Experts Matter. Find Yours.
Connect for media, speaking, professional opportunities & more.
#Expert Research: The Use of AI in Financial Reporting
Artificial intelligence (AI) is developing into an amazing tool to help humans across multiple fields, including medicine and research, and much of that work is happening at Emory University’s Goizueta Business School. Financial reporting and auditing are both areas where AI can have a significant impact as companies and audit firms are rapidly adopting the use of such technology. But are financial managers willing to rely on the results of AI-generated information? In the context of audit adjustments, it depends on whether their company uses AI as well. Willing to Rely on AI? Cassandra Estep, assistant professor of accounting at Goizueta Business School, and her co-authors have a forthcoming study looking at financial managers’ perceptions of the use of AI, both within their companies and by their auditors. Research had already been done on how financial auditors react to using AI for evaluating complex financial reporting. That got Estep and her co-authors thinking there’s more to the story. “A big, important part of the financial reporting and auditing process is the managers within the companies being audited. We were interested in thinking about how they react to the use of AI by their auditors,” Estep says. “But then we also started thinking about what companies are investing in AI as well. That joint influence of the use of AI, both within the companies and by the auditors that are auditing the financials of those companies, is where it all started.” The Methodology Estep and her co-authors conducted a survey and experiment with senior-level financial managers with titles like CEO, CFO, or Controller – the people responsible for making financial reporting decisions within companies. The survey included questions to understand how companies are using AI. It also included open-ended questions designed to identify key themes about financial managers’ perceptions of AI use by their companies and their auditors. In the experiment, participants completed a hypothetical case in which they were asked about their willingness to record a downward adjustment to the fair value of a patent proposed by their auditors. The scenarios varied across randomly assigned conditions as to whether the auditor did or not did not use AI in coming up with the proposed valuation and adjustment, and whether their company did or did not use AI in generating their estimated value of the patent. When both the auditor and the company used AI, participants were willing to record a larger adjustment amount, i.e., decrease the value of the patent more. The authors find that these results are driven by increased perceptions of accuracy. It’s not necessarily a comfort thing, but a signal from the company that this is an acceptable way to do things, and it actually caused them to perceive the auditors’ information as more accurate and of higher quality. Cassandra Estep, assistant professor of accounting “Essentially, they viewed the auditors’ recommendation for adjusting the numbers to be more accurate and of higher quality, and so they were more willing to accept the audit adjustment,” Estep says. Making Financial Reporting More Efficient Financial reporting is a critical process in any business. Companies and investors need timely and accurate information to make important decisions. With the added element of AI, financial reporting processes can include more external data. We touched on the idea that these tools can hopefully process a lot more information and data. For example, we’ve seen auditors and managers talk about using outside information. Cassandra Estep “Auditors might be able to use customer reviews and feedback as one of the inputs to deciding how much warranty expense the company should be estimating. And is that amount reasonable? The idea is that if customers are complaining, there could be some problem with the products.” Adding data to analytical processes, when done by humans alone, adds a significant amount of time to the calculations. Research from the European Spreadsheets Risks Interest Group says that more than 90% of all financial spreadsheets contain at least one error. Some forms of AI can process hundreds of thousands of calculations overnight, typically with fewer errors. In short, it can be more efficient. Efficiency was brought up a lot in our survey, the idea that things could be done faster with AI. Cassandra Estep “We also asked the managers about their perspective on the audit side, and they did hope that audit fees would go down, because auditors would be able to do things more quickly and efficiently as well,” Estep says. “But the flip side of that is that using AI could also raise more questions and more issues that have to be investigated. There’s also the potential for more work.” The Fear of Being Replaced The fear of being replaced is a more or less universal worry for anyone whose industry is beginning to adopt the use of AI in some form. While the respondents in Estep’s survey looked forward to more efficient and effective handling of complex financial reporting by AI, they also emphasized the need to keep the human element involved in any decisions made using AI. What we were slightly surprised about was the positive reactions that the managers had in our survey. While some thought the use of AI was inevitable, there’s this idea that it can make things better. Cassandra Estep “But there’s still a little bit of trepidation,” Estep says. “One of the key themes that came up was yes, we need to use these tools. We should take advantage of them to improve the quality and the efficiency with which we do things. But we also need to keep that human element. At the end of the day, humans need to be responsible. Humans need to be making the decisions.” A Positive Outlook The benefits of AI were clear to the survey participants. They recognized it as a positive trend, whether or not it was currently used in their financial reporting. If they weren’t regularly using AI, they expected to be using it soon. I think one of the most interesting things to us about this paper is this idea that AI can be embraced. Companies and auditors are still somewhat in their infancy of figuring out how to use it, but big investments are being made. Cassandra Estep “And then, again, there’s the fact that our experiment also shows a situation where managers were willing to accept the auditors’ proposed adjustments. This arguably goes against their incentives as management to keep the numbers more positive or optimistic,” Estep continues. “The auditors are serving that role of helping managers provide more reliable financial information, and that can be viewed as a positive outcome.” “There’s still some hesitation. We’re still figuring out these tools. We see examples all the time of where AI has messed up, or put together false information. But I think the positive sentiment across our survey participants, and then also the results of our experiment, reinforce the idea that AI can be a good thing and that it can be embraced. Even in a setting like financial reporting and auditing, where there can be fear of job replacement, the focus on the human-technology interaction can hopefully lead to improved situations.” Cassandra Estep, is an assistant professor of accounting at Goizueta Business School, and a co-author of the forthcoming study looking at financial managers’ perceptions of the use of AI. She's available to speak about this important topic - simply click on her icon now to arrange an interview today.
