Dr. Gary Glover is a Professor of Radiology (Radiological Sciences Lab) at Stanford University , with courtesy appointments in Psychology and Electrical Engineering. His work focuses on the physics and mathematics of MRI, particularly rapid scanning methods using spiral k-space trajectories for functional brain imaging and multimodal neuroimaging (fMRI/EEG/fPET/fNIRS) combined with neuromodulation techniques like TMS and transcranial ultrasound. Academic Appointments: Radiology, Psychology, Electrical Engineering Professional Affiliations: Bio-X, Stanford Cancer Institute, Wu Tsai Neurosciences Institute Research Interests include: Development of blood oxygen level-dependent (BOLD) and viscoelastic contrast in MRI Functional MR Elastography for brain activation mapping Optimization of MR-ARFI for transcranial ultrasound guidance Automated spinal cord segmentation (EPISeg) using machine learning Scientific Awards : National Academy of Engineering (2013) Gold Medal, ISMRM (2000) Steinmetz Award, General Electric (1985) Lauterbur Lecture, ISMRM (2018) Recent Publications analyze: Fast fMRI sampling and spurious signal correction Dissociated patterns in default mode network anti-correlations Neural correlates of collaborative behavior in triadic fMRI Salience network contributions to depression pathophysiology
Paul Wiegert is a Full Professor in the Department of Physics and Astronomy at the University of Western Ontario , where he has been since 1996 after positions at York University and Queen's University. He is a member of the Institute for Earth and Space Exploration (IESX) and the Centre for Planetary Science and Exploration (CPSX) . His research spans asteroid dynamics , exoplanet systems , and celestial mechanics , with notable work on Earth co-orbital asteroids like (3753) Cruithne and Earth's first Trojan asteroid 2010 TK7. Education : PhD in Astronomy (University of Toronto, 1996) Research Domains : Planetary Science, Astronomy, Big Data Analytics His recent publications focus on interstellar transport mechanisms , asteroid impact risks , and exomoon detection . Key findings include quantifying risks from asteroid 2024 YR4's potential lunar impact and demonstrating the feasibility of detecting alpha Centauri-origin material in our solar system. He actively supervises graduate students like Cole Gregg and participates in NSERC-funded summer research programs for undergraduates. For planetary defense, he has analyzed collision probabilities for Apophis and developed meteoroid hazard models for spacecraft. His work appears in Planetary Science Journal , Nature Astronomy , and Astrophysical Journal Letters , with media coverage in 60+ outlets and 126 X (Twitter) mentions .
Dr. Christian Jaeger is a Researcher at the Zurich University of Applied Sciences (ZHAW) School of Engineering, focusing on Machine Learning in Optimal Control for Industry. His work bridges engineering and computer science with applications in industrial automation and building systems. His research interests span Machine Learning , Optimal Control , Reinforcement Learning , Energy Management Systems , and Industrial Automation . Jaeger has led multiple research projects including a preliminary study on automated IBN heat pumps and a feasibility study on Reinforcement Learning Control for heating systems. His work demonstrates a clear trajectory from traditional manufacturing technology toward contemporary AI-driven control systems. Jaeger's publication record shows consistent output from 2005 to 2024, with recent focus on energy optimization in building control using reinforcement learning, 3D printing techniques, and model predictive control. His research demonstrates strong interdisciplinary connections between computer science, engineering, and practical industrial applications. His scientific contributions include publications in journals such as Applied Sciences and the Journal of the British Interplanetary Society, along with numerous conference proceedings from international events including EuroSun and the International Symposium on Nonlinear Theory and its Applications. At ZHAW, Jaeger has served as project leader for multiple completed research initiatives including adaptive energy management systems for buildings and automated heat pump systems. His work demonstrates strong industry connections with applications in building automation and industrial manufacturing processes.
Professor Clinton Fookes is a faculty member at the Queensland University of Technology (QUT) within the School of Electrical Engineering & Robotics . His research focuses on leveraging computer vision and artificial intelligence to develop automated systems that understand, anticipate, and interact with human behaviors, with applications in medical diagnostics, autonomous vehicles, defense, and industrial efficiency . Research areas include AI adaptability, multimodal biosignal analysis, and human-machine interaction Collaborates with CSIRO Data61, Defence Science and Technology Group, Orica, Airbus, and Sentient Vision Systems Develops systems for human action detection, infrastructure monitoring, and stress response prediction His work addresses critical challenges in AI deployment, such as environmental adaptability and reducing diagnostic errors in medical and autonomous systems. Recent publications highlight trends in self-supervised learning, zero-shot knowledge transfer, multimodal integration , and 3D reconstruction for healthcare , while exploring ethical AI use in sectors like mining and defense . Professor Fookes emphasizes interdisciplinary collaboration, bridging engineering, medicine, and social sciences to advance AI systems capable of real-world impact. His research agenda includes improving AI memory capabilities and explainability for safer, more reliable automation.
