جست‌وجوی استاد و پژوهشگر

Jari Marko Lahtiمشاهده پروفایل

استاد

• Psychology
• Behavioral Genetics
• Epigenetics
• +۳ مورد دیگر

Jari Marko Lahti is a Professor in the Department of Psychology at the University of Helsinki, where he serves as Supervisor in both the Doctoral Program in Human Behavior and the Doctoral Program in Cognition, Learning, Teaching and Communication. He also holds an ORCID profile (0000-0002-4310-5297) and maintains contact through the university's Haartmaninkatu 3 address in Helsinki. His academic credentials include a Doctor of Psychology degree (awarded December 12, 2009), Docent status in Clinical Psychology from the University of Helsinki (awarded December 12, 2011), and Advanced Psychotherapist certification from Chydenius Institute / University of Jyväskylä (2011-2013). Earlier in his career, he earned a Master of Psychology from the University of Helsinki (1997) and certification as a cognitive Psychotherapist (1998-2001). Professor Lahti's research focuses on the intersection of psychology, behavioral genetics, and epigenetics, with particular emphasis on how environmental factors interact with genetic predispositions to influence mental health outcomes across the lifespan. His work frequently examines prenatal influences on child development, stress response mechanisms, and the genetic underpinnings of psychological traits. Recent publications reveal a strong concentration on epigenetic pathways linking maternal psychological states during pregnancy to offspring developmental outcomes. Analysis of his publication record shows consistent productivity with over 300 publications, including high-impact articles in Molecular Psychiatry, Psychoneuroendocrinology, and Biological Psychiatry. His research often involves large-scale collaborative efforts, as evidenced by multi-author publications with international research teams. Among his notable recognitions is the Young Investigators Award received at the 6th World Congress on Developmental Origins of Health and Disease in December 2009. His current research leadership includes the IMAGINE project (2022-2025), funded by the Academy of Finland, which investigates mental well-being as a pathway to increasing youth participation. Previously, he led a project on environmental, epigenetic, and genomic factors in depression and related cardiometabolic risks (2013-2016), and participated in the Helsinki Study of Very Low Birth Weight Adults (2003-2015). Professor Lahti has also served as a Collegium Researcher at the University of Turku (2019-2021), demonstrating his standing within the broader Finnish academic community. His research has generated significant media attention, with multiple press mentions related to maternal health conditions and child mental health outcomes.

Julien Mairalمشاهده پروفایل

پژوهشگر

• Machine Learning
• Optimization
• Computer Vision
• +۲ مورد دیگر

Julien Mairal is a Senior Research Scientist at Inria Grenoble within the Thoth research team, focusing on machine learning, optimization, and computer vision. His research spans theoretical foundations and practical applications across multiple domains including signal processing, bioinformatics, and neuroimaging. His primary research interests include machine learning , particularly optimization algorithms for large-scale problems, computer vision with applications in image restoration and super-resolution, and kernel methods for structured data. His work demonstrates strong connections between theoretical optimization and practical implementations, as evidenced by his Cyanure toolbox for empirical risk minimization. Analysis of his recent publications reveals a consistent focus on optimization techniques for machine learning, with increasing emphasis on large-scale applications, self-supervised learning, and connections between kernel methods and deep learning architectures. His work shows strong interdisciplinary applications spanning computer vision, neuroscience, and remote sensing. ERC Consolidator Grant (2023) for the Apheleia project ICML Test of Time Award (2019) ERC Starting Grant (2016) for the SOLARIS project Mairal serves extensively in editorial capacities as area chair for major conferences including ICML, NeurIPS, CVPR, and ECCV. He is an associate editor for the Journal of Machine Learning Research (JMLR) and previously served for IEEE PAMI and IJCV. His Cyanure toolbox has become a significant contribution to the field of large-scale optimization in machine learning. His research demonstrates strong connections between theoretical foundations and practical implementations across multiple application domains.