How college graduates can find success in a tough job market
Commencement season is an exciting time for soon-to-be college graduates – at least for those who will jump into a job once the caps are tossed. For others, it's a time of stress and uncertainty. Jill Gugino Panté, director of the Lerner Career Services Center at the University of Delaware, identified three areas where concerned graduates should focus to boost their chances of scoring interviews and potentially securing employment this summer. • Stay industry-informed: Keeping up with skills, trends and news in your field to stay current and competitive. • Network with purpose: Because many jobs are landed through connections, use LinkedIn to engage with others and grow your brand. • Leverage AI Smartly. Use tools like ChatGPT, Microsoft Copilot or Google Gemini to refine your resume, prep for interviews or analyze job descriptions. One key: Remember to maintain your authentic self. To arrange an interview with Panté visit her profile and click on the "contact" button.
NASA Asks Researchers to Help Define Trustworthiness in Autonomous Systems
A Florida Tech-led group of researchers was selected to help NASA solve challenges in aviation through its prestigious University Leadership Initiative (ULI) program. Over the next three years, associate professor of computer science and software engineering Siddhartha Bhattacharyya and professor of aviation human factors Meredith Carroll will work to understand the vital role of trust in autonomy. Their project, “Trustworthy Resilient Autonomous Agents for Safe City Transportation in the Evolving New Decade” (TRANSCEND), aims to establish a common framework for engineers and human operators to determine the trustworthiness of machine-learning-enabled autonomous aviation safety systems. Autonomous systems are those that can perform independent tasks without requiring human control. The autonomy of these systems is expected to be enhanced with intelligence gained from machine learning. As a result, intelligence-based software is expected to be increasingly used in airplanes and drones. It may also be utilized in airports and to manage air traffic in the future. Learning-enabled autonomous technology can also act as contingency management when used in safety applications, proactively addressing potential disruptions and unexpected aviation events. TRANSCEND was one of three projects chosen for the latest ULI awards. The others hail from Embry-Riddle Aeronautical University in Daytona Beach – researching continuously updating, self-diagnostic vehicle health management to enhance the safety and reliability of Advanced Air Mobility vehicles – and University of Colorado Boulder – investigating tools for understanding and leveraging the complex communications environment of collaborative, autonomous airspace systems. Florida Tech’s team includes nine faculty members from five universities: Penn State; North Carolina A&T State University; University of Florida; Stanford University; Santa Fe College. It also involves the companies Collins Aerospace in Cedar Rapids, Iowa and ResilienX of Syracuse, New York. Carroll and Bhattacharyya will also involve students throughout the project. Human operators are an essential component of aviation technology – they monitor independent software systems and associated data and intervene when those systems fail. They may include flight crew members, air traffic controllers, maintenance personnel or safety staff monitoring overall system safety. A challenge in implementing independent software is that engineers and operators have different interpretations of what makes a system “trustworthy,” Carroll and Bhattacharyya explained. Engineers who develop autonomous software measure trustworthiness by the system’s ability to perform as designed. Human operators, however, trust and rely on systems to perform as they expect – they want to feel comfortable relying on a system to make an aeronautical decision in flight, such as how to avoid a traffic conflict or a weather event. Sometimes, that reliance won’t align with design specifications. Equally important, operators also need to trust that the software will alert them when it needs a human to take over. This may happen if the algorithm driving the software encounters a scenario it wasn’t trained for. “We are looking at how we can integrate trust from different communities – from human factors, from formal methods, from autonomy, from AI…” Bhattacharyya said. “How do we convey assumptions for trust, from design time to operation, as the intelligent systems are being deployed, so that we can trust them and know when they’re going to fail, especially those that are learning-enabled, meaning they adapt based on machine learning algorithms?” With Bhattacharyya leading the engineering side and Carroll leading the human factors side, the research group will begin bridging the trust gap by integrating theories, principles, methods, measures, visualizations, explainability and practices from different domains – this will build the TRANSCEND framework. Then, they’ll test the framework using a diverse range of tools, flight simulators and intelligent decision-making to demonstrate trustworthiness in practice. This and other data will help them develop a safety case toolkit of guidelines for development processes, recommendations and suggested safety measures for engineers to reference when designing “trustworthy,” learning-enabled autonomous systems. Ultimately, Bhattacharyya and Carroll hope their toolkit will lay the groundwork for a future learning-enabled autonomous systems certification process. “The goal is to combine all our research capabilities and pull together a unified story that outputs unified products to the industry,” Carroll said. “We want products for the industry to utilize when implementing learning-enabled autonomy for more effective safety management systems.” The researchers also plan to use this toolkit to teach future engineers about the nuances of trust in the products they develop. Once developed, they will hold outreach events, such as lectures and camps, for STEM-minded students in the community. If you're interested in connecting with Meredith Carroll or Siddhartha Bhattacharyya - simply click on the expert's profile or contact Adam Lowenstein, Director of Media Communications at Florida Institute of Technology at adam@fit.edu to arrange an interview today.
