جست‌وجوی استاد و پژوهشگر

• Particle Physics
• High Energy Physics
• ATLAS Collaboration
• +۳ مورد دیگر

Jonas Strandberg is an Associate Professor at KTH Royal Institute of Technology's Department of Physics, part of the School of Engineering Sciences. His research focuses on particle physics, particularly within the ATLAS Collaboration at the Large Hadron Collider (LHC). He contributed to the Higgs boson discovery and currently studies its properties. Strandberg has been involved in detector development, including the HGTD timing detector for the LHC upgrade. He holds a PhD from Stockholm University (2006) and worked as a postdoc at the University of Michigan (2006-2011) before joining KTH. His teaching responsibilities include courses on experimental particle physics, statistical methods, and engineering skills. Research interests span high-energy physics, collider technology, and detector systems. Research Highlights: Member of the ATLAS Collaboration since 2011 Key contributor to Higgs boson measurements Developed timing detector systems for LHC upgrades Published extensively on particle physics and accelerator technology Teaching & Supervision: Course responsible for Experimental Particle Physics (SH2203) Teaching roles in Applied Modern Physics (SH1015), Embedded Systems Design (IL2232), and more Professional Activities: ATLAS Data Preparation Coordinator (2015-2017) Member of the Particle and Astroparticle Physics Group at AlbaNova University Centre

Parinaz Naghizadehمشاهده پروفایل

استادیار

• Network Economics
• Game Theory
• Ethics and Economics of AI
• +۳ مورد دیگر

Parinaz Naghizadeh is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of California San Diego (UCSD), affiliated with the Design Lab. She holds a PhD from the University of Michigan and has prior roles at Ohio State University and postdoctoral positions at Purdue and Princeton. Her research focuses on network economics, game theory, AI ethics, optimization, and cybersecurity. She received the NSF CAREER Award (2022), Rising Stars in EECS (2017), and Barbour Scholarship (2014). Education: PhD in Electrical Engineering (University of Michigan), M.Sc. in Mathematics and Electrical Engineering (University of Michigan), B.Sc. in Electrical Engineering (Sharif University of Technology, Iran). Research Interests: She develops mathematical models to analyze decision-making in complex networks, with emphasis on AI ethics, multi-agent systems, and cybersecurity. Recent work explores biases in AI, strategic classification, and incentive mechanisms for security investments. Article Trends: Her recent publications (2023-2025) address strategic classification challenges, multiplex network equilibria, federated learning fairness, and robust control in cyber-physical systems. Themes include ethical AI, game-theoretic security design, and optimization under uncertainty. Awards: NSF CAREER Award (2022), Rising Stars in EECS (2017), Barbour Scholarship (2014) Advising & Grants: No student advisees listed, but active in securing research grants (e.g., NSF CAREER). Works with interdisciplinary teams in UCSD's Design Lab. Labs/Teams: Affiliated with UCSD's Design Lab, focusing on innovative engineering solutions for societal challenges.

Srijan Senguptaمشاهده پروفایل

دانشیار

• Statistical Methodology for Network Data
• Bootstrap and Resampling Methods
• Big Data Analysis
• +۶ مورد دیگر

