جست‌وجوی استاد و پژوهشگر

Grey Ballardمشاهده پروفایل

دانشیار

• Numerical Linear Algebra
• High Performance Computing
• Computational Science
• +۶ مورد دیگر

Dr. Grey Ballard is an Associate Professor in the Department of Computer Science at Wake Forest University . He earned a B.S. in Math and Computer Science (2006), M.A. in Math (2008) from Wake Forest, and PhD in Computer Science (2013) from the University of California, Berkeley. He was a Truman Fellow at Sandia National Laboratories before joining Wake Forest. Research Focus: Ballard develops communication-optimal algorithms for high-performance computing , particularly in tensor decompositions , symmetric matrix computations , and nonnegative matrix factorization . His work combines numerical linear algebra with parallel algorithm design to reduce data movement costs in distributed systems. Publications demonstrate expertise in communication lower bounds , randomized tensor rounding , and visualization tools for parallel algorithms. He has contributed software packages such as TuckerMPI , GentenMPI , and PLANC for large-scale data compression and clustering. Scientific Awards: Wake Forest Excellence in Research Award NSF CAREER Award SIAM Linear Algebra Prize Three Conference Best Paper Awards (SPAA, IPDPS, ICDM) C.V. Ramamoorthy Distinguished Research Award (UC Berkeley) ACM Doctoral Dissertation Award – Honorable Mention Teaching: Courses include Introduction to Computer Science , Numerical Linear Algebra , and Parallel Algorithms . He has developed educational tools using the Thread-Safe Graphics Library to visualize parallel dynamic programming and collective communication.

Márk Jelasityمشاهده پروفایل

استاد

• Distributed Systems
• Peer-to-Peer Computing
• Gossip Protocols
• +۷ مورد دیگر

Márk Jelasity is a Full Professor in the Department of Algorithms and AI at the University of Szeged, Hungary, where he has been working since 2016. Previously, he served as a research advisor (equivalent to full professor) and senior research scientist at the Research Group on Artificial Intelligence (RGAI) of the Hungarian Academy of Sciences. His career includes numerous international research positions at institutions in Sweden, Norway, France, Italy, and the Netherlands. Professor Jelasity's research spans distributed systems, peer-to-peer computing, gossip protocols, and decentralized machine learning. His work bridges theoretical foundations with practical applications, particularly in the areas of self-organizing systems and privacy-preserving computation. His research has significant implications for smart grid technologies, secure distributed systems, and robust machine learning. His publication record shows a clear evolution from foundational work in gossip protocols and peer-to-peer systems toward cutting-edge research in decentralized machine learning, adversarial robustness, and privacy-preserving AI. Recent publications demonstrate his leadership in comparing gossip learning with federated learning approaches and exploring novel techniques for enhancing robustness in neural networks. Bolyai Plaquette (2015) 10 years best paper award at ACM/IFIP/USENIX Middleware Conference (2014) Best paper award at IEEE International Conference on Peer-to-Peer Computing (2014) Best paper award at IEEE International Conference on Self-Adaptive and Self-Organizing Systems (2013) Scientific Award of the Faculty of Science and Informatics, University of Szeged (2013) Fulbright Scholarship to visit Cornell University (2013) Multiple Bolyai Scholarships (2007-2014) Professor Jelasity has been actively involved in the academic community as an organizer of major conferences including DAIS'16 (TPC co-chair), SASO 2010 (General Co-Chair), and SASO 2007 (TPC co-chair). His leadership in the field is evidenced by his extensive publication record in top venues and his role in editing special issues and conference proceedings.

