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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.
Global Honors Highlight J.S. Held’s Unmatched Technical and Advisory Expertise
J.S. Held proudly celebrates the numerous industry and expert recognitions earned throughout 2025. As a global consulting firm, J.S. Held continues to be acknowledged for its deep financial, technical, and scientific expertise, with leading outlets highlighting the firm’s capabilities across investigations, risk advisory, forensics, turnaround and restructuring, business intelligence, and litigation support. The firm’s curated team of entrepreneurs — each with an unrivaled understanding of both tangible and intangible assets — reflects a collective strength that is recognized worldwide. Beyond organizational achievements, J.S. Held’s experts received individual distinctions that further demonstrate their standing as leaders within their respective fields. Industry publications and ranking bodies honoured these specialists for excellence in arbitration, construction and engineering, environmental consulting, forensic accounting, investigations, litigation support, intellectual property, specialty finance, and a wide range of other highly specialized domains. Together, these recognitions underscore J.S. Held’s commitment to delivering trusted insight and unparalleled expertise as clients navigate increasingly complex challenges. In a rapidly evolving business landscape, the firm remains dedicated to providing informed, innovative, and practical solutions that enable organizations to move forward with confidence. Click on the link below to learn more about our recognition and respective areas of expertise: Expert recognition by notable organizations serves as a further testament to J.S. Held's agile, collaborative, creative, and client-centric team, reflecting the trusted advisor role the firm has earned over the last 50 years. For any media inquiries, contact: Kristi L. Stathis, J.S. Held +1 786 833 4864 Kristi.Stathis@JSHeld.com
UF team develops AI tool to make genetic research more comprehensive
University of Florida researchers are addressing a critical gap in medical genetic research — ensuring it better represents and benefits people of all backgrounds. Their work, led by Kiley Graim, Ph.D., an assistant professor in the Department of Computer & Information Science & Engineering, focuses on improving human health by addressing "ancestral bias" in genetic data, a problem that arises when most research is based on data from a single ancestral group. This bias limits advancements in precision medicine, Graim said, and leaves large portions of the global population underserved when it comes to disease treatment and prevention. To solve this, the team developed PhyloFrame, a machine-learning tool that uses artificial intelligence to account for ancestral diversity in genetic data. With funding support from the National Institutes of Health, the goal is to improve how diseases are predicted, diagnosed, and treated for everyone, regardless of their ancestry. A paper describing the PhyloFrame method and how it showed marked improvements in precision medicine outcomes was published Monday in Nature Communications. Graim, a member of the UF Health Cancer Center, said her inspiration to focus on ancestral bias in genomic data evolved from a conversation with a doctor who was frustrated by a study's limited relevance to his diverse patient population. This encounter led her to explore how AI could help bridge the gap in genetic research. “If our training data doesn’t match our real-world data, we have ways to deal with that using machine learning. They’re not perfect, but they can do a lot to address the issue.” —Kiley Graim, Ph.D., an assistant professor in the Department of Computer & Information Science & Engineering and a member of the UF Health Cancer Center “I thought to myself, ‘I can fix that problem,’” said Graim, whose research centers around machine learning and precision medicine and who is trained in population genomics. “If our training data doesn’t match our real-world data, we have ways to deal with that using machine learning. They’re not perfect, but they can do a lot to address the issue.” By leveraging data from population genomics database gnomAD, PhyloFrame integrates massive databases of healthy human genomes with the smaller datasets specific to diseases used to train precision medicine models. The models it creates are better equipped to handle diverse genetic backgrounds. For example, it can predict the differences between subtypes of diseases like breast cancer and suggest the best treatment for each patient, regardless of patient ancestry. Processing such massive amounts of data is no small feat. The team uses UF’s HiPerGator, one of the most powerful supercomputers in the country, to analyze genomic information from millions of people. For each person, that means processing 3 billion base pairs of DNA. “I didn’t think