Expert Perspective: Mitigating Bias in AI: Sharing the Burden of Bias When it Counts Most
Whether getting directions from Google Maps, personalized job recommendations from LinkedIn, or nudges from a bank for new products based on our data-rich profiles, we have grown accustomed to having artificial intelligence (AI) systems in our lives. But are AI systems fair? The answer to this question, in short—not completely. Further complicating the matter is the fact that today’s AI systems are far from transparent. Think about it: The uncomfortable truth is that generative AI tools like ChatGPT—based on sophisticated architectures such as deep learning or large language models—are fed vast amounts of training data which then interact in unpredictable ways. And while the principles of how these methods operate are well-understood (at least by those who created them), ChatGPT’s decisions are likened to an airplane’s black box: They are not easy to penetrate. So, how can we determine if “black box AI” is fair? Some dedicated data scientists are working around the clock to tackle this big issue. One of those data scientists is Gareth James, who also serves as the Dean of Goizueta Business School as his day job. In a recent paper titled “A Burden Shared is a Burden Halved: A Fairness-Adjusted Approach to Classification” Dean James—along with coauthors Bradley Rava, Wenguang Sun, and Xin Tong—have proposed a new framework to help ensure AI decision-making is as fair as possible in high-stakes decisions where certain individuals—for example, racial minority groups and other protected groups—may be more prone to AI bias, even without our realizing it. In other words, their new approach to fairness makes adjustments that work out better when some are getting the short shrift of AI. Gareth James became the John H. Harland Dean of Goizueta Business School in July 2022. Renowned for his visionary leadership, statistical mastery, and commitment to the future of business education, James brings vast and versatile experience to the role. His collaborative nature and data-driven scholarship offer fresh energy and focus aimed at furthering Goizueta’s mission: to prepare principled leaders to have a positive influence on business and society. Unpacking Bias in High-Stakes Scenarios Dean James and his coauthors set their sights on high-stakes decisions in their work. What counts as high stakes? Examples include hospitals’ medical diagnoses, banks’ credit-worthiness assessments, and state justice systems’ bail and sentencing decisions. On the one hand, these areas are ripe for AI-interventions, with ample data available. On the other hand, biased decision-making here has the potential to negatively impact a person’s life in a significant way. In the case of justice systems, in the United States, there’s a data-driven, decision-support tool known as COMPAS (which stands for Correctional Offender Management Profiling for Alternative Sanctions) in active use. The idea behind COMPAS is to crunch available data (including age, sex, and criminal history) to help determine a criminal-court defendant’s likelihood of committing a crime as they await trial. Supporters of COMPAS note that statistical predictions are helping courts make better decisions about bail than humans did on their own. At the same time, detractors have argued that COMPAS is better at predicting recidivism for some racial groups than for others. And since we can’t control which group we belong to, that bias needs to be corrected. It’s high time for guardrails. A Step Toward Fairer AI Decisions Enter Dean James and colleagues’ algorithm. Designed to make the outputs of AI decisions fairer, even without having to know the AI model’s inner workings, they call it “fairness-adjusted selective inference” (FASI). It works to flag specific decisions that would be better handled by a human being in order to avoid systemic bias. That is to say, if the AI cannot yield an acceptably clear (1/0 or binary) answer, a human review is recommended. To test the results for their “fairness-adjusted selective inference,” the researchers turn to both simulated and real data. For the real data, the COMPAS dataset enabled a look at predicted and actual recidivism rates for two minority groups, as seen in the chart below. In the figures above, the researchers set an “acceptable level of mistakes” – seen as the dotted line – at 0.25 (25%). They then compared “minority group 1” and “minority group 2” results before and after applying their FASI framework. Especially if you were born into “minority group 2,” which graph seems fairer to you? Professional ethicists will note there is a slight dip to overall accuracy, as seen in the green “all groups” category. And yet the treatment between the two groups is fairer. That is why the researchers titled their paper “a burden shared is a burdened halved.” Practical Applications for the Greater Social Good “To be honest, I was surprised by how well our framework worked without sacrificing much overall accuracy,” Dean James notes. By selecting cases where human beings should review a criminal history – or credit history or medical charts – AI discrimination that would have significant quality-of-life consequences can be reduced. Reducing protected groups’ burden of bias is also a matter of following the laws. For example, in the financial industry, the United States’ Equal Credit Opportunity Act (ECOA) makes it “illegal for a company to use a biased algorithm that results in credit discrimination on the basis of race, color, religion, national origin, sex, marital status, age, or because a person receives public assistance,” as the Federal Trade Commission explains on its website. If AI-powered programs fail to correct for AI