex neurodevelopmental issues such as autism and other neurodevelopmental disorders. The methods he uses include analytical techniques (e.g., EEG source reconstruction, functional connectivity) and modeling (e.g., point neurons, morphologically-detailed neurons, neural masses), as well as the combination of these two approaches through Bayesian model-driven analyses. He is further interested in novel ways to empower the study of neuroscience through AI and to empower AI through biologically inspired neural networks
MaterialsAtlas: A Toolbox for Materials Discovery from UofSC
The availability and easy access of large scale experimental and computational materials data have enabled the emergence of accelerated development of algorithms and models for materials property prediction, structure prediction, and generative design of materials. However, lack of user-friendly materials informatics web servers has severely constrained the wide adoption of such tools in the daily practice of materials screening, tinkering, and design space exploration by materials scientists. Herein Dr. Jianjun Hu's team developed http://materialsatlas.org, a web based materials informatics toolbox for materials discovery, which includes a variety of routinely needed tools for exploratory materials discovery, including materials composition and structure check (e.g. for neutrality, electronegativity balance, dynamic stability, Pauling rules), materials property prediction (e.g. band gap, elastic moduli, hardness, thermal conductivity), and search for hypothetical materials, and fool-proof machine learning of user-specified datasets. These user-friendly tools can be freely accessed at http://materialsatlas.org. This materials informatics apps will greatly ease the pains in exploratory materials discovery.