جست‌وجوی استاد و پژوهشگر

Rickard Sandbergمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning
• Data Analytics
• +۹ مورد دیگر

Rickard Sandberg is an **Associate Professor** and **Center Director** at the **Department of Entrepreneurship, Innovation and Technology** at the **Stockholm School of Economics (SSE)**. His work bridges econometrics, statistics, and business analytics with a focus on time series analysis, machine learning applications, and sustainability measurement. **Research Interests**: Machine Learning, Deep Learning, Data Analytics, Predictive Analytics, Forecasting, Nonlinear Time Series Modelling, Structural Economic Modelling, Econometrics, and Measuring Sustainability. His research emphasizes theoretical advancements in statistical methods and their practical application in economic and business contexts. **Key Contributions**: His publications explore unit root testing in nonlinear models, ESG rating challenges, and the impact of energy policies. Notable works include analyzing Scandinavian unemployment trends, cartel damage calculations, and Nordic companies' data-driven transformations. His 2023 paper on ESG ratings proposes solutions for consistency in ambiguous evaluation systems. **Teaching & Outreach**: Teaches advanced econometric time series courses (e.g., MSc 5314) and actively engages in international academic collaborations through presentations in Japan and Brazil. His work on AI for sustainability highlights interdisciplinary outreach efforts. **Labs/Teams**: Leads research initiatives within SSE’s Department, focusing on entrepreneurship and innovation through data and economic modeling frameworks.

Emily Bouckمشاهده پروفایل

استاد

• Special Education
• Mathematics Education
• Technology in Education
• +۵ مورد دیگر

Emily Bouck is a Professor and Associate Dean for Research at the College of Education, Michigan State University. Her research focuses on mathematics education for students with disabilities and at-risk populations, emphasizing response to intervention (RtI), virtual manipulatives, and technology integration. She holds a Ph.D. from Michigan State University. Her work addresses instructional strategies for students with disabilities, including virtual manipulatives, non-immersive VR, and evidence-based practices in math interventions. Key areas include life skills development, transition planning for students with intellectual disabilities, and online education post-pandemic. Bouck’s research spans elementary to secondary levels and explores topics like fraction instruction, algebra support, and computational fluency through games and technology. Bouck advocates for inclusive education practices and has contributed to systematic reviews on math interventions for autism spectrum disorder (ASD) and intellectual disabilities. Her studies often compare virtual and concrete manipulatives, emphasizing accessibility and generalization of skills. Recent work highlights the use of video modeling, schema-based instruction, and collaborative teacher leadership in special education settings. Her role as Associate Dean for Research underscores her commitment to advancing research in special education policies, transition services, and technology-driven solutions for students with extensive support needs.

Kayo Ideمشاهده پروفایل

دانشیار

• Data Assimilation
• Atmospheric Dynamics
• Oceanography
• +۳ مورد دیگر

Dr. Kayo Ide is an Associate Professor at the University of Maryland's Department of Atmospheric and Oceanic Science, within the College of Computer, Mathematical, and Natural Sciences. Her research focuses on dynamics of atmosphere and oceans, with expertise in data assimilation, scientific prediction, transport/mixing processes, and climate variability. She contributes to NOAA's operational systems and collaborates with teams like the UFS Coastal Applications Team. Her work emphasizes integrating advanced observational technologies (e.g., satellite data from CrIS, Aeolus) into numerical weather prediction and ocean modeling frameworks. Key projects include optimizing data assimilation algorithms, evaluating new sensor constellations (e.g., CubeSats), and improving forecast initialization techniques. Dr. Ide also develops software tools like the System for Analysis of Wind Collocations (SAWC) to intercompare multi-platform wind observations. Publications highlight innovations in satellite data utilization, ensemble-based methods, and the impact of novel observing systems on operational forecasting. Her research bridges computational methods, environmental science, and applied meteorology, addressing challenges in global climate monitoring and predictive modeling.

Efthymios N. Karatzasمشاهده پروفایل

استادیار

• Numerical Analysis
• Computational Mathematics
• Optimal Control
• +۵ مورد دیگر

Efthymios N. Karatzas serves as an Assistant Professor in the Department of Mathematics at Aristotle University of Thessaloniki, Faculty of Sciences, within the Computer Science and Numerical Analysis Section. He maintains an active research profile in computational mathematics with strong institutional affiliations including collaborations with SISSA mathLab and FORTH Institute of Applied and Computational Mathematics. His academic credentials include: PhD in Mathematics, National Technical University of Athens (2015) Master's in Applied Mathematical Sciences – Computational Mathematics, NTUA (2009) Master's in Applied Mathematics, University of Patras (2001) Bachelor's in Mathematics (Computational Mathematics), University of Patras (1999) Dr. Karatzas' research program centers on advanced numerical techniques for partial differential equations , with pioneering work in reduced order modeling , embedded boundary methods , and optimal control systems . His expertise spans computational fluid dynamics, uncertainty quantification, and biomechanical applications, characterized by methodological innovation in handling geometrically complex domains through cut finite element approaches and shifted boundary formulations. Analysis of his 15 most recent publications reveals a cohesive research trajectory focused on developing efficient numerical frameworks for parametrized PDE systems. His work consistently bridges theoretical rigor with practical implementation, particularly in advancing reduced basis methods for fluid-structure interaction and biological modeling, demonstrating significant contributions to computational mathematics through high-impact journal publications. No major scientific awards are documented in the available sources. Dr. Karatzas demonstrates research leadership through project management roles including Scientific Manager for the ELIDEK project at NTUA (2019-2021) and Project Manager for the European Social Fund HEaD initiative at SISSA (2017-2019). His grant administration experience encompasses coordinating interdisciplinary teams and securing external funding for computational mathematics research. He maintains active collaborations with the SISSA mathLab in Trieste (particularly with Prof. Gianluigi Rozza's group) and the FORTH Institute in Crete, participating in international workshops including the Reduced Order Methods in CFD Summer School (2019) and SIAM UQ conferences. His research network spans computational mathematics groups across Europe with emphasis on advancing numerical methodologies for real-world engineering and biological applications.

