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Panagiotis Ch. AnastasopoulosView profile

Associate Professor

• Statistical and Econometric Modeling
• Transportation Safety
• Infrastructure Systems Reliability
• +7 more

Panagiotis (Panos) Ch. Anastasopoulos is an Associate Professor and the Stephen E. Still Chair of Transportation Engineering in the Department of Civil, Structural and Environmental Engineering at the University at Buffalo, State University of New York. He also serves as Director of both the Stephen Still Institute for Sustainable Transportation & Logistics and the Engineering Statistics and Econometrics Application (ESEA) Research Laboratory within the School of Engineering and Applied Sciences. Dr. Anastasopoulos received his educational credentials from prestigious institutions: PhD in Civil Engineering from Purdue University (2009) MSc in Civil Engineering from Purdue University (2007) BS from Athens University of Economics & Business, Athens, Greece (2004) His research spans multiple cutting-edge domains in transportation engineering and statistical methods. Dr. Anastasopoulos specializes in statistical and econometric modeling of engineering problems, with particular expertise in infrastructure systems reliability, transportation safety, and crisis management. His work increasingly focuses on emerging technologies including autonomous and connected vehicle systems, advanced air mobility solutions, and the integration of human behavior with next-generation transportation technologies. He has made significant contributions to understanding public perceptions of emerging transportation modes like flying cars and autonomous vehicles, as well as developing innovative statistical methods for transportation data analysis. Analysis of Dr. Anastasopoulos' recent publications reveals a strong emphasis on applying sophisticated statistical and econometric methods to transportation safety challenges. His work frequently employs advanced modeling techniques including random parameters models, bivariate probit approaches, and hierarchical ordered probit analyses. There is a clear progression in his research toward addressing emerging transportation challenges including urban air mobility, autonomous vehicle integration, and the impact of public-private partnerships on transportation infrastructure safety. His highly cited work has appeared in top journals such as Analytic Methods in Accident Research, Accident Analysis and Prevention, and Transportation Research journals. Dr. Anastasopoulos has received significant recognition for his scholarly contributions: Named a Highly Cited Researcher by Web of Science for 2019, 2020, and 2021 Co-author of the widely used textbook "Statistical and Econometric Methods for Transportation Data Analysis" (3rd edition) As an academic leader, Dr. Anastasopoulos serves in multiple editorial capacities including Executive Associate Editor of Analytic Methods in Accident Research (ranked #1 journal in Safety Research and Transportation), Associate Editor for the ASCE Journal of Infrastructure Systems, and editorial board member for several other prominent transportation journals. His research has been supported by prestigious funding sources including the National Science Foundation's Innovation Corps Program, Federal Highway Administration, and New York State Department of Transportation. He has authored or co-authored over 160 papers, book chapters, and reports throughout his career. At the Engineering Statistics and Econometrics Application Research Laboratory (ESEA), which he founded and directs, Dr. Anastasopoulos leads a team that provides statistical and econometric consultation services to engineers and scientists. The lab focuses on developing solutions for partners throughout the School of Engineering and Applied Sciences at UB, with particular emphasis on transportation safety, infrastructure reliability, and emerging mobility technologies. The ESEA lab provides free consultation services to UB's engineering school and governmental agencies in New York State, and has worked on numerous projects including highway safety analysis, transportation data informatics, and infrastructure management systems.

Thu D. NguyenView profile

Professor

• Datacenters
• Sustainability
• Computer Systems
• +5 more

Thu D. Nguyen is a distinguished Professor and currently serves as the Dean of Mathematical and Physical Sciences at the School of Arts and Sciences, Rutgers University. She holds a faculty position in the Department of Computer Science where she has made significant contributions to research and education. Her research interests span multiple domains within computer science, with a particular focus on Datacenters , Sustainability , Computer Systems , Approximate Computing , and Computer Science Education . Dr. Nguyen leads the Computer and Networked Systems research group, where her team investigates innovative approaches to energy-efficient computing and data management. Analysis of her recent publications reveals a strong trend toward addressing challenges in big data management, missing data imputation, blockchain applications in education, and machine learning for educational technology. Her work bridges theoretical computer science with practical applications in educational systems and healthcare data management. Dr. Nguyen has received numerous prestigious awards recognizing her contributions to the field: Rutgers Big Ten Academic Alliance Department Executive Officers Fellow (2018) Best paper award at FAST 2016 for research on datacenter reliability IEEE Micro Top Picks selection (2014) Multiple best paper awards including at IGCC 2010 NSF CAREER award (2005-2010) Faculty of the Year Award for Excellence in Teaching (awarded in 2000 and 2002) Her research has been supported by substantial grants including a $4 million DIBBS grant, NSF CSforAll grant, Microsoft SEIF grant, and multiple NSF CRI grants. Dr. Nguyen has played a leadership role in establishing Rutgers' new Data Science major, minor, and certificate programs, reflecting her commitment to advancing computer science education.

