Jayasimha Atulasimha, Ph.D.

The National Academy of Inventors (NAI) recently inducted five Virginia Commonwealth University (VCU) College of Engineering researchers as senior members. Chosen for their innovative engineering contributions, the honorees are recognized as visionary inventors whose groundbreaking research and patented technologies are driving meaningful societal and economic advancements across the national innovation landscape. “Invention represents the practical application of knowledge and stands as one of the many ways engineers can make a positive impact on their communities and the world,” said Azim Eskandarian, D.Sc, the Alice T. and William H. Goodwin Jr. Dean of the VCU College of Engineering. “This year’s honorees exemplify the interdisciplinary nature of our field, leveraging advanced concepts from mechanical, biomedical, chemical and pharmaceutical engineering to address today’s most pressing challenges. We are immensely proud that our dedicated researchers have earned recognition as members of the esteemed National Academy of Inventors.” The VCU College of Engineering NAI inductees are: Jayasimha Atulasimha, Ph.D. Engineering Foundation Professor Department of Mechanical & Nuclear Engineering An internationally recognized pioneer of straintronics, an approach to electrically control magnetism for ultra-low-energy computing, Atulasimha has made significant research contributions to next-generation memory, neuromorphic hardware and emerging quantum computing technologies. He holds four U.S. patents spanning energy-efficient magnetic memory, nanoscale computing architectures and medical tools. Atulasimha’s commercially viable inventions are funded by organizations like the Virginia Innovation Partnership Corporation and he leads multi-institutional collaborations that drive innovation in computing hardware, AI and quantum technologies with more than $10 million in funded research. Casey Grey, Ph.D. Postdoctoral Research Associate Department of Mechanical & Nuclear Engineering Bridging engineering and medicine, Grey’s work spans life‑saving stroke technologies, breakthrough respiratory and neurological care, and sustainable packaging. As a lead R&D scientist at WestRock, he helped create and commercialize the CanCollar® portfolio, a recyclable paperboard replacement for plastic beverage rings now used on five continents, eliminating thousands of tons of single‑use plastic annually. In medical device innovation, Grey’s patent and development work on a novel cyclic aspiration thrombectomy platform, currently in clinical trials, is advancing stroke treatment by enhancing clot removal efficiency and reducing long‑term disability. At the VCU College of engineering, Grey built a research and commercialization pipeline around neurological and respiratory technologies, securing eight provisional patents and leading multidisciplinary teams in neurology, neurosurgery, surgery, pharmacology and toxicology, internal medicine, and respiratory medicine. His work includes developing dry powder inhaler strategies for delivering life‑saving drugs to patients with acute respiratory distress syndrome (ARDS), a pediatric bubble CPAP system designed to protect brain development in premature infants, and non‑invasive, non‑pharmacological 40 Hz neuromodulation therapies to treat neurodegeneration and conditions with significant central nervous system complications, like sickle cell disease. In collaborations with the VCU Children’s Hospital and VCU Critical Care Hospital, Grey is leading two clinical studies that are translating these innovations to improve patient care. Ravi Hadimani, Ph.D. Associate Professor and Director of Biomagnetics Laboratory Department of Mechanical & Nuclear Engineering Hadimani founded RAM Phantoms LLC, a VCU startup company, commercializing anatomically accurate, MRI-derived brain phantoms for neuromodulation and neuroimaging applications. These brain phantoms help test and tune transcranial magnetic and deep brain stimulation technologies, improving clinical safety and enabling personalized therapy for patients. RAM Phantoms is also developing a highly-skilled workforce for employment in Virginia’s growing biomedical device industry. Beyond commercialization, Hadimani maintains a productive research program with more than $4.5 million in funding resulting in 125 original peer-reviewed publications, 17 current and pending patents, a book, and several book chapters. His biomagnetics lab serves as a training ground for undergraduate, graduate and Ph.D. students to hone their skills in innovation management, intellectual property strategy and startup development. Several students from Hadimani’s lab have engaged in translational research, patent co-authorship and start-up formation, cultivating a new generation of engineer-entrepreneurs equipped to drive future technological advances. Before joining VCU, Hadimani led the development of hybrid piezoelectric–photovoltaic materials that established FiberLec Inc., which commercialized multifunctional energy-harvesting fibers capable of converting solar, wind and vibrational energy into usable electricity. Worth Longest, Ph.D. Alice T. and William H. Goodwin, Jr. Distinguished Chair Department of Mechanical & Nuclear Engineering Uniting aerosol science, biomedical engineering and computational modeling, Longest is revolutionizing inhaled drug delivery. Working with collaborators, his lab has developed novel devices, formulations and delivery platforms that precisely target medications to the lungs, addressing conditions like cystic fibrosis, pneumonia, acute respiratory distress syndrome and neonatal respiratory distress syndrome. These innovations have resulted in multiple patents. Some of them have been licensed through commercial partnerships like Quench Medical, an organization advancing inhaled therapies for applications like lung cancer. Collaborating with the Gates Foundation and the lab of Michael Hindle, Ph.D., from the VCU Department of Pharmaceutics, Longest’s team developed a low-cost, high-efficacy aerosol surfactant therapy for pre-term infants based entirely on technology developed at VCU. The invention eliminates intubation, reduces dosage by a factor of 10, and cuts treatment costs. Over 9 million infant lives are projected to be saved by this technology between 2030 and 2050. Through a long-term collaboration with the U.S. Food and Drug Administration, Longest’s in vitro and computational methods provide federal regulatory guidance for generic inhaled medications. The VCU mouth-throat airway models developed under his leadership are used globally across the pharmaceutical industry and in government laboratories. Hong Zhao, Ph.D. Associate Professor Department of Mechanical & Nuclear Engineering Zhao holds 40 patents with innovations spanning additive manufacturing, stretchable electronics, inkjet printing technologies and superoleophobic materials that repel oils, greases, and low-surface-tension liquids. Her research has applications across health care, sustainable energy and advanced manufacturing. Prior to joining the College of Engineering, Zhao served as a senior research scientist and project leader at the Xerox Research Center, where she developed high-performance materials and printing technologies for commercial deployment. Her industry experience makes Zhao’s lab a hub for innovation and mentorship, with students engaging in innovative research and co-authoring publications. Zhao is an invited reviewer for more than 50 premier journals and grant agencies. “Working with distinguished researchers and innovators like those inducted into the National Academy of Inventors is a great honor for me,” said Arvind Agarwal, Ph.D., chair of the Department of Mechanical & Nuclear Engineering and NAI fellow. “They are an inspiration and showcase the kind of impact engineers can make. Having all five of these innovators as part of our department amplifies the scientific richness of our college and its societal impact. They advance the college’s mission of Engineering for Humanity, with research that brings a positive change to our world.” The 2026 NAI class of senior members, composed of 231 emerging inventors from NAI’s member institutions, is the largest to date. Hailing from 82 NAI member institutions across the globe, they hold over 2,000 U.S. patents.

