Roman Kuc is a Professor of Electrical Engineering at Yale University, affiliated with the School of Engineering & Applied Science. He directs the Intelligent Sensors Laboratory, focusing on biomimetic sensors for robotics and bioengineering. His research explores brain-based devices (BBDs), sonar sensing, and neuromorphic processing inspired by biological systems. He holds a BSEE from Illinois Institute of Technology and a PhD from Columbia University. Dr. Kuc’s work bridges signal processing, robotics, and bioengineering, with applications in autonomous systems and clinical diagnostics. He has published over 200 papers and authored textbooks like Electrical Engineering in Context and The Digital Information Age . Notable honors include an honorary doctorate from the Glushkov Institute of Cybernetics and the Yale Sheffield Distinguished Teaching Award. His research themes include cognitive mapping via sonar echoes, neural network-based classification of environmental features, and biomimetic approaches to echolocation. Recent work emphasizes sensorimotor integration and robust performance in uncertain environments. Scientific awards highlight his contributions to robotics, signal processing, and education. His lab develops systems that emulate biological sensory mechanisms, aiming to advance robotics, medical applications, and assistive technologies.
Ana Filipa Sequeira is a researcher affiliated with INESC TEC, Porto, Portugal, and the University of Porto. Her work spans biometrics, fairness in AI, and explainable artificial intelligence. She has contributed to advancements in face recognition, synthetic data applications, and bias mitigation through techniques like knowledge distillation and model compression. Institution: INESC TEC (Porto, Portugal) Research Themes: Face recognition, fairness, explainability, synthetic data, biometric security Her recent publications focus on addressing demographic biases in face recognition systems, developing privacy-preserving explainable methods, and evaluating synthetic data's impact. She collaborates extensively with researchers like Pedro C. Neto, Jaime S. Cardoso, and Naser Damer. Sequeira has participated in organizing and analyzing competitions such as FRCSyn, BIOSIG, and SYN-MAD, emphasizing robust evaluation frameworks. Her work intersects technical innovation with ethical considerations, advocating for responsible AI applications in biometrics.
Brian Kirby is the Meinig Family Professor in the Department of Mechanical Engineering at the College of Engineering, Cornell University. He is a leading researcher in microfluidics, biomedical engineering, and cancer diagnostics, with a strong emphasis on circulating tumor cells (CTCs), rare cell isolation, and biophysical forces in disease. His work bridges engineering, biology, and clinical medicine. Institution: Cornell University School: College of Engineering Department: Mechanical Engineering Rank: Professor Education: Stanford University, 2001 Brian Kirby's research focuses on developing and applying microfluidic technologies to solve biomedical challenges. His work centers on microfluidic rare cell capture , particularly circulating tumor cells (CTCs) , enabling early cancer detection and monitoring treatment response. He investigates biophysical forces such as shear stress and surface interactions in conditions like thrombosis and cancer metastasis. His lab also works on dielectrophoresis , acoustophoresis , and electrokinetics for cell separation and analysis. Additional interests include bioinstrumentation , lab-on-a-chip devices , and fluid mechanics in biological systems . His recent publications show a consistent focus on microfluidic diagnostics, cancer biophysics, and smart fluid systems. Articles span topics from CTC isolation in prostate and pancreatic cancers to thrombosis in medical devices and programmable viscosity metamaterials . The research integrates engineering design with clinical applications, often involving interdisciplinary collaboration. Scientific Awards: Creative Teaching Award, Cornell Center for Teaching Innovation Advising Award, College of Engineering, Cornell University, 2015 Research Award, College of Engineering, Cornell University, 2015 Brian Kirby is actively involved in advising and research mentorship. While specific student names are not listed in the provided text, his extensive publication record and leadership of a research group indicate active supervision of graduate students and postdoctoral researchers. His research is supported by grants related to cancer diagnostics, microfluidics, and biomedical engineering, though specific grant details are not provided. He has contributed to the development of novel microfluidic devices such as the GEDI (Geometrically Enhanced Differential Immunocapture) platform for CTC capture and functional analysis. Labs and Teams: Kirby leads a research laboratory at Cornell focused on microfluidics and biomedical instrumentation. His team develops and applies microfluidic platforms for clinical diagnostics, particularly in oncology and hematology. The lab collaborates with clinicians and scientists across disciplines to translate engineering innovations into medical applications.