bias, the company utilizing it can run into trouble with the law. In these cases, human reviews are well worth the extra effort for all stakeholders. The paper grew from Dean James’ ongoing work as a data scientist when time allows. “Many of us data scientists are worried about bias in AI and we’re trying to improve the output,” he notes. And as new versions of ChatGPT continue to roll out, “new guardrails are being added – some better than others.” “I’m optimistic about AI,” Dean James says. “And one thing that makes me optimistic is the fact that AI will learn and learn – there’s no going back. In education, we think a lot about formal training and lifelong learning. But then that learning journey has to end,” Dean James notes. “With AI, it never ends.” Gareth James is the John H. Harland Dean of Goizueta Business School. If you're looking to connect with him - simply click on his icon now to arrange an interview today.
The Hidden Power of Invisible Experts
In a fast-moving landscape shaped by AI, hybrid work, and constant information shifts, organizations can’t afford to overlook their own expertise. Yet many still do — because the most valuable voices are often hiding in plain sight. We call them "invisible experts". These aren’t just the well-known thought leaders or executives quoted in media. They’re the researchers, engineers, clinicians, analysts, and project leads quietly shaping strategy, driving innovation, and influencing outcomes every day. They have deep knowledge, practical insight, and the credibility to build trust — but they’re often left out of the spotlight. And that’s a problem. --- The Expertise Gap Many organizations, both corporate and institutional struggle to define what makes someone an “expert”. Without a clear framework, expertise is often equated with job title, seniority, or public visibility. But in reality, expertise is multidimensional. It includes formal education, yes — but also lived experience, community influence, original research, and the ability to explain complex ideas clearly. If your organization wants to stay competitive, earn media attention, attract speaking engagements, partnerships, or influence your industry, you need a deeper bench of visible expertise. And it starts by identifying who your real experts are — not just the obvious ones. --- 7 Dimensions of Expertise Here are seven ways to think about expertise beyond the traditional credentials: Authority – Known as a go-to source in their domain. Advocate – Actively supports and elevates their professional community. Educator – Shares knowledge through teaching, speaking, or mentoring. Author – Publishes original insights or thought leadership content. Researcher – Contributes new data, analysis, or findings in their field. Practitioner – Applies knowledge in real-world contexts daily. Graduate – Has academic or technical training in a focus area. Not every expert is made for the stage or the media spotlight — and that’s okay. Some are best behind the scenes, helping create compelling content, briefing spokespeople, or surfacing insights from the field. Your job is to recognize the different ways people can contribute and make that part of your strategy. --- Visibility ≠ Seniority In the era of LinkedIn, personal branding, and AI-powered content, professional visibility is no longer tied to hierarchy. A mid-career professional, with a sharp take on current events might be more discoverable — and more in demand — than a long-tenured exec with little digital presence. That’s why organizations need to shift from thinking about expertise as a ladder, to thinking of it as an ecosystem. Not every expert wants to build a personal brand, but many are ready to contribute — if they’re supported and recognized. Here’s the truth: If you don’t tell your story, someone else will. And if you don’t help your experts show up in the right places — search engines, newsrooms, speaker directories, donor meetings — opportunities will go elsewhere. --- Give Your Experts a Digital Home Even after you've identified your internal experts, the next question is: Where do they live online? Too many organizations treat expert content like an afterthought — scattered across bio pages, outdated PDFs, or buried in press releases. To unlock the real value of your expertise, you need to give it a proper home. That means: Expert Profiles that showcase credentials, insights, and media-friendly info Expert Posts that surface their latest research, commentary, and thought leadership Searchable Directories that help media, partners, and the public find the right voice fast Inquiry Management tools that streamline incoming requests and drive results A centralized platform makes it easier for both internal teams and external audiences to discover, engage, and activate your expertise — whether it’s for media interviews, event invitations, donor conversations, or strategic partnerships. Without it, you're leaving visibility and value on the table. --- Is Your Organization Ready? Expertise is one of your most valuable and underutilized assets — but turning it into impact requires more than a list of names. You need to take stock of your internal bench strength, identify the experts who are ready to lead, and invest in the systems that make their voices heard. Start by asking: Who in our organization has untapped insight? Who’s already engaging audiences but flying under the radar? What tools, platforms, and support can we provide to amplify them? Recognizing your invisible experts is just the first step. Giving them a digital home and helping them engage with the right audiences — that’s how you turn knowledge into opportunity. Learn more about how ExpertFile helps organization's shine the light in these Invisible Experts.
