Steven Conolly is a Full Professor of Bioengineering and Electrical Engineering and Computer Sciences at UC Berkeley, holding the Montford G. Cook Endowed Chair. He currently serves as Vice Chair of Instruction in BioE and previously chaired the UC Berkeley-UCSF Joint Graduate Group in Bioengineering (2006-2009). His research focuses on medical imaging hardware and biosensing, particularly Magnetic Particle Imaging (MPI), for applications like stem cell tracking, cancer imaging, and gastrointestinal health assessment. He has pioneered MPI scanner development in the USA and holds 30 patents, with over half licensed commercially. His work has been supported by CIRM, NIH, the Siebel Foundation, and others. Education: PhD in Electrical Engineering (Stanford University, 1989), M.S. and B.S. in Electrical Engineering (Stanford and Boston University). Research Interests: MPI technology advancement, nanoparticle design for imaging, and translational applications in diagnostics and therapy. His lab develops novel tracers and systems for imaging vascular structures, immune responses, and tumor biology using non-radioactive methods. Awards & Honors: Bakar Prize (2022), AIMBE Fellow (2015), NAE Gilbreth Lectureship (2006), and Stanford's Outstanding Inventor Award (2004). Grants & Patents: Secured major funding for MPI research and holds patents in imaging hardware and nanoparticle formulations. His innovations bridge fundamental physics with clinical applications. Lab & Collaborations: Leads a multidisciplinary team advancing MPI as a next-generation imaging modality. Collaborates with institutions on theranostic platforms combining imaging and therapy delivery.
جستوجوی استاد و پژوهشگر
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نمایش ۵۷۱-۵۸۵ از ۶۱۴ استاد راهنما
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Selim Romeroمشاهده پروفایل
پژوهشگر
Selim Romero is a Post-Doctoral Research Associate at Texas A&M University's Single Cell Data Science Core, specializing in computational science and quantum computing. His work bridges physics, chemistry, and biology through high-performance computing (HPC) and algorithm development in C, C++, Python, Fortran, and MATLAB. Education: Ph.D. and M.S. in Computational Science, M.S. in Physics, and B.S. in Engineering Physics Technical Expertise: Parallel computing (MPI/OpenMP), GPU programming (CUDA/OpenMP), Fortran90, and density functional theory His research focuses on quantum materials, self-interaction-corrected density functional theory, and quantum computing applications. Recent publications emphasize self-interaction correction methods, quantum algorithms, and single-cell RNA sequencing data analysis using quantum annealing. He actively participates in workshops like Qiskit Global Summer School and NERSC GPU programming bootcamps. The trend in his publications reveals a dual emphasis on quantum computing (e.g., quantum annealing for feature selection) and computational chemistry/physics (e.g., self-interaction correction in density functional theory). His work spans algorithm development, HPC optimization, and interdisciplinary applications in gene expression analysis and quantum materials. As part of Texas A&M AgriLife and the College of Agriculture & Life Sciences, Selim contributes to cutting-edge computational research while engaging with institutions like NERSC and Intel Developer Tools Optimization programs.
Filip Podjaskiمشاهده پروفایل
پژوهشگر ارشد
Dr. Filip Podjaski serves as a UKRI Research Fellow (Marie Skłodowska-Curie Fellow) in the Department of Chemistry at Imperial College London's Faculty of Natural Sciences, where he leads research on light-driven and ionic processes in organic semiconductors for next-generation photobattery technologies and solar hydrogen production. Based at the Molecular Sciences Research Hub on White City Campus, he employs time-resolved optical spectroscopy to investigate charge storage mechanisms in novel materials, with a focus on environmental adaptation for renewable energy infrastructure. Podjaski's academic foundation spans multiple institutions: he completed undergraduate physics studies at the University of Göttingen (Germany) and University of Lyon (France), followed by a joint PhD program between École Polytechnique Fédérale de Lausanne (EPFL) and the Max-Planck Institute for Solid State Research (MPI-FKF). His interdisciplinary background bridges physics, chemistry, and materials science to address energy conversion challenges. His research centers on photocharging materials that intrinsically combine solar cell and battery functions, enabling electricity supply on demand. Key focus areas include: Photocatalytic hydrogen production using carbon nitride-based materials Development of photo-rechargeable batteries for dark-energy applications Strain-engineered electrocatalysts for enhanced hydrogen evolution Light-driven microswimmers for biomedical drug delivery These investigations reveal fundamental photo-ionic mechanisms while targeting practical implementations in energy storage and medical technology. Analysis of his 15 most recent publications (2022-2025) shows consistent focus on carbon nitride materials (38% of works), electrocatalyst degradation (27%), and photocharging mechanisms (20%), with increasing emphasis on operando characterization techniques and environmental adaptation. His work bridges fundamental photophysics with industrial applications in renewable energy conversion and storage. Scientific recognition includes: Marie Skłodowska-Curie Fellowship (Horizon Europe) Materials Horizons Emerging Investigator designation Organization of the inaugural International Symposium on Photocharging Materials (2024) Podjaski leads collaborative research projects including the med-tech initiative Endosurge through the Max-Planck Innovation incubator Max!mize. His current UKRI fellowship funds fundamental studies on ionic-organic semiconductor interfaces, while external collaborations extend his strain-engineering research for hydrogen evolution catalysis. He has secured patent protection for photo-memristive sensing and solar battery concepts. As principal investigator of the Photocharging Research Group within the Durrant Group ecosystem, he directs experimental efforts in time-resolved spectroscopy and materials characterization. His team maintains active partnerships with the Max-Planck Institute for Solid State Research and leverages Imperial's White City Campus facilities for nanomaterial synthesis and device testing, with particular emphasis on biological and ionic environment compatibility.