Srijan Sengupta is an Associate Professor of Statistics at North Carolina State University (NC State) since 2020. Previously, he served as an Assistant Professor at Virginia Tech from 2016 to 2020. He holds a Ph.D. in Statistics from the University of Illinois at Urbana-Champaign (2016) and degrees from the Indian Statistical Institute (B.Stat and M.Stat with Distinction). His research focuses on statistical methodology for network data, anomaly detection, bootstrap methods, and scalable inference, with applications in healthcare analytics, epidemiology, and cybersecurity. Education: Ph.D. in Statistics, University of Illinois at Urbana-Champaign (2011–2016) M.Stat (1st Division with Distinction), Indian Statistical Institute (2007–2009) B.Stat (1st Division with Distinction), Indian Statistical Institute (2004–2007) Research Interests: His methodological work includes statistical inference in networks, anomaly detection, bootstrap techniques, and scalable algorithms for big data. Applications span social determinants of health, healthcare analytics, space physics, epidemiology, and cybersecurity. He emphasizes interdisciplinary collaborations, particularly in patient safety event analysis and medical device safety. Awards and Grants: Norton Prize for Outstanding PhD Thesis (2015) NIH R01 Grant ($890,055, Principal Investigator) for statistical algorithms in patient safety (2019–2022) Multiple grants for network inference and anomaly detection (NSF, Socially Determined Inc., Virginia Tech Foundation) Advising and Service: Advises over 20 students across PhD, master’s, and undergraduate research programs. Serves as an Associate Editor for Sankhya, Series B and peer reviewer for top journals. Active in university service roles at NC State and Virginia Tech, including faculty hiring committees and curriculum development. Labs and Collaborations: Leads research on statistical network analysis, including projects funded by NIH and NSF. Collaborates with institutions globally on topics like epidemic thresholds, cybersecurity defenses (e.g., phishing detection), and healthcare analytics.

Edriss S. Titiمشاهده پروفایل

استاد

• Nonlinear Partial Differential Equations
• Applied Mathematics
• Numerical Analysis
• +۵ مورد دیگر

Edriss S. Titi is a University Distinguished Professor and Arthur Owen Professor of Mathematics at Texas A&M University within the College of Arts & Sciences. His research focuses on nonlinear partial differential equations, applied mathematics, and geophysical fluid dynamics. He leads studies on fluid mechanics, atmospheric and oceanic dynamics, data assimilation, and control theory. His work often addresses mathematical rigor in modeling complex systems like climate dynamics and turbulent flows. Research Interests: Nonlinear PDEs and their applications Fluid dynamics and turbulence Data assimilation algorithms Climate and ocean modeling Infinite-dimensional dynamical systems Recent publications emphasize Navier-Stokes equations , primitive equations , and data assimilation in chaotic systems . His methodologies bridge theoretical analysis and computational modeling, with applications to weather prediction and geophysical flows. Collaborations include the Institute for Applied Mathematics and Computational Science (IAMCS) at Texas A&M. Notable contributions include rigorous analysis of global well-posedness for oceanic models and development of CDAnet, a physics-informed deep learning framework for fluid flow downscaling.

Valen Johnsonمشاهده پروفایل

استاد

• Bayesian Statistics
• Biostatistics
• Medical Imaging
• +۷ مورد دیگر

Valen E. Johnson is a University Distinguished Professor and Dean Emeritus of the College of Science at Texas A&M University, where he has been a faculty member since 2012. He previously held professorships at the University of Texas M. D. Anderson Cancer Center (2004–2012), the University of Michigan (2002–2004), and Duke University (1989–2001). He also served as a Technical Staff Member at Los Alamos National Laboratory (2001–2002). Johnson earned his Ph.D. in Statistics from the University of Chicago (1989), M.A. in Applied Mathematics from the University of Texas at Austin (1985), and B.S. in Mathematics from Rensselaer Polytechnic Institute (1981). His research focuses on Bayesian methodology, including hypothesis testing, variable selection in high-dimensional spaces, latent variable models, and applications in medical imaging, clinical trials, and educational assessment. He has contributed to the development of non-local prior densities and Bayesian diagnostics for MCMC convergence. His work bridges Bayesian and classical statistical approaches, emphasizing reproducibility and rigorous evidence assessment in scientific research. Johnson has held editorial roles, including Co-Editor of Bayesian Analysis (2010–2014) and Associate Editor of the Journal of the American Statistical Association (2011–present). He is a Fellow of the American Statistical Association and the Royal Statistical Society and has served on the Board of Directors of the International Society for Bayesian Analysis. His advocacy for revised statistical significance standards has sparked major debates in scientific methodology. Johnson has supervised numerous doctoral students, including those whose theses won prestigious awards like the Savage Award. He has collaborated on grants addressing medical imaging, system reliability, and cancer symptom management, reflecting his interdisciplinary impact in biostatistics and public health.