Lisa Wills serves as Assistant Professor of Computer Science at Duke University's Trinity College of Arts & Sciences and holds a joint appointment in Electrical and Computer Engineering at the Pratt School of Engineering since 2019. Her research bridges computer architecture and domain-specific applications, with a focus on hardware acceleration for computationally intensive fields. Dr. Wills earned her Ph.D. from Columbia University in 2014. Her academic journey reflects a deep commitment to advancing hardware-software co-design methodologies for real-world computational challenges. Her research centers on developing efficient hardware accelerators for big data analytics, particularly in genomics, graph processing, and database systems. She pioneers frameworks that simplify accelerator deployment while tackling critical bottlenecks in genomic data analysis, protein structure prediction, and privacy-preserving computing. Current work focuses on hardware-aware machine learning systems and energy-efficient architectures for emerging AI applications. Analysis of her publication record reveals a clear trajectory: from foundational work in database processing units (2014-2016) to specialized genomic accelerators (2019-2021), then evolving toward ML-enhanced design automation (2022-2023) and cutting-edge architectural abstractions (2024-2025). Her research consistently targets the intersection of hardware efficiency and domain-specific computational demands, with increasing emphasis on AI/ML workloads. Google ML and Systems Junior Faculty Award (2025) Dr. Wills actively mentors doctoral students including Chris Kjellqvist (lead architect of Beethoven accelerator framework), Mason Ma (PyTFHE FHE framework), and Mansi Choudhary (COCOSSim accelerator simulator). Her research is supported by significant grants including the NSF AI Institute: Athena ($20M, 2021-2027), Meta-funded ProSE accelerator project (2023-2026), and NSF CAREER award (2021-2026), totaling over $25M in active funding. She directs the APEX Lab (Application-driven Programmable Efficient Accelerated Systems), which develops open-source frameworks like Beethoven for FPGA/ASIC accelerator deployment and focuses on lowering barriers for non-hardware researchers to leverage custom acceleration in genomics, AI, and big data applications.
Adela Isvoranu serves as an Assistant Professor in the Department of Psychology at the National University of Singapore (NUS), where she investigates developmental pathways from mental health to mental illness using complexity-based frameworks. Her work challenges traditional syndrome-focused approaches by emphasizing symptom interactions within psychopathological networks. Academic Background: Ph.D. (cum laude) from University of Amsterdam M.Sc. from University of Amsterdam B.Sc. (Hons.) from University of Leeds Her research program integrates network analysis with clinical psychology to model mental health conditions as dynamic systems. This approach examines fuzzy boundaries between diagnostic categories and aims to develop novel intervention strategies through collaborations with clinical psychologists, practitioners, and methodologists. Her methodological innovations focus on translating complex network theory into practical clinical applications. Recent publications reveal a strong trajectory in advancing network psychometrics methodology while applying these frameworks to psychosis and trauma research. The 2022 book establishes foundational R-based tools for behavioral scientists, the 2021 methodological paper provides crucial estimation guidelines, and the 2017 psychosis study demonstrates clinical utility in mapping trauma-symptom pathways. Scientific Awards: No awards explicitly mentioned in source material While specific student mentorship details are unavailable, her collaborative research environment with clinical teams and methodologists indicates active supervision of research projects. Her work is supported through university affiliations with infrastructure including dedicated research space (AS4-02-32) and computational resources for network analysis. Research Environment: Operates within NUS Psychology Department's clinical research ecosystem Collaborates with GROUP Investigators consortium Integrates statistical methodology with clinical practice Maintains research website (www.adelaisvoranu.com) for dissemination
Craig Kluever is a Professor and Director of Undergraduate Studies in the Department of Mechanical and Aerospace Engineering at the University of Missouri, within the College of Engineering. He holds a strong academic and industry background in aerospace engineering, with extensive contributions to research, education, and professional societies. Education: PhD in Aerospace Engineering, Iowa State University MS in Aerospace Engineering, Iowa State University BS in Aerospace Engineering, Iowa State University His research centers on the guidance, navigation, and control of aerospace vehicles, with deep expertise in orbital mechanics, reentry flight mechanics, and trajectory optimization. These areas form the foundation of modern space mission design and flight dynamics. His work bridges theoretical modeling and practical aerospace applications, particularly in space flight systems and dynamic system control. Kluever is the author of two widely used textbooks: Dynamic Systems: Modeling, Simulation, and Control and Space Flight Dynamics , both published by Wiley, which are instrumental in engineering education. Although specific publications are not listed here, his active editorial role and scholarly output suggest ongoing research contributions. Scientific Awards and Honors: 2020 Kemper Fellow Associate Fellow, American Institute of Aeronautics and Astronautics (AIAA) Fellow, American Astronautical Society (AAS) Kluever has made significant contributions to academic service, currently serving as Deputy Editor of the AIAA Journal of Guidance, Control, and Dynamics . His leadership extends to education as Director of Undergraduate Studies, where he shapes curriculum and mentors students. While no specific grants or student advisees are listed, his textbook authorship and editorial position indicate substantial impact on both teaching and research in aerospace engineering. He is affiliated with active research in aerospace systems and is likely involved in collaborative projects through the Mechanical and Aerospace Engineering department at Mizzou, contributing to high-impact research, student training, and innovation in space flight dynamics.