Tore Brox-Larsen is an Associate Professor in the Department of Informatics at UiT The Arctic University of Norway. His work centers on distributed computing, high-performance systems, and large-scale visualization technologies. He is actively engaged in research involving sensor networks, Arctic observatories, and remote visualization of scientific data. His research interests span distributed shared memory systems, MPI performance, tiled display walls, and networked visualization. He has made significant contributions to improving communication efficiency in cluster computing and enabling scalable interactive visualization environments. His work bridges computer systems engineering with applications in genomics and environmental monitoring. The most recent publications reflect a strong trend toward real-world deployment of large-scale systems, particularly in Arctic observation and healthcare informatics. His work emphasizes practical system design, latency optimization, and cross-platform interoperability in distributed environments. Tore Brox-Larsen has collaborated extensively with researchers such as Otto Anshus, John Markus Bjørndalen, and Brian Vinter. While no formal advising or grant history is listed, his sustained publication record indicates active research leadership and team-based scientific inquiry. He has contributed to major projects including the development of a large-scale Arctic observatory sensor system and interactive tiled display walls. His work supports both academic research and societal applications in health and environmental science.
Şefika Kuzgunمشاهده پروفایل
پژوهشگر
Şefika Kuzgun is a postdoctoral researcher at the Max Planck Institute for Mathematics in the Sciences (MPI MiS) in Leipzig, Germany, since September 2024, working in Prof. Dr. Felix Otto's research group. Previously, she held a postdoctoral position at the University of Rochester's Department of Mathematics. She earned her Ph.D. in Mathematics from the University of Kansas under Prof. David Nualart and completed her undergraduate studies at Boğaziçi University in Istanbul, Turkey. Research Interests: Her work focuses on stochastic analysis and stochastic partial differential equations (SPDEs) , particularly exploring properties of SPDEs using tools from stochastic calculus. She investigates applications of these equations to physical systems and employs techniques like the Malliavin-Stein method for density convergence and probabilistic approximations. Recent Activities: In 2025, she is organizing the Probability and Applied Analysis Summer School in Türkiye and has been an invited speaker at conferences such as the SPDEvent in Bielefeld and the Equadiff Conference in Karlstad. She also participates in initiatives like the Women in Mathematics group at the University of Rochester and cofounded a math support platform to assist Turkish students affected by the 2023 earthquakes. Service & Outreach: She co-founded the Directed Reading Program Türkiye and has mentored multiple students. She previously led the University of Kansas Student Chapter of the Association for Women in Mathematics, organizing professional development seminars and mentoring programs. She is dedicated to promoting equity in academia and community engagement through math outreach.