it would work as well as it did,” said Graim, noting that her doctoral student, Leslie Smith, contributed significantly to the study. “What started as a small project using a simple model to demonstrate the impact of incorporating population genomics data has evolved into securing funds to develop more sophisticated models and to refine how populations are defined.” What sets PhyloFrame apart is its ability to ensure predictions remain accurate across populations by considering genetic differences linked to ancestry. This is crucial because most current models are built using data that does not fully represent the world’s population. Much of the existing data comes from research hospitals and patients who trust the health care system. This means populations in small towns or those who distrust medical systems are often left out, making it harder to develop treatments that work well for everyone. She also estimated 97% of the sequenced samples are from people of European ancestry, due, largely, to national and state level funding and priorities, but also due to socioeconomic factors that snowball at different levels – insurance impacts whether people get treated, for example, which impacts how likely they are to be sequenced. “Some other countries, notably China and Japan, have recently been trying to close this gap, and so there is more data from these countries than there had been previously but still nothing like the European data," she said. “Poorer populations are generally excluded entirely.” Thus, diversity in training data is essential, Graim said. "We want these models to work for any patient, not just the ones in our studies," she said. “Having diverse training data makes models better for Europeans, too. Having the population genomics data helps prevent models from overfitting, which means that they'll work better for everyone, including Europeans.” Graim believes tools like PhyloFrame will eventually be used in the clinical setting, replacing traditional models to develop treatment plans tailored to individuals based on their genetic makeup. The team’s next steps include refining PhyloFrame and expanding its applications to more diseases. “My dream is to help advance precision medicine through this kind of machine learning method, so people can get diagnosed early and are treated with what works specifically for them and with the fewest side effects,” she said. “Getting the right treatment to the right person at the right time is what we’re striving for.” Graim’s project received funding from the UF College of Medicine Office of Research’s AI2 Datathon grant award, which is designed to help researchers and clinicians harness AI tools to improve human health.
As interest in cryptocurrencies move from the fringes to mainstream conversation and public policy debate, Derek Mohr, clinical associate professor of finance at the Simon Business School at the University of Rochester, offers a clear-eyed voice on the subject. Mohr specializes in financial innovation and digital assets, and he’s been in demand with reporters looking to understand the economics behind everything from “Bitcoin-powered” home heaters to gas stations offering discounts for crypto purchases. His message? Not everything that markets itself as a breakthrough actually adds up. For instance, some companies have pitched devices that promise to heat a home using excess energy generated from bitcoin mining. Mohr recently told CNBC the idea might sound clever, but that its practicality collapses under basic financial and engineering realities. “The bitcoin heat devices I have seen appear to be simple space heaters that use your own electricity to heat the room . . . which is not an efficient way to heat a house,” Mohr said. “Yes, bitcoin mining generates a lot of heat, but the only way to get that to your house is to use your own electricity.” Bitcoin mining, he explained, has become so specialized that home computers have virtually zero chance of earning a mining reward. Industrial mining farms operate on custom-built chips far more powerful than any consumer device. In other words, consumers who think they’re heating their homes and earning crypto are, in reality, just paying for electricity and getting no real mining benefit. A pragmatic voice in a volatile space Mohr’s research and commentary help explain not just what is happening in the crypto world, but why it matters for consumers, businesses, and regulators. Whether evaluating the economics of mining or the viability of crypto payments, he brings a steady, analytical perspective to a domain dominated by hype and fast-moving news cycles. For journalists covering cryptocurrency, fintech, and the future of financial transactions, Mohr is available for interviews on digital payments, bitcoin mining economics, crypto regulation, and emerging trends in financial technologies. Top contact him, reach out to University of Rochester media relations liaison David Andreatta at david.andreatta@rochester.edu.
