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Fei YuView profile

Assistant Professor

• Health Informatics
• Health Sciences Librarianship
• Bibliometrics
• +3 more

Dr. Fei Yu is an Assistant Professor at the University of North Carolina's School of Information and Library Science (SILS), where she also serves as a faculty member in the Carolina Health Informatics Program. She holds a PhD in Library and Information Science from the University of Pittsburgh, and advanced degrees in Biomedical and Health Informatics, Information Management, and Library Science from UNC Chapel Hill and Wuhan University. Her research focuses on digital health technology, bibliometrics, research impact evaluation, and user-centered design, with particular emphasis on systematic reviews, AI applications in healthcare, and translational science support. Previously serving as a Health Informatics Librarian at UNC Health Sciences Library for seven years, Dr. Yu has supported researchers across disciplines in research impact assessment, citation analysis, and digital health technology evaluation. She currently leads grant-funded projects such as the iPICS program (US Department of Defense) and contributes to the Lancet Commission on Evidence-based Implementation in Global Health. Her work bridges library science with translational healthcare, emphasizing evidence-based practices and interdisciplinary collaboration. Dr. Yu teaches courses including User Interface Design (INLS718), Systematic Literature Search (INLS490), and Perspectives on Technology in Information (INLS777). Her awards include the 2023 UNC Libraries Peer-to-Peer Award and recognition from Endeavors for her contributions to global health research through librarian partnerships.

Gidon ErnstView profile

Assistant Professor

• Formal Methods
• Software Verification
• Software Testing
• +5 more

Gidon Ernst is a Junior Professor (Assistant Professor) for Software Verification at the Department of Computer Science, LMU Munich, where he leads research in the Software and Computational Systems Lab. His work focuses on formal methods for software engineering, with particular emphasis on verification, testing, and security analysis of complex systems. His research interests span formal methods for software engineering, software testing, interactive and automated proofs, hybrid systems, and falsification. Ernst's work addresses challenges in relational proofs for substitution principle, software testing with Legion (a tool combining concolic execution and fuzzing), concurrent information flow security with SecCSL and SecC, and falsification of temporal logic requirements for hybrid systems. His approach often combines theoretical foundations with practical tool development. Ernst's publication record shows a strong focus on verification of security properties, particularly information flow security in concurrent systems, and falsification of hybrid systems. His recent work has advanced techniques for adaptive probabilistic search in falsification, compositional vulnerability detection, and contract-based software specification. He actively contributes to the verification community through competitions like VerifyThis and ARCH. Best Master Lecture 2022 in Computer Science (awarded by Gruppe Aktiver Fachschaftika) Ernst has supervised PhD student Dongge Liu on software fuzzing with machine learning (2018-2022), co-supervised with Toby Murray and Ben Rubinstein from the University of Melbourne. His teaching includes courses on formal specification and verification, methods in software engineering, and various seminars on software quality assurance and deductive verification. He has also served on numerous program committees and organized major verification events including SPIN 2025 and the VerifyThis challenge series. Ernst maintains active collaborations with researchers worldwide, particularly in the areas of hybrid systems verification (with Ichiro Hasuo, Sean Sedwards, and Zhenya Zhang) and information flow security (with Toby Murray). His work bridges theoretical computer science with practical software engineering challenges, developing tools that have seen adoption in both academic and industrial settings.

Nima MaftoonView profile

Associate Professor

• Biomedical Engineering
• Auditory Mechanics
• Medical Devices
• +9 more

Nima Maftoon is an Associate Professor and Director of the Biomedical Engineering graduate program at the University of Waterloo. He leads the Hearing Lab, which focuses on developing medical devices and methods for hearing loss treatment, and the Computational Metastasis Lab, which creates predictive tools for personalized cancer treatments. His research integrates biomechanics, acoustics, computational modeling, and clinical data analysis. Education: Postdoctoral Fellowship in Hearing Science (2017), Harvard Medical School PhD in Biomedical Engineering (2014), McGill University MSc in Mechanical Engineering (2000), Isfahan University of Technology BSc in Mechanical Engineering (1997), University of Tehran His research interests span auditory mechanics, medical device design, cancer metastasis modeling, and computational fluid/solid mechanics. His work combines experimental measurements with advanced computational techniques to address clinical challenges in hearing and oncology. Recent publications focus on finite-element modeling of biomedical systems, cancer cell mechanics, and acoustic analysis of the middle ear. His labs collaborate across disciplines to bridge engineering and clinical applications. Nima supervises graduate students through his Sole-Supervisory Privilege Status (SSPS) and teaches courses in linear systems and biomedical signal processing.

