جست‌وجوی استاد و پژوهشگر

Jasmyn Goodingمشاهده پروفایل

مدرس

• Bioinformatics
• Computational Biology
• Machine Learning
• +۱۳ مورد دیگر

Jasmyn Gooding is a PhD Researcher and Associate Tutor in the School of Computing Sciences and a Research Associate and Coordinator in the Norwich Medical School at the University of East Anglia (UEA), Faculty of Science and Faculty of Medicine and Health. She holds an MSci in Biological Sciences from UEA and is currently completing her PhD under the supervision of Dr. Taoyang Wu (CMP) and Prof. Cock Van Oosterhout (ENV). Academic Background: Master of Science (MSci), Biological Sciences, University of East Anglia (2020–2024) Currently pursuing PhD in Computational Biology and Evolutionary Genomics Her research lies at the intersection of deep learning and evolutionary biology, focusing on modeling extinction risk and recovery potential using forward-in-time simulations to generate synthetic genomic data. In medicine, she coordinates Patient and Public Involvement (PPI) across over 10 funded projects and supports more than 30 pre-grant applications. She is actively involved in projects such as INfLAIM (AI analysis of intersectionality in health), AISCS (anxiety assessment in stroke and brain injury), and NLP model development. Her work integrates computational modeling, genomics, and public health, demonstrating a strong interdisciplinary approach across computer science and medicine. The absence of publications in the provided text prevents detailed article trend analysis. Scientific Engagement & Recognition: Active participant in the Norwich Science Festival for 5 years Delivered numerous public science communication talks Jasmyn advises on PPI strategy and contributes to grant development across multiple medical research initiatives. She teaches mathematics, machine learning, and programming modules as an Associate Tutor. Her technical expertise includes R, Python, DNA extraction, PCR, gel electrophoresis, and microbiome analysis. She is a member of the Computational Biology Lab and the Lifespan Health research group.

• Environmental Fluid Mechanics
• Physical Oceanography
• Hydraulics
• +۱۲ مورد دیگر

Dr. Adam Jiankang Yang is an incoming Assistant Professor in the Department of Civil and Resource Engineering at Dalhousie University, Faculty of Engineering, starting in Fall 2024. His research focuses on environmental fluid dynamics, with applications in oceanography, climate change mitigation, and sustainable water environments. Educational Background: BE in Theoretical and Applied Mechanics, Sun Yat-sen University (2011–2015) Exchange studies at National Tsing Hua University and National Cheng Kung University, Taiwan (2013, 2015) MPhil in Civil and Environmental Engineering, The Hong Kong University of Science and Technology (2015–2017) PhD in Civil Engineering, The University of British Columbia (2017–2022) Postdoctoral Associate, Yale Center for Natural Carbon Capture & Earth and Planetary Sciences, Yale University (2022–2024) His research interests center on environmental fluid mechanics, particularly stratified shear instabilities, turbulence, wave dynamics, and mixing processes in natural waters. He investigates how these phenomena influence tracer transport, particle settling, and dissolution, with a growing emphasis on marine carbon dioxide removal (mCDR) as a climate intervention strategy. His work integrates numerical simulations , laboratory experiments (e.g., PIV, LIF), and field experiments (e.g., CTD, ADCP) to build fundamental understanding. He is also exploring the use of machine learning for turbulence and mixing parametrization. Dr. Yang is actively recruiting motivated undergraduate students, graduate students (Masters and PhD), and postdoctoral researchers to join his research group. He seeks individuals with strong academic records and backgrounds in engineering, physics, mathematics, or earth sciences, preferably with experience in programming (Python, MATLAB), experimental methods, or fieldwork. Scientific Awards: Dr. Yang has not yet advised any named students, as per the available information. He is currently establishing his research program and is seeking external funding to support graduate students and postdocs. His research has potential for collaboration with climate science and ocean engineering initiatives. Labs and Research Teams: Dr. Yang will lead a research group at Dalhousie University focused on environmental fluid mechanics. His lab will employ computational, experimental, and field-based approaches to study fluid dynamics in natural systems, particularly in the context of climate solutions like ocean-based carbon removal.