Gates Foundation to Fund RPI Research to Develop Low-Cost Monoclonal Antibody Treatments
Professor Todd Przybycien, Ph.D., head of RPI’s Department of Chemical and Biological Engineering, has been awarded a $3.1 million share of a Gates Foundation Global Grand Challenge grant to advance exceptionally low-cost monoclonal antibody (mAb) manufacturing. Monoclonal antibodies have proven effective at treating a wide range of conditions, including infectious diseases like COVID-19, autoimmune disorders, and certain types of cancer. But they are expensive to produce, and current market prices of $50 to $100 per gram put them effectively out-of-reach for millions of people around the world. The goal of the Gates Grand Challenge is to reduce the price of mAbs to just $10 per gram. Last month, the Gates Foundation announced $10.5 million in funding to a team led by the National Institute for Innovation in Manufacturing Biopharmaceuticals (NIIMBL) in order to achieve that goal. Professor Przybycien’s group is part of that team and will focus on improving the process of purifying monoclonal antibodies after they have been produced by engineered cells. “Optimization and intensification of the downstream purification process offer the exciting possibility of breaking through to the $10/g overall target,” Przybycien said. “We are excited to advance the precipitation-based process we have developed with our collaborator at Penn State as part of the manufacturing solution to sustainably meet the global need for monoclonal antibodies.” Przybycien is an internationally recognized researcher in biomanufacturing and applied biophysics, focusing on developing processes to manufacture recombinant proteins, mRNA, and viral vectors. He has won numerous awards including the NSF CAREER Award and the Camille Dreyfus Teacher-Scholar Award. He is also a fellow of the American Chemical Society, AIChE, and AIMBE. “This grant is a testament to the years of work Todd Przybycien and his team have done to optimize and improve biopharmaceutical manufacturing processes,” said Shekhar Garde, Ph.D., the Thomas R. Farino, Jr. ’67 and Patricia E. Farino Dean of the School of Engineering. “It will pave the way for affordable access to lifesaving medications for millions of people who desperately need them.” “We are excited by the opportunity to demonstrate that there are existing solutions developed by industry and academic partners that can significantly reduce cost of goods and accelerate timelines,” said Kelvin Lee, NIIMBL Institute Director. “We are honored to receive this grant from the Gates Foundation, which will enable this exceptional team to deliver meaningful advances to antibody production efficiency.” This Gates Grand Challenge was established in honor of Dr. Steve Hadley, who championed the reduction of mAbs costs to make them affordable in low- and middle-income countries. The team’s first target will be a monoclonal antibody to treat malaria, an infectious disease which kills more than half a million people each year, primarily in Africa.
The School of Science at Rensselaer Polytechnic Institute (RPI) has launched a new minor in quantum computing, positioning students at the forefront of one of the most rapidly developing fields in technology. The minor leverages RPI's unique status as the first university in the world to house an IBM Quantum System One on campus, providing students with unprecedented access to utility-scale quantum computing technology. The minor, which is now available to all currently enrolled students, requires four courses drawn from physics, computer science, mathematics, and engineering. The curriculum provides both theoretical foundations and practical exposure to quantum hardware and software, and gives students a leg up in a field rapidly approaching quantum advantage — the point at which quantum systems outperform classical computing approaches on meaningful tasks. "The quantum computing minor will augment the training of RPI students with insight into an emerging technology that will reshape industries from pharmaceuticals to artificial intelligence," said Steven Tait, Ph.D., Dean of the School of Science. "With direct access to the IBM Quantum System One, our students will gain hands-on experience with cutting-edge tools that are not yet widely available. This minor equips them with the interdisciplinary foundation needed to understand and contribute to quantum-enabled innovation." The minor arrives at a pivotal moment in quantum computing's evolution. IBM's demonstration of quantum utility in 2023 marked the beginning of an era in which quantum systems serve as scientific tools to explore complex problems in chemistry, physics, and materials science — areas where quantum advantage offers transformative potential. Hannah Xiuying Fried, graduating this December, is one of the first students to declare the minor. “I'm not a physics or computer science major, so it allows me an accredited way to prove a relevant background to future employers,” she said. “It prepares me for graduate school where I plan to continue pursuing quantum hardware research.” Currently enrolled students may declare the minor now and pursue it alongside their established degree programs. Interested students should contact Chad Christensen at sciencehub@rpi.edu.