Donghoon KimView profile

Associate Professor

• Cloud Computing Security
• High-Performance Computing
• Software Engineering
• +5 more

Dr. Donghoon Kim is an Associate Professor in the Department of Computer Science at Arkansas State University, part of the College of Engineering & Computer Science. He holds a PhD from North Carolina State University and a Master of Science from Auburn University, following a software engineering role at Samsung Electronics in Korea. His research focuses on cloud computing security, high-performance computing, software engineering, parallel computing, and wireless network security. His work bridges theoretical advancements with practical applications in cybersecurity and machine learning. Dr. Kim’s research interests include analyzing machine learning approaches for detecting Android and IoT malware, enhancing deep hashing models for image retrieval, and improving authentication systems for digital content. He has contributed to optimizing multimedia data processing and evaluating Tor network vulnerabilities through real-time fingerprinting techniques. His teaching expertise spans software engineering and computer architecture. While his Google Scholar publications highlight cutting-edge work in cybersecurity and machine learning, no formal grants or awards are listed. His academic contributions emphasize both foundational research and applied solutions to modern computing challenges.

Paul GreenView profile

Lecturer

• Bayesian Data Analysis
• Categorical Data Analysis
• Transportation Engineering
• +3 more

Paul Green is a Lecturer II in the Department of Statistics at the University of Michigan, affiliated with the College of Literature, Science, and the Arts. He earned his Ph.D. in Biostatistics from the same university in 1995. His research focuses on Bayesian data analysis, categorical data analysis, and applications in transportation systems. Key areas include driver behavior analysis, human factors in automotive interfaces, and emotion recognition via EEG signals. Education: Ph.D. in Biostatistics, University of Michigan, 1995 Research Interests: Bayesian statistical methodologies Driver cognitive load and fatigue Autonomous vehicle decision-making EEG-based emotion recognition Human factors in transportation safety Recent publications emphasize driver-brain interactions in high-speed rail environments, autonomous vehicle lane-change strategies using deep reinforcement learning, and tactile intervention systems for fatigue mitigation. His work bridges biostatistics with engineering, particularly in automotive human factors and simulation technologies. No scientific awards or grants are explicitly listed. No advisee students are recorded in the provided materials. His research extends to interdisciplinary topics like lifestyle mobilities of aging populations and standardization of driver evaluation protocols.

• Infrastructure Resilience
• Network Theory
• Operations Research
• +5 more

Nils Goldbeck is an Honorary Research Fellow in the Department of Civil and Environmental Engineering at Imperial College London, part of the Faculty of Engineering. He is affiliated with the Centre for Transport Studies (CTS), Transport Systems and Logistics Laboratory (TSL), Centre for Systems Engineering and Innovation (CSEI), and Climate-KIC. His research focuses on infrastructure resilience, combining reliability engineering, network theory, and operations research to model interdependencies in urban systems. Goldbeck holds an MPhil in Engineering for Sustainable Development from the University of Cambridge and a BSc in Industrial Engineering from Karlsruhe Institute of Technology (KIT), with a minor in Applied Studies of Culture and Society. He was a visiting student at Istanbul Technical University. His work explores cascading failures, supply chain resilience, and disaster response strategies. Recent publications address autonomous vehicle control, scenario-based simulators, and holistic infrastructure risk assessment. Funded by the EPSRC Centre for Doctoral Training and Climate-KIC, his research informs policy and planning for resilient urban systems.