Should I use AI to write my college entrance essay?
With the rapid advancement of artificial intelligence tools such as generative pre-trained transformers, or GPTs, high school students may be tempted to use the tools to perfect their college applications, particularly their entrance essay. Robert Alexander, a vice provost and the dean of enrollment management at the University of Rochester, cautions prospective college students from relying too heavily on AI tools in their applications. “The sentiment among college admissions professionals is that while AI tools may be helpful in generating essay topics and refining or editing students’ writing, we discourage their use to compose application essays or short answers because AI stifles an applicant’s authentic voice,” Alexander says. That personal voice becomes paramount when admissions officers are sifting through applications and considering how each student will contribute to the campus community and fit into the incoming class. “No college or university is trying to admit perfectly identical automaton students,” Alexander says. “At the University of Rochester, for instance, we’re not looking for 1,300 perfect students. We’re trying to craft the perfect class of 1,300 very different and highly-imperfect, but great-fit students.” The goal, he says, is to invite great students, inclusive of their imperfections, and guide them on a transformative journey through their next four years. “Colleges want students to come in with a growth mindset and potential,” Alexander says. “So, if students think they can use AI to help make their application ‘perfect,’ I think they’re chasing the wrong brass ring.” Alexander is an expert in undergraduate admissions and enrollment management who speaks on the subjects to national audiences and whose work has been published in national publications. Click his profile to reach him.
Chemical and Life Science Engineering Professor Michael “Pete” Peters, Ph.D., is investigating more efficient ways to manufacture biologic pharmaceuticals using a radial flow bioreactor he developed. With applications in vaccines and other personalized therapeutic treatments, biologics are versatile. Their genetic base can be manipulated to create a variety of effects from fighting infections by stimulating an immune response to weight loss by producing a specific hormone in the body. Ozempic, Wegovy and Victoza are some of the brand names for Glucagon-Like Peptide-1 (GLP-1) receptor agonists used to treat diabetes. These drugs mimic the GLP-1 peptide, a hormone naturally produced in the body that regulates appetite, hunger and blood sugar. “I have a lot of experience with helical peptides like GLP-1 from my work with COVID therapeutics,” says Peters. “When it was discovered that these biologic pharmaceuticals can help with weight loss, demand spiked. These drug types were designed for people with type-2 diabetes and those diabetic patients couldn’t get their GLP-1 treatments. We wanted to find a way for manufacturers to scale up production to meet demand, especially now that further study of GLP-1 has revealed other applications for the drug, like smoking cessation.” Continuous Manufacturing of Biologic Pharmaceuticals Pharmaceuticals come in two basic forms: small-molecule and biologic. Small-molecule medicines are synthetically produced via chemical reactions while biologics are produced from microorganisms. Both types of medications are traditionally produced in a batch process, where base materials are fed into a staged system that produces “batches” of the small-molecule or biologic medication. This process is similar to a chef baking a single cake. Once these materials are exhausted, the batch is complete and the entire system needs to be reset before the next batch begins. “ The batch process can be cumbersome,” says Peters. “Shutting the whole process down and starting it up costs time and money. And if you want a second batch, you have to go through the entire process again after sterilization. Scaling the manufacturing process up is another problem because doubling the system size doesn’t equate to doubling the product. In engineering, that’s called nonlinear phenomena.” Continuous manufacturing improves efficiency and scalability by creating a system where production is ongoing over time rather than staged. These manufacturing techniques can lead to “end-to-end” continuous manufacturing, which is ideal for producing high-demand biologic pharmaceuticals like Ozempic, Wegovy and Victoza. Virginia Commonwealth University’s Medicines for All Institute is also focused on these production innovations. Peters’ continuous manufacturing system for biologics is called a radial flow bioreactor. A disk containing the microorganisms used for production sits on a fixture with a tube coming up through the center of the disk. As the transport fluid comes up the tube, the laminar flow created by its exiting the tube spreads it evenly and continuously over the disk. The interaction between the transport medium coming up the tube and the microorganisms on the disk creates the biological pharmaceutical, which is then taken away by the flow of the transport medium for continuous collection. Flowing the transport medium liquid over a disc coated with biologic-producing microorganisms allows the radial flow bioreactor to continuously produce biologic pharmaceuticals. “There are many advantages to a radial flow bioreactor,” says Peters. “It takes minutes to switch out the disk with the biologic-producing microorganisms. While continuously producing your biologic pharmaceutical, a manufacturer could have another disk in an incubator. Once the microorganisms in the incubator have grown to completely cover the disk, flow of the transport medium liquid to the radial