• statistics
• performance evaluation
• adaptive measurement technologies
• +۵ مورد دیگر

Dr. Szabolcs Takacs is an Associate Professor at Gáspár Károli Reformed University (KRE), where he serves as Deputy Head of the Institute of Psychology within the Faculty of Humanities and Social Sciences. Additionally, he holds the position of Deputy Dean of Education. His academic career combines expertise in mathematics, statistics, and psychology, with a focus on applying quantitative methods to psychological and social research questions. Dr. Takacs has established himself as a prominent figure in statistical methodology within the Hungarian academic community. Dr. Takacs earned his educational qualifications at Eötvös Loránd University (ELTE): PhD in Applied Mathematics (2014) - Dissertation: "Solving some problems of hierarchical money distribution systems using random vectors and multidimensional scaling" MA in Applied Mathematics (2004) Dr. Takacs' research spans multiple domains at the intersection of statistics and psychology. His primary focus areas include statistical methodology development, performance evaluation systems, and adaptive measurement technologies. He applies operations research techniques such as linear programming and stochastic optimization to psychological measurement problems. His work often addresses practical applications in workforce analysis, educational assessment, and risk management. Dr. Takacs has made significant contributions to cluster analysis within person-oriented research frameworks and has developed innovative validation procedures for statistical models. Analysis of Dr. Takacs' 15 most recent publications reveals a strong emphasis on statistical methodology development with applications primarily in psychology and social sciences. His work demonstrates expertise in multidimensional scaling, classification methods, and cluster analysis. A notable trend is the application of mathematical optimization techniques to psychological measurement problems. Many publications address workforce and employment issues, reflecting his interest in practical applications of statistical methods. His research shows increasing sophistication in handling complex data structures while maintaining focus on real-world applicability. Dr. Takacs has received prestigious recognition from KRE: Publication of the Year (2014) - awarded in 2015 Teacher of the Year (2014) - awarded in 2015 Dr. Takacs actively mentors the next generation of researchers, currently supervising three doctoral students across institutions (Tilburg University and KRE). His supervisory approach emphasizes methodological rigor in psychological research. He has served as an opponent in multiple doctoral defenses, most recently in 2024 for research on mathematics-related beliefs and academic performance. His leadership extends to the Competency Assessment Research Group, which he leads, focusing on developing and validating assessment tools for various professional contexts. As Deputy Dean of Education and Deputy Head of Institute, Dr. Takacs plays a significant role in shaping educational policy and research direction within the Faculty of Humanities and Social Sciences. His work bridges theoretical statistical methodology with practical applications in psychology and social sciences, contributing to the university's research profile and educational mission.

Hanna Palmérمشاهده پروفایل

استاد

• Mathematics Education
• Early Childhood Mathematics
• Problem Solving and Problem Formulation
• +۴ مورد دیگر

Hanna Palmér is a Professor in Mathematics Education at Linnaeus University, where she serves as Vice Chancellor for Teacher Education programs, overseeing the university's largest educational program. She is affiliated with the Department of Mathematics within the Faculty of Technology, where she teaches mathematics didactics courses and supervises independent projects for teacher education programs. Her research focuses on young children's (0-12 years) mathematics learning and teachers' professional development with emphasis on mathematics instruction. Her work explores problem solving and problem formulation as starting points for preschool mathematics education, the development and implementation of toddlers' number sense and early arithmetic skills, and mathematics through digital tools and programming in preschool and school settings. She collaborates with teachers and children across multiple preschools, preschool classes, and schools. Her recent publications demonstrate strong trends in early mathematics education, particularly examining how young children develop numerical understanding through play, problem-solving activities, and digital tools. Her research shows how mathematical concepts can be integrated into everyday preschool activities while maintaining educational integrity. A significant portion of her work examines the role of teachers in facilitating mathematical learning through intentional pedagogical approaches that bridge play and formal instruction. Professor Palmér leads several significant research projects including PIF (Problem Solving in Preschool Class), SUMI (Study of Scaling and Modeling of Early Numerical Innovation), and participates in VR-funded projects. She is the project leader for the SUMI study (2023-2025), which aims to develop scientifically grounded models for implementing mathematics instruction focused on numbers and early arithmetic in preschool on a large scale. She actively supervises independent projects in mathematics didactics for both primary teacher education and special education teacher programs. Her grant funding comes primarily from the Swedish Research Council for Education and Research (Skolforskningsinstitutet) and the Swedish Research Council (Vetenskapsrådet), supporting multiple longitudinal studies on early mathematics education. Professor Palmér co-leads the Mathematics Education research group at Linnaeus University and is part of the Digi-group, which researches the use of digital tools in preschool and school instruction. She also co-leads the EMLA (Early Mathematics Learning and Awareness) seminar series with Camilla Björklund from the University of Gothenburg, bringing together senior and junior researchers and teacher educators with special interest in mathematics learning in early ages (0-8 years).