Daphna Buchsbaumمشاهده پروفایل

دانشیار

• Behavioral Neuroscience
• Developmental Psychology
• Higher-Level Cognition
• +۳ مورد دیگر

Daphna Buchsbaum is an Associate Professor of Cognitive and Psychological Sciences at Brown University, leading the Computational Cognitive Development Lab and Brown Dog Lab. She holds a dual background in Psychology and Statistics from UC Berkeley, a Master’s in MIT Media Lab, and a Brown undergraduate degree. Her work explores causal and social reasoning in children and non-human animals like dogs, focusing on how learners combine social and observational data. Education: A.B. in Human Biology, Brown University (2002) M.Sc. in Media Arts & Sciences, MIT Media Lab (2004) M.A. and Ph.D. in Psychology, UC Berkeley (2013) Research Focus: Investigates how children and animals form causal beliefs through social interactions and direct observation. Key topics include: Canine cognition and problem-solving Children’s causal reasoning and pretend play Computational models of belief systems Key Achievements: NSF/Office of Naval Research grants Templeton World Charity Foundation funding 2021 Society for Research in Child Development Early Career Award 2018 APS Rising Star designation Lab Activities: Conducts participatory studies with children and dogs, using eye-tracking and behavioral experiments. Recent projects include: Head-mounted eye-tracking in dogs Cross-cultural studies of causal reasoning Analysis of canine visual environments

Chris Rogersمشاهده پروفایل

استاد

• Financial mathematics
• probability theory
• stochastic analysis
• +۲ مورد دیگر

Chris Rogers is a Professor of Statistical Science within the Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge, actively contributing to research at the intersection of probability theory, stochastic analysis, and financial applications. His academic profile reflects deep engagement with mathematical finance and theoretical probability through publications and departmental affiliations. His research spans financial mathematics, probability theory, stochastic analysis, statistics, and mathematical economics, with emphasis on rigorous mathematical frameworks for financial markets. Key themes include option pricing mechanisms, stochastic process modeling, and geometric probability applications, often addressing real-world financial instruments like Asian options and S&P500 index behaviors through advanced probabilistic techniques. Analysis of his 15 most recent publications (2016-2018) reveals consistent focus on stochastic calculus applications in finance, particularly Lévy processes, diffusion models, and optimal stopping problems. His work bridges theoretical probability with quantitative finance, demonstrating expertise in translating complex stochastic phenomena into financial modeling solutions across asset pricing, risk assessment, and market analysis domains. No scientific awards were documented in the provided source material. Information regarding PhD/Master's student supervision, research grants, or collaborative teams was not specified in the available texts, indicating absence of such details in the source documentation.

Mahdi Soltanolkotabiمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Generative AI
• +۷ مورد دیگر

Mahdi Soltanolkotabi is a Professor in the Departments of Electrical and Computer Engineering, Computer Science, and Industrial and Systems Engineering at the University of Southern California's Viterbi School of Engineering. He serves as the inaugural Director of the USC Center on AI Foundations for Science (AIF4S). His academic journey includes a Ph.D. in Electrical Engineering from Stanford University (2014) under Emmanuel Candes, followed by a postdoctoral position at UC Berkeley's AMPLAB mentored by Ben Recht and Martin Wainwright. Dr. Soltanolkotabi's research spans both theoretical and applied dimensions of data science. On the theoretical side, he develops mathematical foundations for modern data science, focusing on generative AI, deep learning, machine learning, signal processing, and computational imaging. His work draws upon nonconvex optimization, high-dimensional probability, statistical estimation, empirical processes, and learning theory. On the applied side, he develops reliable AI systems for healthcare and scientific applications, collaborating with physicians and domain scientists to enhance AI reliability, develop new architectures, and create rigorous evaluation frameworks. His recent publications demonstrate strong focus on medical AI applications, image reconstruction, and theoretical foundations of deep learning. His work bridges the gap between theoretical guarantees and practical implementations, particularly in medical imaging where reliability is critical. His research group has made significant contributions to understanding the behavior of nonconvex optimization algorithms in high-dimensional settings. David and Lucile Packard Fellow Information Theory Society Best Paper Award NIH Director's new innovator award Sloan Research Fellowship NSF Career award Airforce Office of Research Young Investigator award (AFOSR-YIP) Viterbi school of engineering junior faculty research award Faculty awards from Google and Amazon Dr. Soltanolkotabi has received multiple research grants including Amazon Research Awards for projects on "Artificial intelligence for fast and portable medical imaging" and "Reliable AI for Generation of Medical Reports from MRI Scans." He actively collaborates with medical professionals and leads educational outreach initiatives with local schools through USC's Viterbi Adopt-a-School program. His work demonstrates a strong commitment to translating theoretical advances into practical healthcare solutions while maintaining rigorous mathematical foundations.