• Sustainable Synthesis of Materials
• Ceramics Processing
• Hydrogen-Based Reduction Pathways
• +۴ مورد دیگر

Dr. Barak Ratzker is a researcher at the Max Planck Institute for Sustainable Materials , affiliated with the Microstructure Physics and Alloy Design department. His work focuses on the sustainable synthesis of materials, particularly through hydrogen-based reduction pathways and advanced sintering techniques like spark plasma sintering (SPS) and hot isostatic pressing (HIP). His research spans transparent ceramics, MAX/MXene phases, and alloy design. Key research areas include: Hydrogen reduction of oxides for sustainable metallurgy Pressure-assisted sintering (SPS/HIP) of transparent ceramics Microstructure engineering in refractory materials Development of MXene-based composites for electronics Thermodynamic and kinetic analysis of solid-state reactions His recent publications highlight trends in: Environmentally conscious processing of ferromanganese oxides High-pressure synthesis of MAX phases and MXenes Optimization of optical and mechanical properties in ceramics Dynamic deformation behavior under extreme conditions Biological material interactions (e.g., crusticul-chitin systems)

Elizabeth Wolfمشاهده پروفایل

مدرس

• Mathematical Education
• Computational Methods
• Stochastic Models
• +۳ مورد دیگر

Elizabeth Wolf is a Lecturer II in the Department of Mathematics at the University of Michigan. She teaches undergraduate courses such as Calculus, Probability, and Honors Mathematics, and occasionally coordinates introductory math courses (Math 105, 115, 116). With a PhD and MA in Mathematics from the University of Wisconsin-Madison (2014, 2011) and a BS from Bucknell University (2009), her academic background spans pure and applied mathematics. Research Interests : Elizabeth develops computational methods for stochastic models in biosciences, particularly chemical reaction networks, combining probability theory, stochastic processes, and numerical analysis. She also contributes to mathematical education. Recent Publications : Her work focuses on sensitivity analysis in stochastic systems (2015), finite difference methods for chemical reaction networks (2012), and geometric-algebraic connections (2011). Teaching & Awards : 2025: Matthews Underclass Teaching Award for inspiring undergraduate students Contact & Office : Office 3060/3823 East Hall, 530 Church Street, Ann Arbor, MI 48109-1043.

Heiner Giefersمشاهده پروفایل

استاد

• Cloud Computing
• Hardware Acceleration
• Field-Programmable Gate Arrays
• +۷ مورد دیگر

Heiner Giefers is a Professor for Cloud Computing at the Department of Computer Science and Natural Sciences at Southwestphalia University of Applied Sciences since 2018. Prior to this position, he worked as a Research Staff Member at IBM Research - Zürich (2013-2018), focusing on hardware acceleration in cloud environments, implementation of big data algorithms on FPGAs, and development of hardware platforms for approximate and in-memory computing. Dr. Giefers received his doctorate (Dr. rer. nat.) from Universität Paderborn in 2012 with a dissertation titled "Design and Programming of Reconfigurable Mesh based Many-Cores." His academic journey at Universität Paderborn includes serving as an Academic Council Member (Akademischer Rat a.Z.) from 2008-2013 and as a Scientific Staff Member from 2006-2012, where he taught digital technology and computer architecture. Professor Giefers' research focuses on energy-efficient computing, particularly through hardware acceleration using FPGAs for cloud and AI workloads. His work spans cloud computing infrastructure, hardware-software co-design, approximate computing, in-memory computing, and energy-efficient implementations of machine learning algorithms. He has made significant contributions to the field of reconfigurable hardware for high-performance computing applications. His recent publications show a strong trend toward applying hardware acceleration techniques to artificial intelligence and machine learning workloads, with a particular focus on energy efficiency. His work bridges the gap between theoretical computer science and practical hardware implementation, often resulting in patented technologies that address real-world computing challenges in cloud environments. Best Paper Award for "Stochastic Matrix-Function Estimators: Scalable Big-Data Kernels with High Performance" (2016) Best Paper Award Nomination for "Energy-Efficient Stochastic Matrix Function Estimator for Graph Analytics on FPGA" (2016) Best Paper Award Nomination for "Analyzing the energy-efficiency of dense linear algebra kernels by power-profiling a hybrid CPU/FPGA system" (2014) Best Paper Award Nomination for "A Triple Hybrid Interconnect for Many-Cores: Reconfigurable Mesh, NoC and Barrier" (2010) Professor Giefers actively supervises numerous Bachelor's and Master's students, with over 50 completed theses covering topics from machine learning and cloud computing to IoT systems and hardware acceleration. He leads the "Energy-efficient AI" project (eki), which aims to increase the energy efficiency of AI systems through approximation techniques for FPGA implementation. Additionally, he collaborates with Prof. Dr. Christian Plessl on the "Digital teaching materials with Jupyter Notebooks" project, creating interactive learning materials that integrate teaching content, program code, and results into a single document. His work extends to practical applications through multiple patents related to FPGA implementations, neural networks, and memory systems, demonstrating his commitment to translating research into real-world solutions.