Chao Liمشاهده پروفایل
پژوهشگر
Chao Li is a Senior Research Scientist and Group Leader in the Department of Climate Variability at the Max Planck Institute for Meteorology (MPI-M), Hamburg, Germany. He leads the Climate Impacts and Adaptation research group, focusing on climate dynamics, variability, and future climate scenarios. He also holds a Distinguished Guest Professor position at the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences, since 2024. PhD in Natural Sciences, University of Hamburg & Max Planck Institute for Meteorology (2012) MSc in Natural Sciences with distinction, South China Sea Institute of Oceanology, Chinese Academy of Sciences (2006) BSc in Natural Sciences with distinction, Sun Yat-Sen University, China (2004) Chao Li's research centers on climate dynamics and variability, particularly the transient and equilibrium responses of the climate system to anthropogenic forcing. His work investigates temperature and precipitation extremes under global warming, internal climate variability, and climate mitigation strategies. He is actively involved in developing plausible climate futures to enhance societal resilience, with research funded by the DFG under the Excellence Strategy EXC 2037 'CLICCS - Climate, Climatic Change, and Society'. His recent publications demonstrate a strong focus on large-scale atmospheric circulation changes (e.g., Walker and Hadley cells), climate extremes, ocean-atmosphere interactions, and the stability of key climate systems like the AMOC. His work utilizes advanced climate models and large-ensemble simulations to understand uncertainty and variability in future climate projections. Notable scientific recognition includes having a 2020 Science Advances paper highlighted as a cover article and by the journal's chief editor. Chao Li mentors several postdoctoral researchers and serves on multiple PhD advisory and examination committees, including joint programs with the Chinese Academy of Sciences and the Ocean University of China. His research is integrated within major modeling initiatives such as WarmWorld and the Cluster of Excellence CLICCS.
GANESH GOPALAKRISHNAN is a Professor of Computer Science at the University of Utah's School of Computing. His work focuses on formal verification of parallel/distributed systems, GPU programming, and numerical error analysis. He has contributed to tools like ISP for MPI verification, ARCHER for OpenMP race detection, and FLiT for floating-point consistency testing. His research spans theoretical foundations (e.g., concurrency models) and practical applications (e.g., GPU error analysis). Recent work includes advancing formal methods for mixed-precision computing and resilience in exascale systems. Notable projects include rigorous error estimation for floating-point operations and compiler-assisted verification techniques. Research Interests: Formal Verification of Parallel Systems | GPU & HPC Correctness | Floating-Point Numerical Analysis | Concurrency Bugs | Tools for Distributed Systems. Current work emphasizes hybrid approaches combining formal methods with dynamic analysis to address emerging challenges in heterogeneous computing architectures. Articles Trends: Recent publications (2020–2025) emphasize GPU verification (data races, error analysis), mixed-precision computing (matrix operations, tensor cores), and resilience in HPC systems. Tools like FPDetect and BinFPE highlight practical contributions to error detection in production runs. Workshops (DOE/NSF) indicate leadership in defining correctness strategies for exascale computing. Labs/Teams: Leads research groups focused on formal methods for parallel computing and numerical system reliability. Collaborations include Argonne National Lab, NVIDIA, and LLNL on verification tools and HPC correctness frameworks.
Francisco J. Andújar Muñoz is an Associate Professor at the University of Valladolid in the Department of Computer Science since January 2024. His career spans multiple institutions including Universidad de Castilla-La Mancha (2008-2015) and Universitat Politècnica de València (2017-2018), with academic roles ranging from Research Assistant to Juan de la Cierva Formación Researcher. PhD in Advanced Computer Science Technologies (2011-2015) MsC in Advanced Computer Science Technologies (2010-2011) Computer Science Engineering (2008-2010) Computer Science Technical Engineering (2004-2008) His research focuses on high-performance interconnection networks , with significant contributions to quality-of-service mechanisms, energy-efficient network topologies, and heterogeneous programming optimization. He maintains the open-source VEF Traces framework for network workload modeling. Recent publications (2023-2025) demonstrate expertise in FPGA high-level synthesis portability, SYCL-based GPU optimization, and machine learning applications for Twitch streaming analysis. His work combines theoretical network design with practical implementations in the Journal of Supercomputing and IEEE Transactions on Computers .
