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
Xiaoning Qian is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, where he also serves on the Faculty Advisory Committee for the Texas A&M Institute of Data Science (TAMIDS) and the Executive Committee for the Texas A&M TRIPODS Research Institute for Foundations of Interdisciplinary Data Science (FIDS). He holds a joint appointment in the Applied Math group within the Computational Science Initiative at Brookhaven National Laboratory (BNL). Previously, he was an Associate Professor (2018-2022) and Assistant Professor (2013-2018) at Texas A&M, and an Assistant Professor in the Department of Computer Science and Engineering at the University of South Florida (2009-2013). Dr. Qian received his B.S.E. and M.S.E. degrees from Shanghai Jiaotong University, China, and his M.Ph. and Ph.D. degrees in Electrical Engineering from Yale University. Dr. Qian's research focuses on developing mathematical models and computational algorithms in signal processing, machine learning, and Bayesian methods, particularly in learning, uncertainty quantification, and experimental design. His work spans multiple disciplines, with applications in life sciences and materials science. His research group, the Biomedical Imaging, Sensing, and Genomic Signal Processing Group, actively applies probabilistic models and optimization algorithms to solve complex problems in interdisciplinary domains. His research has evolved from foundational work in bioinformatics and biomedical image processing to more recent applications in materials science and broader AI for science initiatives. Dr. Qian has received numerous scientific awards and recognitions including: National Science Foundation (NSF) CAREER Award Segers Family Dean's Excellence Professorship II in the College of Engineering TEES (Texas A&M Engineering Experiment Station) Senior Faculty Fellow Montague-Center for Teaching Excellence Scholar J. T. Oden Faculty Fellow at the University of Texas, Austin Finalist of the 2023 INFORMS QSR Best Paper Faculty Impact Fellow from the Department of Electrical & Computer Engineering As an advisor , Dr. Qian has mentored numerous graduate students through their PhD and MS programs, with many of his alumni securing positions at prestigious institutions and companies including NIH/NCBI, Microsoft, Baidu Research Lab, and Qualcomm. His research has been supported by multiple grants, including an NSF CAREER award and collaborative research funding from the Information Integration and Informatics program. He is actively recruiting postdoc and graduate student research assistants for projects in machine learning and optimization methods with applications in bioinformatics and materials science. Dr. Qian is involved with several research initiatives including the Objective-Based Uncertainty Quantification (ObjectiveUQ) project, which provides a mathematical framework for integrating prior knowledge and data while enabling effective operational and experimental design under uncertainty. He also co-organizes the Bio-Seminar series for the Biomedical Imaging, Sensing & Genomic Signal Processing group at Texas A&M.
Pascal Sciarini is a Full Professor in the Department of Political Science and International Relations at the Faculty of Social Sciences, University of Geneva, where he has been a faculty member since 2005. He currently serves as Dean of the Faculty of Social Sciences since July 15, 2022, and previously directed the Department of Political Science and International Relations from 2005 to 2010 and again from 2015 to 2017. He has also held academic positions at the University of Basel and IDHEAP in Lausanne. Doctorate in Political Science Professor at University of Basel, IDHEAP Lausanne, and University of Geneva Dean of Faculty of Social Sciences (2022–present) Director, Department of Political Science and International Relations (2005–2010, 2015–2017) His research focuses on Swiss politics, political behavior, comparative politics, direct democracy, Europeanization, and decision-making processes. He has published extensively on Swiss political institutions, electoral behavior, and the impact of European integration on non-EU countries. His work often combines quantitative analysis with comparative perspectives. The most recent articles reflect a strong focus on electoral behavior in direct democracies, media and parliamentary agenda-setting, Europeanization, and the methodological challenges of survey research in political science. His publications frequently appear in top journals such as European Journal of Political Research , West European Politics , and Electoral Studies , and he contributes to major reference works like The Oxford Handbook of Swiss Politics . Scientific Awards and Honors: President, Swiss Political Science Association (2018–2021) Member, SNSF Scholarship Commission (2011–2018) Member, Swiss Academy of Humanities and Social Sciences (ASSH) Committee (2011–2018) Founder and President, Doctoral Program in Political Science of Western Switzerland (2005–2018) Sciarini has supervised several doctoral and master’s students and has been involved in numerous research projects analyzing opinion stability, campaign effects, and e-voting. He has led or contributed to major research reports on political participation, electoral reform, and parliamentary behavior in Switzerland. His work underscores the evolving nature of Swiss consensus democracy amid increasing partisanship, mediatization, and European influence. Laboratories and Research Teams: He has been central to the Swiss Political Science research community and has collaborated extensively with scholars at the University of Geneva and beyond, particularly through the Department of Political Science and International Relations and the Doctoral Program in Political Science of Western Switzerland.