Prof. Serge A. Shapiro is a Full Professor of Geophysics at Freie Universität Berlin since 1999 and Director of the PHASE consortium since 2004. He holds a Diploma in Applied Geophysics from Lomonosov Moscow State University (1982), a PhD from the Moscow Research Institute of Geosystems (1987), and a Habilitation from Karlsruhe University (1995). His research focuses on seismogenic processes, induced seismicity, rock physics, and subduction zone dynamics, with applications to geothermal energy, CO2 storage, and hydraulic fracturing. Education: Diploma in Applied Geophysics, Lomonosov Moscow State University (1982) PhD in Geophysics, Moscow Research Institute of Geosystems (1987) Habilitation, Karlsruhe University (1995) Research Interests: Induced seismicity from fluid operations CO2 storage and hydraulic fracturing risks Seismic hazard assessment Rock physics under stress Key Contributions: Developed the Seismogenic Index Model for induced earthquakes Pioneered DAS-based seismic monitoring techniques Advanced understanding of fault stability and pressure diffusion effects Awards: Virgil Kauffman Gold Medal (2013) for work in microseismic monitoring and rock physics Grants & Projects: PHASE consortium leader (2004–present) Utah FORGE EGS project advisor Labs/Teams: Seismology Group, Freie Universität Berlin PHASE university consortium
Maarten De Vos is a Professor at the Department of Electrical Engineering (ESAT) , KU Leuven , with dual appointments in the Faculty of Medicine and Faculty of Engineering Science . He leads interdisciplinary research at the intersection of artificial intelligence and biomedical signal processing.
Karthik Dantu is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York, within the School of Engineering and Applied Sciences. His research focuses on mobile sensor networks, robot networks, networked embedded systems, mobile computing, wireless networks, and embedded operating systems. He leads the Distributed Robotics and Networked Embedded Sensing (DRONES) Lab and has received significant funding including an NSF CAREER Award. Dr. Dantu's educational background includes: PhD in Computer Science from University of Southern California (2009) BE in Computer Science from Sri Jayachamarajendra College of Engineering (1999) His research interests center on algorithmic and systems challenges in Edge Computing Systems, with particular focus on enabling seamless vision sensing in cloud-edge environments. Dantu's work bridges mobile systems and robotics, developing novel approaches for UAV software, visual SLAM, and distributed sensing. His research addresses critical challenges in resource-constrained environments, security, and real-time performance for mobile and robotic systems, with emphasis on practical implementations that solve real-world problems in autonomous systems. Dr. Dantu's publication record shows a strong trajectory in mobile systems and robotics research, with increasing focus on edge computing applications for visual sensing. His recent work demonstrates expertise in adapting visual SLAM to edge environments, securing mobile systems through technologies like Rushmore, and developing novel approaches for UAV software reliability and depth sensing. The research spans theoretical algorithms and practical system implementations, with particular strength in bringing academic research to practical applications in robotics and mobile computing. Dr. Dantu has received several scientific honors: NSF CAREER Award on Enabling Seamless Vision Sensing in Cloud-Edge Systems Outstanding service award from the Office of International Services NSF Travel Grant for SenSys 2005 Conference Travel Grant for SIGCOMM 2002 As an advisor, Dr. Dantu has mentored numerous PhD students to completion, with graduates now working at companies like Samsung Research and Zoox Inc., or continuing academic careers as Assistant Professors. His research is supported by substantial grants including a DARPA OFFSET Sprint 4 award ($470k), an NSF CAREER award ($550k), and multiple NSF collaborative grants totaling over $1.5 million. He serves on numerous conference committees including Mobicom, MobiSys, and ICRA, demonstrating leadership in the mobile systems and robotics research communities. Dr. Dantu leads the Distributed Robotics and Networked Embedded Sensing (DRONES) Lab at UB, which focuses on developing algorithms and systems for mobile sensor networks, robot networks, and embedded sensing applications. The lab's work spans theoretical foundations to practical implementations, with particular expertise in UAV systems, visual SLAM, and edge computing for robotics, maintaining strong collaborations with industry partners and other academic institutions to advance the state of the art in mobile and robotic systems.