• Artificial Intelligence
• Multi-Agent Path Finding
• Robotics
• +۴ مورد دیگر

Jiří Švancara is a Lecturer at the Department of Theoretical Computer Science and Mathematical Logic (KTIML) within the Faculty of Mathematics and Physics at Charles University in Prague. His academic career focuses on Artificial Intelligence, particularly Multi-Agent Path Finding (MAPF), where he has established himself as a prominent researcher with numerous publications in top-tier conferences. His research interests span across Multi-Agent Path Finding , Robotics , Algorithm Design , and Constraint Satisfaction Problems . Švancara has made significant contributions to the field of MAPF, developing novel approaches for large-scale maps, temporal uncertainty handling, and efficient solving methods. His work bridges theoretical foundations with practical applications in robotics and transportation systems. Analysis of his recent publications reveals a strong focus on improving the scalability and robustness of MAPF algorithms. His research explores graph pruning techniques for large maps, handling temporal uncertainty in path execution, and comparing different objective functions for optimization. He has also investigated applications of MAPF in autonomous intersections and train routing systems, demonstrating the practical relevance of his theoretical work. Best Paper Award for 'Multi-agent Path Finding on Real Robots: First Experience with Ozobots' at IBERAMIA 2018 Švancara actively teaches multiple courses including Introduction to Artificial Intelligence, Propositional and Predicate Logic, and Algorithms and Data Structures. His teaching spans both theoretical foundations and practical implementation, with students engaging in programming assignments that reflect current AI challenges. His research collaborations are extensive, with frequent co-authorship with Roman Barták and other researchers in the field, indicating strong integration within the international AI research community. His laboratory work involves practical implementations of MAPF algorithms, including testing on real robots as evidenced by several publications describing experiments with Ozobots and other robotic platforms. This hands-on approach connects theoretical research with tangible robotic applications, providing valuable validation for his algorithmic contributions.

Paavo Ritalaمشاهده پروفایل

استاد

• Strategy and Management
• Artificial Intelligence
• Technology and Innovation Management
• +۷ مورد دیگر

Paavo Ritala is a Professor in the Business Studies department at LUT School of Economics, LUT University, located in Lappeenranta, Finland. His academic work bridges strategic management with emerging technologies, focusing on how organizations can leverage digital transformation for competitive advantage and sustainable growth. Professor Ritala's research spans multiple interconnected domains of modern business strategy. His primary focus areas include Strategy and Management, Artificial Intelligence applications in business contexts, Technology and Innovation Management, Circular Economy frameworks, Digital Transformation strategies, Innovation Ecosystems dynamics, Platform Business Models, and Data Analytics. His work frequently examines the intersection of these fields, particularly how AI and digital technologies are reshaping traditional business models and creating new strategic opportunities. He has developed significant expertise in understanding organizational adaptation to technological disruptions and sustainability challenges. His recent publication record (2024-2025) reveals strong trends toward examining AI's impact on workplace wellbeing, circular economy business models enabled by digital technologies, platform ecosystems governance, and strategic approaches to digital transformation. These works appear in top-tier journals across management, innovation, and technology domains including Journal of Business Research, Research Policy, International Journal of Management Reviews, and R&D Management, demonstrating his interdisciplinary approach to contemporary business challenges. His research methodology combines theoretical development with empirical investigation, often employing case studies, large-scale surveys, and experimental approaches across international contexts. Professor Ritala actively contributes to advancing knowledge in strategic management through his extensive publication record and engagement with both academic and practitioner communities. His research provides practical frameworks for organizations navigating the complex landscape of digital transformation, sustainability implementation, and innovation management in today's rapidly evolving business environment.

