As an interdisciplinary research center, WNCG supports research with applications in several areas. Our faculty and students are leading the way in topics from 6G to artificial intelligence and machine learning, to healthcare technology and beyond.
WNCG graduate students showcased some of their work in a virtual open house for affiliates last fall. In lieu of a traditional poster session, the students filmed five-minute videos introducing their research.
Check out a curated selection of our students’ research videos on WNCG’s YouTube channel here.
A committee of WNCG faculty and staff recognized three student videos with a Best Video Award.
Above: Marius Arvinte won Best Video
Marius Arvinte was named the overall winner. Arvinte’s video “Deep Diffusion Models for Robust Channel Estimation” covered his research on machine learning algorithms that work out-of-the-box in new environments, with a focus on physical layer processing and medical imaging.
“Robustness is important in digital communications, since channel conditions can change rapidly and it's the modem's job to make sure the connection is seamless.” Arvinte explained. “In our latest work, we use deep diffusion models for wireless channel estimation and show that this approach can be reliably used in new channel models with zero adaptation required.”
Marius Arvinte is a fifth-year doctoral student advised by Prof. Jon Tamir.
Above: Second-place winner Andrew Graff
Second place was Andrew Graff’s video, “Purposeful OFDM Co-Design for Ranging and Communications,” which addressed the demand for precise-positioning services in next-generation wireless networks. Graff’s research analyzes the theoretical bounds of both ranging precision and communications throughput, examines the tradeoffs and impact of OFDM parameters, and guides the purposeful co-design of new OFDM signals to meet ranging and communications requirements in different propagation environments.
Andrew Graff is a third-year doctoral student advised by Prof. Todd Humphreys.
Above: Third-place winner Mónica Ribero
Mónica Ribero was recognized in third place for her video, “Federated Learning Under Time-Varying Communication Constraints and Intermittent Client Availability.” Ribero’s research focuses on enabling privacy-preserving machine learning systems—studying how to model communication constraints and client intermittency in federated learning and efficient differentially private algorithms.
Mónica Ribero is a fifth-year doctoral student advised by Prof. Haris Vikalo.
You can watch the winning talks as well as the full list of available videos on WNCG’s YouTube channel or via the playlist below.