Dr. Brett J. Borghetti is a Professor of Computer Science in the Department of Electrical and Computer Engineering at the Air Force Institute of Technology (AFIT), Graduate School of Engineering and Management, Wright-Patterson AFB, OH. He was promoted to Professor in July 2022, following prior appointments as Associate Professor (2017) and Assistant Professor (2008/2013). His expertise lies in artificial intelligence, machine learning, deep learning, cybersecurity, and human-machine teaming. Education: Ph.D. in Computer Science, University of Minnesota, Twin Cities (2008) M.S. in Computer Systems, Air Force Institute of Technology (1996) B.S. in Electrical Engineering, Worcester Polytechnic Institute (1992) Dr. Borghetti's research focuses on applying machine learning to physical science sensors (hyperspectral, seismic, RF), cybersecurity, and enhancing human-machine team performance. He teaches graduate courses in machine learning, AI, data security, and algorithm design, and advises numerous MS and PhD students in areas such as sensor exploitation, cognitive workload, and cyber situational awareness. His recent publications demonstrate strong trends in deep learning for multimodal sensor fusion, nuclear security, and neuroergonomics. Scientific Awards: AETC Educator of the Year (2021, Civilian) AFIT Ezra Kotcher Teaching Award (2021) AFIT Teaching Excellence Award (2019) AF STEM Outstanding Science and Educator Award (2015) Multiple Eta Kappa Nu Outstanding Instructor Awards Air Force Meritorious Service Medal and other military honors Dr. Borghetti has advised numerous graduate students and led research projects with significant funding and applications in defense and national security. He has directed research in AI-driven sensor analysis, cyber defense systems, and adaptive automation. His work often involves collaboration with national labs and DoD agencies. He has contributed to major research initiatives in human factors, cyber intruder detection, and machine learning for operational environments. Labs and Research Teams: His work is associated with AFIT's research in cyber security, sensor exploitation, and human-machine systems. He collaborates with teams working on the Cyber Intruder Alert Testbed (CIAT), neuroergonomic modeling, and machine learning for defense applications.
Jonas Schorlemer is a Researcher at the Department of High Frequency Systems within the Faculty of Electrical Engineering and Information Technology at Ruhr University Bochum. His work focuses on radar systems, AI integration in sensor technologies, and applications in humanitarian demining. He collaborates with Prof. Dr.-Ing. Ilona Rolfes and contributes to projects like KI-ROJAL and Terahertz-NRW. His research emphasizes radar echo simulation, GPR-based localization, and SAR algorithm development. Research interests include radar-based particle tracking, sensor fusion in indoor environments, and electromagnetic localization in granular materials. Recent work highlights AI-driven approaches for improving training data generation and scenario augmentation in demining applications. Publications span high-impact journals like Sensors and conferences such as IEEE MTT-S and ICEAA. He actively participates in academic events including the Faculty Colloquium and international workshops. He maintains a lab website at www.etit.ruhr-uni-bochum.de/hfs/ and holds an ORCID ID for scholarly tracking.
Nam Ling is a Ph.D. holder and IEEE/IET Fellow currently serving as the Wilmot J. Nicholson Family Chair Professor in the Department of Computer Science and Engineering at Santa Clara University's School of Engineering. His career spans academic leadership roles, including Chair of the Department (2010-2023) and Associate Dean for the School of Engineering (2002-2010). He has held visiting/chair professorships at Tianjin University, Xi’an University of Posts & Telecommunications, Lanzhou University, Fuzhou University, Shanghai Jiao Tong University, National Dong Hwa University, and National University of Singapore. B.Eng. in Electrical Engineering from National University of Singapore M.S. and Ph.D. in Computer Engineering from University of Louisiana, Lafayette His research focuses on video/image compression, deep learning for coding/processing, and machine-centric media coding. He has authored 280+ publications, contributed to seven international standards, and secured 20+ U.S./European/PCT patents. He pioneered DNN approaches to video coding, VVC/HEVC optimization, and compressive sensing methods. As an academic leader, he chaired IEEE Hot Chips, ICME, VCIP, and U-Media conferences, served as IEEE/APSIPA Distinguished Lecturer, and held editorial roles for IEEE TCAS I, J-STSP, and Springer journals. His accolades include six Santa Clara University awards, three IEEE Umedia Best Paper Awards, and recognition as IEEE ICCE Best Paper winner in 2003. Honors & Awards: IEEE Fellow (2008-present) IET Fellow (2011-present) AAIA Fellow (2022-present) IEEE ICCE Best Paper Award (2003) IEEE Umedia Best Paper Awards (2016, 2017, 2019) University/School-level awards at Santa Clara University
Zhiyuan Wu is a Doctoral Research Fellow at the University of Oslo , affiliated with the Digital Signal Processing and Image Analysis research group under the Faculty of Mathematics and Natural Sciences . Education: Bachelor’s degree in Communication Systems and Information Technology from Lanzhou University, China Master’s degree from the Technical University of Munich, School of CIT Research Focus: Zhiyuan Wu specializes in machine learning, with particular emphasis on probabilistic graphical models, information theory, and tackling real-world challenges such as distributional shifts and privacy concerns. His work explores entropy regularization techniques to address label shift in distributed learning systems, aiming to improve model robustness and data privacy across diverse domains like medical applications. Publications & Research Trends: His recent publication at the International Conference on Learning Representations highlights a novel approach to mitigating label shift through entropy regularization. This aligns with his broader research goals of enhancing the adaptability and interpretability of machine learning models in dynamic, privacy-sensitive environments. Labs & Teams: He is actively involved with the Digital Signal Processing and Image Analysis (DSB) research group, contributing to collaborative projects that bridge theoretical advancements with practical implementations in machine learning.