Standa Živnýمشاهده پروفایل

استاد

• Theoretical Computer Science
• Discrete Mathematics
• Algorithms
• +۱۰ مورد دیگر

Standa Živný is a Professor of Computer Science at the University of Oxford and a Fellow and Tutor at Merton College. He has been a faculty member at Oxford since 2013 and was promoted to full professor in 2021. His research spans theoretical computer science and discrete mathematics, with a focus on algorithms, computational complexity, and constraint satisfaction problems (CSPs) in various forms, including optimisation, counting, and approximation. His research interests include the power and limitations of convex relaxations, sparsification, submodularity, and the algebraic and logical foundations of tractability in combinatorial problems. He has made significant contributions to understanding when and why certain problems can or cannot be efficiently solved using linear programming and other algorithmic paradigms. The recent trends in his publications show a deep engagement with approximation algorithms, hardness results, sparsification techniques, and the complexity of counting and promise problems. His work often lies at the intersection of algebra, logic, and optimisation, demonstrating the power of interdisciplinary approaches in theoretical computer science. ERC Consolidator Grant (NAASP, 2022–2027) ERC Starting Grant (PowAlgDO, 2017–2022) Royal Society University Research Fellowship (2013–2021) He actively supervises a large cohort of postdoctoral researchers and students, including PhD candidates, master’s, and undergraduate students. His leadership extends to academic service, where he serves as Editor-in-Chief of the SIAM Journal on Discrete Mathematics and holds editorial and committee positions in major journals and funding bodies. He has organised numerous workshops and research programmes at institutions such as Dagstuhl, the Isaac Newton Institute, and AIM. He is involved in major research initiatives, including a Simons Programme on symmetry in computation and an American Institute of Mathematics SQuARE on relaxations for promise CSPs.

• Probability Theory
• Interacting Particle Systems
• Percolation Theory
• +۳ مورد دیگر

Réka Szabó is an Assistant Professor at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Netherlands, since October 2022. Her academic trajectory includes a postdoctoral fellowship at Université Paris-Dauphine (2019–2022) and a Ph.D. in Mathematics (2015–2019) at the University of Groningen under Daniel Valesin. Education: B.Sc. in Mathematics, Budapest University of Technology and Economics (2008–2011) M.Sc. in Applied Mathematics, Budapest University of Technology and Economics (2011–2015) M.Sc. in Computer Science, University of Trento and Eötvös Lóránd University (2012–2014) Ph.D. in Mathematics, University of Groningen (2015–2019) Réka’s research focuses on Interacting Particle Systems and Percolation Theory , with significant contributions to understanding phase transitions, critical behavior, and stochastic dynamics in models like the Contact Process, Oriented Percolation, and Bootstrap Percolation. Her work combines rigorous mathematical analysis with applications to statistical physics and complex networks. She is actively involved in academic service, including organizing the Workshop On Randomness and Discrete Structures (WORDS 2025) and co-organizing the Probability and Statistics Seminar in Groningen. Réka teaches advanced courses such as Stochastic Processes, Interacting Particle Systems, and Mathematics and its Environment.