Brenda Ogleمشاهده پروفایل

استاد

• Cardiac Tissue Engineering
• 3D Bioprinting
• Stem Cell Differentiation
• +۶ مورد دیگر

Brenda Ogle is a Professor in the Department of Biomedical Engineering at the University of Minnesota's College of Science and Engineering. She leads the System Regeneration Lab, where her research focuses on cardiac tissue engineering, stem cell differentiation, and advanced 3D bioprinting technologies. Her work bridges multiple disciplines including stem cell biology, extracellular matrix science, and engineering principles to develop novel approaches for cardiovascular regeneration. Dr. Ogle's research interests primarily center on understanding the mechanisms that govern stem cell fate, particularly in the context of the cardiovascular system. Her lab is pioneering 3D bioprinting for cardiac tissue engineering, creating complex model systems that go beyond simple geometric shapes. Key areas of investigation include the role of extracellular matrix proteins in guiding stem cell differentiation, the development of novel tools for analyzing stem cell behavior, and the delivery of stem cells or associated progeny to the body. Her work has led to breakthroughs in creating patch-like structures with micron-scale features that support cardiac cell organization and can be adhered to failing hearts. Dr. Ogle's research has resulted in significant scientific contributions, including the development of unique bioink formulations coupled with multiphoton-based 3D printing to create chambered heart structures based on digital templates. These engineered tissues can sustain flow profiles and exhibit pressure-volume dynamics characteristic of the native heart, making them valuable for studying cardiac disease progression and testing drug efficacy. Her work has received recognition including an NIH R01 award for Epicardial Regulation of Myocardial Function and being named a BMES Fellow. NIH R01 Awarded, Epicardial Regulation of Myocardial Function BMES Fellow (2021) Dr. Ogle mentors a diverse team of researchers including postdoctoral associates, graduate students, and undergraduate researchers. Her lab has produced numerous PhD graduates who have gone on to successful careers in academia and industry. Current research projects in her lab include heart organoid formation using hiPSC-derived cardiomyocytes, investigation of hypertrophic cardiomyopathy mechanisms, cardiomyocyte maturation studies, and development of ECM-based bioinks for cardiac constructs. The lab is also working on creating integrated platforms for high-throughput cardiac organoid production and developing models to study the impact of radiation exposure on cardiac function.

Marc Teboulleمشاهده پروفایل

استاد

• Continuous Optimization
• Convex Optimization
• Complexity Analysis of Optimization Algorithms
• +۵ مورد دیگر

Marc Teboulle is a distinguished Professor holding The Eric and Sheila Samson Chair of Optimization in the School of Mathematical Sciences at Tel Aviv University. With a career spanning over three decades, he has established himself as a leading figure in optimization theory and applications. His work bridges theoretical foundations with practical implementations across multiple scientific domains. Professor Teboulle's research focuses on continuous optimization, with particular emphasis on convex optimization, complexity analysis of algorithms, Lagrangian and dual decomposition methods, variational inequalities, and nonconvex nonsmooth large-scale optimization. His work has significant applications in engineering science, machine learning, and finance, demonstrating the interdisciplinary impact of optimization techniques. He has developed novel frameworks for center-based clustering algorithms and contributed to the theoretical understanding of first-order methods beyond traditional Lipschitz gradient continuity assumptions. His publication record shows a clear evolution toward increasingly sophisticated optimization frameworks, with recent work focusing on nonconvex composite optimization, Lagrangian-based methods, and complexity analysis of gradient-based algorithms. The trend indicates growing interest in non-Euclidean geometries for optimization and applications to high-dimensional data problems, reflecting the evolving challenges in modern optimization. As an educator and mentor, Professor Teboulle has supervised numerous PhD and MSc students since 1990, including prominent researchers like Amir Beck, Ron Shefi, and Yoel Drori. His graduate courses include Convex Analysis and Optimization, Advanced Topics in Modern Optimization, Algorithms for Continuous Optimization, and Advanced Seminar in Continuous Optimization. He has been exceptionally active in the academic community, delivering invited lectures at major international conferences from 2003 through 2024 across Asia, Europe, and North America. His book 'Asymptotic Cones and Functions in Optimization and Variational Inequalities' (co-authored with A. Auslender) has become a standard reference in the field, while his edited volume 'Grouping Multidimensional Data: Recent Advances in Clustering' has influenced data science applications.

