جست‌وجوی استاد و پژوهشگر

Julian Priddleمشاهده پروفایل

مدرس ارشد

• Student engagement
• Independent learning
• Differential student attainment
• +۴ مورد دیگر

Dr. Julian Priddle serves as Academic Lead for Learning Partnerships within Anglia Ruskin University's Anglia Learning & Teaching unit, holding the title of University Teaching Fellow. His role focuses on curriculum enhancement through student-staff partnerships, active learning initiatives, and inclusive education strategies. He collaborates extensively with the Students' Union, faculty learning leads, University Library, and Student Services to improve student engagement and success across ARU. Priddle's academic credentials include a BSc (Hons) Zoology, PhD, and PGCert eLearning. His professional recognitions feature Senior Fellowship of the Higher Education Academy, Fellowship of the Staff and Educational Development Association, and Certified Membership of the Association for Learning Technology. His research spans two distinct domains: contemporary educational development and historical environmental science. Current work emphasizes student engagement, differential attainment (particularly for BAME students), assessment practices, and sustainability education. Earlier career phases involved Antarctic marine research with significant contributions to Southern Ocean biogeochemistry. This dual expertise informs his innovative approaches to curriculum design, including the 'learning journey' framework and the MSc Sustainability course developed with the Global Sustainability Institute. Recent publications (2015-2019) reveal a concentrated focus on sustainability pedagogy, student partnership models, and technology-enhanced learning, while pre-2003 works document foundational Antarctic ecosystem research. This transition reflects his strategic shift from laboratory science to educational leadership. Professional honors include: Senior Fellow of Higher Education Academy Fellow of Staff and Educational Development Association Certified Member of Association for Learning Technology Priddle supervises doctoral candidates and contributes to staff development through Course Design Intensive workshops and ARU's accredited recognition scheme as mentor and assessor. His project leadership includes major initiatives like OSIER (Open Sustainability in Education Resource), NUMBAT (Numeracy Bank), and Simshare, all focused on open educational resources and learning technologies. Current work addresses differential student success through partnerships with Student Services and the Students' Union. Within Anglia Learning & Teaching, he co-developed the Study Skills Plus programme and contributes to the Active Curriculum framework. His collaborative network spans the Global Sustainability Institute, Eden Project (for MSc Sustainability), and multiple UK universities where he maintains honorary teaching positions.

Björn Usadelمشاهده پروفایل

استاد

• Plant Genomics
• Bioinformatics
• Pangenomics
• +۷ مورد دیگر

Prof. Dr. Björn Usadel serves as Head of the Institute of Biological Data Science at Heinrich Heine University Düsseldorf (HHU) and Head of the Institute of Bioinformatics at Forschungszentrum Jülich (IBG-2). His dual appointments position him at the intersection of computational biology and plant sciences, where he leads research initiatives focused on leveraging data science approaches to understand plant biology and improve crop performance. Usadel is also actively involved with CEPLAS (Cluster of Excellence on Plant Sciences), having joined in 2019. Dr. Usadel's research program centers on the development and application of computational methods for plant omics data analysis. His group specializes in comparative analyses of plant genomes, with particular focus on identifying mechanisms underlying abiotic stress tolerance and valuable compound accumulation. Key technological platforms developed under his leadership include the Mercator platform for automated protein classification and annotation, which utilizes hand-curated hidden Markov models to categorize over 6,000 functional processes, and the Plant Knowledge Hub for sustainable data management and interactive web-based analysis. His team extensively employs nanopore sequencing technology for pangenomics studies and collaborates with the German ELIXIR plant nodes to maintain comprehensive databases tracking protein annotation across more than 1,000 sequenced plant genomes. Analysis of Dr. Usadel's recent publication record reveals a consistent focus on high-quality genome assembly and comparative genomics across diverse plant species. His work spans staple crops like potato, tomato, and faba bean, as well as specialty crops like tea. A notable trend is the increasing use of long-read sequencing technologies (particularly nanopore) to achieve chromosome-scale assemblies and haplotype-resolved genomes, enabling deeper insights into structural variation and its implications for crop breeding. His research bridges computational innovation with practical agricultural applications, particularly in understanding stress tolerance mechanisms and metabolic pathways relevant to food security. Dr. Usadel has successfully mentored numerous doctoral students, with several interview excerpts highlighting how the research questions posed by early-career scientists have led to unexpectedly significant findings. His group actively participates in both fundamental plant science and agricultural applications, with an emphasis on FAIR (Findable, Accessible, Interoperable, Reusable) data management principles. The team maintains strong collaborative networks across international research institutions, as evidenced by the multi-institutional authorship on his publications. Usadel's laboratory operates cutting-edge platforms including the Plant Metabolism and Metabolomics Facility and an Imaging Platform, which support integrated multi-omics analyses. His group also contributes to the CEPLAS Data initiative and maintains public datasets and tools that serve the broader plant science community. Current research directions include optimizing plant performance by mapping interfaces between development and metabolism, studying plant microbiota metabolic networks, advancing synthetic biology approaches, and developing theoretical frameworks in plant biology through data science.