Nadia Lahrichiمشاهده پروفایل

استاد

• Operational Research
• Healthcare Analytics
• Logistics
• +۴ مورد دیگر

Nadia Lahrichi is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal, where she holds a Tier 1 Canada Research Chair in Healthcare Analytics and Logistics (HANALOG). She serves as Deputy Director at CIRRELT and is an active member of multiple research centers including IVADO (Institute for Data Valorization) and the Interuniversity Research Center on Enterprise Networks, Logistics and Transport. Her research focuses on applying operational research and mathematical modeling techniques to healthcare systems, with particular emphasis on patient flow optimization, resource allocation, scheduling, and logistics. She has developed innovative approaches that integrate machine learning with traditional optimization methods to address complex healthcare challenges. Her work spans multiple healthcare domains including radiology, chemotherapy scheduling, emergency department operations, and pandemic response systems. Professor Lahrichi's publications demonstrate consistent growth in applying advanced analytics to healthcare problems, with recent work focusing on pandemic response systems (particularly related to COVID-19 testing in Nepal), integration of machine learning with optimization techniques, and resilience planning for healthcare systems. Her research bridges theoretical operations research with practical healthcare applications, resulting in tangible improvements to healthcare delivery systems. Tier 1 Canada Research Chair in Healthcare Analytics and Logistics (HANALOG) Deputy Director at CIRRELT Member of CIRRELT (Interuniversity Research Center on Enterprise Networks, Logistics and Transport) Member of IVADO (Institute for Data Valorization) Professor Lahrichi has supervised an impressive number of graduate students throughout her career, including 5 PhD students and 34 Master's students, demonstrating her commitment to training the next generation of researchers in healthcare analytics. Her research has been supported by significant grants that have enabled large-scale projects addressing critical healthcare system challenges. She collaborates extensively with healthcare institutions to ensure her research has direct practical applications and impacts real-world healthcare delivery systems.

• Bioinformatics
• Computational Biology
• Genomics
• +۴ مورد دیگر

Chien-Yu Chen is a Professor of Biomechatronics Engineering at National Taiwan University, where he has been on faculty since 2005, progressing from Assistant Professor to Associate Professor and currently serving as Professor. His research integrates computational approaches with biological systems, focusing on bioinformatics, genomics, and machine learning applications in medicine. Dr. Chen's educational background includes: PhD in Computer Science and Information Engineering from National Taiwan University (1999-2003) MS in Electrical Engineering from Stanford University (1996-1998) BS in Electrical Engineering from National Taiwan University (1992-1996) His research interests span multiple interdisciplinary domains at the intersection of computation and biology. Dr. Chen has made significant contributions to bioinformatics methodology development , particularly in genomic data analysis, variant interpretation, and integration of multi-omics data. His work frequently applies machine learning and deep learning approaches to solve challenging problems in genomics and precision medicine. A substantial portion of his research focuses on cancer genomics , particularly acute myeloid leukemia, as well as immunogenetics including HLA and KIR gene complex analysis. He has also contributed to population genomics with studies focused on the Taiwanese population, and to reproductive medicine through mitochondrial DNA research. Analysis of Dr. Chen's recent publications (2023-2024) reveals a strong focus on advancing computational methods for genomic analysis. His work increasingly incorporates deep learning techniques, including BERT models and variational autoencoders, to tackle complex biological questions. There's a clear emphasis on translational research with medical applications, particularly in cancer diagnostics and risk stratification, immunogenetics, and population-specific genomic medicine. His collaborative work spans multiple institutions and reflects an interdisciplinary approach that bridges computer science, engineering, and clinical medicine. Dr. Chen has been actively involved in mentoring students and leading research projects, though specific details about advisees are not provided in the available information. His research has been supported by various grants that enable large-scale genomic studies and methodological development in computational biology. His work appears to be conducted within a collaborative research environment that likely includes bioinformatics specialists, clinicians, and laboratory scientists, though specific lab or team names are not mentioned in the available information.