RPI to Host Holiday Concert and Troy Victorian Stroll Kickoff December 6
RPI President Martin A. Schmidt ’81, and his wife, Lyn, invite the Capital Region to join them for the RPI Holiday Concert and Troy Victorian Stroll Kickoff, which will take place on Saturday, December 6, at 7 p.m. in the Concert Hall of the Curtis R. Priem Experimental Media and Performing Arts Center (EMPAC) on RPI’s campus. This event is free and open to the public, and a reception with refreshments will follow. The concert, titled “Light in Winter,” will include performances from the RPI Wind Symphony, Concert Choir, and Orchestra, all under the direction of Robert Whalen, RPI lecturer in music and conducting. For the second year in a row, the concert will also serve as the kickoff for the 43rd annual Troy Victorian Stroll, made possible through RPI’s partnership with the Rensselaer County Regional Chamber of Commerce. “This concert is a wonderful opportunity for our students, who work so hard all year, to share their passion for music and create something meaningful for the entire community,” said Whalen. The program, following a “Light in Winter” theme, celebrates the various ways we bring light and joy to the darker winter season. “There is a common connection across cultures of lighting up the darkness in the late fall and winter, said Whalen. “Consider Diwali, Hannukah, Christmas, Alban Arthan, and others. Music and the arts, as well as education, each represent a way of sharing our inner creative light to illuminate the darkness.” During the concert, students will perform music by Smetana, Tchaikovsky, Gershwin, Lauridsen, and Saint-Saens, all following the “Light in Winter” theme. The program will feature two winners of the 2025 RPI Concerto Competition, Reese Bush '27 in Saint-Saens' "Introduction and Rondo capriccioso" for Violin and Orchestra, and Avery Roach '25 singing Gershwin's gorgeous and evocative "Summertime". Performers across the three groups represent 160 students from 34 different majors and all 5 schools, a reminder of just how curious and well-rounded RPI students are, and that art and science don’t just coexist, they actually enhance each other. The opportunity to continue growing as a musician while pursuing an engineering degree is something that connects many of us and inspires the music we create,” said Bush. “We’re excited to share our hard work and passion with the community.” The Holiday Concert is an annual tradition that celebrates peace and unity, the creativity and hard work of RPI students, and the confluence of science, engineering, and art. Combining the concert with the Troy Victorian Stroll has been a great opportunity for RPI to partner with its home city and further engage with the community. RSVP here: Media are welcome to attend.
