جست‌وجوی استاد و پژوهشگر

Peter Schneiderمشاهده پروفایل

استاد

• Arithmetic geometry
• Representation theory
• Cohomology theories
• +۷ مورد دیگر

Prof. Dr. Peter Schneider is a faculty member at the University of Münster, affiliated with the Faculty of Mathematics and Computer Science and the Department of Mathematics and Computer Science . He is a principal investigator in the CRC 1442 Geometry: Deformations and Rigidity and contributes to the Mathematics Münster cluster. University: University of Münster School: Faculty of Mathematics and Computer Science Department: Department of Mathematics and Computer Science Academic Rank: Professor His research focuses on arithmetic geometry and representation theory , particularly the p-adic Langlands program , cohomology theories in mixed characteristics, and noncommutative algebraic structures. He investigates derived categories of p-adic representations, moduli spaces of Galois representations, and geometric approaches to automorphic forms. He has supervised numerous students, including Torsten Schoeneberg (2009), Marten Bornmann (2009), and Marius Kley (2016, 2019). His collaborative works with Rachel Ollivier, Otmar Venjakob, and Marie-France Vigneras explore modular Hecke algebras, Iwasawa theory, and (phi, Gamma)-modules. Emails: pschnei@wwu.de pschnei@uni-muenster.de Workshops Organized: Noncommutative Algebras (2006) Instructional workshop on noncommutative main conjectures (2011) International workshop on K-types for p-adic groups (2003)

• Statistical Network Analysis
• Machine Learning
• Data Privacy
• +۷ مورد دیگر

Tianxi Li is an Assistant Professor in the Department of Statistics at the University of Minnesota, Twin Cities, within the College of Science and Engineering. Their research integrates statistical methodology with applications in network science, data privacy, and biomedical data analysis. Their research interests lie at the intersection of statistics and network science, focusing on statistical modeling of complex networks , data privacy , network security , and biomedical applications such as neuroimaging and genomics. They develop adaptive and scalable methods for network estimation, community detection, and differential correlation analysis. The recent publications demonstrate a consistent focus on advancing statistical tools for network-structured data, with increasing applications in neuroscience and cancer genomics. The work spans theoretical development (e.g., network growth models) and practical applications (e.g., glioblastoma gene modules), reflecting a balance between methodology and real-world impact. Tianxi Li leads an active research program funded by the National Science Foundation, indicating recognition and support for their innovative work. Principal Investigator, Statistical tools for network security protection: from data privacy to threat detection , NSF (2024–2025) They advise graduate students in statistics and data science, though specific advisees are not listed. Their collaborative network includes researchers in biostatistics, computer science, and machine learning, as evidenced by co-authorships and interdisciplinary projects. Li's work contributes to the UN Sustainable Development Goals, particularly through advancements in data-driven solutions for secure and ethical data analysis.

Xiaoqing Panمشاهده پروفایل

استاد

• Materials Science
• Electron Microscopy
• Atomic-scale Characterization
• +۶ مورد دیگر

Xiaoqing Pan is a Professor and Henry Samueli Endowed Chair in Engineering at the University of California, Irvine, with dual appointments in the Department of Materials Science and Engineering and the Department of Physics and Astronomy. He serves as Director of the Irvine Materials Research Institute (IMRI) and the Center for Complex and Active Materials (NSF MRSEC). A renowned electron microscopy expert, Pan has developed advanced transmission electron microscopy (TEM) techniques for atomic-scale material characterization. Ph.D., Universität des Saarlandes, Germany (1991) His research focuses on atomic-scale structure-property relationships in oxide heterostructures, ferroelectrics, nanocatalysts, and 2D functional materials. Pan leads development of novel 4D-STEM and momentum-resolved vibrational electron microscopy methods to study single-atom catalysts and complex oxides. With over 400 high-impact publications in Nature , Science , and Nature Materials , his work has been recognized by major fellowships and awards from the American Ceramic Society, American Physical Society, and National Science Foundation. Pan's recent work includes: Atomic-scale analysis of grain boundary phonon anisotropy Advances in FeSe/SrTiO 3 interface electron-phonon coupling Plastic waste upcycling through carbon intermediate interception Control of metal-support interactions in photocatalysts Strain engineering in high-entropy oxide films His laboratory at UCI represents the forefront of materials characterization technology development.