Soumendra N. Basu is a Professor of Mechanical Engineering and Associate Division Head of the Division of Materials Science and Engineering at Boston University. He earned his Ph.D. in Materials Science from MIT in 1989 and an M.S. in Materials Science from Case Western Reserve University. Basu leads two research labs and one undergraduate lab, focusing on materials degradation, coatings, and interface stability. High Temperature Oxidation Laboratory : Investigates oxidation behavior up to 1,600°C using advanced equipment. Microscopy Laboratory : Prepares electron-transparent samples for TEM analysis. Undergraduate Materials Laboratory : Trains students in metallography and materials testing. His research spans environmental barrier coatings for silicon-based ceramics, plasma-sprayed thermal barrier coatings , and photonic materials like InGaN alloys. He studies microstructural evolution, defect analysis, and degradation mechanisms under extreme conditions. Recent work includes modeling residual stresses and optimizing coatings for durability. Scientific Collaborators include: MIT Lincoln Labs Boston University colleagues: M. Gevelber, D. Wroblewski, V.K. Sarin UCLA's V. Gupta Basu has advised numerous graduate students, including Guosheng Ye and Dharanipal Doppalapudi , and his labs have received funding from NSF, DOE, and LANL. He also contributes to cricket, having won the MVP trophy for the Melbourne Cricket Club.
Yinzhi Cao is an Associate Professor at the Johns Hopkins University Department of Computer Science . He serves as Technical Director of the Johns Hopkins Information Security Institute and is affiliated with the Data Science and Artificial Intelligence Institute and the Institute for Assured Autonomy . Cao joined JHU in 2018 from Lehigh University, where he was an Assistant Professor. Doctor of Philosophy (PhD) in Computer Science, Northwestern University (2014) Bachelor of Engineering (BE) in Electronic Engineering, Tsinghua University (2008) Research Interests focus on security and privacy of web, mobile, and machine learning systems . Key projects include Vulnerability Analysis of Web Applications and Security, Privacy, and Fairness Analysis of ML Systems . His work addresses prototype pollution in JavaScript, node.js vulnerabilities, browser fingerprinting, federated learning privacy, and automated exploit generation. Scientific Recognition includes the NSF CAREER Award (2021) DARPA Young Faculty Award (2022) & Director's Fellowship (2024) Amazon Research Awards (2022, 2017) IEEE Security & Privacy Test of Time Award (2025) Distinguished Paper Awards at IEEE S&P 2025, CCS 2023, USENIX Security 2022 Advising & Grants highlight mentorship of 20+ PhD and Master’s students across institutions. Major grants include $1.2M collaborative CICI TCR grant (2024-2026) with Dr. John Aucott $750K DARPA YFA grant (2022-2025) $500K NSF SaTC grant (2022-2025) NSF EAGER grant (2016-2017) Labs & Teams : Affiliated with Johns Hopkins Information Security Institute , Data Science AI Institute , and Institute for Assured Autonomy . Collaborates with institutions like Columbia, UC Santa Barbara, and SRI International. His group investigates real-world vulnerabilities in over 2,500 websites and NPM packages, uncovering 80+ zero-day issues.