Matthew C. Kellerمشاهده پروفایل

استاد

• Behavioral Genetics
• Complex Trait Genetics
• Genetic Architecture
• +۶ مورد دیگر

Matthew C. Keller is a Professor in the Psychology & Neuroscience department at the University of Colorado Boulder and serves as Director of the Institute for Behavioral Genetics. His research focuses on using measured genetic data, family data, and simulations to understand the causes of human complex trait variation, with particular emphasis on psychiatric disorders and behavioral phenotypes. Dr. Keller received his formal training in statistics and psychology and has developed expertise in statistical genetics, evolutionary psychology, and quantitative methods. His lab is currently recruiting students for fall 2026 and postdoctoral fellows interested in developing innovative methods to understand complex trait genetic architecture. His research interests span several interconnected areas: developing extended pedigree models to uncover genetic and environmental architecture of traits; creating models that use whole-genome data to uncover genetic architecture; studying the effects of distal inbreeding on complex traits; investigating gene-by-environment interactions; and utilizing whole-genome data to estimate assortative mating and vertical transmission. His work has evolved from studying depression heterogeneity to developing methods in extended twin family designs, and more recently to analyzing whole-genome data to understand trait genetic architecture. Analysis of his recent publications reveals a strong focus on structural equation modeling approaches using polygenic scores, estimation of full heritability spectra across the relatedness spectrum, and understanding the environmental influences of parents on offspring. His work increasingly integrates whole-genome data with family-based designs to disentangle genetic and environmental influences. Fulker Award for best paper published in Behavioral Genetics (2011, 2021) Fuller/Scott Early Career Award from the Behavioral Genetics Association (2012) Faculty Research Award in Psychology & Neuroscience department (2019) Multiple awards for mentees including the Dozier/Muenzinger Award and Thompson Award Dr. Keller has successfully mentored numerous postdocs and graduate students who have gone on to successful careers in behavioral genetics. His lab is funded by seven NIH grants totaling approximately $16.6M in direct costs, with Dr. Keller serving as Principal Investigator on four of these grants. He also leads the Workshop on Statistical Genetic Methods for Human Complex Traits, a major training venue for the next generation of behavioral geneticists. His research group maintains strong collaborations with leading institutions worldwide, particularly with Peter Visscher and Naomi Wray's lab in Brisbane, Australia. The lab combines computational approaches with theoretical developments to advance our understanding of how genes and environments interact to shape human differences.

Himadri Shekhar Dharمشاهده پروفایل

دانشیار

• Quantum information
• Entanglement
• Resource theories
• +۷ مورد دیگر

Himadri Shekhar Dhar is an Associate Professor in the Department of Physics at the Indian Institute of Technology Bombay, specializing in theoretical quantum physics with applications in quantum information processing and quantum technology development. His research focuses on four interconnected domains: Quantum entanglement and resource theories in many-body systems and quantum optics Theoretical modeling of light-matter interactions in cavity QED and hybrid quantum platforms Quantum dynamics analysis using tensor networks, quantum trajectories, and variational algorithms Optimal control frameworks for quantum device engineering enhanced by machine learning Recent publications (2020-2025) demonstrate sustained contributions to photon condensation phenomena, quantum coherence preservation, and entanglement characterization, frequently appearing in Physical Review Letters and Nature Photonics through international collaborations. His work bridges fundamental quantum theory with practical quantum technology applications.