• Air Pollution
• Particulate Matter Source Apportionment
• Chemometrics
• +۵ مورد دیگر

Philip K. Hopke, Ph.D., serves as an Adjunct Professor in both the Department of Public Health Sciences and Department of Environmental Medicine at the University of Rochester School of Medicine and Dentistry. With a distinguished career spanning over five decades since earning his Ph.D. from Princeton University in 1969, Dr. Hopke has established himself as a global leader in environmental chemistry and air pollution research. Ph.D. in Chemistry, Princeton University (1969) M.A. in Chemistry, Princeton University (1967) B.S. in Chemistry, Trinity College (1965) Dr. Hopke's pioneering work focuses on particulate matter source apportionment , where he developed innovative methodologies like dispersion-normalized positive matrix factorization (DN-PMF). His research integrates chemometrics , atmospheric chemistry , and environmental epidemiology to address critical air quality challenges worldwide. Recent projects span China's megacities, Iran's water crisis, and New York State's vehicle emission regulations, with particular emphasis on health impacts of source-specific pollutants. Analysis of his 15 most recent publications reveals consistent focus on advanced source apportionment techniques , health outcomes linked to air pollution , and policy evaluation using sophisticated statistical modeling. His work increasingly incorporates machine learning approaches while maintaining rigorous chemical characterization of environmental samples. Scientific Recognition Fellow of both the Air and Waste Management Association (2021) and American Association for Aerosol Research Recipient of the Lifetime Achievement Award in Chemometrics (2014) Albert Nelson Marquis Lifetime Achievement Inductee (2018) Multiple international awards including the Fissan-Pui TSI International Aerosol Research Award (2018) Dr. Hopke maintains an exceptionally active research program with collaborators across six continents, evidenced by his 2025 publications from China, Iran, Kazakhstan, and the United States. His work bridges fundamental atmospheric science with practical public health applications, particularly in evaluating regulatory impacts on air quality and health outcomes.

Jan Salmenمشاهده پروفایل

پژوهشگر

• Machine Learning
• Computer Vision
• Neural Networks
• +۳ مورد دیگر

Dr. Jan Salmen is a researcher at Ruhr University Bochum's Faculty of Computer Science, affiliated with the Institute of Neuroinformatics (INI). His work focuses on real-time systems, computer vision, and machine learning. Doctoral thesis: Efficient video-based driver assistance systems Salmen's research spans autonomous driving, traffic sign recognition, stereo vision, and sports analytics. He has contributed to benchmarks in traffic sign detection and soccer analysis. Publications highlight his expertise in image processing, pattern recognition, and sensor fusion for autonomous systems. Key trends include optimization of machine learning algorithms for real-time applications. He collaborates with interdisciplinary teams at INI, which integrates experimental psychology, neurophysiology, and robotics into artificial cognitive systems research.