Tommy Lundberg is a Senior Lecturer and Docent in Physiology at Karolinska Institutet. He works at the Department of Laboratory Medicine, Division of Clinical Physiology. His email address is tommy.lundberg@ki.se, and his postal address is H5 Laboratoriomedicin, H5 Klinisk Fysiologi Gustafsson, 141 52 Huddinge. Lundberg is affiliated with the university library and has held positions since 2022. Research Interests: Lundberg's research focuses on skeletal muscle mass and function adaptation, particularly in athletic performance, disease contexts, aging, and transgender individuals undergoing hormone therapy. He investigates molecular, metabolic, morphological, and functional responses to resistance and aerobic exercises. His work also explores biological maturity selection biases in youth sports, bio-banding applications in soccer and ice hockey, and the impact of anti-inflammatory drugs on muscle hypertrophy. Article Trends: Lundberg's recent publications (2025–2024) cover topics like sex differences in disc golf, muscle atrophy in space exposome, longitudinal hormone therapy effects in transgender individuals, and bio-banding in youth sports. Earlier works delve into molecular pathways in muscle hypertrophy, concurrent training effects, mitochondrial function, and imaging techniques (CT/MRI) for muscle assessment. Scientific Recognition: Most prominent young researcher in Sport Science, Swedish Central Association for Sport Promotion (SCIF), 2017 Teaching and Editorial Roles: Lundberg teaches human physiology and sports science in nursing, physiotherapy, and biomedical analytics programs. He leads a contract education course in advanced exercise physiology and contributes to a PhD course on scientific writing. He is an Associate Editor for Frontiers in Physiology - Exercise Physiology (2022) and a member of the editorial board for Translational Exercise Biomedicine (2024). Collaborations and Expertise: He collaborates with the Swedish Football Association and Swedish Ice Hockey Association on bio-banding studies. Lundberg served as an invited speaker at the ACSM Annual Meeting (2024) on transgender athletes and as an expert panelist for World Rugby's transgender workshop (2020). His supervision includes Andrea Tryfonos (2021) and thesis evaluations at Mid Sweden University and Linköping University.
Elena Anatolyevna Babushkina is a Professor at the Department of Construction and Economics of Siberian Federal University. She serves as director and scientific consultant of the Scientific and Educational Laboratory 'Dendroecology and Environmental Monitoring' . Her work spans dendrochronology, climate change impacts on tree growth, wood anatomy, and environmental monitoring in Siberian ecosystems. Doctor of Biological Sciences (2020) Corresponding Member of the Russian Academy of Sciences Extensive collaborations with international institutions like University of Arizona, University of Cambridge, and Swiss Federal Institute for Forest, Snow and Landscape Research Her research focuses on climatic reconstruction through tree rings , moisture-limited forest ecosystems , and environmental drivers of xylogenesis . Recent studies analyze earlywood/latewood dynamics, drought sensitivity, and cross-species growth patterns in Siberian larch, spruce, and Scots pine populations. Elena’s publications (100+ scientific, 10+ methodological) include 15 recent articles on tree-ring-based climate proxies , crop yield modeling , and seasonal growth regulation . Key journals include Forests , Dendrochronologia , and Scientific Reports . Notable scientific awards include the 2021 Honorary Worker of Education of the Russian Federation title and multiple Presidential and Ministerial Certificates of Appreciation . She leads national grants (RFBR, RSF) on climate-crop interactions and genetic adaptation to environmental stress .