• Big Data
• Business Analytics
• Healthcare Analytics
• +۴ مورد دیگر

Haipeng Shen is a Professor of Innovation and Information Management at HKU Business School, The University of Hong Kong, serving as Associate Dean (EMBA and IMBA) and holding the Patrick S C Poon Professorship in Analytics and Innovation. He chairs the Business Analytics and Innovation program and joined HKU in 2015 after previously holding a professorship at the University of North Carolina at Chapel Hill. His academic credentials include: PhD in Statistics, The Wharton School of Business, University of Pennsylvania, 2003 MA in Statistics, The Wharton School of Business, University of Pennsylvania, 2000 BS in Mathematics, School of Mathematical Sciences, Peking University, 1998 Professor Shen's research focuses on data-driven decision making under uncertainty, with expertise spanning big data analytics, business analytics, healthcare analytics, and service engineering. He develops advanced statistical and machine learning methodologies to solve complex operational problems in call centers, optimize stroke care protocols, and enhance financial risk modeling, emphasizing real-time applications in high-stakes environments. Analysis of his recent publications reveals a consistent interdisciplinary approach bridging operations research, statistics, and domain-specific knowledge. His work demonstrates strong methodological innovation in time-series forecasting for service systems, risk assessment frameworks for medical complications, and covariance structure analysis for financial markets, with direct translational impact on business operations and clinical outcomes. His scientific contributions have been recognized with prestigious awards including: Most Influential Publication Award from China Stroke Association (2018) Fellow of the American Statistical Association (2015) Best Advisor of the Year Award from Academy of Asian Business (2018) Elected Member of International Statistical Institute (2015) Cluster Chair for Big Data Analytics at INFORMS International (2015) As an academic leader, Professor Shen has secured significant research funding from organizations including The Xerox Foundation and National Institute on Drug Abuse. He serves as Associate Editor for Management Science, Journal of the American Statistical Association, and Technometrics, while mentoring graduate students in statistical methodology and applied analytics. His current initiatives position HKU Business School at the forefront of healthcare innovation through big data analytics, driving collaborations with medical institutions to transform stroke care and hospital operations in Asia.

Matt Nassarمشاهده پروفایل

دانشیار

• Computational Neuroscience
• Cognitive Psychology
• Decision Making
• +۵ مورد دیگر

Matt Nassar is an Associate Professor of Neuroscience and Assistant Professor of Cognitive and Psychological Sciences at Brown University. He leads the Learning, Memory and Decision Lab, which is part of the Department of Neuroscience and the Robert J. & Nancy D. Carney Institute for Brain Science. His research focuses on understanding how the brain flexibly processes information to achieve complex and adaptive behaviors through computational approaches that bridge cognitive psychology and neuroscience. Education: PhD, University of Pennsylvania (2012) BA, Colgate University (2004) Nassar's research examines how different cognitive systems—learning, memory, and perception—leverage common computational principles to optimize decision-making. His work particularly focuses on how the brain balances stability and flexibility in processing information, how uncertainty is represented and utilized in learning, and how neural computations underlie complex behaviors. Through computational modeling and empirical research, he investigates how modular information-processing systems impact decisions and complex behavior in dynamic environments. His research integrates methods from cognitive psychology, neuroscience, and computational modeling to address fundamental questions about human cognition. Analysis of Nassar's recent publications (2020-2024) reveals a strong focus on computational neuroscience applied to decision-making, learning, and psychiatric conditions. His work frequently employs Bayesian modeling approaches to understand belief updating, uncertainty processing, and structure learning. Key themes include the neural basis of flexibility in learning, computational mechanisms underlying psychiatric symptoms, and age-related changes in cognitive processing. His research bridges cognitive psychology, neuroscience, and computational modeling to provide insights into both healthy cognition and disorders such as depression and schizophrenia. Scientific Contributions: Developed computational models of belief updating and learning under uncertainty Investigated neural mechanisms of stability-flexibility tradeoffs in cognition Examined age-related differences in learning and memory processes Explored computational mechanisms underlying psychiatric conditions Studied the role of noise correlations in neural learning systems Investigated how prefrontal cortex representations shape decision processes Nassar actively mentors researchers in his lab, with recent announcements highlighting postdocs joining from prestigious institutions like Max Planck UCL and Freie Universität Berlin. His lab appears to receive significant research funding, supporting multiple postdoctoral positions and research projects. Collaborations span multiple departments at Brown University, particularly with researchers in Cognitive and Psychological Sciences, Neurology, and Psychiatry. The lab has produced numerous high-impact publications in top journals including Nature Human Behaviour, Brain, and eLife. The Learning, Memory and Decision Lab, led by Nassar, is an active research group that uses computational models to understand how the brain represents and stores information for effective decision making. Recent lab announcements (as of February 2025) indicate the lab is expanding with new postdoctoral researchers joining from Harvard, Max Planck UCL, and Freie Universität Berlin, suggesting strong research momentum and funding support. The lab appears to be well-integrated within Brown's neuroscience community, with collaborations spanning multiple departments and research centers.

