Burak Kurkcu is an Assistant Professor in the Department of Electrical and Computer Engineering at Santa Clara University's School of Engineering. He previously served as an Assistant Professor at Hacettepe University and as a Senior Control System Design Engineer at Aselsan Inc. Education: Ph.D., TOBB University of Economics and Technology (2019) M.S., TOBB University of Economics and Technology (2015) B.S., Istanbul Technical University (2010) Research Interests: Dr. Kurkcu specializes in robust control systems, soft robotics, switched neural networks, and autonomous systems. His work focuses on disturbance estimation, simultaneous learning algorithms, and control of nonlinear systems. Recent Publication Trends: His research includes soft pneumatic actuator modeling, disturbance observer-based control methods, and evolutionary optimization for state-space models. Key themes involve soft robotics, autonomous control, and computational intelligence applications. Scientific Awards: IEEE Turkey Ph.D. Thesis Award (2020) Editorial Roles: Associate Editor for TIMC, Measurement and Control , and Turkish Journal of Electrical Engineering and Computer Science . Principal Investigator for defense-related control system projects.
Xuan Zhang is an Associate Professor at the Department of Information and Communication Technology, University of Agder. His research focuses on Tsetlin Machines, learning automata, and their applications in machine learning, computer vision, and hyperspectral imaging. Research Trends: Zhang’s recent work includes developing interpretable machine learning models (e.g., Tsetlin Machines), optimizing convolutional architectures for image processing, and applying automata theory to solve multi-armed bandit problems and channel selection in cognitive networks. His field spans theoretical analysis and practical implementations in AI, remote sensing, and health informatics. Scientific Contributions Co-developed advanced Tsetlin Machine variants for XOR/NOT operator convergence, disease forecasting, and image restoration Published in journals like IEEE Transactions on Pattern Analysis and Machine Intelligence , Information Sciences , and Applied Intelligence Explored Bayesian pursuit algorithms, hierarchical learning automata, and particle swarm optimization techniques Contact: xuan.zhang@uia.no
Pan Xu is a tenure-track assistant professor with joint appointments in the Department of Biostatistics & Bioinformatics, Department of Computer Science, and Department of Electrical & Computer Engineering at Duke University's Pratt School of Engineering. Prior to joining Duke, he was a Postdoctoral Scholar Research Associate at the California Institute of Technology, and he earned his Ph.D. in Computer Science from UCLA. His research bridges theoretical foundations with practical applications in machine learning and artificial intelligence. Dr. Xu's research focuses on developing computationally- and data-efficient machine learning algorithms with strong theoretical guarantees, particularly in reinforcement learning, optimization, and high-dimensional statistics. His work addresses two fundamental challenges in sequential decision-making: efficient exploration with minimal interactions and robustness against distributional shifts. His research spans theoretical algorithm design, practical implementation, and real-world applications in bioinformatics and healthcare. His publication record demonstrates consistent high-impact contributions to top-tier conferences including ICML, NeurIPS, ICLR, AAAI, and AISTATS. The research trends show a progression from foundational work in non-convex optimization and multi-armed bandits toward increasingly sophisticated frameworks for robust reinforcement learning, with particular emphasis on distributional robustness, efficient exploration strategies, and practical applications. His work often bridges theoretical guarantees with empirical validation. NSF award on approximate sampling based exploration for sequential decision making Whitehead Scholar award from Duke University School of Medicine PIMCO Postdoctoral Fellowship in Data Science UCLA Outstanding Graduate Student Research Award Rising Stars in Data Science by University of Chicago Best Paper Award for Queer In AI: A Case Study in Community-Led Participatory AI at FAccT 2023 Featured Certification for Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits at TMLR Oral Presentation award at AAAI 2024 Dr. Xu actively mentors students and researchers, seeking highly motivated individuals with strong mathematical backgrounds for Ph.D. programs in Biostatistics & Bioinformatics, Computer Science, and Electrical & Computer Engineering at Duke. He has received multiple research grants including an NSF award on approximate sampling based exploration for sequential decision making. His service to the academic community includes roles as area chair for NeurIPS, ICML, ICLR, and AISTATS, as well as action editor for Transactions on Machine Learning Research. His research group develops algorithms that address fundamental challenges in sequential decision-making, with applications spanning healthcare, bioinformatics, and multi-agent systems. Current research directions include distributionally robust reinforcement learning, efficient exploration strategies, and applications of graph neural networks to biological problems.