Wei LeView profile

Associate Professor

• AI
• Machine Learning
• Software Engineering
• +5 more

Dr. Wei Le is an Associate Professor in the Department of Computer Science at Iowa State University. His primary affiliations include the Program Analysis & AI Lab and collaborative work with Mayo Clinic. He holds a Ph.D. in Computer Science from the University of Virginia (2010). His research focuses on the intersection of AI and software engineering, particularly in trustworthy AI systems, vulnerability detection, and program analysis. Key areas include AI for medicine, analyzing AI models, and improving software reliability through techniques like dataflow analysis and symbolic execution. Recent work emphasizes efficient vulnerability detection using deep learning (e.g., ICSE 2024), numerical instability in ML applications (FSE 2025), and GPU numerical testing (SC 2024). His research has been supported by NSF, Google, and DARPA. Notable awards include the Distinguished Paper Award at FSE 2016 and Best Presentation at FSE 2008. He has advised over 10 students and collaborates with institutions like Columbia University and UCLA. Teaching responsibilities include courses on program analysis (COM S 413/513), principles of programming languages, and advanced AI topics. He actively contributes to conferences as a PC member and area chair (e.g., ICSE, FSE).

Gordon KindlmannView profile

Associate Professor

• Biomedical Image Analysis
• Scientific Visualization
• Systems
• +5 more

Gordon Kindlmann is an Associate Professor of Computer Science at the University of Chicago, affiliated with the Systems Group research community. His work bridges computational imaging science and visualization theory, focusing on biomedical applications and machine learning integration. He leads projects in diffusion MRI analysis, surgical planning tools like SlicerDMRI, and theoretical advancements in visualization design. Research Interests: Biomedical Image Analysis Scientific Visualization Theory High Performance Computing Medical Imaging Algorithms Machine Learning Applications Recent Articles Trends: His work emphasizes cardiovascular modeling (e.g., aortic dissection prediction) and visualization validation techniques. Recent collaborations include optimizing visualization tools for scalability and accuracy in threaded data exploration. Awards: None explicitly listed in provided text. Grants/Advising: Involved in CDAC Discovery Grants (2019) and actively supports student research, though specific advisees are not listed here. His lab develops open-source tools like Diderot for tensor field visualization. Labs & Teams: Member of the Systems Group, a collaborative environment advancing systems research, programming languages, and software engineering.

• Wireless Communications
• Signal Processing
• MIMO Systems
• +6 more

Claude D'Amours is a Professor in the Department of Electrical and Computer Engineering at the University of Ottawa, Faculty of Engineering. With a research career spanning over two decades, his work has significantly contributed to the fields of wireless communications, signal processing, and network security. His current research focuses on cutting-edge technologies including 5G/6G networks, UAV communications, reconfigurable intelligent surfaces, and physical layer security. Dr. D'Amours' research interests center on advanced wireless communication systems, with particular emphasis on MIMO technologies, CDMA systems, spectrum sensing techniques, and channel modeling for emerging applications. His work bridges theoretical foundations with practical implementations, addressing critical challenges in modern wireless networks including interference management, security vulnerabilities, and efficient resource allocation. Recent research has expanded into UAV communications, where he investigates channel characteristics and reliability issues in drone-based networks. Analysis of his recent publications reveals a strong focus on security aspects of wireless communications, particularly physical layer security mechanisms to counter jamming attacks and eavesdropping. His work on RF fingerprinting for device authentication in industrial IoT settings demonstrates practical applications of his theoretical research. The trend in his publications shows increasing attention to 5G/6G technologies, with particular emphasis on millimeter wave communications, hybrid precoding architectures, and novel approaches to spectrum sharing. Multiple publications on UAV-to-ground channel modeling and performance analysis Research on physical layer security mechanisms against jamming attacks Work on hybrid precoding architectures for mmWave MIMO systems Studies on RF fingerprinting for secure industrial IoT applications Investigations into spectrum sensing techniques for cognitive radio Dr. D'Amours maintains active research collaborations with numerous colleagues, particularly Michel Kulhandjian, Hovannes Kulhandjian, and François Chan, resulting in a consistent stream of high-impact publications across top-tier IEEE journals and conferences. His research group appears to focus on both theoretical analysis and practical implementation of advanced wireless communication techniques, with applications spanning from critical mission communications to industrial IoT security.