- Neuroscience
- Behavioral Biology
- Zebrafish Research
- +۶ مورد دیگر
Herwig Baier is Director of the Department for Genes - Circuits - Behavior at the Max Planck Institute for Biological Intelligence (formerly Max Planck Institute of Neurobiology), where he leads a prominent research group investigating the neural basis of behavior using zebrafish as a model organism. His laboratory combines genetic, imaging, and behavioral approaches to understand how neural circuits generate behavior. Dr. Baier received his biology education at the University of Konstanz, completed his doctorate in 1995 under Friedrich Bonhoeffer at the MPI for Developmental Biology in Tübingen, and conducted postdoctoral research at the University of California, San Diego (1995-1998). He served as Professor at the University of California, San Francisco (1998-2012) before becoming Director at the Max Planck Institute in 2011. His research focuses on understanding how genes, neural circuits, and behavior are interconnected, with particular emphasis on visual processing, decision making, and social behavior in zebrafish. His laboratory has pioneered techniques for whole-brain imaging, optogenetic manipulation, and connectomic analysis in larval zebrafish, making significant contributions to mapping the neural circuits underlying prey capture, escape responses, and social recognition. The lab's work spans molecular, cellular, circuit, and behavioral levels of analysis, creating a comprehensive understanding of brain function. Analysis of Dr. Baier's recent publications (2021-2025) reveals a strong focus on neural circuit mapping at increasingly higher resolution, from cellular to synaptic levels, while connecting these structural findings to behavioral outputs. His work demonstrates how specific neural populations process visual information, make decisions, and generate appropriate motor responses, with significant contributions to understanding topographic organization in the brain and how genetic programs shape neural circuitry. Dr. Baier leads a large, collaborative research team with numerous postdocs, technicians, and students working on various aspects of zebrafish neuroscience. His laboratory has developed critical transgenic tools and methodological approaches that have been widely adopted by the neuroscience community, including optogenetic tools, calcium indicators, and brain atlases that facilitate circuit-level analysis in zebrafish.
Maria-Paola Forte is a Postdoctoral Researcher at the Max Planck Institute for Intelligent Systems (MPI-IS) in Stuttgart, Germany, holding dual appointments in the Haptic Intelligence Department and Perceiving Systems Department under Dr. Katherine J. Kuchenbecker and Dr. Michael J. Black respectively. Her academic credentials include: BSc in Biomedical Engineering from University of Genova (2015) MSc in Bioengineering from Politecnico di Milano (2018) with exchange at University of Queensland PhD from MPI-IS as IMPRS-IS Candidate (2025) Dr. Forte's research pioneers assistive technologies for sensory and motor impairments through integrated vision-sensor systems. Her work spans sign language capture, robotic surgery enhancement, and bioimpedance sensing applications, targeting practical healthcare solutions for disability support. Current efforts focus on commercialization pathways via Germany's EXIST-Women program. She operates within MPI-IS's collaborative ecosystem, leveraging institutional resources for translational research while maintaining active partnerships with department leads. Her trajectory demonstrates strong interdisciplinary movement between biomedical engineering, robotics, and human-centered computing.
Stephan Schulz is a Professor at the Baden-Wuerttemberg Cooperative State University Stuttgart (DHBW Stuttgart) in the Faculty of Engineering, where he serves as the Program Director for Computer Science. His office is located in room B 3.14 at Lerchenstraße 1, 70174 Stuttgart, Germany. Professor Schulz is a leading researcher in automated reasoning and theorem proving, with extensive experience teaching computer science courses including Formal Languages and Automata, Logic and Foundations of Computer Science, Compiler Construction, and Algorithms. Professor Schulz's primary research interest lies in automated reasoning, specifically developing efficient algorithms and intelligent search control for automatic theorem proving. His long-term goal is integrating high-performance inference mechanisms with machine learning techniques to create robust reasoning systems across diverse domains. He is the principal developer of the E Theorem Prover, a high-performance system for full first-order logic with equality that has performed exceptionally well in international competitions like CASC. His recent publications demonstrate a clear trend toward extending theorem proving capabilities to higher-order logic while maintaining performance. Schulz has made significant contributions to practical aspects of automated reasoning, including watchlist implementations, contradiction detection in large theories, and the integration of machine learning techniques to improve search heuristics in theorem provers. Professor Schulz has received multiple prizes for his work on the E Theorem Prover, though specific award names are not detailed in the available information. His contributions to the field have been recognized through leadership roles in major conferences. Professor Schulz actively mentors students through project work (Studienarbeiten) at DHBW Stuttgart and has taught numerous courses throughout his career at institutions including the University of Miami, Mona Institute of Applied Sciences, Universität Hildesheim, and INRIA/MPI. While specific grant information isn't provided, his sustained development of the E Theorem Prover suggests ongoing research support. Professor Schulz is significantly involved with several workshop and conference series including the International Workshop on the Implementation of Logics (IWIL), Practical Aspects of Automated Reasoning (PAAR), and Artificial Intelligence and Theorem Proving (AITP). His current roles include PC co-chair for the 15th IWIL (2024) and 9th AITP (2024), and PC member for multiple other conferences including the 25th LPAR (2024) and 12th IJCAR (2024).