• High-Performance Computing
• Parallel Programming
• Compiler Optimization
• +۳ مورد دیگر

Richard Schulze is a Researcher at the University of Münster, contributing to projects such as SkelCL, PACXX, and dOpenCL. His work focuses on parallel computing, compiler optimization, and auto-tuning frameworks for high-performance and distributed systems. He explores portable code generation for heterogeneous architectures using Multi-Dimensional Homomorphisms (MDH) and develops abstractions for OpenCL/CUDA programming. His research interests include advancing scheduling languages and systematic composition models, alongside probabilistic data linkage techniques. Recent publications emphasize auto-tuning methodologies for Python and interdependent parallel program parameters. Publications since 2018 highlight contributions to portable compiler design, performance optimization, and cross-platform parallelism. No scientific awards are explicitly mentioned. Consultation hours are by appointment, and he is affiliated with the university's computer science research groups.

• Space Physics
• Ionospheric Physics
• Magnetosphere-Thermosphere-Ionosphere Coupling
• +۴ مورد دیگر

Daniel Billett is a space physicist specializing in magnetosphere-thermosphere-ionosphere coupling dynamics, currently holding the prestigious ESA Living Planet Fellowship. His research leverages the Super Dual Auroral Radar Network (SuperDARN) alongside Fabry-Perot Interferometers, Incoherent Scatter Radars, and all-sky imagers to investigate energy transfer during space weather events. His educational foundation includes: PhD in Space Physics from Lancaster University (2016-2019), thesis: "The great space weather washing machine: Examining the dynamics of high-latitude ionosphere-thermosphere coupling" PGCE in post-compulsory education from Cardiff University (2015-2016) MPhys in Astrophysics from Aberystwyth University (2011-2015) Billett's research centers on quantifying energy pathways between solar wind drivers and Earth's upper atmosphere, with particular focus on aurorally driven conductivity enhancements and thermospheric wind dynamics . His work examines how geomagnetic storms redistribute energy through frictional heating and Poynting flux, addressing critical questions about atmospheric responses to charged particle bombardment and implications for space weather forecasting. Analysis of his 15 most recent publications reveals consistent emphasis on ion-neutral coupling mechanisms, with recurring methodological themes in multi-instrument validation (SuperDARN with satellite data), high-resolution mapping techniques, and quantification of under-sampled energy transfer scales. Key topical threads include GNSS scintillation during cusp events, thermospheric responses to extreme storms like the 2022 Starlink incident, and turbulence characterization around auroral arcs. His scientific recognition includes: ESA Living Planet Fellowship (2021) JSPS Fellowship for Research in Japan (2019) Lancaster University Dean's Award for PhD excellence (2018) Billett has secured competitive research funding through major international fellowships, with the ESA Living Planet Fellowship supporting his current work on global thermospheric responses. His publications demonstrate extensive collaboration across North American, European, and Japanese institutions, reflecting deep integration within the space physics community. He contributes to infrastructure development through projects like Borealis radar systems and pyDARN software, enhancing community-wide observational capabilities. As a core SuperDARN collaboration member, Billett participates in global networks of radar facilities and complementary instrumentation teams. His recent work with the 2022 Starlink storm response highlights growing focus on space weather impacts on commercial space assets, indicating strategic expansion toward applied space climate research while maintaining fundamental ionospheric physics investigations.

Anne Barberمشاهده پروفایل

پژوهشگر

• atmospheric convection
• catastrophe modeling
• high-resolution cloud modeling
• +۴ مورد دیگر

Dr. Anne Barber is a Software Development Scientist at the Centre for Environmental Modelling and Computation (CEMAC), University of Leeds. Her expertise spans atmospheric convection, catastrophe modeling, and high-resolution cloud modeling, alongside technical skills in Python programming and scientific computing. Education: Masters in Applied Meteorology and Climatology, University of Birmingham (2012) PhD in Atmospheric Science, University of Leeds (2021) Research Interests: Anne focuses on atmospheric processes such as convective updraft initiation, sub-cloud variability, and gravity wave interactions. Her work bridges academic research and industrial applications, including flood risk modeling and software development for environmental systems. Experience: Met Office (2012–2016): Operational Meteorologist providing forecasts for aviation, commercial, and marine sectors. JBA Risk Management (Post-PhD): Catastrophe flood risk analyst and contributor to US flood model development. Labs/Teams: CEMAC at the University of Leeds, where she advances computational tools for environmental science.