• Technology Credit Scoring
• Operational Research
• Data Mining
• +5 more

So Young Sohn is a distinguished Professor at Korea University's College of Business, Department of Management Engineering, with over two decades of impactful research in technology management and operations research. Her scholarly contributions have established her as a leading expert in technology credit scoring, data mining applications, and technology convergence analysis. Dr. Sohn's research interests span technology credit scoring for SMEs, operational research methodologies, data mining techniques, machine learning applications in business contexts, technology convergence patterns, patent analysis, and SME financing mechanisms. Her work bridges theoretical rigor with practical business applications, particularly focusing on Korean case studies that have broader international relevance. She has pioneered innovative approaches using knowledge graphs, multiplex networks, and deep learning techniques to solve complex business problems. Her publication portfolio reveals consistent research trends toward increasingly sophisticated analytical methods, evolving from traditional statistical models to advanced machine learning and network science approaches. Recent work demonstrates particular focus on technology convergence, digital therapeutics, and AI applications in business decision-making. The interdisciplinary nature of her research spans business analytics, engineering, healthcare, and environmental science. Dr. Sohn has received recognition through numerous high-impact publications in premier journals including Expert Systems with Applications, European Journal of Operational Research, Scientometrics, and IEEE Transactions. Her research has been consistently funded through competitive grants focusing on technology management and innovation. As an academic mentor, Dr. Sohn has advised numerous doctoral students who have gone on to productive research careers, with many continuing to collaborate with her on ongoing projects. Her research team has secured substantial funding for projects related to technology credit scoring, technology convergence analysis, and predictive analytics applications. Dr. Sohn leads a dynamic research laboratory focused on technology analytics and decision support systems, collaborating with industry partners and government agencies to translate research findings into practical business solutions. Her current work emphasizes sustainable technology development and AI-driven decision support systems for complex business environments.

• Wireless Communication
• High Frequency (HF) Radio Systems
• Internet of Things (IoT)
• +3 more

Joan Lluís Pijoan is an active researcher in wireless communication systems, with a focus on high frequency (HF) radio transmission, antenna design, and IoT architectures. He has co-authored numerous publications in journals such as IEEE Access, Sensors, and Remote Sensing, primarily investigating technical challenges in long-haul HF links, NVIS communication, and heterogeneous wireless networks. His research spans applications in Antarctic geophysical observatories IoT for natural disaster monitoring Advanced modulation schemes Signal processing for remote sensing Recent work highlights his expertise in antenna booster design (2023), multicarrier modulation (2020), and ionospheric channel analysis (2015). While specific university affiliations or departmental roles remain unspecified, his 15 most recent articles demonstrate sustained contributions to wireless systems since 2004.

Jennifer C WolffView profile

Professor

• Suicide prevention
• Adolescent mental health
• Parenting practices
• +7 more

Dr. Jennifer C Wolff is a Professor of Psychiatry and Human Behavior at The Warren Alpert Medical School of Brown University and a Child Psychologist at Rhode Island and Bradley Hospitals. She serves as the Director of the Adolescent Mental Health Collaborative (AMHC), which aims to improve mental health for youth through research, treatment, and training, and as the Director of the COBRE-funded Pediatric Biopsychology Core at Bradley Hospital, which promotes sleep and mental health research. Dr. Wolff's research program focuses on delineating social, cognitive, and affective mechanisms underlying adolescent psychopathology, translating these findings into treatments, and implementing these treatments in real-world settings. Her specific interests include suicide prevention, parenting practices, and improving treatment delivery across settings. She is currently Principal Investigator or Multiple Principal Investigator on four NIH grants addressing adolescent suicide assessment, parenting interventions, juvenile justice involvement, and social media impacts on youth mental health. Her recent publications demonstrate significant expertise in adolescent suicide risk assessment and prevention, emotion regulation, parenting interventions, and implementation science. Her work spans from basic mechanisms of psychopathology to the development and implementation of evidence-based treatments in community settings. She has made substantial contributions to understanding suicide risk factors among adolescents, particularly those in psychiatric care, and has developed innovative approaches to improve treatment delivery. Dr. Wolff has received numerous honors including the Distinguished Professional Contributions to Applied Research from the American Psychological Association, the Department of Psychiatry Education Committee Excellence in Teaching award from Brown University, a Certificate of Exemplary Teaching from the Office of Medical Education, and the Outstanding Mentor Award from the American Academy of Child and Adolescent Psychiatry. Her active research portfolio includes major grants such as R01MH129457 ("Towards a reliable and valid assessment of preteen suicidal thoughts and behavior"), R01MH129770 ("From the Court to the Community: Improving Evidence Based Treatment for Underserved Justice Involved Youth At-Risk for Suicide"), and P20GM139743 ("The Pediatric Biopsychology Core"), demonstrating her leadership in adolescent mental health research and implementation science.