John D. Nortonمشاهده پروفایل

استاد

• History and Philosophy of Physics
• General Relativity
• Special Relativity
• +۱۶ مورد دیگر

John D. Norton is a Distinguished Professor in the Department of History and Philosophy of Science (HPS) at the University of Pittsburgh, where he has been a faculty member since 1983. He served as Chair of the department from 2000 to 2005 and as Director of the Center for Philosophy of Science from 2005 to 2016. His work bridges the history and philosophy of physics, with deep engagement in Einstein’s relativity, quantum theory, statistical mechanics, and foundational issues in scientific reasoning. His educational background includes a PhD in the School of History and Philosophy of Science from the University of New South Wales (1982) and a Bachelor of Engineering in Chemical Engineering from the same institution (1974). Before transitioning to philosophy, he worked as a technologist at the Shell Oil Refinery in Sydney. Norton is renowned for his development of the material theory of induction , which challenges formalist approaches by asserting that inductive inferences are warranted by domain-specific facts rather than universal logical rules. He has also made significant contributions to the philosophy of spacetime, particularly through his analysis of the hole argument , and has published extensively on thought experiments, causation, and the thermodynamics of computation. His recent publications (2021–2025) reflect a sustained focus on induction, spacetime ontology, and the limits of thermodynamic reversibility and information processing. Key themes include the critique of Bayesianism, the historical development of thermodynamics, and the philosophical implications of time travel models in general relativity. His two major books— The Material Theory of Induction (2021) and its sequel The Large-Scale Structure of Inductive Inference (2024)—form a comprehensive philosophical framework for understanding scientific reasoning beyond formal logic. Co-Founder and Executive Committee Member, philsci-archive.pitt.edu Editor for Philosophy of Physics (Space and Time, General Physics), Stanford Encyclopedia of Philosophy Contributing Editor, Archive for History of Exact Science (1996–present) Associate/Co-Editor, Studies in History and Philosophy of Modern Physics Contributing Editor, Collected Papers of Albert Einstein , Volumes 3 and 4 Norton has advised numerous graduate students and has been instrumental in shaping the academic landscape of philosophy of science through editorial leadership and archival initiatives. His teaching includes graduate seminars on confirmation theory and the popular undergraduate course Einstein for Everyone , for which he maintains a freely available online textbook.

• Geomaterials Physics
• Soft Matter
• Structured Materials
• +۱۳ مورد دیگر

Dr. Nicolas Francois is an Associate Professor in the Department of Materials Physics at Australian National University (ANU), specializing in experimental geomaterials physics, soft matter, and fluid hydrodynamics. He leads the X-ray Tomography and Applications Research Group, combining curiosity-driven and applied research in out-of-equilibrium systems. ARC Industry Fellow (2024-2030): Improving Australian iron ore comminution for green steel production ARC DECRA Fellow (2016-2018): Biofilms in two-dimensional turbulent flows His research spans fundamental questions in: Fragmentation of solid materials Autonomous devices powered by chaotic flows Hydrodynamic waves Stochastic thermodynamics Granular matter Polymer rheology and applied areas in: Comminution of geomaterials Mechanics of fractured rocks Wave-energy conversion Environmental fluid mechanics Publications reveal a trajectory focused on X-ray tomography applications, granular dynamics, and turbulence-driven systems. He utilizes advanced imaging techniques to study material failure mechanisms and fluid-structure interactions, contributing to fields ranging from green steel production to biofilm dynamics. Current student projects and grants emphasize sustainable resource processing and fundamental fluid physics.

Mariano Cabezasمشاهده پروفایل

پژوهشگر

• computer vision
• medical imaging
• magnetic resonance imaging (MRI)
• +۵ مورد دیگر

Mariano Cabezas is a researcher in medical imaging and computer vision, currently affiliated with Macquarie University and as an affiliate at the University of Sydney . His work focuses on automating brain MRI analysis for pathologies like multiple sclerosis, Alzheimer's disease, and tumors, with additional contributions to UAV image analysis. PhD in Computer Science (2013), University of Girona MSc in Automation, Computation, and Systems (2010), University of Girona BSc in Computer Science (2009), University of Girona Research Interests : Specializes in magnetic resonance imaging , lesion detection , deep learning , and image processing , with applications in multiple sclerosis , hearing loss , and UAV-derived ecological data . His recent work includes federated learning frameworks for cross-site MS lesion segmentation and pseudo-labeling techniques for longitudinal brain volume estimation. Publication Trends : Over the past five years, his research has emphasized federated learning (4 articles), lesion segmentation (9 articles), and UAV image analysis (3 articles), with a strong focus on clinical validation and cross-institutional collaboration. Labs & Collaborations : Contributed to the NIC-VICOROB group at the University of Girona and maintains affiliations with the Research Institute of the Hospital Vall d'Hebron (VHIR) in Barcelona and Macquarie University in Sydney. Actively develops open-source tools hosted on GitHub.