Steven R. Caliariمشاهده پروفایل

دانشیار

• Biomaterials
• Mechanobiology
• Musculoskeletal Tissue Engineering
• +۱ مورد دیگر

Steven R. Caliari is an Associate Professor in the Department of Chemical Engineering with a secondary appointment in Biomedical Engineering at the University of Virginia’s School of Engineering and Applied Science. He serves as the ChE Graduate Program Director and is a SEAS Copenhaver Fellow (2023). His research focuses on designing biomaterials to study cell-microenvironment interactions, addressing challenges in disease and tissue engineering. He holds a B.S. (2007, University of Florida), M.S. (2010), and Ph.D. (2013) in Chemical Engineering from the University of Illinois, followed by an NIH postdoctoral fellowship at the University of Pennsylvania. His research interests include biomaterials, mechanobiology, musculoskeletal tissue engineering, and advanced manufacturing for biological applications. His lab has pioneered viscoelastic hydrogel platforms and conductive collagen scaffolds, supported by NIH, NSF, DoD, and industry grants. Notable awards include the NSF CAREER Award (2021) and NIH MIRA (2020). Grants: NIH (NIGMS), NSF CAREER, V Foundation, UVA-Coulter Partnership Courses: Tissue Engineering (BME/CHE 4417), Transport Processes I (CHE 3321) Labs: Caliari Lab focuses on biomaterial design and mechanobiological studies His work bridges fundamental science and translational applications, emphasizing dynamic material systems for regenerative medicine and disease modeling.

Manolis Savvaمشاهده پروفایل

دانشیار

• Computer Graphics
• Computer Vision
• 3D Scene Analysis
• +۴ مورد دیگر

Manolis Savva is an Associate Professor in the School of Computing Science at Simon Fraser University and holds the Canada Research Chair in Computer Graphics. He specializes in 3D scene analysis, generative methods for 3D content, and computer graphics for AI. His research bridges computer graphics, vision, and robotics. Education: Ph.D. (Computer Science, Stanford University, 2016), MS (Computer Science, Stanford, 2012), B.A. (Physics & Computer Science, Cornell, 2009). Research Interests: Human-centric 3D scene analysis, generative 3D content creation, AI-driven rendering, and applications in robotics. Key projects include Habitat (Embodied AI platform), ScanNet , and ShapeNet datasets. Recent Articles: Focus on articulated object modeling, 3D scene synthesis, and AI-driven visualization. Notable work includes SceneMotifCoder (generating object arrangements) and R3DS (panoramic scene understanding). Awards: CHCCS Early Career Award (2022), ICLR 2023 Outstanding Paper Award, ICCV 2019 Best Paper Nomination, and SGP 2020 Dataset Award (ScanNet). Lab/Teams: Leads research groups in 3DLG (3D Learning and Graphics) and GrUVi (Graphics and Vision). Collaborates on projects like AI Habitat and HomeRobot .