Magdalena Szymczyk is a Lecturer in the Department of Biocybernetics and Biomedical Engineering at AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering. Her work bridges embedded systems, biomedical signal processing, and geophysical data analysis. Research focuses on energy-efficient sensor networks, neural networks for GPR data classification, and mathematical transforms in signal analysis Expertise in parallel computing, real-time systems, and biomedical engineering applications Her publications (2015–2025) demonstrate a trajectory from parallel neural networks and S-transform/GPR methodologies to recent work on MicroPython in embedded systems. Key themes include energy optimization in distributed architectures and AI-driven signal processing across biomedical and geophysical domains. She has authored works on deterministic chaos in simulations, GPU image processing, and cybersecurity in microcontroller systems. Her current research emphasizes embedded systems security, medical signal diagnostics, and computational methods for geological analysis. She utilizes tools like OpenCL for GPU acceleration and MATLAB for parallel computing implementations.
Ashley Moseman serves as an Assistant Professor of Integrative Immunobiology and Assistant Professor of Cell Biology at Duke University School of Medicine. She holds significant affiliations as a Faculty Network Member of the Duke Institute for Brain Sciences and a Member of the Duke Cancer Institute. Her pioneering research examines the delicate balance between neuronal function and immune protection at the olfactory neuroepithelial barrier, where sensory neurons directly interface with the external environment while protecting the central nervous system from pathogens. Dr. Moseman completed her Ph.D. at Harvard University in 2011, establishing the foundation for her interdisciplinary career at the intersection of immunology and neuroscience. Her research program focuses on understanding how immunological surveillance operates at the unique olfactory barrier, where neurons must contact the external environment to perform chemosensory functions while preventing pathogens from entering the CNS. The Moseman Lab employs cutting-edge multiphoton intravital imaging to visualize immune responses in vivo, revealing dynamic cellular interactions during viral infections and responses to pathogens like Naegleria fowleri. Current projects investigate olfactory barrier mechanisms, neuroimmune crosstalk, host-pathogen dynamics, and immune responses to deadly neurotropic pathogens. Analysis of Dr. Moseman's publication record demonstrates a cohesive research trajectory centered on neuroimmunology and mucosal defense mechanisms. Her work spans fundamental immunological processes, host-pathogen interactions at neural interfaces, and translational applications for understanding neurological complications of infections. A significant portion of her recent research addresses SARS-CoV-2-related olfactory dysfunction and the immunological basis of pathogen invasion through the olfactory system into the central nervous system. Dr. Moseman has secured substantial research funding including 'Using tissue-specific Naegleria opportunism to dissect olfactory immunity' (2025-2030), 'Characterizing olfactory plasma cell dynamics and survival niche within the upper airway' (2024-2029), and the 'Advanced Immunobiology Training Program for Surgeons' (2019-2029). She actively contributes to graduate education through the Medical Scientist Training Program (2022-2027) and teaches advanced immunology courses including IMMUNOL 736 and IMMUNOL 494. The Moseman Lab represents a leading center for neuroimmunology research, utilizing in vivo imaging to visualize immune responses within the central nervous system. Their work has significant implications for understanding how pathogens breach neurological barriers and how the immune system protects the brain while preserving essential sensory functions, with potential applications for treating neurological infections and inflammatory conditions.
Pouya Bashivan is an Assistant Professor in the Department of Physiology at McGill University's Faculty of Medicine. His research focuses on developing computational models to explain and regulate neural responses during visual tasks requiring memory, combining machine learning, neuroscience, and cognitive science. Education : Ph.D. in Computer Engineering (2016), Postdocs in Machine Learning (2020) and Computational Neuroscience (2016-2020) His lab investigates: Topographical neural networks for visual cortex simulation Massively-multitask models for prefrontal cortex Saccade-driven visual exploration models Predictive hippocampus models for episodic memory Recent publications explore adversarial robustness, memory-augmented networks, and brain-state decoding. Current projects emphasize causal models, brain-AI alignment, and translating computational neuroscience into therapeutic applications. The lab is located in the McIntyre Medical Sciences Building, Room 1117, Montreal, Quebec.