Benjamin Lee Greenmanمشاهده پروفایل

استادیار

• Programming Languages
• Gradual/Migratory Type Systems
• Language Interoperability
• +۴ مورد دیگر

Benjamin Lee Greenman is an Assistant Professor at the Kahlert School of Computing , part of the John and Maria Price College of Engineering at the University of Utah. His research focuses on programming languages, gradual/migratory type systems, formal methods, and human factors in software development. He holds a Ph.D. from Northeastern University (2020), a CIFellows postdoc at Brown University (2020–2022), and degrees from Cornell University (B.S. in ILR, M.Eng. in CS). Key projects include: Forge: A tool for teaching formal methods with lightweight model finding. FlowFPX: Tools for debugging floating-point exceptions in scientific computing. LTL Tutor: An adaptive learning system addressing temporal logic misconceptions. Gradual Typing Benchmarks: Evaluating performance and guarantees of type systems. His work emphasizes rigorous methods for language design, including empirical studies, performance evaluation, and human-centered approaches. Recent contributions include exploring misconceptions in LTL education and advancing type system interoperability between typed and untyped code. Teaching roles include courses on compilers, software verification, and programming languages. He advocates for practical tools like Rhombus (Python-like syntax with Lisp macros) and Static Python ’s sound gradual typing system.

• Micro-robotics
• Measurement Instrumentation
• Image Processing
• +۷ مورد دیگر

Ali Zarei is a Research Fellow at Tampere University's Faculty of Medicine and Health Technology within the Department of Biomedical Technology. Based at the Hervanta campus Festia building, he serves as Grant Holder for a European Union Horizon 2020 project under Marie Skłodowska-Curie grant agreement No. 764713, focusing on measurement instrumentation and micro-robotics automation for biomedical applications. He holds a Bachelor of Science in Electronics Engineering (2014) and Master of Science in Communications Systems Engineering (2018). His technical foundation spans embedded systems, communications, and signal processing, directly enabling his current interdisciplinary work in biomedical instrumentation. Dr. Zarei's research integrates Micro-robotics and Measurement Instrumentation to automate mechanical testing of bio-based fibers like flax. He employs advanced Image Processing and Machine Vision for object detection, combined with Machine Learning and Deep Learning algorithms (DNN, CNN) for sensor data analysis. His technical implementation leverages Python, MATLAB, and C/C++ for both data analytics and embedded device programming, significantly enhancing micro-manipulation throughput. His scientific recognition includes the prestigious Marie Skłodowska-Curie Fellowship, which funds his mission to increase micro-robotic platform efficiency for biomaterial testing. This award supports his development of high-precision instrumentation within the EU's Horizon 2020 framework. As part of the BioMediTech research unit, he collaborates on interdisciplinary projects merging engineering and medicine. His current work involves optimizing micro-robotic systems at the Festia facility, with future implications for sustainable biomaterials research and tissue engineering applications.