Lisa Winhausenمشاهده پروفایل

پژوهشگر ارشد

• Experimental geomechanics
• Thermo-hydro-mechanical coupled problems
• Multi-scale analysis of fracture and shear zones
• +۳ مورد دیگر

Lisa Winhausen serves as a Senior Scientist and Research Associate at the Department of Engineering Geology and Hydrogeology within the Faculty for Georessources and Material Science at RWTH Aachen University. Her work focuses on experimental investigations of clay shale behavior with particular relevance to nuclear waste repository applications. She maintains an active research program examining the thermo-hydro-mechanical properties of clay formations, especially Opalinus Clay, which is considered a potential host rock for radioactive waste disposal. Her educational background includes: Dr.rer.nat (2022/2023) from RWTH Aachen University, Faculty for Georessources and Material Science MSc Applied Geosciences (2015-2018) from RWTH Aachen University with specialization in Energy and Mineral Resources BSc Geoscience (2012-2015) from University of Mainz Semester abroad at National University of San Juan, Argentina (2017) Winhausen's research interests center on experimental geomechanics, particularly thermo-hydro-mechanical coupled problems in clay formations. She investigates multi-scale analysis of fracture and shear zones from nano to meter scale, and focuses on quantifying effective thermo-hydro-mechanical and petrophysical parameters. Her work has significant implications for understanding long-term rock behavior in nuclear waste repositories. Analysis of her recent publications reveals a strong focus on the mechanical behavior of Opalinus Clay under various conditions. Her research consistently examines anisotropy, time-dependent deformation, and the influence of environmental factors like temperature and burial history. Winhausen employs advanced experimental techniques including triaxial testing, microstructural analysis with BIB-SEM, and comparative permeability measurements to understand clay shale behavior across multiple scales. While no specific awards are listed in the available materials, her research has generated substantial scientific impact with numerous publications in reputable journals and conference proceedings. Her work is frequently cited in the field of clay shale geomechanics, particularly regarding nuclear waste repository applications. Winhausen has been actively involved in multiple research projects related to nuclear waste disposal, including the Einstein Telescope feasibility study in the Euregio Meuse-Rhine region. She collaborates extensively with Florian Amann's research group and has participated in international research initiatives, including a planned research stay at the University of Minnesota in 2025. Her work contributes significantly to understanding the long-term stability of clay formations for nuclear waste storage applications. She is affiliated with the Laboratory for Engineering Geology and Hydrogeology (LIH) at RWTH Aachen, which conducts research on geomechanics, rock mechanics, and natural hazards. The laboratory participates in major research initiatives including the ERC-Synergy Grant project on Fault Activation and Earthquake Rupture (FEAR) and the Einstein Telescope feasibility study.

Kanishka Pereraمشاهده پروفایل

استاد

• Variational methods for nonlinear PDEs
• Critical growth elliptic problems
• Fractional p-Laplacian equations
• +۲ مورد دیگر

Kanishka Perera is a Professor in the Department of Mathematics and Systems Engineering , College of Engineering and Science at Florida Institute of Technology . Active in both research and teaching, his office is located in Frederick C. Crawford Building, Room 325 , and he can be reached at kperera@fit.edu . His research focuses on advanced mathematical techniques for nonlinear partial differential equations, particularly variational and topological methods , critical growth problems , fractional p-Laplacian equations , and nonlocal operators . He has authored more than one hundred peer-reviewed articles since 2009, demonstrating sustained productivity in these areas. Recent publications (2024–2025) are dominated by investigations into • Schrödinger–Poisson–Slater type systems, • Fractional and nonlocal elliptic equations in Sobolev spaces, • Double phase and Kirchhoff-type problems, • Multiplicity and bifurcation phenomena for critical growth equations. These works collectively advance the understanding of existence, regularity, decay estimates, and qualitative properties of solutions to challenging nonlinear PDEs. Research Profiles ORCID Google Scholar ResearchGate Scopus Contact Email: kperera@fit.edu Phone: (321) 674-8869