Sai Venkatesh Pingaliمشاهده پروفایل

پژوهشگر

• Neutron Scattering
• Small-Angle Neutron Scattering (SANS)
• Biophysics
• +۶ مورد دیگر

Sai Venkatesh Pingali is a Neutron Scattering Scientist at Oak Ridge National Laboratory (ORNL), a Department of Energy laboratory, where he has worked since 2008. He is part of the Neutron Sciences Directorate, specifically within the Neutron Scattering Division's Large Scale Structures Section and Biological Labeling and Scattering Group. His work focuses on using scattering techniques to probe nano- to mesoscale structure and morphology of biological and chemical systems, with a primary emphasis on understanding structure-function correlations. Dr. Pingali earned his doctoral degree in Physics from the University of Illinois at Chicago. He completed postdoctoral training under Dr. Thiyagarajan Pappannan at Argonne National Laboratory and Dr. Volker S. Urban at Oak Ridge National Laboratory. Dr. Pingali's research centers on the structural and morphological study of materials relevant for biofuels and biomedicine. His primary focus is on lignocellulose materials, particularly understanding deconstruction and assembly processes at the nanoscale using scattering techniques. He has expertise in Small-Angle Neutron Scattering (SANS) and has been instrumental in facilitating the use of the Bio-SANS instrument for scientific research. His work spans membrane biophysics, biomass deconstruction, and the development of advanced materials for bioenergy applications, with recent publications showing increasing focus on interdisciplinary research bridging physics, materials science, and biology. Dr. Pingali has received several prestigious awards for his contributions to science: The Secretary's Honor Award, 2021: 'Molecular design and analysis to inform therapeutics related to COVID-19' Significant Event Award, 2016: 'Bio-SANS Detector Expansion Project, West Wing Detector System' Significant Event Award, 2012: 'Bio-SANS Detector Upgrade Project, Gas to Tube Detector System' As a scientist at ORNL, Dr. Pingali plays a dual role: facilitating the use of the Bio-SANS instrument for the broader scientific community and driving his own independent research program. His work is supported by access to world-class facilities including the Center for Structural Molecular Biology, High Flux Isotope Reactor, and Spallation Neutron Source. His research appears connected to DOE-funded initiatives in bioenergy and structural biology, with applications ranging from biofuel production to therapeutic development. Dr. Pingali is an active member of the Biological Labeling and Scattering Group within ORNL's Large Scale Structures Section. His work leverages the capabilities of the High Flux Isotope Reactor and Spallation Neutron Source user facilities, collaborating with researchers from various institutions to advance understanding of complex biological and chemical systems at the nanoscale.

Chris Wilsonمشاهده پروفایل

استاد

• Quantum Computing
• Quantum Simulation
• Superconducting Circuits
• +۳ مورد دیگر

Chris Wilson is a Professor at the University of Waterloo, located at QNC 3122. His research focuses on quantum computing, quantum simulation, superconducting circuits, and condensed matter physics. He actively contributes to advancing quantum technologies, including quantum radar, quantum communication, and topological quantum states. His work bridges theoretical and experimental quantum physics, with a particular emphasis on leveraging superconducting systems for quantum computation and simulation. Key research interests include lattice gauge theories, quantum many-body systems, nonclassical photonics, and quantum state manipulation. Recent work explores three-body interactions in superconducting quantum simulators, robustness of quantum correlations in topological models, and microwave-to-optical quantum transducers for hybrid quantum networks. His experimental contributions involve parametric cavities, NV-center photon detection, and variational quantum eigensolvers. Publications from 2023-2025 highlight advancements in quantum simulation, quantum sensing, and foundational studies of non-Hermitian dynamics in topological systems. While no specific awards are listed, his work is recognized in leading journals and conferences in quantum science.