Ricardo Valerdi is a Professor and Department Head in the Department of Systems and Industrial Engineering at the University of Arizona's College of Engineering. He is a Distinguished Outreach Professor, Faculty Athletics Representative for the Big 12 Conference and NCAA, and a member of the Graduate Faculty. His academic journey includes positions at MIT (2005–2011) and continuous service at the University of Arizona since 2011, with current roles beginning in 2018 and ongoing leadership since 2020. His educational background includes a PhD in Industrial and Systems Engineering from the University of Southern California, an MS in System Architecture and Engineering from the same institution, and a BS in Electrical Engineering from the University of San Diego. Valerdi's research spans systems engineering, cost estimation, model-based systems engineering (MBSE), digital engineering, sports analytics, and test and evaluation of complex systems. He is renowned for his work on the Constructive Systems Engineering Cost Model (COSYSMO) and has pioneered the integration of virtual reality with MBSE. His recent publications reflect a strong focus on executable modeling, systems thinking education, cost modeling convergence, and applications in space and defense systems. His body of work from 2020 to 2025 shows a consistent trajectory in advancing digital engineering tools, integrating immersive technologies into systems design, refining parametric cost models, and assessing systems thinking competencies in education. The publications emphasize interdisciplinary applications, including space missions, ERP systems, and cyber resiliency, demonstrating a blend of theoretical and applied systems engineering. Best paper award, Journal of Systems Engineering International Council of Systems Engineering, Summer I 2016 Foreign Member, Mexican Academy of Engineering, Summer I 2016 Frank Freiman Award for Lifetime Achievement in Cost Estimation and Parametric Modeling, International Cost Estimating & Analysis Association, Fall 2015 Dr. Valerdi has advised numerous graduate students and led educational initiatives integrating industry-focused projects. He founded and co-edited the Journal of Enterprise Transformation and served as editor-in-chief of the Journal of Cost Analysis and Parametrics. He has received significant recognition and grants supporting research in systems engineering cost modeling, human systems integration, and digital transformation. His leadership extends to service as a Fulbright Scholar, visiting professor at West Point, and visiting fellow of the UK Royal Academy of Engineering. He leads research teams focused on cost estimation, digital engineering, and systems integration, often collaborating with defense and aerospace stakeholders. His labs and initiatives emphasize virtual reality integration, executable modeling, and systems thinking assessment. Future work is expected to further explore AI-driven cost models, digital twins for complex systems, and scalable frameworks for MBSE adoption across domains.
Christian Engwer is a full Professor at the University of Muenster in the Institute for Applied Mathematics, specializing in Analysis and Numerics. He leads the Engwer Group focused on Applications of Partial Differential Equations and is actively involved in the Cells in Motion initiative as a supervisor in the CiM-IMPRS Graduate Programme. His research centers on developing numerical methods for partial differential equations, particularly addressing challenges in complex geometries and multi-physics applications. He specializes in Unfitted Discontinuous Galerkin methods, which allow simulations on complex geometries without requiring domain-fitted meshes. His work spans porous media modeling, biological systems, and bioelectromagnetism applications, with significant contributions to EEG/MEG forward modeling in neuroscience. Analysis of his recent publications reveals a strong focus on model order reduction techniques, stabilized numerical schemes for cut-cell meshes, and applications in bioelectromagnetism. His work demonstrates a consistent trajectory toward developing robust, efficient numerical methods applicable to real-world problems in medical imaging and biological modeling, with increasing emphasis on high-performance computing implementations. Professor Engwer actively supervises doctoral students, with recent completions including Lukas Renelt (2025), Michael Wenske (2021), and Maria Carla Piastra (2019), among others working on topics related to numerical methods and biomedical applications. He leads several major research projects including BrainStorm: Highly Extensible Software for Advanced Electrophysiology and MEG/EEG Imaging (NIH-funded since 2019), multiple EXC 2044 Cluster of Excellence projects through 2025, and the InterKI interdisciplinary teaching program on machine learning and artificial intelligence. His group develops several important software packages including DUNE (Distributed and Unified Numerics Environment), duneuro (for bioelectromagnetism applications), and TPMC (Topology Preserving Marching Cubes). These tools support research in numerical methods and their applications to complex scientific problems.