• MRI
• Diffusion MRI
• Perfusion MRI
• +۶ مورد دیگر

Professor Fernando Calamante is a Professor of Biomedical Engineering at The University of Sydney and Director of Sydney Imaging Core Research Facility. He leads the National Imaging Facility node and focuses on advanced MRI methodologies, particularly Diffusion and Perfusion MRI, to study brain connectivity and neurological disorders. His work includes developing the MRtrix software, widely used in diffusion MRI analysis. He holds extensive funding (~$50M) and has been recognized with awards like ISMRM Fellowship and NHMRC grants. His research spans super-resolution imaging, brain connectomics, and clinical applications in stroke and tumors. Education: BSc (Physics, Argentina), PhD (Magnetic Resonance Imaging, University College London). Career highlights include leadership roles at The Florey Institute and ISMRM presidency (2021-2022). Research interests include: Novel MRI methods for brain connectivity and super-resolution imaging Applications of Diffusion and Perfusion MRI in neurology Integration of structural and functional connectomics Key achievements: Over 200 publications, software innovations, and leadership in global MRI societies.

• Healthcare Innovation
• Public Health and Health Services
• Applied Economics
• +۳ مورد دیگر

Professor James Barlow is Co-Director of Imperial College London's Centre for Sectoral Economic Performance and holds a Professorship in the Department of Economics and Public Policy at the Imperial College Business School. He also serves as Academic Director for the MBA programme and Visiting Professor at Halmstad University (Sweden) and Honorary Professor at UCL Bartlett Real Estate Institute. His research focuses on structural challenges in healthcare innovation, housing, and construction sectors, with a particular emphasis on embedding innovations into healthcare systems. Barlow's education includes a background in geography and economics from the London School of Economics. He has held previous roles at the University of Westminster and Policy Studies Institute. His advisory work spans governments, healthcare organizations, and industries including medical technology and pharmaceuticals. He contributes to major initiatives like AGE-WELL (Canada) and the Industry Commons Foundation (Sweden). Research interests include healthcare innovation ecosystems, institutional logics, and frugal innovation. His recent book *Managing Innovation in Healthcare* synthesizes his work. He collaborates across disciplines, addressing challenges in telehealth, AI integration, and regulatory frameworks post-Brexit. Key affiliations include the Centre for Health Economics and Policy Innovation, Policy Innovation Research Unit (PIRU), and the NIHR Health Tech Research Centre. His work bridges academic research with practical policy and industry solutions, emphasizing scalable and sustainable business models.

Robert M. Weikle, IIمشاهده پروفایل

استاد

• Millimeter-wave electronics
• Terahertz technology
• Applied electromagnetics
• +۹ مورد دیگر

Robert M. Weikle, II is a Professor in the Charles L. Brown Department of Electrical and Computer Engineering at the University of Virginia, with a courtesy appointment in the Department of Physics. He earned his B.S. from Rice University (1986), M.S. (1987), and Ph.D. (1992) in Electrical Engineering from Caltech, followed by postdoctoral work at Chalmers University of Technology (1992). His research focuses on millimeter-wave and terahertz electronics , applied electromagnetics, integrated antennas, low-noise sensors, and heterogeneous integration of compound semiconductors. His work bridges electronics and photonics for spectrum access, with applications in astronomy, spectroscopy, and metrology. He has published extensively on micromachined silicon substrates, superconducting materials, and emerging technologies. Scientific Awards: IEEE Microwave Prize (1993) David A. Harrison III Award (1999) University of Virginia All-University Outstanding Teaching Award (2000) Edlich-Henderson Innovator of the Year (2016) Fulbright Scholar (2001) As Chief Technology Officer and co-founder of Dominion Microprobes, Inc., he commercializes micromachined wafer probes for high-frequency metrology. His lab, located in E220 Thornton Hall and the Jesse W. Beams Physics Building, has produced 15+ recent publications on submillimeter-wave devices, THz probes, and calibration techniques.

Paola Leardiniمشاهده پروفایل

دانشیار

• Sustainable Architecture
• Building Energy Efficiency
• Urban Resilience
• +۷ مورد دیگر