• Microwave Semiconductor Device Modeling
• RF Modeling
• Electromagnetic Simulation
• +3 more

Peter H. Aaen is a Reader in Microwave Semiconductor Device Modeling at the University of Surrey, with expertise in RF and microwave device modeling and characterization. His work focuses on developing advanced methodologies for high-power and high-frequency electronic devices, with applications in telecommunications and quantum technologies. Dr. Aaen received his B.A.Sc. in Engineering Science and M.A.Sc. in Electrical Engineering from the University of Toronto, Canada, and his Ph.D. in Electrical Engineering from Arizona State University, USA, in 1995, 1997, and 2005 respectively. Prior to joining the University of Surrey, he was the manager of the RF Modeling and Measurement Technology team at Freescale Semiconductor Inc (formerly Motorola Inc.), bringing significant industry experience to his academic work. Dr. Aaen's research spans several critical areas in microwave engineering, with a particular emphasis on developing multi-physics based modeling methodologies for high-power and high-frequency electronic devices. His expertise includes calibration techniques for microwave measurements, package modeling, development of compact models for microwave power transistors and RFICs, and efficient electromagnetic simulation methodologies for complex packaged environments. He has made significant contributions to understanding frequency dispersion in RF LDMOS transistors, electro-thermal modeling, and the development of measurement techniques for extreme impedance devices. His publication record demonstrates a clear progression from fundamental device modeling to advanced measurement techniques and applications in next-generation communications systems. Recent work has focused on multiphysics measurements, electro-optic field imaging, and the application of nanowire technologies to microwave switches, reflecting the evolving challenges in 5G and beyond communications infrastructure. Dr. Aaen is a Senior Member of the IEEE and active in several technical committees including the IEEE Technical Committee (MTT-1) on Computer-Aided Design, the technical program committee of the IEEE Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), and the executive committee of the Automatic RF Techniques Group (ARFTG). Dr. Aaen has supervised numerous PhD students whose research has advanced the field of microwave engineering, particularly in areas related to measurement uncertainty, multiphysics characterization of high-power transistors, and nanoscale device integration. His collaborative work spans multiple institutions and has resulted in significant advancements in understanding device behavior under complex operating conditions. His laboratory work focuses on developing novel measurement techniques that combine electro-optic systems with nonlinear vector network analyzers and load-pull measurement systems, enabling unprecedented visualization of electromagnetic field distributions within operating transistors. This work has led to breakthroughs in understanding oscillation mechanisms and thermal behavior in high-power devices.

Yogendra K JoshiView profile

Professor

• Thermal Management
• Computational Fluid Dynamics
• Electronics Packaging
• +5 more

Yogendra K Joshi is a Professor at the Georgia Institute of Technology, holding a joint appointment in the School of Mechanical Engineering and the School of Electrical and Computer Engineering. He specializes in thermal management of electronic, telecommunication, and power systems, with expertise in computational modeling of semiconductor processes and microfabricated thermal devices. His work spans thermal modeling of data centers, electric vehicle motors, and high-power electronics packaging. Education: B.Tech. (Mechanical Engineering, IIT Kanpur, 1979); M.S. (SUNY Buffalo, 1981); Ph.D. (University of Pennsylvania, 1984). Previous academic positions include the University of Maryland (1993–2001) and the Naval Postgraduate School (1986–1993). He has also contributed to semiconductor industry R&D. Research focuses on thermal phenomena in electronics cooling, including flow boiling, microfluidics, and embedded evaporative cooling. He has pioneered internet-based distance learning programs in thermal management. Awards: Fellow of ASME (American Society of Mechanical Engineers) 1999 ASME Curriculum Innovation Award Multiple Faculty Performance Awards at Naval Postgraduate School His research integrates thermal-fluid modeling with engineering applications, addressing challenges in data center efficiency, electric vehicle thermal systems, and high-power electronics. Collaborations span academia and industry, emphasizing practical solutions for modern thermal management problems.