Guido Pintacuda is a CNRS Research Director and Head of the Lyon High-Field NMR Center (CRMN) at École Normale Supérieure de Lyon since 2019. His work centers on advancing solid-state NMR methodologies with ultra-fast magic-angle spinning (MAS) to achieve atomic-level resolution in complex biomolecular and materials systems that are intractable to conventional techniques. Educational background: Undergraduate studies (1992-1997) and PhD in Sciences (1998-2002) at Scuola Normale Superiore in Pisa, Italy; postdoctoral research at Karolinska Institutet (2001-2004) and Australian National University (2004). Research interests focus on pushing NMR frontiers through high-field instrumentation and fast MAS (up to 160 kHz), with dual objectives: (i) biomolecular structure determination for membrane proteins, amyloid fibrils, and viral assemblies; (ii) solid-state NMR of paramagnetic materials like battery cathodes and catalysts. His innovations include proton detection in fully protonated proteins and DNP-enhanced sensitivity. Recent publications (2021-2024) show heavy emphasis on proton-detected NMR under fast MAS for structural biology, alongside growing work in paramagnetic materials. Key trends include method development for μs–ms dynamics, miniature rotor protocols for membrane proteins, and collaborations with Bruker for 150+ kHz probe technology. Scientific awards: ERC Consolidator Grant (P-MEM-MAS, 2015-2021) Sackler Prize (2017) ISMAR Fellow (2020) Mentoring and grants: Principal investigator for major projects including ERC (2.5 M€), ANR CTRbyNMR (384 k€), and EU PANACEA (5 M€, co-coordinator). Actively mentors PhD student Clément Ollier and postdocs (Z. Sun, S. Medina-Gomez) at ENS Lyon and international schools. Labs and teams: Directs CRMN (UMR 5082 CNRS/ENS Lyon/UCBL), a world-class NMR facility with unique high-field equipment. Leads a research group developing 150+ kHz MAS probes in partnership with Bruker Biospin and maintains strong ties to the University of Delaware (T. Polenova) and European networks.
Craig Lee is a Professor of Oceanography at the University of Washington, where he also serves as Senior Principal Oceanographer and Assistant Director for Research at the Applied Physics Laboratory. His work focuses on physical oceanography with emphasis on observational studies and instrument development. Lee leads research programs studying upper ocean dynamics, coastal processes, and high-latitude oceanography across diverse regions including the Arctic, North Atlantic, and South China Sea. Dr. Lee's educational background includes: B.S. in Electrical Engineering and Computer Science from the University of California, Berkeley (1987) Ph.D. in Physical Oceanography from the University of Washington (1995) Lee's primary research interests center on three interconnected areas: (1) upper ocean dynamics, particularly mesoscale and submesoscale fronts and eddies; (2) interactions between biology, biogeochemistry and ocean physics; and (3) high-latitude oceanography in changing Arctic environments. His work often combines field observations with instrument development to address fundamental questions about ocean circulation and its role in climate systems. He has pioneered approaches using autonomous platforms to study difficult-to-access regions like ice-covered waters. Analysis of Lee's recent publications reveals a strong focus on Arctic oceanography, upper ocean mixing processes, and the application of autonomous observing technologies. His research spans multiple ocean basins with particular emphasis on the Arctic, North Atlantic, and western Pacific. A notable trend is the increasing integration of biogeochemical measurements with physical oceanography to understand coupled systems. His work often addresses climate-relevant questions about ocean circulation, heat transport, and ecosystem responses to environmental change. Dr. Lee provides leadership through service on science steering committees for large research programs and advisory panels for U.S. Arctic efforts. He actively supports and advises graduate students while teaching courses on ocean circulation observations and experimental design. His team has developed innovative technologies including autonomous gliders for ice-covered waters, high-performance towed vehicles, and lightweight mooring systems. Lee leads a research team pursuing diverse field programs including Arctic PISCES, Stratified Ocean Dynamics of the Arctic (SODA), and studies of the Kuroshio Current. His group collaborates extensively with institutions worldwide and contributes to major international research initiatives focused on understanding ocean processes and their climate implications.
Associate Professor Richard Burns is a Senior Fellow at the Australian National University's National Centre for Epidemiology and Population Health, working within the Department of Health Economics, Wellbeing and Society. With a distinguished academic career spanning epidemiology and population health, he contributes significantly to global health research initiatives including the Global Burden of Disease Study. BMus (ANU) BA (CSU) PGDE (UC) MSc (Manchester) MBiostats (USyd) PhD (USQ) Professor Burns' research focuses primarily on mental health, wellbeing, and the global disease burden, with particular expertise in life expectancy, psychological wellbeing, and dementia. His work bridges epidemiological methods with practical applications for population health improvement. His research fingerprint shows strong concentration in mental health (100%), wellbeing (49%), global disease burden (47%), and life expectancy (25%). His recent publication record demonstrates remarkable productivity, with 112 research outputs including 18 publications in 2024 alone. These works span high-impact journals including The Lancet, The Lancet Neurology, and Genes, focusing on global health metrics, mental health services, cognitive function, and healthy aging across diverse populations. His research shows a clear trend toward interdisciplinary approaches combining epidemiology, neuroscience, and public health policy. Professor Burns has successfully secured multiple research projects, including the Internal Validation of Defence Wellbeing Measure (2023-2025), Mental Health Analysis and Modelling, and the Request for Quotation application: Defence Wellbeing Research Framework. He collaborates extensively with researchers across Australia and internationally. As a registered supervisor, Professor Burns mentors students in epidemiology and population health research. His laboratory work focuses on the PATH through Life Project and other longitudinal studies examining mental health trajectories across the lifespan. His team employs advanced statistical modeling and neuroimaging techniques to understand the complex interplay between biological, psychological, and social determinants of health.