flow bioreactor is shut off. The disk is replaced and then the transport medium flow resumes. That’s minutes for a production changeover instead of the many hours it takes to reset a system in the batch flow process.” The Building Blocks of Biologic Pharmaceuticals Biologic pharmaceuticals are natural molecules created by genetically manipulating microorganisms, like bacteria or mammalian cells. The technology involves designing and inserting a DNA plasmid that carries genetic instructions to the cells. This genetic code is a nucleotide sequence used by the cell to create proteins capable of performing a diverse range of functions within the body. Like musical notes, each nucleotide represents specific genetic information. The arrangement of these sequences, like notes in a song, changes what the cell is instructed to do. In the same way notes can be arranged to create different musical compositions, nucleotide sequences can completely alter a cell’s behavior. Microorganisms transcribe the inserted DNA into a much smaller, mRNA coded molecule. Then the mRNA molecule has its nucleotide code translated into a chain of amino acids, forming a polypeptide that eventually folds into a protein that can act within the body. “One of the disadvantages of biologic design is the wide range of molecular conformations biological molecules can adopt,” says Peters. “Small-molecule medications, on the other hand, are typically more rigid, but difficult to design via first-principle engineering methods. A lot of my focus has been on helical peptides, like GLP-1, that are a programmable biologic pharmaceutical designed from first principles and have the stability of a small-molecule.” The stability Peters describes comes from the helical peptide’s structure, an alpha helix where the amino acid chain coils into a spiral that twists clockwise. Hydrogen bonds that occur between the peptide’s backbone creates a repeating pattern that pulls the helix tightly together to resist conformational changes. “It’s why we used it in our COVID therapeutic and makes it an excellent candidate for GLP-1 continuous production because of its relative stability,” says Peters. Programming The Cell Chemical and Life Science Engineering Assistant Professor Leah Spangler, Ph.D., is an expert at instructing cells to make specific things. Her material science background employs proteins to build or manipulate products not found in nature, like purifying rare-earth elements for use in electronics. “My lab’s function is to make proteins every day,” says Spangler. “The kind of proteins we make depends entirely on the project they are for. More specifically I use proteins to make things that don’t occur in nature. The reason proteins don’t build things like solar cells or the quantum dots used in LCD TVs is because nature is not going to evolve a solar cell or a display surface. Nature doesn’t know what either of those things are. However, proteins can be instructed to build these items, if we code them to.” Spangler is collaborating with Peters in the development of his radial flow bioreactor, specifically to engineer a microorganismal bacteria cell capable of continuously producing biologic pharmaceuticals. “We build proteins by leveraging bacteria to make them for us,” says Spangler. “It’s a well known technology. For this project, we’re hypothesizing that Escherichia coli (E. coli) can be modified to make GLP-1. Personally, I like working with E. coli because it’s a simple bacteria that has been thoroughly studied, so there’s lots of tools available for working with it compared to other cell types.” Development of the process and technique to use E. coli with the radial flow bioreactor is ongoing. “Working with Dr. Spangler has been a game changer for me,” says Peters. “She came to the College of Engineering with a background in protein engineering and an expertise with bacteria. Most of my work was in mammalian cells, so it’s been a great collaboration. We’ve been able to work together and develop this bioreactor to produce GLP-1.” Other Radial Flow Bioreactor Applications Similar to how the GLP-1 peptide has found applications beyond diabetes treatment, the radial flow bioreactor can also be used in different roles. Peters is currently exploring the reactor’s viability for harnessing solar energy. “One of the things we’ve done with the internal disc is to use it as a solar panel,” says Peters. “The disk can be a black body that absorbs light and gets warm. If you run water through the system, water also absorbs the radiation’s energy. The radial flow pattern automatically optimizes energy driving forces with fluid residence time. That makes for a very effective solar heating system. This heating system is a simple proof of concept. Our next step is to determine a method that harnesses solar radiation to create electricity in a continuous manner.” The radial flow bioreactor can also be implemented for environmental cleanup. With a disk tailored for water filtration, desalination or bioremediation, untreated water can be pushed through the system until it reaches a satisfactory level of purification. “The continuous bioreactor design is based on first principles of engineering that our students are learning through their undergraduate education,” says Peters. “The nonlinear scaling laws and performance predictions are fundamentally based. In this day of continued emphasis on empirical AI algorithms, the diminishing understanding of fundamental physics, chemistry, biology and mathematics that underlie engineering principles is a challenge. It’s important we not let first-principles and fundamental understanding be degraded from our educational mission, and projects like the radial flow bioreactor help students see these important fundamentals in action.”