Michael NorrishView profile

Associate Professor

• Formal Methods
• Interactive Theorem Proving
• Formal Semantics
• +3 more

Dr. Michael Norrish is an Associate Professor at The Australian National University , affiliated with the School of Computing and the Mathematical Sciences Institute . He specializes in formal methods, interactive theorem proving (particularly with the HOL4 system), and formal semantics for complex systems. His work emphasizes rigorous software verification and foundational computer science research. Dr. Norrish holds a PhD in Computer Science from the University of Cambridge (1999) , along with a BA and BSc(Hons) from Victoria University of Wellington. His research has led to significant contributions in verified compilers (e.g., CakeML), formalized algorithms (e.g., AKS primality test), and reproducibility challenges in scientific software. Research Interests include: - Formal verification of theorem provers - Verified systems programming - Machine learning integration in automated reasoning - Reproducibility in computational research His recent articles focus on advancing theorem proving tools (e.g., HOL4), reproducibility debt in scientific software, and verified compiler frameworks like PureCake and Pancake. He co-organized the 15th International Conference on Interactive Theorem Proving (ITP 2024) and leads projects on AI trustworthiness in Health, Safety & Environment (Tech4HSE). Current projects include: - Tech4HSE: AI Trust & Trustworthiness (ANU, 2024–2029) - Formalization of mathematical algorithms in HOL4

• Augmented Reality
• Virtual Reality
• Human-Computer Interaction
• +3 more

Jeronimo Grandi is an Assistant Professor in the Department of Computer & Cyber Sciences at the School of Computer and Cyber Sciences, Georgia Southern University. His academic work spans augmented reality (AR), virtual reality (VR), and human-computer interaction, with a focus on collaborative technologies and cybersecurity in extended reality (XR). He holds a Ph.D. and M.S. in Computer Science from Federal University of Rio Grande do Sul (2018 and 2014), and a B.A. in Game and Interactive Media Design from University of Caxias do Sul (2011). His research emphasizes AR/VR interfaces for first responders, public safety applications, and continuous authentication systems. Notable achievements include the Best 3DUI Contest Award (2023), Unreal Faculty Fellowship (2023), and a Ph.D. distinction (Cum Laude) in 2018. He teaches courses like Human-Computer Interaction and Dissertation Research, and actively reviews for conferences like ACM ISMAR and IEEE VR. Key research directions include modular AR frameworks for emergency response, cross-reality collaboration tools for sustainability education, and visual search optimization in VR. His work bridges theoretical HCI principles with practical implementations in law enforcement, healthcare, and disaster management scenarios.

• Bioinformatics
• Machine Learning in Agriculture
• Protein Engineering
• +7 more

Prof. Dr. Dominik Grimm is a Professor in the Department of Forestry and Forestry Management at the University of Applied Sciences Straubing (HSWT). He leads the Straubing site for sustainable resource use and is affiliated with the Faculty of Forestry and Forest Management. His research focuses on interdisciplinary applications of machine learning, computational biology, and optimization in agriculture, biotechnology, and environmental science. Key areas include precision agriculture (e.g., UAV-based weed detection in sorghum fields), protein engineering (thermostability prediction), and reinforcement learning for process synthesis. He has pioneered tools like permGWAS2 for genome-wide association studies and EVARS-GPR for seasonal time series forecasting. His work integrates AI with real-world challenges, such as optimizing hospital bed management and improving crop yields through data-driven strategies. Education : Advanced degrees in computer science and bioinformatics (details not explicitly stated). Research interests span computational methods in agriculture, genomics, and combinatorial optimization. He supervises doctoral candidates like Josef Eiglsperger and Jonathan Pirnay. His lab (Grimmlab) develops open-source frameworks for time series analysis and phenotypic data processing. Key projects include automated process design using reinforcement learning and machine learning for perishable product forecasting in horticulture. Publications highlight contributions to protein stability modeling, agricultural robotics, and data stream algorithms. Collaborations involve institutions like the University of Bayreuth and the Helmholtz Center Munich. His work emphasizes practical applications, such as reducing herbicide use through precision agriculture and enhancing biomanufacturing via AI-driven enzyme discovery. Grants and research projects funded include methods for flowsheet synthesis, generative AI in enzyme discovery, and sustainable resource management. He leads initiatives integrating machine learning with horticultural sales prediction and patient bed assignment optimization in hospitals.