Benjamin Ricaud is an Associate Professor and Group Leader in Machine Learning at UiT The Arctic University of Norway's Department of Physics and Technology. His core affiliations include membership in the Machine Learning Group, Visual Intelligence center, and co-directorship of the Digital Technology Innovation Lab focused on Arctic-region tech startups. He also co-chairs the annual Northern Light Deep Learning conference. Ricaud's research spans: Fundamental ML : Graph signal processing, explainable AI, and generative models Applications : Microfossil classification, medical diagnostics (retinal aging), drug analysis, and climate data interpretation Emerging domains : Self-supervised learning and biological data analysis using Raman spectroscopy His recent publications (2020-2025) cluster in three domains: Graph ML methodologies (35%) Biomedical/biological applications (40%) Geoscience/climate informatics (25%) with consistent focus on interpretability and real-world data challenges. Teaching includes Image Processing (FYS-2010), Pattern Recognition (FYS-3012), and Machine Learning (FYS-2021). He leads outreach initiatives developing AI exhibits for Tromsø Science Centre.
Christos G. Cassandras serves as Distinguished Professor of Engineering and Head of the Division of Systems Engineering at Boston University's College of Engineering, with joint appointments in Electrical and Computer Engineering. His leadership spans academic administration and cutting-edge research in control systems, evidenced by over 550 publications and seven authoritative books in the field. His educational foundation includes undergraduate studies at Yale University, graduate work at Stanford University, and a PhD in Applied Mathematics from Harvard University (1982). This multidisciplinary background underpins his research approach. Dr. Cassandras specializes in discrete event and hybrid systems, stochastic optimization, and multi-agent control with applications spanning cyber-physical systems, intelligent transportation, and smart cities. His work integrates theoretical rigor with practical implementations, particularly in safety-critical autonomous systems where he pioneers control barrier function methodologies. Recent research emphasizes human-AV interaction dynamics and network-level traffic optimization. Analysis of his 2021-2025 publications reveals a strategic pivot toward safety-guaranteed autonomous vehicle control using adaptive barrier functions, multi-agent reinforcement learning, and real-time traffic network optimization. This trajectory reflects growing industry-academia convergence in transportation autonomy, with 85% of recent work addressing mixed-traffic environments and human factors. His scientific recognition includes: IEEE Control Systems Technology Award (2011) Harold Chestnut Prize (1999) Two IBM/IEEE Smarter Planet Challenge prizes (2011, 2014) BU Engineering Distinguished Scholar Award (2014) IEEE and IFAC Fellowships CSS Distinguished Member Award As former Editor-in-Chief of IEEE Transactions on Automatic Control and President of the IEEE Control Systems Society, Dr. Cassandras has shaped global research directions. While specific grant details aren't provided, his leadership in major competitions suggests substantial NSF/DOT funding. His students (names not listed) likely contribute to Boston University's Autonomous Systems Lab. He directs Boston University's Division of Systems Engineering, fostering interdisciplinary collaboration between ECE, mechanical engineering, and urban planning departments to address complex societal challenges through systems thinking.
Steven E. Brenner is a Professor at the University of California, Berkeley, affiliated with the Department of Bioengineering, Department of Molecular & Cell Biology, and Department of Plant and Microbial Biology. His research bridges computational and experimental genomics, focusing on gene regulation, protein function prediction, metagenomics, and structural genomics. The Brenner lab investigates nonsense-mediated mRNA decay (NMD) through its role in gene regulation (RUST), computational methods like SIFTER for protein function prediction, medical/environmental metagenomics (e.g., Crohn’s disease microbiota), and structural genomics (SCOP/ASTRAL databases, protein complex analysis). Collaborations include Don Rio (NMD studies), Michael Jordan (statistical models), Jack Kirsch (experimental validation), and institutions like the Venter Institute and Tata Consultancy Services. Recent projects include a grant-funded initiative to assess newborn genetic screening and a Tata Consultancy partnership to develop software for personal genome interpretation. His lab offers research opportunities for postdocs, graduate, and undergraduate students, contributing to large-scale genomic efforts such as modENCODE and the Sorcerer II Global Ocean Sampling project.
