Major Grants Initiative

Faculty Champions: Dimitrios Nikolopoulos (CS), Todd Lowe (AOE), Chris Roy (AOE), Adrian Sandu (CS)

Goal: VT-ALT-F will leverage Virginia Tech's exceptional strengths in fluid dynamics  experimentation, computational modeling, scalable AI, and high-performance computing to tackle the grand challenge of understanding turbulent boundary layer separation and mode switching, a problem for all ground, air, and sea vehicles, urban structures, and wind turbines. Centered on the BeVERLI Hill experiment, the laboratory will integrate adaptive experiment control, multi-modal data fusion, and intelligent decision-making to accelerate discovery—laying the groundwork for a future center-scale research program at the intersection of experimental fluid dynamics, AI-driven control, and autonomous scientific investigation. 

Faculty Champions:  Eli Vlaisavljevich (BEAM), Adam Maxwell (BEAM), Shima Shahab (ME)

Goal: We propose to establish a Focused Ultrasound (FUS) One Health Center that leverages the strengths of our faculty to accelerate the worldwide adoption of FUS for human and veterinary patients. VT is uniquely situated to host this Center based on our institutional resources and rapidly growing FUS programs with faculty across campus working on a growing portfolio of FUS projects with exciting translational potential. Investment from COE will help move us towards our goals of establishing core facilities and a centralized administrative hub to connect COE faculty to key research, industry, and clinical partners.

Faculty Champions: Anuj Karpatne (CS), Anne Staples (ME), Danesh Tafti (ME)

Goal: We aim to enable transformative advances at the confluence of artificial intelligence (AI), mechanics, and biology to develop a new paradigm of decentralized AI inspired by the nature of embodied intelligence in biological systems. Our proposed research addresses challenges in AI for science by developing AI models that are modular, trustworthy, and  generalizable. Our work also addresses challenges in harnessing the full potential of AI to accelerate discoveries in science, especially to understand and implement the principles of intelligence embodied in insects, bats, and other organisms; as a preliminary project, we will build decentralized AI to engineer bio-inspired medical devices.

Faculty Champion: Chandan Reddy (CS)

Goal: To launch a new interdisciplinary initiative—FIND—that integrates interpretable and physically grounded AI techniques into both scientific discovery and engineering design, enabling rapid hypothesis generation, simulation-free design optimization, and scalable knowledge synthesis. 

Faculty Champions: Doug Bowman (CS) and Joe Gabbard (ISE)

Goal: The Intelligent XR project aims to enable future augmented/virtual reality systems that can be used anywhere to support people’s work and broader life goals. In contrast to traditional XR, intelligent XR systems reason about the context of use to adapt the user interface, offer intelligent suggestions, and provide helpful information. Our objective is to solidify Virginia Tech’s position as a world leader in intelligent XR—an emerging, cross-disciplinary field that many believe will represent the next computing paradigm. We will accomplish this through team-building and research, leading to multiple external grant submissions, with the long-term goal of a center-scale grant.

Faculty Champion: Justin Jaworski (AOE)

Goal:  Advanced air mobility (AAM) is a rapidly-advancing paradigm to realize electrified regional and urban air transportation. This goal requires technical advancements through multidisciplinary graduate research and workforce development of current professionals seeking to develop into this nascent area. Virginia Tech is uniquely-poised technically and geographically to pioneer research and education programming focused on AAM. The proposed effort will (i) build the necessary partnerships to pursue collaborative funding en route to center-level grants; (ii) structure a curriculum of courses across the University that is market-relevant and financially sustainable; and (iii) establish clear long-term  objectives and funding goals to drive immediate actions.

Faculty Champions: James Kong (ISE), Lingjia Liu (ECE/IAC), Chris Williams (ME), Tao Sun (ISE)

Goal: To establish the foundational research, partnerships, and infrastructure critical to secure large-scale federal funding, such as an NSF ERC. By developing a 6G-enabled wireless control framework for reconfigurable hybrid manufacturing workcells, the team aims to transform U.S. manufacturing into a resilient, intelligent, and future-ready ecosystem. This effort will address critical technical and systemic gaps, support workforce development, and catalyze national leadership in advanced manufacturing through interdisciplinary collaboration, real-world testbeds, and alignment with strategic priorities of federal funding agencies, such as NSF, DoD, DOE, DARPA, and NIST. 

Faculty Champion: Ali Mehrizi-Sani (ECE)

Goal: Data centers are a national priority and a Virginia urgency. The goal of POWER-DATA is to establish a convergent research center that pioneers autonomous, scalable, and secure data center ecosystems. By integrating innovations in power systems, 5G/6G communications, cybersecurity, thermal management, and sustainability, POWER-DATA will address urgent national challenges in digital infrastructure. POWER-DATA will enable resilient, net-zero data centers through interdisciplinary research, industry collaboration, and workforce development. The initiative will position Virginia Tech as a national leader in sustainable digital systems while advancing fundamental knowledge in alignment with the priorities of federal and state agencies.

Faculty Champions: Cindy Yi (ECE/IAC), Peter Vikesland (CEE), Lingjia Liu (ECE/IAC)

Goal: We seek to develop a unified, edge-native Internet of Things (IoT) framework with extremely efficient computing and communication capabilities. The on-board intelligence of IoT will be enabled through ultra-low-power neuromorphic computing systems while efficient connectivity will be achieved by leveraging NextG federated-learning over resilient distributed massive-MIMO (multiple input and multiple output) networks. The developed IoT framework will be tailored to advance real-time water quality monitoring and control, automotive hazard detection, and precision agriculture. Through use[1]inspired co-design of spiking-neural hardware, distributed AI, and distributed communication protocols, we will demonstrate autonomous, “self-driving” sensing-to-action loops in these three critical fields.