Gullu Kızıltaş Şendurمشاهده پروفایل

دانشیار

• Design Optimization
• Material Design and Fabrication
• Computational Mechanics
• +۵ مورد دیگر

Dr. Gullu Kızıltaş Şendur is an Associate Professor in the Mechatronics Engineering Department at Sabancı University's Faculty of Engineering and Natural Sciences in Istanbul, Turkey. She received her B.S. and M.S. degrees in Mechanical Engineering from Middle East Technical University in Ankara, Turkey in 1995 and 1998, respectively, and completed her Ph.D. in Mechanical Engineering at the University of Michigan, Ann Arbor in 2003. Her research spans multiple interdisciplinary fields with a focus on: Design Optimization of mechanical, biomedical and electromagnetic systems Material Design and Fabrication using topology optimization and advanced 3D fabrication techniques Computational Mechanics including advanced micro-material modeling and Finite Element Methods Electromagnetics applications for miniature antennas and RF devices Tissue Engineering for artificial tissues and organs Dr. Kızıltaş Şendur's recent work demonstrates a strong trend toward additive manufacturing, multi-material design, and computational methods for creating functionally graded structures. Her research integrates computational design with experimental validation across mechanical engineering, biomedical applications, and electromagnetic systems. She has developed novel optimization frameworks that bridge multiple disciplines to solve complex engineering problems. Her scientific achievements have been recognized with prestigious awards including: 2008 Graduating Class Teaching Award from Sabancı University 2008 TUBA-GEBIP (Young Scientist Award) from the Turkish Academy of Sciences Dr. Kızıltaş Şendur has successfully advised numerous graduate students through their M.Sc. and Ph.D. programs, with many continuing to academic and research careers at institutions worldwide. She leads the 3D Systems Design and Realization Research Laboratory at Sabancı University's Nanotechnology Research and Application Center (SUNUM), which provides state-of-the-art facilities for multi-disciplinary research. Her laboratory focuses on the modeling and fabrication of multi-functional micro/nanostructures, with applications spanning mechanical engineering, biomedical devices, and electromagnetic systems. The group maintains strong collaborations with international research institutions and industry partners to advance the frontiers of computational design and advanced manufacturing.

Christopher Kulpمشاهده پروفایل

استاد

• Nonlinear Dynamics
• Complex Systems
• Machine Learning
• +۷ مورد دیگر

Christopher Kulp is the John P. Graham Teaching Professor of Physics in the Department of Astronomy and Physics at Lycoming College. He holds a B.A. from McDaniel College, and M.S. and Ph.D. from The College of William & Mary. Since joining Lycoming College in 2008, he has taught physics at all levels, along with courses in machine learning, data analysis, and mathematical modeling. Education: B.A., McDaniel College M.S., The College of William & Mary Ph.D., The College of William & Mary His research centers on nonlinear and complex systems, with a focus on nonlinear time series analysis, machine learning, and mathematical modeling. He applies these techniques to data from diverse fields including astrophysics, climatology, medicine, economics, biology, and combustion. His group emphasizes undergraduate research, and he has published over 30 peer-reviewed articles, book chapters, and conference papers. The recent publications highlight a strong trend toward applying machine learning and agent-based modeling to socioeconomic systems, particularly in understanding wealth distribution and taxation impacts. Other works explore symbolic dynamics in chaotic systems, reflecting a deep engagement with nonlinear time series analysis and complex behavior characterization. Scientific Awards: Mike Resnick Memorial Award for Best Science Fiction Short Story by a New Author Kulp has secured research support enabling sustained undergraduate involvement in computational and theoretical projects. His advising emphasizes hands-on experience in data science and modeling. He is also a co-author of influential textbooks, including Classical Mechanics: A Computational Approach (2020) and Mathematical Methods using Python (2024), which integrate programming into physics education. His interdisciplinary approach bridges scientific rigor with creative expression. He leads an active research group focused on complex systems and maintains a strong commitment to pedagogy and innovation in STEM education. His work often involves network representations of time series and intelligent agent design, fostering collaboration across disciplines.