Naftali WeinbergerView profile

Research Fellow

• Causation
• Causal Modeling
• Philosophy of Science
• +9 more

Naftali Weinberger is a Postdoctoral Fellow at the Munich Center for Mathematical Philosophy within Ludwig Maximilian University of Munich's Faculty of Philosophy, Philosophy of Science and Religious Studies. His research focuses on causal modeling methods applied to foundational questions in causal inference and explanation across multiple scientific disciplines. Education PhD, University of Wisconsin, Madison (2015) Dr. Weinberger's research addresses the theoretical foundations and practical applications of causal modeling, with particular emphasis on dynamical systems and discrimination. His work spans population genetics, psychometrics, neuroscience, and economics, examining how causal concepts operate in diverse scientific contexts. Current projects investigate causation in dynamical systems and develop causal frameworks for modeling discrimination. His publication record (2011-2022) demonstrates consistent output in leading philosophy of science journals, with evolving focus from evolutionary biology and psychometrics toward dynamical systems and causal frameworks. Key themes include equilibrium models, time-scale relativity, non-factual scientific disagreement, and path-specific causal effects across disciplines. No scientific awards were mentioned in the source materials. No information regarding student advising or research grants was provided in the available documentation. Dr. Weinberger is currently affiliated with the Munich Center for Mathematical Philosophy. Previously, he contributed to the 'Causation, Continuity, and Complexity' project at the University of Pittsburgh's Center for Philosophy of Science and the 'Bridging Causal and Explanatory Reasoning' project at Tilburg University.

Deniz Dilan Karaman ÖrsalView profile

Adjunct Professor

• Econometrics
• Panel Data Analysis
• Financial Econometrics
• +2 more

Dr. Deniz Dilan Karaman Örsal is an Adjunct Professor at Leuphana University Lüneburg and Visiting Lecturer at University of Hamburg. Her research develops advanced econometric methodologies for analyzing economic relationships in panel data with structural breaks and dependencies. Örsal's expertise includes: Likelihood-based cointegration testing Cross-sectional dependence modeling Time series analysis with structural breaks Financial market applications Macroeconomic forecasting She currently leads a DFG-funded project on panel cointegration methodology and its applications in macroeconomics and financial markets.

• Organizational Behavior Management (OBM)
• Behavioral Systems Analysis
• Culturo-Behavior Science
• +4 more

Dr. Jovonnie Esquierdo-Leal is an Assistant Professor in the Department of Psychology at California State University, Fresno (Fresno State). She holds a BA and MA from Fresno State, and a PhD from the University of Nevada, Reno. Her work spans clinical practice with developmental disabilities, organizational culture improvement, and academic leadership. Education: Bachelor’s in Psychology, Fresno State Master’s in Psychology (ABA emphasis), Fresno State PhD in Psychology, University of Nevada, Reno Research Focus: Dr. Esquierdo-Leal’s work emphasizes cultural-behavior science, leadership development, and equity initiatives. She explores how systemic change and behavioral principles can advance social justice, particularly in healthcare and academic settings. Her expertise includes verbal behavior analysis and organizational behavior management (OBM). Professional Roles: Coordinator, ABAI Diversity, Equity, and Inclusion Board Co-coordinator, ABAI DEI Program Area Member/Planner, Behaviorists for Social Responsibility (ABAI SIG) Teaching: Courses include Introduction to Applied Behavior Analysis, Ethics in Behavior Analysis, and Research Methods in Behavior Analysis at both undergraduate and graduate levels. Her publications address diversity in academic medicine, inclusive cultural practices, and feedback systems in education. She has contributed chapters on graduate student success and prejudice remediation in healthcare.

Sherryse CorrowView profile

Professor

• Cognitive Neuroscience
• Developmental Psychology
• Face Perception
• +5 more

Dr. Sherryse Corrow is a Professor of Psychology at Bethel University, affiliated with the Department of Psychology within the College of Arts and Sciences. She joined Bethel in 2017 and is actively involved in the university's faculty development team. Her research focuses on cognitive neuroscience, particularly face perception, prosopagnosia, and developmental disorders. She teaches courses in cognitive neuroscience and research methodologies, emphasizing student engagement in both classroom and lab settings. Education: B.A. in Psychology, Minnesota State University, Moorhead (2008) M.A. in Child Psychology, University of Minnesota (2011) Ph.D. in Child Psychology, University of Minnesota (2013) Postdoctoral training in neuroscience (neurology/ophthalmology and visual sciences), University of British Columbia, Vancouver (2013–2017) Research Interests: Dr. Corrow’s work examines face recognition impairments in developmental and psychiatric disorders, prosopagnosia (face blindness), and the intersection of cognitive neuroscience with education. She explores topics such as the role of auditory and visual cues in face processing, multisensory integration, and the psychological impact of prosopagnosia on individuals and families. Her research also extends to pedagogical innovations, such as course-based undergraduate research experiences (CUREs) in statistics. Publications: Her recent work highlights trends in understanding face perception deficits, the neural mechanisms underlying prosopagnosia, and the application of perceptual learning techniques for rehabilitation. She has also contributed to educational pedagogy, advocating for efficient teaching strategies and student-centered learning. Professional Contributions: As a member of Bethel’s faculty development team, Dr. Corrow supports colleagues in curriculum design and effective teaching practices. Her research has practical implications for addressing challenges in facial recognition disorders and enhancing undergraduate research opportunities. Labs/Teams: While no specific lab is named, her work aligns with cognitive neuroscience research groups focused on sensory integration and developmental disorders.