AI-powered model predicts post-concussion injury risk in college athletes
Athletes who suffer a concussion have a serious risk of reinjury after returning to play, but identifying which athletes are most vulnerable has always been a bit of a mystery, until now. Using artificial intelligence (AI), University of Delaware researchers have developed a novel machine learning model that predicts an athlete’s risk of lower-extremity musculoskeletal (MKS) injury after concussion with 95% accuracy. A recent study published in Sports Medicine details the development of the AI model, which builds on previously published research showing that the risk of post-concussion injury doubles, regardless of the sport. The most common post-concussive injuries include sprains, strains, or even broken bones or torn ACLs. “This is due to brain changes we see post-concussion,” said Thomas Buckley, professor of kinesiology and applied physiology at the College of Health Sciences. These brain changes affect athletes’ balance, cognition, and reaction times and can be difficult to detect in standard clinical testing. “Even a minuscule difference in balance, reaction time, or cognitive processing of what’s happening around you can make the difference between getting hurt and not,” Buckley said. How AI is changing injury risk assessment Recognizing the need for enhanced injury reduction risk tools, Buckley collaborated with colleagues in UD’s College of Engineering, Austin Brockmeier, assistant professor of electrical and computer engineering, and César Claros, a fourth-year doctoral student; Wei Qian, associate professor of statistics in the College of Agriculture and Natural Resources; and former KAAP postdoctoral fellow Melissa Anderson, who’s now an assistant professor at Ohio University. To assess injury risk, Brockmeier and Claros developed a comprehensive AI model that analyzes more than 100 variables, including sports and medical histories, concussion type, and pre- and post-concussion cognitive data. “Every athlete is unique, especially across various sports,” said Brockmeier. “Tracking an athlete’s performance over time, rather than relying on absolute values, helps identify disturbances, deviations, or deficits that, when compared to their baseline, may signal an increased risk of injury.” While some sports, such as football, carry higher injury risk, the model revealed that individual factors are just as important as the sport played. “We tested a version of the model that doesn’t have access to the athlete’s sport, and it still accurately predicted injury risk,” Brockmeier said. “This highlights how unique characteristics—not just the inherent risks of a sport—play a critical role in determining the likelihood of future injury,” said Brockmeier. The research, which tracked athletes over two years, also found that the risk of MSK injury post-concussion extends well into the athlete’s return to play. “Common sense would suggest that injuries would occur early in an athlete’s return to play, but that’s simply not true,” said Buckley. “Our research shows that the risk of future injury increases over time as athletes compensate and adapt to small deficits they may not even be aware of.” The next step for Buckey’s Concussion Research Lab is to further collaborate with UD Athletics’ strength and conditioning staff to design real-time interventions that could reduce injury risk. Beyond sports: AI’s potential in aging research The implications of the UD-developed machine-learning model extend far beyond sports. Brockmeier believes the algorithm could be used to predict fall risk in patients with Parkinson’s disease. Claros is also exploring how the injury risk reduction model can be applied to aging research with the Delaware Center for Cognitive Aging. “We want to use brain measurements to investigate whether baseline lifestyle measurements such as weight, BMI, and smoking history are predictive of future mild cognitive impairment or Alzheimer’s disease,” said Claros. To arrange an interview with Buckley, email UD's media relations team at MediaRelations@udel.edu
A final disbursement of $8.8 million completes the $17.8 million grant awarded by the Department of Defense (DoD) to Virginia Commonwealth University’s (VCU) Convergence Lab Initiative (CLI). The funding allows CLI to continue advancing research in the areas of quantum and photonic devices, microelectronics, artificial intelligence, neuromorphic computing, arts and biomedical science. “The Convergence Lab Initiative represents a unique opportunity to drive innovation at the intersection of advanced technologies, preparing our students to tackle the critical challenges of tomorrow,” said Nibir Dhar, Ph.D., electrical and computer engineering professor and CLI director. “By combining cutting-edge research in electro-optics, infrared, radio frequency and edge computing, we are equipping the next generation of engineers with the skills to shape the future of both defense and commercial industries.” Working with Industry Partnership is at the heart of CLI and what makes the initiative unique. CivilianCyber, Sivananthan Laboratories and the University of Connecticut are among several collaborators focusing