Parker Shectmanمشاهده پروفایل

استادیار

• Computational Engineering
• Operations Research
• Financial Engineering
• +۶ مورد دیگر

Parker Shectman, Ph.D., serves as an Assistant Professor of Engineering Management in the Department of Engineering Education at St. Mary's University, San Antonio. With over 30 years of academic and professional experience, he previously lectured at the University of Illinois at Urbana-Champaign and the University of Texas at San Antonio. His international consulting career spans Bulk Materials Handling, Mineral and Agricultural Processing, Industrial Simulation, and Risk Assessment across 48 facilities in 24 countries, including recent projects in Brazil (2013). His academic credentials include: Ph.D. in Mechanical & Industrial Engineering, University of Illinois at Urbana-Champaign (1999) M.S. in Mechanical & Industrial Engineering, University of Illinois at Urbana-Champaign (1995) B.S. in Engineering Science, Trinity University, San Antonio (1992) Dr. Shectman's research bridges industrial, mechanical, and financial engineering through computational methods for real-world applications. His primary focus areas include discrete-event simulations of bulk import/export terminals, discrete element modeling of bulk flow, economic assessment of engineering projects, and recreational mathematics. He applies advanced techniques in statistics, design of experiments, mathematical programming, facility layout, queuing processes, and discrete-event simulation to solve complex problems in ports, mining, agribusiness, and finance. Analysis of his publication history reveals a consistent interdisciplinary trajectory: early work (1990s-2000s) centered on mathematical programming and portfolio optimization, while recent publications (2011-2021) address critical port capacity issues in South American agribulk export systems. His research demonstrates seamless integration of operations research methodologies across engineering and financial domains. No scientific awards are documented in available sources. Dr. Shectman teaches Engineering Economy, Python Programming for Engineering, Industrial Statistics, and Engineering Project Management. While specific graduate student advising details are unavailable, his three decades of applied industry experience—including international project leadership—demonstrate significant professional engagement beyond traditional academia. His professional activities include participation in project teams at 48 industrial facilities across 24 countries, with expertise applied in ports & terminals, mining, and agribusiness sectors. Beyond engineering, he performs with the St. Mary’s SET Players and contributes to the Online Encyclopedia of Integer Sequences, reflecting his dual passion for music and recreational mathematics.

Julien Arinoمشاهده پروفایل

استاد

• Mathematical Epidemiology
• Mathematical Population Dynamics
• Data Science
• +۳ مورد دیگر

Julien Arino is a Professor in the Department of Mathematics at the University of Manitoba , specializing in Mathematical Epidemiology and Mathematical Population Dynamics . He is an active member of the Mathematical Biology group and focuses on the role of movement in disease transmission, with applications to both human and animal health. Department of Mathematics Faculty of Science University of Manitoba His research spans: Metapopulation dynamics with explicit movement Multi-pathogen and multi-species transmission modeling Computational epidemiology with data science integration Evaluation of travel control measures for disease variants Recent work includes publications on: Cholera transmission in Chad Multi-species bovine tuberculosis dynamics Measles resurgence patterns Software tools for R compilation and data visualization He maintains a public YouTube channel with lecture videos on Mathematical Epidemiology and contributes open-source code for scientific computing in R and Python. His methodological contributions include novel approaches to plot formatting and computational efficiency in epidemic modeling.

Tobias Schrimpfمشاهده پروفایل

پژوهشگر

• Fake News
• Conspiracy Theories
• Digital Protest Communication
• +۳ مورد دیگر

Tobias Schrimpf is a Researcher at the Department of Media Research and Media Use, University of Hohenheim. He contributes to the BMBF-Project "AIDAHO – AI & Data Science Certificate Hohenheim" and focuses on fake news, conspiracy theories, and digital protest communication. Education: Master's in Communication Management and Analysis (University of Hohenheim) and Bachelor's in Media Studies and German Studies (University of Tübingen). His research examines the psychological and media system factors driving conspiracy beliefs, the persistence of misinformation, and the organization of digital protests on social platforms. His 2023 publication highlights network analysis techniques applied to Telegram-based protest movements like Querdenken. He has presented at major conferences, including the European Communication Conference (ECREA) and the International Association for Media and Communication Research (IAMCR), addressing topics such as climate protest perception, prompt engineering for LLMs, and cross-cultural links between press freedom and pandemic-related misinformation. Scientific Awards: Germany Scholarship (Deutschlandstipendium) Particularly committed project supervisor in a Humboldt reloaded project – October 2021 Tobias Schrimpf teaches courses on Python, R, SQL, and computational methods within social sciences as part of the AIDAHO certificate program. His work integrates social network analysis and AI-driven text analysis to study misinformation dynamics.