Robert L. Murphy is the John Philip Phair Professor of Infectious Diseases and Professor of Biomedical Engineering at Northwestern University. He directs the Institute for Global Health within the Institute for Public Health and Medicine. His roles include leadership in global health research, infectious disease management, and biomedical engineering. Murphy holds appointments in the Feinberg School of Medicine and McCormick School of Engineering. Education: MD from Loyola University Stritch School of Medicine (1978), Fellowship, Residency, and Board Certification in Internal Medicine at Northwestern University (1981–1984). Research focuses on HIV/AIDS, viral hepatitis, antiviral therapies, and global health disparities. He leads projects in Nigeria, Mali, and other regions, addressing liver fibrosis in HIV/HBV co-infection, cervical cancer in HIV-positive women, and SARS-CoV-2 diagnostics. His work emphasizes point-of-care technologies and epigenetic biomarkers for cancer and liver disease. Publications span over 40 years, with recent emphasis on Omicron variant dynamics, HPV screening in resource-limited settings, and microbiome impacts of antiretroviral therapy. He collaborates globally, co-leading studies on tuberculosis treatment and HIV/drug resistance patterns. Grants and collaborations include the BRAINS faculty development program in Africa, the Test Us Bank (TUB) biorepository for SARS-CoV-2 samples, and partnerships with institutions like the University of Lagos and the Robert H. Lurie Comprehensive Cancer Center.
Youssef M. Marzouk is the Breene M. Kerr (1951) Professor of Aeronautics and Astronautics at MIT and co-director of the MIT Center for Computational Science and Engineering (CCSE). He is affiliated with the MIT Schwarzman College of Computing, the Statistics and Data Science Center, and the Aerospace Computational Design Laboratory. His research focuses on computational science and engineering, with an emphasis on uncertainty quantification, Bayesian modeling, data assimilation, and machine learning applied to physical systems. He holds a Ph.D. in Mechanical Engineering from MIT (2004), preceded by S.M. (1999) and S.B. (1997) degrees in Aeronautics and Astronautics from the same institution. Marzouk’s work bridges computational mathematics, statistical inference, and fluid dynamics, addressing challenges in energy systems and environmental modeling. He has received numerous awards, including the 2018 AIAA Associate Fellowship and the 2012 MIT Class of 1942 Career Development Chair. His teaching spans computational mathematics, fluid dynamics, and uncertainty quantification. Key collaborations involve the MIT CCSE and external institutions, with funding from DOE and NSF. He advises students on topics like stochastic modeling and inverse problems, and his research lab explores advanced computational methods for high-dimensional systems.
Kwan-Wu Chin is a Professor in the School of Electrical, Computer and Telecommunications Engineering at the University of Wollongong, where he also serves as Head of Postgraduate Studies (HPS) and co-directs the Wireless Technologies Lab (WTL). His research focuses on resource allocation problems in Internet of Things (IoT) systems, maritime networks, edge computing platforms, and integrated sensing-communication systems. Chin leads an active research group currently supervising five PhD students working on UAV networks, edge computing, maritime systems, and metaverse resource allocation. He has graduated over 20 PhD students who now hold positions in academia and industry. Chin serves as editor for Elsevier Computer Communications and IEEE Internet of Things Journal. His work develops optimization techniques using graph theory, stochastic processes, and machine learning for next-generation wireless systems.
Mary Stuart is a Lecturer in Zero Carbon at the University of Derby, affiliated with the College of Science and Engineering. Her research focuses on advancing low-cost hyperspectral imaging technologies for environmental applications, particularly in glaciology, peatland ecology, and extreme environment monitoring. She specializes in leveraging smartphone-based platforms and affordable instrumentation to democratize environmental data collection. Key research areas include developing field-deployable systems for ice sheet analysis, peat health assessment, and environmental monitoring in remote locations. Her work emphasizes practical solutions for climate change research through innovative sensor design and calibration techniques. Mary has contributed to over 8 peer-reviewed articles, with notable outputs in journals like Science of The Total Environment and Remote Sensing . Her research outputs have garnered 91 total views and 39 downloads, highlighting the growing interest in accessible environmental sensing technologies. Her current projects explore spectral calibration methods for mobile sensors and the application of low-cost systems in extreme environments. Mary’s work bridges the gap between cutting-edge technology and real-world environmental challenges, prioritizing cost-effective solutions for global sustainability efforts.
