SPComNet2024 Workshop on "Data-driven Signal Processing, NextG Communications, and Networking"

Registration Program Venue

We are planning a two-day workshop in Minneapolis on May 16-18, 2024 that will comprise invited talks, panel discussions, and brainstorming sessions involving world-class researchers, and practitioners from academia, government, and industry.

We have identified the following thematic areas to organize panels and talks around:

• Data-driven signal processing

Statistical signal processing involves the use of statistical models and algorithms to interpret, analyze, and extract information from signals, encompassing sounds, images, sensor data, and diverse forms of information that vary over time or space. On the other hand, model-based learning refers to constructing and leveraging models to understand data patterns, make predictions, or optimize decision-making processes. Both statistical signal processing and model-based learning offer robust frameworks to process data, utilizing statistical methods and models to derive meaningful insights and address complex problems across various domains.

• NextG wireless communications

Wireless sensing stands at the forefront of technology, allowing data collection without physical connections, employing a network of distributed sensors. Next-generation (NextG) networks are the latest advancements in wireless communication, promising unparalleled connectivity, speed, and efficiency. The fusion of wireless sensing with NextG networks ushers in an era of seamless, high-speed data transmission and real-time information gathering across diverse applications. This synergy between wireless sensing and NextG networks holds immense promise in revolutionizing connectivity, enabling rapid and reliable data collection for various industries, including healthcare, smart cities, environmental monitoring, and beyond

• AI for grid, graphs, and networks

AI for networks including power grids, social graphs, and wireless networks marks a significant stride toward environmentally conscious and efficient technological advancement. This integration aims to develop AI solutions that optimize energy consumption, enhance network efficiency, and reduce environmental impact. By deploying intelligent algorithms within NextG networks, sustainable AI endeavors to streamline operations, manage resources more effectively, and minimize carbon footprints. The convergence of AI and networks holds promise in shaping a greener, more efficient future for wireless communication, ensuring technological progress aligns with environmental sustainability.

Sponsors

TBD