• Materials Science
• Microstructure Analysis
• Magnetic Properties
• +۷ مورد دیگر

Dr. Thomas George Woodcock is a Research Fellow at the Leibniz Institute for Solid State and Materials Research Dresden (IFW Dresden) within the Magnetic Materials Group. His work focuses on the interplay between material microstructure and functional properties, particularly in magnetic materials. Education : Master's in Materials Science and Engineering from the University of Sheffield, PhD in Materials Science from the University of Birmingham. Employing multi-scale characterization techniques such as aberration-corrected TEM/STEM, EBSD, and synchrotron diffraction, he investigates atomic-scale interfaces, grain-level phenomena, and magnetic domain structures. His recent publications emphasize permanent magnet development, computational data analysis, and phase stability in MnAl-C and Ni-Mn-Ga systems. Scientific contributions include: Marie Curie Postdoctoral Fellowship Editorial Assistant for Journal of Alloys and Compounds (2010-2020) Co-organizer of nanomagnetism symposia Program committee member for major magnetism conferences He actively contributes to Python-based data analysis tools and micromagnetic modeling, advancing sustainable magnet technologies without rare-earth elements.

• Quantum Computing
• Machine Learning
• Condensed Matter Physics
• +۳ مورد دیگر

Prof. Giuseppe Carleo is a Professor specializing in quantum computing and machine learning for quantum many-body systems, renowned for developing the NetKet open-source software library. His research bridges theoretical physics and artificial intelligence to solve complex quantum problems. His primary research interests include: Variational quantum algorithms and neural quantum states Simulation of quantum dynamics and non-equilibrium phenomena Hamiltonian reconstruction and learning Open-source tool development for quantum simulation Recent publications (2022-2025) demonstrate leadership in applying machine learning to quantum systems, with breakthroughs in Bose gas dynamics, noise resilience in quantum state propagation, and scalable software frameworks. The NetKet project has become a cornerstone for researchers globally, enabling neural-network-based quantum simulations. While specific mentorship details are unreported, his collaborative publications indicate active supervision of computational physics projects. His work drives innovation in quantum computing infrastructure, with implications for quantum hardware development and algorithm design.

Michael Bruyns-Haylettمشاهده پروفایل

استاد

• Neuroscience
• Electrophysiology
• Materials Engineering
• +۵ مورد دیگر

Dr. Michael Bruyns-Haylett is a Contracted Professor in the Department of Business Management at IQS School of Management, Universitat Ramon Llull. With a PhD in Neuroscience from the University of Sheffield (2013) and a Master's in Computational and Cognitive Neuroscience (2009), he bridges neuroscience expertise with business management education. His research expertise spans neuroscience , electrophysiology (EEG, LFP, ECOG & spiking), and materials engineering , with particular focus on neural excitation-inhibition balance, neurotrauma, and biomaterials applications. His work integrates advanced data analysis techniques using MATLAB and Python to address complex neuroscience questions. Bruyns-Haylett's recent publications reveal a strong interdisciplinary approach, connecting neuroscience with materials science and business applications. His 2024 work on EEG hyperexcitability following traumatic brain injury demonstrates his neuroscience expertise, while his 2023 machine learning research for cellular uptake prediction highlights his computational skills. The 2022 bioinspired transducer paper exemplifies his ability to bridge engineering and healthcare applications. As a researcher in the GEMAT (Materials Engineering Group), he contributes to three main areas: development of new functional materials, surface engineering, and biomaterials. His current projects include 'Uncovering patterns of unconscious reactions to fake content' (2024) and the ongoing GEMAT research initiative (2022-2025). His teaching focuses on integrating scientific and technical knowledge with business management principles, particularly in the Master in Industrial Business Management program where he helps technical graduates develop management careers. His unique background enables him to connect neuroscience research with practical business applications in healthcare technology and materials science industries.