Hang DinhView profile

Associate Professor

• Quantum computing
• Post-quantum cryptography
• Computer security
• +3 more

Hang Dinh is an Associate Professor in the Department of Computer and Information Sciences at Indiana University South Bend. She joined the faculty in 2010 as a full-time member and has since contributed significantly to teaching and research in theoretical computer science. Her research focuses on quantum computing, post-quantum cryptography, algorithm analysis, and heuristic search methods. Dinh holds a Ph.D. in Computer Science from the University of Connecticut, an M.S. from the Asian Institute of Technology, and a B.S. from Vietnam National University, Hanoi. Education: Ph.D. in Computer Science & Engineering, University of Connecticut (2010) M.S. in Computer Science, Asian Institute of Technology (2002) B.S. in Computer Science, Vietnam National University, Hanoi (2000) Research Interests: Quantum computing and post-quantum cryptographic systems Algorithm design and complexity analysis Heuristic search optimization Theoretical computer science education Awards & Honors: Best Paper Award at ComManTel 2015 Taylor L. Booth Memorial Scholarship (2008-2009) AAAI Student Travel Award (2007) Vietnamese Government Fellowship (2002) Canadian Government Scholarship (2000) Grants & Projects: 2013: $8,500 for 'Quantum Algorithms for Post-quantum Cryptography' 2011: $8,500 for 'Empirical Analysis of A* Search for Hard Problems' 2011: $700 for conference travel (QIP 2011) Teaching Contributions: Courses include Discrete Structures, Security in Computing, and Foundations of Computing Theory. She has developed new courses like CSCI-B401 and CSCI-C250 to strengthen the computer science curriculum. Professional Activities: Co-founder of ExtentWorld, a social media platform emphasizing privacy. Maintains an academic profile on social media and actively publishes in journals like Quantum Information & Computation and Journal of Artificial Intelligence Research .

Da HuView profile

Assistant Professor

• Smart Infrastructure Systems
• Robotics and Sensing
• Infrastructure Inspection and Management
• +7 more

Dr. Da Hu is an Assistant Professor in the Department of Civil and Environmental Engineering at Kennesaw State University (KSU), joining in Spring 2023. His research focuses on smart infrastructure systems, leveraging AI, robotics, and data analytics for disaster response, infrastructure inspection, and facility management. He holds a Ph.D. from the University of Tennessee, Knoxville, and has extensive postdoctoral and industry experience. His work spans robotic systems, geotechnical engineering, and building information modeling, with over 20 publications in top journals. Education: Ph.D. in Civil Engineering, University of Tennessee, Knoxville (2022) M.S. in Civil Engineering, Texas Tech University (2018) M.S. in Civil Engineering, University of the Chinese Academy of Sciences (2017) B.S. in Safety Engineering, Zhengzhou University (2014) B.B.A. in Business Administration, Zhengzhou University (2014) Research Interests: Robotics and Sensing, Infrastructure Inspection, Human-Robot Collaboration, VR/AR, Disaster Management, and AI-driven solutions for sustainable infrastructure. His lab develops technologies like drone-based damage assessment, GPR for subsurface mapping, and robotic disinfection systems for hospitals. Grants & Awards: NSF ERI Award ($199,642) as PI (2024–2026) SPCEET Outstanding Early Career Faculty Award (2025) Best Paper Award, Building and Environment (2020) Advising & Labs: Supervises PhD and master’s students in smart infrastructure and robotics. Active in the Geotechnical Lab, developing affordable learning resources like CE3708 lab videos. Former lab members include Tracy Le and Frank-Cedric Kadjo. Teaching: Teaches courses like CE 2003, ENGR 2214, and CE 3708 Geotechnical Engineering Lab.