Joel Zindelمشاهده پروفایل

استاد

• Tissue Repair
• Macrophage Biology
• Mesothelial Cell Biology
• +۳ مورد دیگر

Prof. Dr. Joel Zindel is a faculty member at ETH Zurich's Institute of Molecular Health Sciences, leading a translational research group focused on tissue repair and scarring. He previously established his lab at the University of Bern (2023) before migrating to ETH Zürich in August 2025. His work bridges clinical surgery and immunology, targeting post-surgical complications like adhesions and fibrosis. Education: Medical Degree & MD, University of Bern PhD in Immunology, University of Bern SNSF-sponsored Research Fellowship, Canada Research Focus: The Zindel Lab investigates three key areas: (i) peritoneal macrophage biology in injury detection, (ii) mesothelial-to-mesenchymal transition in scarring, and (iii) foreign body responses in body cavities. Using intravital microscopy and clinical cohort studies, the lab explores immune-mesothelial interactions and druggable pathways. Publication Trends: Recent work emphasizes macrophage roles in regeneration, EGFR signaling in fibrosis, and technological innovations in surgical skill assessment. Key methodologies include spatial transcriptomics and multiphoton imaging. Scientific Awards: Max Cloëtta Research Fellowship SNSF Starting Grant Advising & Collaborations: Leads a team including PhD students (John Li Flores Alvarez, Agnes Huber, Brooke Slade) and postdoc Tea Kocijan. Collaborates with (bio)material engineers like Prof. Inge Herrmann and Prof. Mark Tibbitt.

P.M. Mohiteمشاهده پروفایل

استاد

• Composite Structures Analysis
• Micromechanics of Materials
• Finite Element Analysis
• +۶ مورد دیگر

Dr. P.M. Mohite is a Professor at the Department of Aerospace Engineering , Indian Institute of Technology Kanpur , with a career spanning over two decades in composite materials research. He holds a PhD in Aerospace Engineering from IIT Kanpur (2007) and has been a Visiting Assistant Professor (2008-2009) and later Assistant , Associate , and Professor (2019-present) at IIT Kanpur. Research Interests include: Advanced composite structures analysis Micromechanics and damage modeling Finite element adaptive methods Metal plasticity and polymer composites Aerostructural optimization Key Publications focus on damage mechanics, structural optimization, and micromechanical characterization, with recent works on evolutionary algorithm-based composite design and boundary layer effects in laminates. Scientific Awards received: Best Student Award (B.E. Mechanical, 1998) AIAA Students Paper Contest Finalist (2006) Teaching Contributions include core courses like Composite Materials and Finite Element Methods , alongside advanced postgraduate topics in aerospace structural analysis. Advising has impacted 30+ M.Tech/Ph.D. students, including international collaborations with Université de Biskra (Algeria) and Ecole Centrale de Nantes (France).

Andrea Fossatiمشاهده پروفایل

استادیار

• Bacterial Defense Systems
• Phage Biology
• Virology
• +۵ مورد دیگر

Andrea Fossati is a DDLS Fellow and Principal Investigator at Karolinska Institutet, leading the Fossati Lab at SciLifeLab. His research focuses on bacterial defense systems and phage biology, utilizing interaction proteomics and machine learning to develop novel strategies against antibiotic-resistant bacteria by disabling bacterial immunity during phage-bacterial warfare. Dr. Fossati's work centers on discovering bacterial defense mechanisms and phage counter-defense systems through advanced proteomic techniques like DIP-MS and quantitative interaction mapping. His lab integrates systems biology and machine learning to analyze host-pathogen interactions, with emphasis on jumbo phage infection mechanisms and lipid compartment formation. This research directly targets the enhancement of phage therapy efficacy by tilting the evolutionary balance toward phages. His publication trends reveal deep specialization in virology and microbiology, with dominant themes in bacterial immunity, phage-bacteria interactions, and proteomic methodology development. Over 80% of recent work involves jumbo phages and defense system characterization, while computational frameworks like PCprophet demonstrate cross-disciplinary integration of machine learning. Scientific awards: DDLS Fellow Dr. Fossati actively mentors the next generation of scientists, currently supervising two PhD students and collaborating with three postdoctoral researchers. His DDLS fellowship provides substantial research funding enabling cutting-edge instrumentation for proteomic analysis and high-throughput screening of phage-bacterial interactions, with recent grants focusing on quantitative interaction mapping and defense system discovery. The Fossati Lab operates within SciLifeLab's national infrastructure, maintaining close collaborations with leading proteomics and virology groups across Europe. The team specializes in next-generation interaction proteomics, developing novel methodologies for complex deconvolution while maintaining strong computational biology capabilities for data analysis and machine learning applications in host-pathogen systems.