Associate Professor Paola Leardini holds dual roles as Director (Research) and Associate Professor at the University of Queensland's School of Architecture, Design and Planning, within the Faculty of Engineering, Architecture and Information Technology. Her work bridges design and performance in built environments, focusing on net-zero targets, urban resilience, and circular economy strategies in construction. She is a key researcher in the Centre for Future Building Structures and advises on Queensland’s social/affordable housing through the Design Excellence Panel. Leardini earned a PhD in building energy efficiency and environmental quality from Politecnico di Milano, supervised by Prof. P. Ole Fanger. Her educational journey includes studies in Milan, Berlin, Leicester, and Copenhagen. Research highlights include eco-retrofitting of historical districts (e.g., Milan’s Quartiere Mazzini), Passive House adaptations for subtropical climates, and timber construction innovations with the ARC Advance Timber Hub. She co-founded Passive House Institute New Zealand (PHINZ) and collaborates globally on projects like water-sensitive cities and flood-resilient urban design. Her recent publications emphasize timber’s role in sustainable construction, circular economy frameworks, and climate-responsive building systems. Advising includes leading an ARC Linkage project on adaptable housing while fostering industry partnerships through projects like Norman Creek Catchment flood resilience studies. She actively contributes to labs/teams advancing timber use and urban resilience, aligning with UQ's mission for future-ready built environments.

Ross Thyerمشاهده پروفایل

استادیار

• Synthetic Biology
• Protein Engineering
• Genetic Code Expansion
• +۶ مورد دیگر

Ross Thyer is an Assistant Professor in the Department of Chemical and Biomolecular Engineering at Rice University. He holds a BSc (Hons) from the University of Western Australia and a PhD from the Harry Perkins Institute of Medical Research under Drs. Rackham and Filipovska. His postdoctoral training at the University of Texas at Austin with Prof. Andrew Ellington focused on engineered biosynthesis pathways and non-canonical amino acids. He co-founded GRO Biosciences, a Boston-based biotech startup, and leads the Thyer Lab at Rice. His research bridges synthetic biology, protein engineering, and molecular programming to address global challenges. Key areas include expanding genetic codes for therapeutics, engineering biosynthetic pathways via genetic circuitry, and developing microbial systems for environmental bioremediation. Core technologies include deep learning for protein design, modular DNA assembly, and high-throughput selections. The lab also develops tools like MutCompute for enzyme engineering and domesticates non-model bacteria for bioproduction. His work emphasizes technology innovation, with recent advances in selenocysteine incorporation, L-DOPA sensing systems, and actinobacteria toolkits. The Thyer Lab actively collaborates on biocatalyst development and translational applications in healthcare and industry.

• Lean construction
• Takt planning
• Industrialized Construction
• +۱۲ مورد دیگر

Iris D. Tommelein serves as the Roy W. Carlson Distinguished Professor in the Civil and Environmental Engineering Department at the University of California, Berkeley's College of Engineering, where she directs the Project Production Systems Laboratory (P2SL). A globally recognized pioneer in Lean Construction, she has revolutionized architecture-engineering-construction (AEC) practices through research, industry workshops, and leadership since co-founding the Lean Construction Institute in 1997. Her educational foundation spans multiple disciplines: Ph.D. in Civil Engineering (Construction Engineering and Management), Stanford University, 1989 M.S. in Computer Science (Artificial Intelligence), Stanford University, 1989 M.S. in Civil Engineering (Construction Engineering and Management), Stanford University, 1985 B.S. (5-year degree) in Civil Engineer-Architect, Vrije Universiteit Brussel, Belgium, 1984 Professor Tommelein's research centers on transforming construction processes through Lean principles and digital innovation . Her work pioneers takt planning for workflow reliability, industrialized construction for labor and sustainability challenges, and mistakeproofing to eliminate errors. She integrates digital twins , AI , and optimization to develop practical decision-support systems for supply chains, logistics, and production management. Recent focus includes modular offsite construction and Industry 4.0 applications. Analysis of her 2023-2025 publications reveals intensifying research on takt planning maturity models and industrialized construction feasibility , with growing emphasis on mass timber automation and visual management systems. Her work consistently bridges lean theory with practical implementation across megaprojects, subcontracting networks, and heavy civil engineering. Her exceptional contributions have earned: Lean Pioneer Award (Lean Construction Institute, 2015) National Academy of Construction induction (2019) PPI Technical Achievement Award (2022) Robert B. Harris Award (University of Michigan, 2024) ASCE Construction Management Award (2024) - first woman recipient in 51 years Through the P2SL, she leads industry-collaborative research on production system design, mistakeproofing frameworks, and digital transformation. Her grant-funded projects develop assessment tools for industrialized construction adoption and takt planning methods adaptable to diverse project types. She actively mentors graduate students and drives knowledge transfer via workshops and the annual Construction Innovation Day. The Project Production Systems Laboratory (P2SL) operates as a global hub for construction innovation, partnering with owners, contractors, and suppliers to implement lean production systems. Current initiatives include developing serious games for mistakeproofing training, optimizing work density methods for heavy civil projects, and creating digital twins for real-time construction management.