Peter Melchiorمشاهده پروفایل

استادیار

• Statistical Astronomy
• Machine Learning
• Data Fusion
• +۵ مورد دیگر

Peter Melchior is an Assistant Professor of Astrophysical Sciences at Princeton University, with a joint appointment at the Center for Statistics and Machine Learning. He leads the Princeton Astro Data Lab, where his team develops novel algorithms to extract information from astronomical observations despite instrumental limitations and noise. His educational background includes a Ph.D. in Physics (2010) and a Diplom/M.S. in Physics (2006), both from the University of Heidelberg. Prior to his current position at Princeton, he held postdoctoral positions at The Ohio State University (2011-2015) and the University of Heidelberg (2010). Dr. Melchior's research focuses on statistical methods for large astronomical surveys. His primary interests include: Physics-based machine learning for astronomical data analysis Source separation and data fusion techniques Optimal combination of multiple datasets from different surveys Development of neural network approaches for astronomical problems Application of statistical methods to hydrologic modeling His recent publications demonstrate a strong trend toward interdisciplinary work that combines astronomy with machine learning and environmental science. The research spans from fundamental astronomical data analysis techniques to practical applications in water resource management across the United States. Among his notable achievements: PI of a project funded by the Schmidt Futures Foundation to optimize target selection for the Prime Focus Spectrograph survey Lead developer of the HydroGEN project funded by NSF for hydrologic scenario generation Author of approximately 300 papers in major peer-reviewed journals Developer of open-source software including pyGMMis for Gaussian mixture modeling Dr. Melchior actively mentors students and has organized the Undergraduate Summer Research Program and Data Science Seminar at Princeton. His work bridges astronomy, statistics, and machine learning, with growing applications in environmental science.

• Engineering Geology
• Geotechnical Engineering
• Subsurface Modeling
• +۶ مورد دیگر

Dominique J.M. Ngan-Tillard serves as Associate Professor of Engineering Geology within the Department of Geo-engineering at Delft University of Technology's Faculty of Civil Engineering & Geosciences. With expertise spanning subsurface modeling and non-destructive testing techniques, Dr. Ngan-Tillard has established a distinctive research profile that bridges engineering geology with cultural heritage applications. Dr. Ngan-Tillard specializes in modeling the subsurface in terms of structure and material properties, with particular focus on conceptual modeling of soil microstructures supported by multi-scale, multi-physics data fusion. Their fingerprint reveals strong connections to Netherlands geology, soils, clays, computed tomography, and X-radiation research areas, reflecting their technical expertise in subsurface characterization. Analysis of their recent publications shows a clear trend toward interdisciplinary research that applies engineering geology techniques to archaeological and cultural heritage contexts. Their work increasingly combines advanced imaging technologies like computed tomography with traditional geotechnical approaches to solve problems in both civil engineering and heritage conservation fields. European Heritage Award for Scanning for Syria project Dr. Ngan-Tillard maintains an active research program with significant contributions to both academic and practical applications. Their work with the Centre for Digital Heritage demonstrates commitment to preserving cultural artifacts through advanced imaging techniques, while their participation in geoscience conferences shows continued engagement with core engineering geology communities. The supervised work listed indicates mentorship activities, though specific students are not named in the available information. Through projects like the Scanning for Syria initiative and research on archaeological materials from South Africa to the Netherlands, Dr. Ngan-Tillard has built collaborative networks that span engineering, archaeology, and cultural heritage preservation, operating at the intersection of multiple scientific disciplines.