on cutting-edge, multidisciplinary research and workforce development. The lightweight, low-power components CLI helps develop are capable of transforming military operations and also have commercial applications. The Convergence Lab Initiative has 25 collaborative projects in this area focused on: Electro-optic and Infrared Technologies: Enhancing thermal imaging for medical diagnostics, search-and-rescue operations and environmental monitoring. This improves military intelligence, surveillance and reconnaissance capabilities. Radio Frequency and Beyond 5G Communication: Developing ultra-fast, low-latency communication systems for autonomous vehicles, smart cities and telemedicine. Accelerating advancements in this area also address electronic warfare challenges and security vulnerabilities. Optical Communication in the Infrared Wavelength: Increasing data transmission rates to create more efficient networks that support cloud computing, data centers, AI research and covert military communications. Edge Technologies: Creating low size, weight and low power-consuming (SWaP) computing solutions for deployment in constrained environments, such as wearables, medical devices, internet of things devices and autonomous systems. These technologies enhance real-time decision-making capabilities for agriculture, healthcare, industrial automation and defense. Benefits for Students College of Engineering students at VCU have an opportunity to engage with cutting-edge research as part of the DoD grant. Specialized workforce development programs, like the Undergraduate CLI Scholars Program, provide hands-on experience in advanced technologies. The STEM training also includes students from a diverse range of educational backgrounds to encourage a cross-disciplinary environment. Students can also receive industry-specific training through CLI’s Skill-Bridge Program, which facilitates direct connections between business needs and academic education. Unlike the DoD program for transitioning military personnel, the CLI Skill-Bridge is open to students from VCU and other local universities, creating direct connections between industry needs and academic training. This two-way relationship between academia and industry is unlike traditional academic research centers. With the College of Engineering’s focus on public-private partnerships, VCU becomes a registered partner with the participating businesses, collaborating to design individualized training programs focused on the CLI’s core research areas. This approach ensures students receive relevant, up-to-date training while companies gain access to a pipeline of skilled talent familiar with the latest industry trends and innovations. “The significance of this grant extends beyond immediate research outcomes. It addresses critical capability gaps for both the DoD and commercial sectors,” says Dhar. “This dual-use approach maximizes DoD investment impacts and accelerates innovation in areas that affect everyday life — from healthcare and environmental monitoring to communication networks and smart infrastructure. Breakthroughs emerging from these collaborations will strengthen national security while creating commercial spinoffs that drive economic growth and improve quality of life for communities both locally and globally. Advances in infrared technology, in particular, will position the VCU College of Engineering as a center for defense technologies and new ideas.”
Why generative AI 'hallucinates' and makes up stuff
Generative artificial intelligence tools, like OpenAI’s GPT-4, are sometimes full of bunk. Yes, they excel at tasks involving human language, like translating, writing essays, and acting as a personalized writing tutor. They even ace standardized tests. And they’re rapidly improving. But they also “hallucinate,” which is the term scientists use to describe when AI tools produce information that sounds plausible but is incorrect. Worse, they do so with such confidence that their errors are sometimes difficult to spot. Christopher Kanan, an associate professor of computer science with an appointment at the Goergen Institute for Data Science and Artificial Intelligence at the University of Rochester, explains that the reasoning and planning capabilities of AI tools are still limited compared with those of humans, who excel at continual learning. “They don’t continually learn from experience,” Kanan says of AI tools. “Their knowledge is effectively frozen after training, meaning they lack awareness of recent developments or ongoing changes in the world.” Current generative AI systems also lack what’s known as metacognition. “That means they typically don’t know what they don’t know, and they rarely ask clarifying questions when faced with uncertainty or ambiguous prompts,” Kanan says. “This absence of self-awareness limits their effectiveness in real-world interactions.” Kanan is an expert in artificial intelligence, continual learning, and brain-inspired algorithms who welcomes inquiries from journalists and knowledge seekers. He recently shared his thoughts on AI with WAMC Northeast Public Radio and with the University of Rochester News Center. Reach out to Kanan by clicking on his profile.