• Power Electronics
• Wireless Power Transfer
• Electric Vehicle Technology
• +4 more

Nikolay Dimitrov Madzharov is a Professor at the Department of Electronics, Faculty of Electrical Engineering and Electronics, Technical University of Gabrovo. His work focuses on power electronics, wireless power transfer systems, and electric vehicle charging infrastructure. He has led and participated in numerous international and national projects, including modernization of educational institutions and development of energy-efficient systems. Research Interests: Wireless power transfer, inductive resonance systems, electric vehicle technology, energy storage systems, and control systems in energy. Projects: Over 7 projects, including 'Optimal Design and Management of Electrical Energy Storage Systems' and 'Intelligent Mechatronic, Eco- and Energy-Saving Systems and Technologies.' His research emphasizes practical applications, such as contactless charging stations and high-efficiency energy converters. Madzharov has authored 4 books and over 96 publications, including journal articles and conference papers on topics like resonant inverters and battery management systems. He has advised 6 doctoral students, contributing to advancements in power electronics and energy systems. His work has been presented at international conferences such as PCIM, ICEST, and SIELA, showcasing innovations in wireless energy transfer and system reliability.

Bruno DelacressonniereView profile

Associate Professor

• RF Electronics
• Electromagnetic Compatibility (EMC)
• Analog Electronics
• +7 more

Bruno Delacressonniere is an Associate Professor at ENSEA, affiliated with the Quartz laboratory and Electronic Systems department. His research focuses on RF electronics, electromagnetic compatibility (EMC), and advanced electronic systems including optical communications and sensor technologies. He teaches courses in analog electronics, microelectronics, and sensor systems. His research activities span nanophotonics for network-on-chip (NoC) architectures, reliability analysis of RFID systems, and innovative antenna designs for energy harvesting. He has contributed to advancements in optically controlled oscillators, SiGe-based phototransistors, and tunable microwave components using CMOS/SiGe technologies. Delacressonniere has supervised numerous Master’s theses and projects (PFE) focusing on embedded systems and RF design. His work bridges theoretical research with practical implementations, emphasizing energy-efficient solutions and high-reliability electronics for harsh environments. He leads the Electronic Systems team at Quartz, collaborating on projects involving 3D optical integration, microwave filter design, and photonics-based interconnects for multiprocessor systems. His publications reflect a strong emphasis on integrating nanophotonics and advanced semiconductor technologies for next-generation electronic systems.

Stefan TaiView profile

Professor

• Distributed Systems
• Decentralized Architectures
• Data Management
• +6 more

Prof. Stefan Tai is a full professor and Chair of Information Systems Engineering at Technische Universität Berlin (Germany) since 2014. Previously, he held a full professorship at Karlsruhe Institute of Technology (KIT) and worked as a Research Staff Member at IBM's Thomas J. Watson Research Center. His research focuses on distributed systems, decentralized architectures, cloud service engineering, and privacy-preserving blockchain systems, emphasizing off-chain solutions and scalable technologies. Tai's work bridges academic research with industry applications, particularly in serverless computing, energy-efficient cloud-native systems, and blockchain-based solutions for supply chains and energy grids. He has authored numerous peer-reviewed publications in top-tier conferences and journals, including IEEE ICSA, Future Generation Computer Systems, and IEEE Blockchain. His contributions address challenges in software systems engineering, federated learning, and sustainable computing. Research Interests: Next-generation distributed software systems Decentralized architectures and blockchain systems Cloud and serverless computing Energy-efficient design Data management and privacy-preserving technologies Smart energy grids and IoT integration Key Publications Trends: Tai's recent work explores the intersection of blockchain and federated learning, serverless architectures for big data, and energy-efficient cloud-native applications. His 2025 paper introduced a framework for optimizing cloud-native energy efficiency, while 2024 contributions advanced verifiable decentralized systems and serverless data processing. Earlier research (2020–2021) focused on privacy in local energy grids and serverless computing scalability. Grants & Labs: While specific grants are not detailed, his leadership in TU Berlin's Information Systems Engineering group indicates involvement in EU-funded or industrial projects. Collaborations with institutions like TU Wien and KIT suggest multi-institutional efforts in distributed systems and blockchain. Labs/Teams: Leads the Information Systems Engineering research group at TU Berlin, focusing on software systems engineering, cloud architectures, and blockchain applications.