David A. Joynerمشاهده پروفایل

استاد مدعو

• Computer Science Education
• Online Education
• Educational Technology
• +۳ مورد دیگر

David A. Joyner is an educator and researcher at Georgia Tech's College of Computing, serving as Associate Dean for Off-Campus & Special Initiatives and Executive Director of Online Education & OMSCS. His work focuses on improving online learning through artificial intelligence, social communities, and interface design. Education: Ph.D. in Human-Centered Computing from Georgia Tech Research Interests: Joyner's research spans computer science education, online learning, and AI in education. He investigates scalable instructional materials, AI-driven student support, and fostering online communities for large courses. Article Trends: His recent publications emphasize AI integration in online education, sentiment analysis for peer feedback, peer assessment systems, and strategies for maintaining academic integrity in large-scale programs. Scientific Awards: Georgia Tech Outstanding Professional Education Award, 2023 Georgia Tech Alumni Association 40 Under 40, 2022 College of Computing Outstanding Faculty Leadership Award, 2022 USG Board of Regents’ Teaching Excellence Award for Online Teaching, 2019 Reimagine Education Gold and Bronze Awards, 2019 Multiple Georgia Tech Teaching and Curriculum Innovation Awards Advising and Grants: While no explicit grants are mentioned, Joyner leads the OMSCS program and has influenced thousands of students through scalable online curricula. He authored four books, including Teaching at Scale and The Distributed Classroom , and served as General Chair for the ACM Learning @ Scale conference. Labs & Teams: Joyner contributes to Georgia Tech's Learning@Scale research initiatives and the School of Interactive Computing, focusing on global online education scalability and AI-driven instructional design.

Dong Anمشاهده پروفایل

استادیار

• Quantum Computing
• Applied Mathematics
• Quantum Algorithms
• +۳ مورد دیگر

Dong An is an Assistant Professor at the Beijing International Center for Mathematical Research (BICMR) , Peking University, since September 2024. He previously held a QuICS Hartree Postdoctoral Fellowship at the University of Maryland (2021-2024). He earned his Ph.D. in Applied Mathematics (2021) from UC Berkeley under Professor Lin Lin and a B.S. in Computational Mathematics (2016) from Peking University. Research Focus: Quantum algorithms for classical scientific computing problems and classical algorithms for quantum many-body problems. Key Areas: Quantum simulation, Hamiltonian dynamics, quantum linear systems, differential equations, and adiabatic quantum computing. Publications highlight advancements in quantum differential equation solvers, linear combinations of Hamiltonian simulations, and multi-product methods with explicit commutator scaling. His work bridges theoretical quantum computing and practical scientific computation, emphasizing optimal parameter dependence and error bounds. Scientific Awards: Co-Principal Investigator of NSF Robust Quantum Simulation Seed Grant QuICS Hartree Fellowship Bernard Friedman Memorial Prize in Applied Mathematics Multiple honors from Peking University Teaching: Currently instructs graduate-level quantum algorithms courses at Peking University. Previously served as a teaching assistant for differential equations and calculus at UC Berkeley. Service: Organizer for workshops on quantum differential equations and SIAM special sessions. Active reviewer for journals like PRX Quantum and conferences including QIP and TQC.

Michael J. O'Sheaمشاهده پروفایل

استاد

• Condensed Matter Physics
• Magnetic Materials
• Financial Time Series Analysis
• +۳ مورد دیگر

Michael J. O'Shea is a Professor of Physics at Kansas State University , with a research career spanning experimental condensed matter physics, financial time series analysis, and physics education. His work on magnetic materials includes studies of rare-earth alloys in thin film and nanoparticle forms, while his financial research explores quasi-periodic signals in stock market indices through novel folding techniques. Research areas include: Experimental condensed matter physics (Nd2Fe14B and SmCo5 magnets, neutron irradiated superconductors) Financial signal analysis (DJIA periodicity studies) Outdoor sports modeling (climbing, snowboarding, canoeing) Teaching portfolio spans conceptual physics to graduate-level statistical mechanics. Current service roles include Chair of the Graduate Entrance Committee and Departmental Graduate Advisor. Recent publications focus on: Human frame scaling models for backpacking Periodic components in financial indices Canoe river-crossing dynamics Climber force analysis Collaborations and grants include NSF, Army Research Office, and NATO funding. Graduate advisees include multiple PhD candidates working on magnetic materials and physics education topics.