Proteins, often called the building blocks of life, play a central role in drug development. When scientists develop new treatments, they must understand how drugs interact with proteins involved in disease mechanisms and with proteins in the human body that influence drug response. Scientists commonly use cryo-electron microscopy (cryo-EM) 3D imaging data to study proteins. While recent advances have enabled higher-resolution images that are easier to analyze, medium-resolution images—which are more difficult to interpret—are still the most common for larger protein complexes. Salim Sazzed, Ph.D., an assistant professor in the computer science department of Georgia Southern University’s Allen E. Paulson College of Engineering and Computing, has been awarded a two-year National Science Foundation grant of about $175,000 to lead a groundbreaking project to develop novel Artificial Intelligence (AI) techniques for determining protein secondary structures from medium-resolution cryo-electron microscopy (cryo-EM) images. Improved modeling from medium-resolution images will help researchers study more proteins efficiently, giving new insights into diseases and potentially guiding the development of new treatments and future drugs. At its core, this research will combine biology and machine learning to study protein structures. The multidisciplinary approach and potential impacts on public health are what most excite Sazzed. “The impetus behind this research is the positive impact on public health and possibly contributing to the biomedical workforce,” he said. “Seeing biology and computer science combine for that kind of impact is incredibly moving.” As the Principal Investigator (PI) for the project, Sazzed will use his expertise in deep learning computer models to focus on a major challenge in structural biology: identifying the two main secondary structures of proteins—the alpha helix and the beta sheet. These structures are critical for a protein’s overall shape and function, but in medium-resolution cryo-EM images they often appear indistinct or lack clear detail, making them particularly difficult to analyze. Sazzed’s research will focus on two main goals. First, he will quantify the variability of alpha helices and beta sheets in medium-resolution images, comparing them to idealized structures. Second, by integrating this structural variability with the image data in a deep learning model, he will aim to generate more precise and accurate representations of protein secondary structures. “When we feed this information into a deep learning model along with the image data, the model should be able to determine protein secondary structures more precisely,” Sazzed elaborated. Sazzed believes students will greatly benefit from this multi-disciplinary approach. In addition to a Ph.D. student, several undergraduate students will be directly engaged in the research. A full-day workshop will also be organized, allowing Georgia Southern students from diverse disciplines to participate. This initiative will build on Georgia Southern’s strong tradition of involving undergraduates in research and will support the University’s recent focus on biomedical and health sciences. “There are many different knowledge areas coming together in this work,” Sazzed said. “It involves computer science, biology, chemistry, and even public health. I look forward to students following the research and exploring these different fields themselves.” Allen E. Paulson College of Engineering & Computing Interim Associate Dean of Research, Masoud Davari, Ph.D., echoes this sentiment and emphasizes its importance to the University’s research profile. “Sazzed’s interdisciplinary research, which bridges the gap between biology and computer science, will foster multidisciplinary research in our college—as it is cutting-edge and potentially groundbreaking in drug development to impact people’s lives nationally and globally,” Davari said. “It’s also well aligned with the college’s strategic research plan—as we make the move to R1 status to be aligned with ‘Soaring to R1,’ which is among the transformational initiatives for the University.” Looking to know more about Georgia Southern University or connect with Salim Sazzed — simply contact Georgia Southern's Director of Communications Jennifer Wise at jwise@georgiasouthern.edu to arrange an interview today.
The University of Florida’s ‘AI Queen’ is using AI technology to help prevent dementia
To help the 50 million people globally who live with dementia, the National Institute on Aging is finding researchers to develop tech-based breakthroughs that target the disease — researchers like the University of Florida’s “AI Queen.” It’s a fitting nickname for Aprinda Indahlastari Queen, Ph.D., who is applying artificial intelligence technology to study transcranial direct current stimulation, or tDCS — a technique that involves placing electrodes on the scalp to deliver a weak electrical current to the brain — as a possible way to prevent dementia. The assistant professor in the UF College of Public Health and Health Professions’ Department of Clinical and Health Psychology is using UF’s supercomputer, HiPerGator, to perform neuroimaging and machine learning analyses to study how anatomical differences may affect tDCS outcomes. “Investigating working memory in patients with mild cognitive impairment offers an opportunity to understand how cognitive processes are disrupted in the early stages of Alzheimer’s disease,” said Queen, whose study — funded by a National Institute on Aging research career development grant — integrates neuroimaging with information on brain structure that is unique to older adults and those with mild cognitive impairment. Refining the treatment with AI Using neuroimaging, Queen captures real-time changes during tDCS to the parts of the brain associated with working memory, which is the type of memory that allows humans to temporarily keep track of small amounts of information. Think of this as a mental “scratchpad.” Her study includes older adults with mild cognitive impairment as well as individuals who are cognitively healthy. In tDCS, a safe, weak electrical current passes through electrodes placed on a person’s head. The stimulation is being used in research and clinical settings for a variety of conditions and has shown partial success as a nonpharmaceutical intervention that can improve cognitive and mental health in older adults. But tDCS results can vary across individuals, and the suspected cause is both simple and complex: Everyone’s head is different. “One potential reason tDCS may not work for some individuals is the variation in head tissue anatomy, including differences in brain structure,” Queen said. “Since electrical stimulation must travel through multiple layers of tissue to reach the brain, and every individual’s anatomy is unique, these differences likely affect outcomes.” To address this further, Queen is using AI. “Artificial intelligence will play a major role in the modeling pipeline, including constructing individualized head models, conducting predictive analyses to identify which participants will respond to the stimulation, and disentangling multiple individual factors that may contribute to these outcomes,” Queen said. An estimated 10 to 20% of adults over age 65 have memory or thinking problems characterized as mild cognitive impairment. Their symptoms are not as severe as Alzheimer’s disease and other dementias, but they may be at increased risk for developing dementia. “The fact that not all individuals with mild cognitive impairment progress to Alzheimer’s disease emphasizes the need to identify effective interventions that can slow the progression to dementia,” Queen said. “This project presents an opportunity to differentiate between multiple types of mild cognitive impairment and investigate how tDCS affects the brain across these subtypes.” An AI visionary Queen, who joined the UF faculty under the university’s AI hiring initiative, is an instructor in the College of Public Health and Health Professions’ undergraduate certificate program in AI and public health and health care, and the co-chair of the college’s AI Workgroup. She is also the assistant director for computing and informatics at the UF Center for Cognitive Aging and Memory Clinical Translational Research and a member of UF’s McKnight Brain Institute. Queen received her Ph.D. training in engineering with a focus on building and running computational models to investigate medical devices. She experienced a career “a-ha” moment as a postdoc, when she was a co-investigator on a large clinical trial that paired brain stimulation with cognitive training to enhance cognition in older adults. “This experience was transformative for me. I had the chance to interact directly with participants, which was both fulfilling and eye-opening. These interactions allowed me to see the immediate, real-world implications of my work and sparked a passion for pursuing aging research,” Queen said. “I realized that, through this type of research, I could have a more direct impact on addressing age-related challenges, which prompted a shift in my career plans.” The new grant will help Queen further improve her understanding of the neurobiology and progression of Alzheimer’s disease and other dementias. “These experiences will ultimately prepare me to become a well-rounded aging investigator, capable of making meaningful contributions to the field of aging research,” Queen said. She also credits her mentors and collaborators — Ronald Cohen, Ph.D.; Adam Woods, Ph.D.; Steven DeKosky, M.D.; Ruogu Fang, Ph.D.; Joseph Gullett, Ph.D.; and Glenn Smith, Ph.D. — with supporting her as an early career scientist. “It really takes a village to get here!” Queen said.
Masoud Davari, an associate professor in Electrical and Computer Engineering at Georgia Southern University, has been awarded the 2024 IEEE Region 3 Outstanding Engineer Award, making him the first faculty member in the university’s 55-year history to receive this honor. Davari was recognized for his contributions to reinforcement-learning optimal controls for power-electronic converters, his work on integrating power-electronic systems with cyber-attack considerations in modern power grids, and for his leadership in hardware-in-the-loop testing and standards development, including service on the IEEE P2004 standards working group. In addition to the award, Davari was inducted into the IEEE-Eta Kappa Nu (HKN) honor society. His research program at Georgia Southern has earned significant support, including more than $1.17 million in National Science Foundation funding, a 2024 Gulfstream Aerospace Research Fellowship, inclusion in the Stanford/Elsevier Top 2% Scientists list, and selection as a finalist for the 2024 Curtis W. McGraw Research Award. You can find out more about Davari's research by visiting his Georgia Southern Scholars profile below: To arrange an interview or to learn more about this award - Looking to know more about Georgia — simply contact Georgia Southern's Director of Communications Jennifer Wise at jwise@georgiasouthern.edu to arrange an interview today.