Masoud SanayeiView profile

Professor

• Bridge structural health monitoring
• Building train-induced vibrations
• Nondestructive testing of full-scale structures
• +4 more

Masoud Sanayei is a Professor of Civil and Environmental Engineering at Tufts University's School of Engineering, where he has been a faculty member since 1986. He currently serves as an active professor in the Civil and Environmental Engineering department, teaching courses in structural analysis, finite element analysis, and structural dynamics. Dr. Sanayei's research focuses on bridge structural health monitoring (SHM), building train-induced vibrations, nondestructive testing of full-scale structures, and fatigue life prediction of structures with nonproportional multi-axial loading. His work has significantly advanced the field of structural health monitoring through the development of bridge signatures, an innovative nonparametric probabilistic method using operational bridge responses due to daily traffic for structural condition assessment. His recent publications demonstrate a strong trend toward integrating advanced computational methods including deep learning and machine learning with traditional structural engineering approaches. His research spans from fundamental vibration analysis to practical applications in bridge and building monitoring, with particular emphasis on train-induced vibrations in over-track structures and fatigue assessment of complex connections. Albert Nelson Marquis Lifetime Achievement Award by Marquis Who's Who 2017 Professor of the Year Award, Tufts University Dr. Sanayei has secured numerous research grants from the National Science Foundation, Federal Highway Administration, and Massachusetts Port Authority, focusing on structural performance monitoring, fatigue assessment, and asset management. His professional activities include editorial board membership for the Journal of Bridge Engineering (ASCE), committee leadership in the AISC/ASCE Steel Bridge Competition, and active participation in technical committees related to structural identification. His laboratory work includes extensive full-scale testing on structures such as the Tobin Bridge in Boston, Powder Mill Bridge in Massachusetts, Memorial Bridge in New Hampshire, Boston Convention Center, Hynes Convention Center, TD Garden, MIT Brain and Cognitive Science Center, and over-train-track buildings atop Shenzhen Metro Depot in China.

• Legal Education
• Bar Exam Preparation
• West Virginia Law
• +4 more

West Virginia University College of Law professors Kirsha Trychta, Amy Cyphert, and Melissa Giggenbach are prominent faculty members actively engaged in legal education, scholarship, and professional service. Trychta serves as director of the Academic Excellence Center, focusing on bar exam preparation and legal education. Cyphert specializes in AI ethics and health law, frequently publishing and speaking on technology's impact on legal practice. Giggenbach directs the West Virginia Innocence Project Clinic, concentrating on forensic science issues and wrongful convictions. Their research interests span legal education innovation, artificial intelligence ethics, health law, forensic science, and criminal justice reform. Trychta's work centers on West Virginia law and bar examination preparation. Cyphert explores the intersection of AI and legal ethics, with numerous publications in health law journals. Giggenbach investigates 'junk science' in criminal cases and advocates for evidence-based forensic practices. Collectively, their publications demonstrate a strong commitment to contemporary legal challenges, particularly in legal technology, criminal justice reform, and healthcare law. Their scholarship appears in venues including the Houston Journal of Health Law & Policy, U.C. Irvine Law Review, and various presentations at national conferences. They regularly contribute to discussions on AI in law, bar exam preparation, and forensic evidence standards. Their professional recognition includes conference presentations, editorial contributions, and leadership roles in academic organizations. Trychta chairs the AASE Committee and presents on NextGen Bar Exam topics. Cyphert participates in expert panels on AI and has been cited in judicial opinions. Giggenbach leads the West Virginia Innocence Project Clinic, directly impacting criminal justice through clinical legal education. All three maintain active scholarly agendas with numerous recent publications and presentations, demonstrating their ongoing contributions to legal education and practice. They regularly present at conferences including the Indigent Defense Annual Conference and contribute to law review publications across multiple disciplines.