Mohammed Y Niamat is a full-time Professor in the Department of Electrical Engineering and Computer Science at the University of Toledo's College of Engineering . His research focuses on hardware security, FPGA vulnerabilities, and blockchain applications in cybersecurity. Research Interests : Physical Unclonable Functions (PUFs), FPGA Security, Blockchain-based Security Frameworks, IoT Security, Smart Grid Authentication, Machine Learning Vulnerability Analysis Publications : Over 85 publications from 1986-2024, with recent works on integrations of blockchain and PUFs for secure supply chains, neural network modeling attacks on PUFs, and hardware Trojan detection techniques. Collaborations : Co-authored with Junghwan Kim (4), Weiqing Sun (2), Richard Molyet (1). Recent Article Trends : 2024 works on zero-trust architecture for FPGA supply chains using blockchain and ROPUFs; 2023 studies on IoT device authentication, hardware Trojan detection, and NFT-based IP protection; 2021-2019 research on machine learning attacks against PUFs, lightweight cryptographic designs for IoT, and BER optimization in wireless systems.
Thomas Longden is an Associate Professor in the Department of Physiology at the University of Maryland School of Medicine. He leads a research group focused on neurovascular interactions in health and disease, with particular emphasis on understanding how blood flows through the brain under normal conditions and how this process is disrupted in diseases like Alzheimer's. Dr. Longden received his B.Sc (Hons) and Ph.D. in Pharmacology from the University of Manchester in the UK (2006 and 2010), followed by postdoctoral training at the University of Vermont under Professor Mark Nelson (2011-2015). He was promoted to Assistant Professor at Vermont in 2015 before joining the University of Maryland in February 2019. His research focuses on the control of blood flow in the brain, particularly the mechanisms of neurovascular coupling where neuronal activity triggers changes in blood flow. His lab has made significant discoveries including identifying the brain's capillary network as a 'sensory web' that translates neural activity into vasodilatory electrical signals, and demonstrating how pericytes function as metabolic sentinels that control blood flow through KATP channel-dependent mechanisms. Analysis of Dr. Longden's recent publications reveals a strong focus on pericyte function in neurovascular coupling, electrical signaling in the capillary network, and how these mechanisms are disrupted in Alzheimer's disease and other dementias. His work increasingly incorporates advanced imaging techniques, computational approaches, and innovative tools to study vascular plasticity. 2023: Fellow of the American Physiological Society Cardiovascular Section 2020: NIH Director's New Innovator Award 2017: American Heart Association Scientist Development Grant Multiple travel awards and postdoctoral fellowships Dr. Longden currently mentors several graduate students and postdoctoral fellows in the Longden Lab, which is supported by multiple NIH grants including an NINDS New Innovator Award and an NIA R01 grant. His lab develops and employs advanced techniques including multiphoton microscopy, electrophysiology, optogenetics, and molecular biology to study vascular cells in the brain. The lab is particularly focused on understanding vascular signaling plasticity and how pericytes control brain blood flow in health and Alzheimer's disease.
Jessica A. Mong, PhD , is a Professor in the Department of Pharmacology & Physiology at the University of Maryland School of Medicine , where she also serves as Assistant Dean for Graduate & Post-Doctoral Studies and Director of Graduate Education for the Program in Neuroscience. Her research focuses on the neuroendocrine mechanisms underlying sex differences in sleep circuitry and the estrogenic modulation of sleep-wake cycles. Primary Appointment: Pharmacology & Physiology Administrative Title: Assistant Dean for Graduate & Post-Doctoral Studies Laboratory Director: Program in Neuroscience Research Interests: Dr. Mong's work investigates how ovarian steroids influence sleep-wake behavior through sexually differentiated neuroanatomical substrates. Key areas include: Mechanisms of estrogenic modulation in the median preoptic nucleus (MnPN) Developmental programming of sex differences in sleep sensitivity Translational studies using rodent and nonhuman primate models of menopause Functional significance of hormonal influences on sleep quality and recovery Scientific Trends: Her recent publications (2023-2025) emphasize: Role of KCNMA1 channelopathy in sleep regulation Adenosinergic signaling in MnPN Translational menopause models Estrogen's protective effects against noise-induced hearing loss Sex-dependent responses to kynurenine pathway challenges Awards & Appointments: NIH BIRCWH Scholar (Building Interdisciplinary Research Careers in Women's Health) Co-Chair, Society for Women’s Health Research Interdisciplinary Research Network on Sex-Differences in Sleep Health NIH/NHLBI R01 HL129138 grant recipient Education & Training: B.S., Biology, Gettysburg College (1987-1991) Ph.D., Neuropharmacology, University of Maryland Baltimore (1994-2000) NIH Postdoctoral Fellowship in Endocrinology, Rockefeller University (2000-2003)