Christine Muschikمشاهده پروفایل

دانشیار

• Quantum Computing
• Quantum Information Science
• Quantum Simulation
• +۳ مورد دیگر

Christine Muschik is an Associate Professor and University Research Chair at the University of Waterloo. Her research focuses on quantum computing, quantum simulation, and theoretical physics, particularly in the context of lattice gauge theories and quantum algorithms. She explores applications of quantum technologies to problems in particle physics, condensed matter physics, and high-energy phenomena. Her work bridges quantum information science with experimental implementations in superconducting circuits, trapped ions, and optical systems. She is affiliated with research groups in Photonics, Atomic/Molecular/Optical Physics, and Quantum Information & Computing. Her research interests emphasize developing scalable quantum algorithms for simulating complex physical systems, error mitigation techniques, and hybrid quantum-classical methods. She investigates topological phases, gauge symmetries, and real-time dynamics in quantum systems. Recent work includes quantum simulations of quantum chromodynamics, variational approaches for hadron dynamics, and superposed quantum gate protocols. Muschik’s publications span quantum error correction, measurement-based algorithms, and the integration of quantum technologies with classical computational frameworks. She collaborates across disciplines to advance both theoretical and experimental quantum computing. Her research contributes to foundational understanding of quantum systems while addressing practical challenges in quantum hardware and algorithm design. Her articles highlight advancements in simulating lattice gauge theories, optimizing variational quantum eigensolvers, and applying neural networks to quantum state tomography. These contributions underscore her role as a leader in applying quantum computing to fundamental physics problems.

• Artificial Intelligence for Materials Science
• Alloy Design
• Metallic Glasses
• +۵ مورد دیگر

Dr. Anurag Bajpai is a Research Fellow and Project Group Leader of the "Artificial Intelligence for Materials Science" group at the Max Planck Institute for Sustainable Materials in Düsseldorf, Germany. He holds a Ph.D. and M.Tech in Materials Science and Engineering from the Indian Institute of Technology, Kanpur (2017–2023), and a B.E. in Materials and Metallurgical Engineering from Punjab Engineering College, Chandigarh (2011–2015). His research focuses on integrating AI with experimental materials science to advance sustainable alloy design, particularly in metallic glasses and multicomponent alloys. Research Interests include: Machine learning-driven alloy/process design Thermal and mechanical behavior of metallic glasses Electronic waste recycling and green metallurgy Molecular dynamics simulations Process kinetics optimization His current work emphasizes sustainable materials development using scrap steel and reducing environmental impacts. Key Contributions include pioneering machine learning approaches for glass formation prediction (2022–2023), developing AI models for high-entropy alloy design (2024), and advancing cryomilling techniques for waste beneficiation. His group's work on attention-enhanced variational learning (2025) has pushed boundaries in ultra-hard metallic glass design. Awards include the prestigious Alexander von Humboldt Postdoctoral Fellowship (2024–present). His research has been published in 20+ articles, with notable contributions in AI-driven materials discovery and sustainable waste management.

Venkat R. Subramanianمشاهده پروفایل

استاد

• Advanced Materials Science and Engineering
• Complex Systems
• Electrochemical Engineering
• +۳ مورد دیگر

Professor Venkat R. Subramanian holds the Ernest Dashiell Cockrell II Professorship in Engineering at the University of Texas at Austin, affiliated with the Cockrell School of Engineering. He specializes in advanced materials science, complex systems, and electrochemical engineering, with a focus on battery technology and model-based design. His research group develops next-generation energy storage systems, particularly in lithium-ion and lithium-metal batteries, emphasizing safety, longevity, and efficiency. He has pioneered fast-impedance simulation methods and robust solvers for battery models, improving battery life by 2x in 18Ah cells through model-based charging profiles. Education: B.Tech. in Chemical and Electrochemical Engineering from Central Electrochemical Research Institute (CECRI), India (1997); Ph.D. in Chemical Engineering from the University of South Carolina (2001). Research Interests: Advanced battery management systems (BMS), capacity fade mechanisms, phase-field modeling, electrochemical impedance spectroscopy, and model-based design for next-gen energy storage. His work bridges fundamental science and engineering applications, addressing challenges in battery degradation, thermal management, and multi-scale modeling. Key Awards: Elected ECS Fellow; Past Chair of IEEE Division (Electrochemical Society); Past Technical Editor of Electrochemical Society; Past Chair of Area 1e: Electrochemical Engineering (AIChE). Lab Affiliation: M.A.P.L.E. Lab (Modeling and Analysis of Processes in Lithium Electrochemistry), focused on high-energy batteries for clean energy grids and transportation. The lab’s innovations include the fastest battery simulators and IP-protected solvers, contributing to safer and more efficient energy storage systems.