• Machine Learning
• Artificial Intelligence
• Data Analysis
• +۷ مورد دیگر

Jonathan Voersaa Wenshøj is an academic researcher at the Department of Computer Science, University of Copenhagen. He contributes to the Machine Learning section's activities spanning theoretical foundations and applications in diverse domains like information retrieval, medical data analysis, remote sensing, sustainability, and biological modeling. The section participates in the SCIENCE AI Centre and collaborates with initiatives like TreeSense for global tree resource analysis. His research intersects machine learning with quantum computing, medical informatics, and sustainability. Recent publications highlight applications in environmental monitoring, healthcare diagnostics, and energy-efficient AI systems. The department provides advanced compute resources including a powerful cluster for intensive machine learning tasks. This researcher's work appears in diverse machine learning domains, with recent publications addressing quantum-inspired architectures, explainable AI in medical imaging, and sustainable computing practices. The section actively hosts events including seminars, conferences, and PhD defences related to machine learning advancements.

• Machine Learning
• Artificial Intelligence
• Quantum Computing
• +۵ مورد دیگر

Sophia Natasha Wilson is a Research Fellow in the Department of Computer Science (DIKU) at the University of Copenhagen, specializing in machine learning applications across interdisciplinary domains. She is affiliated with the SCIENCE AI Centre and holds a cross-departmental position at the Niels Bohr Institute . Her research bridges theoretical machine learning with practical implementations in healthcare, quantum computing, and environmental sustainability. University of Copenhagen Department of Computer Science (DIKU) Niels Bohr Institute SCIENCE AI Centre Her research focuses include: Quantum-enhanced machine learning algorithms Explainable AI for healthcare applications Environmental sustainability in computing Emotion-aware language models Quantum computing hardware optimization Public health risk modeling Her recent publications demonstrate cross-disciplinary work in quantum machine learning (hybrid optical processors, qubit stabilization), health informatics (hypothyroidism analysis, nursing values evaluation), and ethical AI (sustainable AI, fairness in recommender systems). Technical work also appears in non-Euclidean generative models and real-time adaptive systems . Current projects include quantum dot array simulation (QDarts platform) and federated learning for personalized medicine . She contributes to the TreeSense center for remote sensing of global tree resources and works on climate-aware AI frameworks.

• Quantum Information
• Quantum Computing
• Quantum Many-Body Physics
• +۹ مورد دیگر

Univ.-Prof. Dr. Norbert Schuch is a Professor of Physics and Mathematics at the University of Vienna, leading the Quantum Information and Quantum Many-Body Physics group. His research bridges Quantum Information Theory, Quantum Computing, and the study of complex quantum many-body systems, with a focus on Tensor Networks, Topological Order, and Symmetry Breaking. He has offices at both the Faculty of Physics (Boltzmanngasse 9) and Faculty of Mathematics (Oskar-Morgenstern-Platz 1). Research Interests : Quantum Information at the interface of Many-Body Physics, including Entanglement Theory, Topological Quantum Computation, Tensor Network algorithms, and Symmetry-Protected Topological (SPT) phases. His work develops numerical and analytical frameworks to study entanglement order parameters and prepare/experiment with topological states in quantum simulators. Teaching : Courses on Quantum Information, Quantum Computing, Theoretical Physics, and seminars on quantum many-body topics. Prior to Vienna, he was a tenured group leader at Max-Planck-Institute of Quantum Optics and a lecturer at Technical University Munich. Scientific Awards : ERC Consolidator Grant SEQUAM (2020–2025) FWF ESPRIT Programme ESP 306 FWF SFB BeyondC FWF Entanglement Order Parameters Research Trends : His recent publications explore Quantum Algorithms, Tensor Networks for Topological Phase Transitions, Entanglement Spectra, and Symmetry-Protected Phases. Key subfields include Non-Abelian Anyons, Chiral Spin Liquids, and Computational Complexity in Many-Body Systems. Group Members : Current team includes postdocs like Dr. Ilya Kull and Dr. András Molnár, with historical alumni spanning PhDs, Masters, and BSc students now at institutions like Xanadu, MIT, and Quantinuum.