Aneta Podkalicka is a Senior Lecturer in Communications and Media Studies at the School of Media, Film and Journalism (MFJ), Monash University, Melbourne. She is actively engaged in research, teaching, and supervision, with a strong focus on media's role in social innovation, environmental sustainability, and everyday life. Her work bridges academic research with real-world applications across non-profit, government, and commercial sectors. Her research interests include: Media and everyday life Environmental communication Media for social innovation Digital and automated technologies and work futures Thrift, secondhand, and circular economies Media analytics and research training Aneta’s recent publications reflect a consistent trajectory in examining how media shapes sustainable practices, digital economies, and social inclusion. Her work spans cultural studies, sociology of consumption, and media policy, often employing qualitative and interdisciplinary methods. She has contributed to journals such as International Journal of Cultural Studies , Media International Australia , and Convergence , as well as book publications with Intellect and Palgrave. She is currently a Chief Investigator on the international project 'Workers in transition through automation, digitalisation and robotisation of work' (2020–2025), and previously led a project on virtual housing policy modeling. She has served as guest editor for Media International Australia and Linkoping University Electronic Press, demonstrating her active role in academic publishing. Aneta supervises postgraduate students in media, communication, sociology of consumption, and design. She is part of the 'Media and Environment' research group at MFJ and contributes to advancing the UN Sustainable Development Goals, particularly those related to sustainable consumption and environmental sustainability.
Evita Papazikou serves as a Lecturer in Transport Engineering at the School of Engineering, University of the West of England (UWE Bristol), where she contributes to the Centre for Transport and Society and collaborates with the Bristol Robotics Laboratory's Connected & Autonomous Vehicles Centre. Her academic qualifications include: Civil Engineering (BEng and MEng) from Aristotle University of Thessaloniki MSc in Planning, Organisation, and Management of Transport Systems, Aristotle University of Thessaloniki PhD in Automated Systems and Driver Behaviour (Road Safety) from Loughborough University, sponsored by the Insurance Institute for Highway Safety with access to SHRP2 NDS data Dr. Papazikou's research focuses on road safety, connected and automated vehicles, driver behaviour analysis, and smart infrastructure. She investigates accident causation through statistical modeling, develops driver monitoring systems, and explores human factors in transportation. Her work integrates traffic simulation with mobility data fusion from vehicles, sensors, and infrastructure to enhance safety in future mobility systems, particularly in cooperative, connected, and automated environments. Her recent publications (2023-2025) reveal a concentrated research trajectory examining safety impacts of dedicated lanes for autonomous vehicles, parking policy implications in automated eras, and driver fatigue management. She consistently employs naturalistic driving data and traffic microsimulation to analyze driver-vehicle-environment interactions, with increasing emphasis on real-world intervention effectiveness and environmental sustainability in mobility systems. Scientific Awards: No specific awards were mentioned in the provided information. Dr. Papazikou has secured significant research funding through competitive programs including Horizon 2020, Innovate UK, and the Department for Transport. Her project portfolio demonstrates substantial industry collaboration, particularly with Ford, and includes: LEVITATE: Assessing societal impacts of Connected and Automated Vehicles SafetyCube: Developing an innovative road safety decision support tool i-DREAMS: Creating a smart driver and road environment assessment system DDRST: Building a data-driven road safety tool for hotspot identification TRIP: Developing a driver culpability assignment tool for road injury prevention She actively contributes to interdisciplinary research through her affiliations with the Centre for Transport and Society and the Bristol Robotics Laboratory's Connected & Autonomous Vehicles Centre, where she bridges engineering, human factors, and policy development for next-generation transportation systems.