David B. Dunson is the Arts and Sciences Distinguished Professor of Statistical Science at Duke University, with a joint appointment in the Department of Mathematics. He is also a Faculty Network Member of the Duke Institute for Brain Sciences. His research bridges theoretical statistics with practical applications across multiple scientific domains, focusing on developing new tools for probabilistic learning from complex data. Dr. Dunson earned his Ph.D. from Emory University in 1997 and his B.S. from Pennsylvania State University in 1994. Dr. Dunson's research focuses on developing statistical methods directly motivated by challenging applications in ecology/biodiversity, neuroscience, environmental health, and criminal justice/fairness. His methodological work spans models for low-dimensional structure in data (latent factors, clustering, geometric and manifold learning), flexible/nonparametric models (neural networks, Gaussian/spatial processes), Bayesian inference frameworks, and models for "object data" (trees, networks, images, spatial processes). His approach emphasizes creating practical tools that scientists and decision makers can use routinely. Dunson's recent publications demonstrate a strong focus on advancing Bayesian methodology for complex data structures across applications in biodiversity mapping, brain connectomics, environmental health, and infectious disease modeling. His work shows consistent innovation in nonparametric Bayesian methods, computational efficiency, and the handling of high-dimensional and structured data, always with an eye toward solving real-world scientific challenges. Dr. Dunson has received numerous prestigious awards including: IMS Medallion Lecturer (2019) Mitchell Prize from the International Society of Bayesian Analysis (2018) Carnegie Centenary Professorship (2018) DeGroot Prize (2017) COPSS Award: President's Award (2010) Fellow of the Institute of Mathematical Statistics (2010) His extensive publication record with numerous co-authors suggests an active research group mentoring graduate students and postdocs. His research on projects like biodiversity mapping (funded by a European Research Council Grant) and brain connectomics indicates well-funded research programs addressing significant scientific challenges across multiple domains. Dr. Dunson's work involves collaborations across multiple labs and teams, particularly through his affiliation with the Duke Institute for Brain Sciences. His research on biodiversity mapping, brain connectomics, and environmental health suggests involvement in large, interdisciplinary teams addressing complex scientific questions that require sophisticated statistical approaches.
Jenna Ann McHenry is an Assistant Professor of Psychology and Neuroscience and Neurobiology at Duke University, holding a primary appointment in the School of Medicine's Department of Neurobiology. As a Faculty Network Member of the Duke Institute for Brain Sciences, she leads the McHenry Lab focused on neural circuit mechanisms underlying social and motivated behaviors. Her educational background includes: Ph.D., Florida State University (2013) B.S., Florida State University (2007) Dr. McHenry's research defines fixed and flexible features of molecularly defined neural circuits controlling social and nonsocial motivated behaviors, with emphasis on hypothalamic subnuclei and connections to midbrain dopaminergic reward systems. Her lab employs optogenetics, in vivo deep-brain calcium imaging, viral/genetic targeting, and advanced behavioral analysis to investigate circuit processing in disorders including Autism Spectrum Disorders, Reproductive Mood Disorders, Eating Disorders, and Major Depression. Specializing in chronic deep-brain microscopy, her team tracks neural networks during awake behavioral states over time. Her publication record demonstrates consistent innovation in neural circuit analysis, with recurring themes of hormonal modulation of social reward circuits, state-dependent sensorimotor processing, and neural mechanisms of social homeostasis. Key contributions include identifying prepronociceptin neurons in arousal responses and elucidating how adolescent sleep shapes adult social preferences. Major recognitions include: NARSAD Young Investigator Award (2017) K99 Pathway to Independence Award (NIMH, 2017) Notable Nole Alumni Award (Florida State University, 2018) NRSA Postdoctoral Fellowship (NIMH, 2013) Dr. McHenry actively mentors graduate students and postdocs, with current lab openings advertised. Her research is supported by substantial grants including the Neurobiology Training Program (2024-2029), Establishing Neural Circuits for Social Homeostasis (2022-2027), and Illuminating Socially-Modulated Homeostatic Control Circuits (2022-2025). She teaches Behavioral Neuroendocrinology and research practicums across psychology and neuroscience programs. The McHenry Lab operates at the forefront of systems neuroscience, utilizing GSRB II facilities for deep-brain imaging and circuit manipulation. As part of the Duke Institute for Brain Sciences, the lab collaborates extensively on translational research bridging basic neural mechanisms with clinical applications in mental health disorders.