Artificial intelligence is a resource-intensive technology. A paper recently published in Nano Letters by collaborators at the Virginia Commonwealth University (VCU) College of Engineering and Georgetown University hopes to improve AI’s ability to parse the vast amounts of information it creates by applying magneto-ionics to the established concept of physical reservoir computing (PRC). “Demonstrating we can make solid-state devices with magneto-ionic materials is an important step into further energy-efficient computing research, and this Nano Letters publication reinforces that,” said Muhammad (Md.) Mahadi Rajib, Ph.D., a postdoc with Jayasimha Atulasimha, Ph.D., Engineering Foundation Professor in the Department of Mechanical & Nuclear Engineering. What makes a decision? Our brains make countless complex decisions everyday. Input comes in, we weigh options and decide what to do. Within that simple path are countless identical loops of input, consideration and output as neurons fire in a chain that takes you from cause to effect. For artificial intelligence, nodes within a neural network receive inputs and provide output, much like the neurons in our brains. These outputs can be sent to other nodes for continued processing, but those outputs need weight to have value. For AI, weight signifies one input or connection is more important than another. Traditional neural networks have multiple layers consisting of countless nodes like this. Each node requires training in order to weigh things properly. Training consumes processing power, and processing power takes time and energy. Making tasks like analysis and prediction more efficient is how to continuously improve AI technology. Less training, more efficiency. Physical reservoir computing reduces the number of nodes an AI needs to train. Only the final output layer needs training in PRC, using a simple method for classification or prediction tasks. A physical “black box” replaces neural network nodes and synapses, like the ones used for AI inference, in PRC and processes inputs by implementing a nonlinear mathematical function with temporal memory. To explain the inner workings of the black box, imagine two stones thrown into still water. One stone is thrown with high force and the other with low force, creating big and small ripples respectively. If the stones are thrown so the second stone lands before the previous ripples have dissipated, the new ripple is affected by the earlier one. This illustrates the concept of temporal memory. In this analogy, if multiple stones are thrown one after another into still water according to some complex trend, observing the ripples over time allows you to understand the trend and train a simple set of weights to predict the force of the next stone throw from the ripple pattern. Repeatedly performing this cycle of input, interaction and observation is PRC. It reveals patterns over time that can predict chaotic systems, like market trends or the weather, using techniques like linear regression modeling to plot each output as a single point. The magneto-ionic approach. Using this same example above, the “water” in a magneto-ionic PRC is represented by a positive and negative electrode with solid-state electrolyte between them through which ions move when voltage is applied. The application of voltage is equivalent to throwing a stone and the ripple effect is comparable to the movement of oxygen ions in the system. “In addition to its energy efficiency, a useful feature of the magnetoionic system is that time scales for ion diffusion can be controlled from microseconds to minutes,” Atulasimha said. “This leads to simple experimental demonstration, as no megahertz and gigahertz measurements are needed. One can work at the natural time scales of the target application in practical systems and remove the need for complex frequency conversion, which takes both energy and space due to complex electronics.” Atulasimha imagines these energy-efficient reservoir systems have applications in edge computing devices like drones, automated vehicles and surveillance cameras. Tasks such as household energy load forecasting, weather prediction or processing hourly readings from wearable devices, which operate on hour-scale data, can also be performed using magneto-ionic PRC without additional preprocessing. “We showed that the magneto-ionic physical reservoir has both memory and nonlinear behavior, two important properties necessary for using it as a reservoir block,” Rajib said. “Our system stands out because voltage-controlled ion migration is a highly energy efficient method of manipulating magnetization. We demonstrated the required reservoir properties in a physical system and did so using a very energy efficient approach.” Two labs came together in order to pursue this research. Virginia Commonwealth University collaborators included Atulasimha, Rajib, and VCU Ph.D. students Fahim Chowdhury and Shouvik Sarker. The Georgetown University team included Kai Liu, Ph.D., Professor and McDevitt Chair in Physics, Dhritiman Bhattacharya, Ph.D., Christopher Jensen, Ph.D. and Gong Chen, Ph.D. Atulasimha’s group illustrated physical reservoir computing using numerical models of spintronic devices and sought a material system to experimentally demonstrate PRC. Liu’s team worked with magneto-ionic materials and was intrigued by the possibility of using them for computing applications.