• Applied Signal Processing
• Inverse Problems
• Greedy Algorithms
• +۵ مورد دیگر

Florian Bossmann is an Associate Professor at the College of Mathematics, Harbin Institute of Technology (HIT), specializing in Applied Signal Processing with a focus on algorithms and applications. His research encompasses inverse problems, greedy algorithms, sparsity, and compressed sensing, with applications in seismic exploration, ptychography, and video processing. He holds a PhD in Mathematics from the University of Göttingen (2013) and a Diplom in Mathematics from the University of Duisburg-Essen (2009). Current projects include NSFC-funded seismic data interpolation and a start-up grant for applied signal processing research. He teaches courses like 'Signal and Image Processing' and 'Calculus for Civil Engineering'. Supervising Master/PhD students in signal processing, he emphasizes collaboration and innovation. His lab, the ASP Group, explores cutting-edge methods in multidimensional data reconstruction and algorithm design. Prominent publications focus on impedance inversion, neural network applications in geoscience, and object reconstruction techniques. He has led collaborative research initiatives with institutions like the Fraunhofer Institute and the Helmholtz Center for Environmental Health.

Alison Ramageمشاهده پروفایل

دانشیار

• Numerical Linear Algebra
• Scientific Computation
• Partial Differential Equations
• +۶ مورد دیگر

Alison Ramage is a Reader in Applied and Industrial Mathematics at the University of Strathclyde's Department of Mathematics and Statistics. Her research focuses on numerical linear algebra, preconditioning techniques for partial differential equations, and applications in liquid crystal modeling, geotechnical engineering, and data assimilation. She holds prestigious fellowships from the Leverhulme Trust and EPSRC, and has led numerous grants and collaborations with industry partners like Hewlett-Packard and Oasys Ltd. Education: PhD in Preconditioned Conjugate Gradient Methods from the University of Bristol (1991), BSc from the University of St Andrews (1987). Research Interests : Numerical Linear Algebra, Scientific Computing, Preconditioning, Liquid Crystals, Data Assimilation, Computational Fluid Dynamics. Her work bridges theoretical mathematics with practical industrial applications, emphasizing efficient algorithms and iterative solvers. Grants and Awards : Multiple EPSRC grants, including a £43K Leverhulme Fellowship (2017) for data assimilation research. Active in editorial roles for SIAM journals and professional societies like SIAM and EMS. Academic Service : Long-standing roles in SIAM, including Board of Trustees and editorial boards. Organized international conferences and workshops on numerical analysis and applied mathematics. Labs/Teams : Co-developed the IFISS software package for incompressible flow simulations, collaborating with global researchers in numerical methods and data science.

• Applied Mathematics
• Partial Differential Equations
• Numerical Mathematics
• +۴ مورد دیگر