Edmund Hollis is an Assistant Professor of Neuroscience at the Brain and Mind Research Institute within Weill Cornell Medical College . He has been affiliated with the institution since 2016 and leads a research lab focused on neural circuit remodeling and recovery after spinal cord injury. Education: Ph.D. in Neurosciences, University of California, San Diego, School of Medicine (2008) B.S., University of Southern California (2002) Research Interests: Hollis's lab investigates the neural mechanisms underlying movement and recovery from spinal cord injury. Using genetic, molecular, and behavioral tools, along with optogenetics and optical imaging, the lab explores how neural circuits respond to injury and how they can be therapeutically enhanced. Key areas include cortical plasticity, axon regeneration, astrocyte responses, and neuromodulation of motor circuits. Scientific Contributions: His recent publications demonstrate a strong focus on corticospinal tract function, spinal interneuron modulation, and the use of advanced behavioral assays like the Kinematic Deviation Index (KDI) to assess motor recovery in rodent models. His work spans from molecular signaling pathways (e.g., RANKL, Wnt, IGF-I) to large-scale circuit remodeling and rehabilitation strategies. Collaborations & External Roles: Hollis has professional affiliations with Texas A&M University and the National Institutes of Health, and has served as a speaker and consultant for various academic and governmental organizations.
Dr. Anne Koelewijn is an Assistant Professor leading the Biomechanical Motion Analysis and Creation (BioMAC) group at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) since 2019. Her research bridges biomechanics, computational modeling, and wearable technology to analyze human movement. She holds a Junior Professorship in Computational Movement Science within the Department of Electrical-Electronic-Communication Engineering. Her educational background includes a Doctor of Engineering in Mechanical Engineering from Cleveland State University (focus: prosthesis design and gait simulations), an MSc in Mechanical Engineering (BioMechanical Design specialization), and a BSc in Aerospace Engineering , both from Delft University of Technology. She completed postdoctoral work at École Polytechnique Fédérale de Lausanne on neuromuscular control. Research interests center on human movement optimization , neuromuscular control mechanisms , and in-the-wild movement analysis . Her work integrates musculoskeletal modeling, optimal control theory, and machine learning to study gait adaptations, exoskeleton design, and pathological movement patterns (e.g., Parkinson’s disease). Publications emphasize predictive simulations , wearable sensor technology , and biomechanical energy optimization , with recent advances in radar-based motion capture, inertial pose estimation, and digital twin applications for medical engineering. Promising Scientist Award , International Society of Biomechanics (2023) Best Paper Award , 5th International Symposium on Wearable Robotics (2020) She leads the BioMAC research group, focusing on computational methods for movement science and collaborating internationally on projects involving exoskeletons, injury prevention, and neuroprosthetics.
Dr. Sameer Mulani is an Associate Professor, Associate Department Head, and Director of Graduate Programs in the Department of Aerospace Engineering and Mechanics at the University of Alabama's College of Engineering. He leads the Stochastic Mechanics and Multi-Disciplinary Optimization Laboratory (SMO Lab) and is an integral part of the Remote Sensing Center and Alabama Materials Institute. Dr. Mulani's research spans uncertainty quantification, random vibrations, multi-disciplinary optimization, and composite structures' multi-scale analysis and design. His work combines computational methods with machine learning to develop innovative solutions for aerospace engineering challenges. He has made significant contributions to self-healing composite materials, uncertainty quantification techniques, and optimization of composite structures. His research group has published extensively on topics including polynomial chaos expansion for uncertainty quantification, self-healing composites, stochastic buckling analysis, and machine learning applications in structural mechanics. The publications demonstrate a strong trend toward integrating probabilistic methods with traditional engineering analysis to improve reliability and safety of aerospace structures. AIAA Associate Fellow, Class of 2025 2025 Department of the Air Force Summer Faculty Fellowship Program 2024 Department of the Air Force Summer Faculty Fellowship Program MSC Software Contest Winner (2011) Night on the Town: General Electric Award (2007) DAAD Fellowship (1999-2000) Dr. Mulani has advised numerous graduate students who have gone on to successful careers at institutions including Los Alamos National Laboratory, Cirrus Aircraft, L3Harris, and Lockheed-Martin. His lab collaborates with various research centers including the Remote Sensing Center where they work on antenna design, manufacturing, and integration for aircraft systems. The SMO Lab utilizes advanced software including MSC NASTRAN/PATRAN, ANSYS Mechanical/FLUENT, ABAQUS, SOLIDWORKS, and CATIA for their simulations and analyses.