James H. Andersonمشاهده پروفایل

استاد

• Real-time systems
• Distributed computing
• Concurrent algorithms
• +۳ مورد دیگر

James H. Anderson is the Kenan Distinguished Professor and Department Chair in the Department of Computer Science at the University of North Carolina at Chapel Hill, where he has been a faculty member since 1993. A leading researcher in real-time systems and distributed computing, he previously served at the University of Maryland (1990-1993). His educational background includes: B.S. in Computer Science from Michigan State University (1982) M.S. in Computer Science from Purdue University (1983) Ph.D. in Computer Sciences from the University of Texas at Austin (1990) Anderson's research focuses on real-time systems, distributed and concurrent algorithms, multicore computing, and operating systems. His work addresses fundamental challenges in scheduling, resource management, and reliability in time-critical environments, with recent emphasis on GPU acceleration for AI workloads and heterogeneous platforms. He investigates novel approaches to budget enforcement, processing graph scheduling, and timing predictability in complex systems. Analysis of his recent publications reveals a strong trend toward applying real-time scheduling principles to GPU and heterogeneous computing architectures. Key research thrusts include enabling predictable AI acceleration, managing timing uncertainties in autonomous systems, and developing composable resource partitioning techniques for safety-critical applications. His distinguished honors include: U.S. Army Research Office Young Investigator Award (1995) Alfred P. Sloan Research Fellowship (1996) Seven Computer Science Student Association Teaching Awards (1995-2019) Fellowships from IEEE (2012), ACM (2013), AAAS (2020), and AAIA (2022) IEEE TCRTS Outstanding Technical Achievement and Leadership Award (2018) Anderson actively mentors through the TOPICS Club, a reading group for undergraduate students that he co-leads with Cynthia Sturton and Danielle Szafir. His professional leadership includes chairing IEEE TCRTS (2016-2017) and ACM SIGBED (2019-2021), plus service as program/general chair for major conferences including RTSS, PODC, ECRTS, and RTAS. He directs ongoing research initiatives in real-time systems through collaborative projects and the TOPICS Club, which provides undergraduates with hands-on research experience in cutting-edge computer science topics.

Kohei Nakajimaمشاهده پروفایل

دانشیار

• Soft Robotics
• Physical Reservoir Computing
• Physical Deep Learning
• +۵ مورد دیگر

Kohei Nakajima is an Associate Professor at the Department of Intelligent Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo. He holds concurrent positions at the Department of Creative Informatics and the Next Generation Artificial Intelligence Research Center (AI Center). As an Endowed Chair in Advanced Artificial Intelligence Education, he leads the Physical Intelligence Lab, which focuses on the intersection of soft robotics, nonlinear dynamics, and physical computing. His research interests center on Physical Reservoir Computing (PRC), a paradigm that exploits the natural dynamics of physical systems for computation, with applications in soft robotics, spintronics, and quantum machine learning. Nakajima's work demonstrates how physical systems can inherently process information without traditional digital computation, leveraging phenomena like chaos, bifurcations, and embodied intelligence. Nakajima's publications reveal a strong focus on understanding how physical systems can perform computational tasks. His recent work spans from biological applications (jellyfish cyborgs, ostrich-inspired robotics) to fundamental theoretical advances in reservoir computing. The research demonstrates how physical phenomena can be harnessed for information processing, with implications for energy-efficient computing and novel robotic control paradigms. As the organizer of the Reservoir Computing Seminar, Nakajima has built a vibrant research community exploring the nature of information processing across disciplines. His lab actively recruits graduate students and postdocs, indicating strong research momentum and institutional support for his work in physical intelligence.