Bruce Jacob is a Keystone Professor and Full Professor in the Department of Electrical & Computer Engineering at the University of Maryland College Park's College of Engineering. His research primarily focuses on memory systems design and exascale computing architectures, with significant contributions to DRAM simulation and high-performance computing systems. Dr. Jacob received his A.B. in Mathematics from Harvard University (1988), followed by his M.S. and Ph.D. in Computer Science & Engineering from the University of Michigan (1995 and 1997 respectively). His research interests include memory systems design, exascale computing architectures, embedded systems, circuit integrity, and algorithmic composition. His recent publications demonstrate a strong focus on next-generation memory technologies, particularly ReRAM and advanced DRAM architectures. His work bridges the gap between theoretical modeling and practical implementation, with significant contributions to memory system simulation through projects like DRAMsim. His research shows a consistent trajectory toward solving the memory bottleneck problem in high-performance computing systems. Named Fellow, IEEE (2021) Multiple University of Maryland Research Leader awards (2006, 2010, 2012, 2016, 2017) Clark School of Engineering Keystone Professor (2006) National Science Foundation CAREER Award (2000) University of Maryland Award for Teaching Excellence (2004) Dr. Jacob has led significant research initiatives including the University of Maryland Exascale Systems Research and Memory-Systems Research groups. He has developed important computational artifacts such as DRAMsim (a public-domain DRAM-system simulator) and BioBench (a set of bioinformatics workloads). His work has influenced both academic research and industry practices in memory system design.
Antonio Alguacil Cabrerizo is an Assistant Professor (starting 2025) at Université de Sherbrooke, where he currently serves as a Postdoctoral Fellow (2023-2025). His academic trajectory includes dual doctoral degrees in Mechanical Engineering from Université de Sherbrooke and École Nationale Supérieure d'Aéronautique et de l'Espace, complemented by aerospace engineering degrees from ENSEEIHT and Universidad Politécnica de Madrid. His research integrates computational fluid dynamics with machine learning, focusing on: Aeroacoustic prediction and noise source identification Deep learning surrogates for fluid and acoustic systems Turbomachinery and airfoil aerodynamics Data-driven modeling of spatiotemporal physical systems This work advances computational efficiency in simulating complex wave propagation, turbulence effects, and fluid-structure interactions. Publication analysis reveals consistent focus on developing neural network-based computational methods for aeroacoustics and fluid dynamics. His 15 most recent works demonstrate progressive refinement in applying convolutional architectures to predict acoustic scattering, refraction phenomena, and turbomachinery noise with increasing physical accuracy and computational efficiency. Awards and recognition include: Top 5 in AIAA Best Student Paper in Aeroacoustics (2024) Graduate Scholarship Award from CFD Society of Canada (2022) Eureka Scholarship from Université de Sherbrooke (2021) Best Poster Prize at CRASH Day (2021) He secured a $70,000 CAD startup grant (2025-2028) from Université de Sherbrooke for establishing his research program. No student advising relationships or laboratory affiliations are currently documented.
Beichuan Zhang serves as Associate Department Head and Professor in the Department of Computer Science at the University of Arizona, maintaining office GS 723 with contact details 520-621-4817 and bzhang@cs.arizona.edu. His academic leadership spans network architecture research and departmental administration within the university's computing ecosystem. Zhang holds a Ph.D. from the University of California at Los Angeles (2003), establishing his foundation in advanced networking systems. His doctoral work catalyzed a career focused on internet infrastructure evolution. Research centers on computer networks with specific expertise in Internet routing architecture, protocols, topology, and multicast systems. Zhang is a principal investigator in Named Data Networking (NDN), driving innovations in stateful forwarding planes, in-network caching (e.g., Nb-cache, BLEnD), and congestion control mechanisms. His work bridges theoretical networking models with practical implementations for wireless, satellite, and live-streaming environments. Analysis of 2021-2025 publications reveals strategic expansion into Low Earth Orbit satellite networks, where Zhang pioneers NDN adaptations for handover resilience and outage detection. Concurrently, his group optimizes video streaming protocols and wireless performance through interest bundling techniques. This dual trajectory demonstrates systematic progression from terrestrial networking to space-ground integrated architectures.
Luis Sentis is a Professor in the Department of Aerospace Engineering and Engineering Mechanics at The University of Texas at Austin and holds the Frank and Kay Reese Endowed Professorship in Engineering . He leads the Human Centered Robotics Laboratory, focusing on control systems, human-robot interaction, and exoskeleton robotics. His affiliations include UT Austin's Good Systems initiative and Apptronik Systems as an innovation advisor. Ph.D. , Electrical Engineering, Stanford University B.S. , Telecommunications and Electronics Engineering, Polytechnic University of Catalonia His research spans humanoid robotics , agile manipulation , autonomous systems , and human-robot teaming . Recent work emphasizes FAIR datasets , EEG monitoring , and collision detection for legged robots, with applications in industrial automation and ethical AI. Scientific awards include the NASA Elite Team Award and La Caixa Foundation Fellowship . Funding sources include DARPA , NSF , NASA , and ONR .