Ignasi Sau Vallsمشاهده پروفایل

پژوهشگر

• Graph Theory
• Parameterized Complexity
• Algorithmic Graph Theory
• +۵ مورد دیگر

Ignasi Sau Valls is a Directeur de Recherche (DR2) at CNRS, affiliated with the LIRMM laboratory at Université de Montpellier, France. He is a member of the AlGCo team, focusing on algorithms for graphs and combinatorics. His academic background includes dual degrees in Mathematics and Telecommunications Engineering from UPC (Barcelona), a PhD in joint supervision between UPC and Projet Mascotte (Sophia Antipolis), and a postdoctoral position at the Technion (Israel). He has been with CNRS since 2010 and was promoted to his current role in October 2024. He also served as a Visiting Professor at UFC (Brazil) from 2016–2017. His research interests lie primarily in Graph Theory and Parameterized Complexity , with a focus on structural graph properties, kernelization, and algorithm design. He has made significant contributions to problems involving minor-closed graph classes, treewidth, and graph modification. His work bridges theoretical foundations with algorithmic applications, particularly in discrete optimization and network problems. The recent articles highlight a strong trend in parameterized algorithms, especially for graph modification, kernelization, and structural graph problems. Topics such as hitting minors, dynamic programming on tree decompositions, and edge contractions reflect a deep engagement with structural parameterizations and fixed-parameter tractability. His publications frequently appear in top-tier journals like SIAM Journal on Computing, Journal of Combinatorial Theory, and Algorithmica, as well as major conferences such as ICALP, SODA, and IPEC. Best paper award of Track C of ICALP'10 Best student paper award of WG'09 Ignasi Sau has been a principal investigator of the ANR JCJC project ELIT (ANR-20-CE48-0008-01), funded with 169k€ from 2021 to 2026. He serves as an editor for DMTCS and Information and Computation , and has held significant organizational roles, including PC member of numerous conferences (MFCS, WG, IPEC, COCOON) and as co-chair and main organizer of WG 2019, ICGT 2022, and JCALM 2023. He has delivered invited courses at international schools in France, Argentina, and Brazil. He is actively involved in the research community through editorial duties, conference organization, and collaborative research. His lab affiliation is the AlGCo team at LIRMM, a leading group in algorithmic graph theory and combinatorics.

Rahul Kapoorمشاهده پروفایل

استاد

• Innovation Management
• Business Ecosystems
• Technology Strategy
• +۱ مورد دیگر

Rahul Kapoor is a Professor at the Wharton School of the University of Pennsylvania, where he also serves as the Chair of the Management Department. His research focuses on innovation management, business ecosystems, and technology strategy, with a particular emphasis on how firms navigate technological and organizational challenges in dynamic industries. University: University of Pennsylvania School: Wharton School Department: Management Department Academic Rank: Professor His research explores the interplay between organizational design, external collaboration, and innovation outcomes. Recent work includes studies on forecasting strategies, ecosystem interdependencies, and the role of setbacks in technology development. He has contributed to leading journals such as Strategic Management Journal and Research Policy , with a recurring focus on technology ecosystems and modular innovation. Kapoor’s research spans both theoretical and applied dimensions. He has examined how firms can optimize value creation in business ecosystems, the impact of organizational design on invention sourcing, and the dynamics of technology emergence. His studies often integrate historical case analyses and simulation models to uncover patterns in innovation management. Scientific Awards: Inaugural Academy of Management Emerging Scholar Award Strategic Management Journal Best Paper Prize Wharton Teaching Excellence Award (multiple years) Editorial Roles: Associate Editor, Strategic Management Journal Contributing Editor, Strategy Science Teaching: Undergraduate, MBA, Executive MBA, and PhD courses on technology and innovation strategy Leadership in Wharton’s Executive Education programs He has advised firms on innovation initiatives and draws on over seven years of industry experience in high-tech, including roles at Texas Instruments and co-founding a startup, to inform his academic work.