Prof. Dr. Johannes Kinder is a Professor and Chair of Programming Languages and Artificial Intelligence at the Institute of Informatics , Ludwig Maximilian University of Munich. His research focuses on software security through program analysis and machine learning, particularly targeting malware detection , vulnerability analysis , and reverse engineering . He has held faculty positions at Royal Holloway, University of London, and Bundeswehr University Munich. Research Interests include: Securing software systems via program and machine learning techniques Detection of software vulnerabilities and malware Preventing exploitation through binary analysis Applications of formal methods in systems security Recent Publications highlight advancements in binary function embedding , malware detection in npm , and speculative execution attack modeling . His work appears in top venues like USENIX Security and IEEE S&P . Education : Diplom from TU Munich (2005), Doctorate from TU Darmstadt (2010). Professional Roles : General Chair, ACM CCS 2019 Doctoral Symposium Chair, ESSoS 2016 Program Committee member for NDSS 2026, IEEE S&P 2022-2025
Prof. Ilia Polian serves as Head of the Institute of Computer Engineering and Chair of the Hardware-Oriented Computer Science (HOCOS) department at the University of Stuttgart. His leadership spans research, teaching, and institutional coordination across multiple high-impact projects. Prof. Polian's research focuses on developing circuit and system architectures based on both traditional and novel principles, including neuromorphic, stochastic, and approximate architectures. His second major research focus is systematic design methodology and design automation, with particular emphasis on safety and reliability properties of developed systems. Current research directions include quantum computing engineering, secure mixed-signal neural networks, and resource-efficient stochastic circuits for near-sensor computing applications. His recent publications demonstrate strong trends in quantum computing (particularly circuit partitioning and compilation for multi-QPU architectures), hardware security (including memristive cryptographic implementations), and AI-driven approaches to hardware testing and reliability. These works bridge fundamental computer architecture research with practical industrial applications. University of Stuttgart's Publication Prize for Paper on Partitioning of Quantum Circuits Prof. Polian actively supervises doctoral students including Devanshi Upadhyaya, and leads significant research grants such as the DFG Priority Program Nano Security which he coordinates. His department offers numerous thesis and research opportunities for students interested in cutting-edge hardware research. The Hardware-Oriented Computer Science department maintains strong collaborations with industry partners including IBM, Infineon Technologies, and Advantest, as well as academic institutions through the IQST Graduate School and QuantumBW initiatives.
Bernhard Aichernig is an Associate Professor at the Institute of Software Engineering and Artificial Intelligence. His work bridges formal methods, model-based testing, and artificial intelligence, with a focus on automata learning, digital twins, and AI-assisted programming. Institution: Institute of Software Engineering and Artificial Intelligence Key Research Areas: Model-Based Testing, Automata Learning, AI-Driven Verification His research explores the integration of machine learning into formal verification, enabling scalable testing of complex systems like IoT devices and reinforcement learning agents. Recent projects include AI-Augmented DevOps frameworks (AIDOaRT) and digital twin validation (LearnTwins). Notable scientific awards include multiple best paper recognitions at SEFM (2020, 2021) and the TAYSIR Competition first place (2023). His publications emphasize hybrid approaches combining genetic programming, SMT solving, and neural networks for system modeling. 2025 : AI-assisted programming, timed automata via domain knowledge 2024 : Stochastic environment modeling, Git system learning 2023 : Reinforcement learning under partial observability, digital twins for VPN servers He actively contributes to testing frameworks like AALpy and investigates explainable AI for fault diagnosis in cyber-physical systems.
Dr. Matteo Fasiolo is a Senior Lecturer in the School of Mathematics at the University of Bristol, specializing in Statistical Science. His research focuses on advanced statistical modeling with significant applications in electricity demand forecasting and medical statistics, leveraging Generalized Additive Models (GAMs) as a core methodology. His primary research interests span: Generalized Additive Models and their extensions for complex data structures Covariance matrix modeling for high-dimensional energy forecasting Probabilistic forecasting techniques for uncertainty quantification Statistical machine learning including variational inference and contrastive learning Applications in electricity grid management and medical diagnostics Recent publications (2023-2025) reveal a dual focus: developing scalable statistical methods for electricity net-demand prediction in Great Britain using additive covariance models, and applying distributional regression to medical challenges like kidney function decline and cardiovascular risk prediction. His work on SoftCVI demonstrates innovation in variational inference, while extensions to GAMs address both mean modeling and full distributional forecasting. Dr. Fasiolo has supervised at least one student as indicated by university records. His research outputs include 16 publications and 2 publicly available datasets, reflecting active contributions to methodological statistics and domain-specific applications in energy systems.