Bradford L. Chamberlainمشاهده پروفایل
عضو هیئت علمی
- Parallel programming languages
- Compilers for high-performance computing
- Scalable parallel algorithms
- +۳ مورد دیگر
Bradford L. Chamberlain is a Distinguished Technologist at Hewlett Packard Enterprise and an Affiliate Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. He is the founding technical lead of the open-source Chapel programming language, which targets productive parallel computing from laptops to supercomputers. Education: Ph.D. in Computer Science & Engineering, University of Washington, 2001 M.S. in Computer Science & Engineering, University of Washington, 1995 B.S. (with honors) in Computer Science, Stanford University, 1992 Research interests center on user productivity for high-performance computing, spanning programming languages, compilers, algorithms, and applications. His work emphasizes scalable parallel computation and the design of language abstractions—such as regions, distributions, and iterators—that enable both high productivity and high performance. Over the past two decades he has led the Chapel project from a small research effort to a thriving open-source language with nearly twenty full-time developers at HPE and numerous external collaborators. His recent publications explore locality-aware compiler optimizations, fabric-attached memory, and the expression of sophisticated parallel patterns like stencil tiling and data re-allocation. Teaching & service: He created and regularly teaches UW’s graduate parallel computation course (CSEP 524), has advised graduate students, and serves on departmental committees, fostering tight ties between the Allen School and HPE/Cray.
Isaías A. Comprés Ureña serves as a Researcher at the Chair of Computer Architecture and Parallel Systems within the Department of Informatics at the Technical University of Munich (TUM), focusing on advancing exascale computing infrastructure and distributed memory systems. His research centers on High Performance Computing with specialized expertise in parallel programming standards including MPI and PMIx, distributed resource management, batch scheduling optimization, and automatic performance tuning for supercomputing environments. These areas directly support the development of next-generation HPC tools and frameworks for scientific computing workloads. Dr. Comprés actively contributes to international HPC initiatives through membership in the MPI Forum, Virtual Institute - High Productivity Supercomputing, and Eurolab4HPC collaborations, driving standardization efforts for process management and distributed memory programming models.
Panagiotis E. Hadjidoukas is an Adjunct Assistant Professor at the Department of Computer Science, University of Ioannina, Greece. He currently holds a Visiting Scientist position at IBM Research Zurich. His work bridges parallel computing with applications in medical physics, computational biology, and high-performance numerical optimization. His research focuses on parallel and distributed computing, runtime support for parallel programming models, and thread libraries. He has contributed to hybrid programming frameworks and task-parallelism deployment in multicore environments. The articles in his portfolio highlight a strong trend in Monte Carlo simulations applied to radiation biology, parallelization techniques for hierarchical data clustering, and performance optimization in multicore systems. His work often integrates OpenMP and hybrid MPI/OpenMP models for distributed computing. He has developed notable software projects like PSthreads (a runtime library for lightweight threads) and UthLib (a portable non-preemptive user-level threads package).
David Lowenthal is a Professor at the University of Arizona specializing in parallel and distributed computing, operating systems, and run-time systems, with his office located in GS 705. His research directly addresses critical challenges in modern high-performance computing infrastructure. His educational background includes: Ph.D. in Computer Science from the University of Arizona (1996) Professor Lowenthal's research centers on optimizing large-scale computing systems through innovative scheduling algorithms, power management techniques, and network optimization strategies. Key contributions include mitigating inter-job interference in cluster environments, developing quality-of-service mechanisms for MPI applications, and pioneering coscheduling approaches like the Jigsaw scheduler. His work bridges theoretical computer science with practical system implementation to enhance resource utilization under power constraints. Analysis of his 15 most recent publications (2016-2023) reveals three dominant research thrusts: intelligent job scheduling (particularly coscheduling and backfilling), power-aware computing for energy-constrained HPC systems, and network performance optimization in dragonfly/fat-tree topologies. These publications consistently demonstrate collaborative NSF-funded research with emphasis on real-world system implementation and performance evaluation. No scientific awards were documented in the provided text. While no student advisees were listed, his research program has secured substantial NSF funding through collaborative grants including "Collaborative Research: SHF: Medium: Co-Optimizing Computation and Data Transformations for Sparse Tensors" (2022) and "Collaborative Research: OAC Core: Improving Utilization of High-Performance Computing Systems via Intelligent Co-scheduling" (2021), indicating active grant management and interdisciplinary collaboration. No specific laboratories or research teams were mentioned in the source material.