Andrej Novak is an Associate Professor at the Theoretical Physics Department , Faculty of Science, University of Zagreb. He holds a PhD in Mathematical Models of Flow in Porous and Mixed Media (2017) and a Master's in Mathematical Model of Piano String (2011), both from University of Zagreb. Research Interests span: (1) Partial Differential Equations & Mathematical Modeling, (2) Numerical Solutions of PDEs, (3) Data Analysis Algorithms (bioinformatics/medical applications). Current focus includes shock filter equations, medical image processing, and cardiovascular pharmacotherapy modeling. Key Projects include leading the 2024 HRZZ-funded 'From PDEs to Deep Learning: Advancing Medical Image Processing' and contributing to projects like 'Analysis of partial differential equations and shape optimization' (HRZZ, 2023). He has participated in international collaborations including Austrian-funded 'Vanishing Capillarity on Smooth Manifolds' (2019). Teaching responsibilities include courses like Computational Neuroscience, Introduction to Computer Science, and Numerical Mathematics. He advises on C++ programming, AI fundamentals, and mathematical modeling across undergraduate and graduate levels. Publications highlight contributions in journals like Archive for Rational Mechanics and Analysis (2024), Applied Soft Computing (2025), and Canadian Journal of Cardiology (2025), focusing on interdisciplinary applications of PDEs, machine learning in healthcare, and image processing techniques.

Robert Bamlerمشاهده پروفایل

استاد

• Deep Generative Models
• Machine Learning
• Probabilistic Inference
• +۳ مورد دیگر

Robert Bamler is a Professor of Data Science and Machine Learning at the University of Tübingen, Germany , and a member of the Cluster of Excellence "Machine Learning: New Perspectives for Science" and the Tübingen AI Center . Current affiliation: University of Tübingen (since November 2020) Prior roles: Postdoctoral Scholar at UC Irvine (with Stephan Mandt), Machine Learning Researcher at Disney Research (Pittsburgh/Los Angeles) Education: PhD in Theoretical Statistical and Quantum Physics from University of Cologne (2016), advised by Achim Rosch Research Focus : Algorithm development for deep generative models (including large language models) Resource-efficient inference and model compression Probabilistic machine learning applications in natural sciences Theoretical foundations in statistics, information theory, and physics Scientific Contributions : German Telekom Foundation PhD scholarship awardee Co-founder of the BamlerLab reading group on probabilistic modeling and compression Key Collaborations : Stephan Mandt (UC Irvine) Bernhard Schölkopf (MPI-IS) Cluster of Excellence "Machine Learning" (Tübingen) Teaching : Lecturer for "Data Compression With and Without Deep Probabilistic Models" (2021-2025) Organizer of weekly reading groups on function-space inference and compression algorithms

• Microelectronics
• Nanotechnology
• VLSI Design
• +۷ مورد دیگر

Sumeet Gupta is the Elmore Associate Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School of Electrical and Computer Engineering. His research focuses on device-circuit co-design in emerging nanotechnologies, spintronics, and low-power VLSI design. He leads interdisciplinary efforts in ferroelectric devices, neuromorphic computing, and energy-efficient hardware for AI applications. Education: B.Tech from IIT Delhi (2006), M.S. and Ph.D. from Purdue University (2008, 2012). Research Interests: Ferroelectric and spintronic materials for next-gen computing Non-Boolean computing architectures Compute-in-memory accelerators Low-power and variation-tolerant VLSI design Domain wall dynamics in thin films Neuromorphic engineering Key Contributions: Over 50+ peer-reviewed publications on ferroelectric transistors, crossbar array optimizations, and fault-tolerant neural network hardware. His work bridges material science, device physics, and circuit design to enable energy-efficient computing systems.

• Quantum Information
• Quantum Computation
• Quantum Optics
• +۲ مورد دیگر

Professor Ignacio Cirac is Director at the Max Planck Institute for Quantum Optics and leads the Theory Division. His pioneering work in quantum information theory has fundamentally advanced quantum computing and quantum simulation frameworks. Research breakthroughs include: Developing theoretical foundations for quantum computers and quantum networks Creating new algorithms for quantum communication Designing quantum simulation methods for many-body systems Establishing theoretical tools for quantum entanglement characterization His group develops concepts for quantum gates and algorithms implemented by experimental physicists worldwide. Current investigations focus on quantum simulation of solid-state systems using ultracold atoms in optical lattices, advancing understanding of magnetism and superconductivity. Major Awards: Wolf Prize in Physics (2013) Niels Bohr Medal (2013) Prince of Asturias Prize (2006) Benjamin Franklin Medal (2009) Quantum Electronics Prize (2005)