James McLaughlinمشاهده پروفایل

استاد

• Solar Physics
• Astrophysics
• Plasma Physics
• +۳ مورد دیگر

James McLaughlin is a Professor of Physics at Northumbria University, specializing in solar physics and magnetohydrodynamics. He holds a PhD from the University of St Andrews and previously worked at NASA Goddard Space Flight Center and the University of St Andrews as a Research Fellow. His research focuses on magnetic reconnection, solar coronal dynamics, and MHD wave behavior. He leads the Solar and Space Physics Group and secured a £1.29M STFC grant (2023–2026). McLaughlin supervises PhD students exploring oscillatory reconnection dynamics and has authored over 50 peer-reviewed papers. He is a Fellow of the Royal Astronomical Society and a Member of the Institute of Physics. Education: MSci (Mathematics & Physics), Durham University, 2002 PhD (Applied Mathematics & Solar Physics), University of St Andrews, 2002–2006 Research Interests: Magnetic reconnection mechanisms, solar flare dynamics, coronal heating, MHD wave propagation, and plasma diagnostics in extreme astrophysical environments. His work bridges theoretical modeling, numerical simulations, and observational data from instruments like SDO/AIA and DKIST. Recent Projects: STFC Consolidated Grant: Solar and Space Physics Group (£1.29M, 2023–2026) Awards: Fellow of the Royal Astronomical Society (2002) Member of the Institute of Physics (1998) Advising & Grants: Supervises PhD students Ryan Smith and Jordan Talbot. His research explores oscillatory reconnection’s role in generating solar waves and energy release. He collaborates internationally on space physics missions and heliophysics studies.

• Operations Research
• Management Science
• Mathematical Programming
• +۱۳ مورد دیگر

Benjamin Lev is a Professor in the Department of Decision Sciences and Management Information Systems (DS&MIS) at the LeBow College of Business, Drexel University. He previously served as Trustee Professor (2014–2021) and Department Head (2009–2014) at Drexel. His academic leadership extends to prior roles as Professor, Department Head, and Dean at the University of Michigan-Dearborn (1990–2009), Professor and Department Head at Worcester Polytechnic Institute (1987–1990), and Professor and Department Head at Temple University (1970–1987). He has held short appointments at institutions in China and the U.S., including the Wharton School and Tel Aviv University. Lev’s research spans Operations Research, Management Science, and Decision Sciences , with expertise in mathematical programming, operations planning, inventory control, supply chain management, and optimization under uncertainty. His recent work focuses on applications in disaster management, sustainable supply chains, AI in operations, water resource allocation, and emergency logistics. He has published over 150 journal articles and authored or edited 18 books, with a strong emphasis on real-world problem-solving using quantitative methods. The trends in his recent publications (2022–2025) reflect a focus on complex optimization under uncertainty , particularly in humanitarian logistics, environmental sustainability, and digital commerce. His work frequently employs advanced methodologies such as bi-level programming, stochastic optimization, fuzzy logic, and data envelopment analysis (DEA), often applied to critical societal challenges like disaster response, air pollution, and resource scarcity. Lev is an INFORMS Fellow (2003) and has received several honors, including the 2023 Top Cited Article award in Naval Research Logistics and the 2023 First Prize from the Jiangxi Province Social Science Outstanding Achievement Award. His editorial leadership is most notably demonstrated by his 23-year tenure as Editor-in-Chief (2002–2025) of OMEGA – The International Journal of Management Science , one of the premier journals in the field. He has advised numerous scholars and presented his work globally, particularly on his experience as EiC of OMEGA . He has been actively involved in academic collaborations, especially in China, serving on advisory boards and as an external reviewer for institutions like Sichuan University. He has also received significant grant funding from the U.S. National Institutes of Health, U.S. Public Health Service, and U.S. Air Force for research in medical information systems and operations research applications. Lev has been instrumental in organizing major international conferences and has served on the editorial boards of over 20 journals, including Interfaces, IIE Transactions, OR Journal, and Financial Innovation . His role as Vice President of TIMS and INFORMS further underscores his leadership in the global operations research community.

• Large-scale nonparametric and kernel methods
• Robust and trustworthy machine learning
• Multiresolution data and data fusion
• +۳ مورد دیگر

Professor Dino Sejdinovic is a faculty member in the School of Computer and Mathematical Sciences at the University of Adelaide, part of the Faculty of Sciences, Engineering and Technology. Previously, he held positions as Lecturer and Associate Professor at the University of Oxford's Department of Statistics (2014–2022). His academic qualifications include a PhD in Electrical and Electronic Engineering from the University of Bristol (2009) and a Diplom in Mathematics and Theoretical Computer Science from the University of Sarajevo (2006). His research focuses on the intersection of statistical methodology and machine learning, encompassing large-scale nonparametric methods, robust machine learning, multiresolution data fusion, and measures of dependence. He has contributed to kernel methods, Bayesian inference, causal discovery, and applications in climate science, quantum computing, and social science data analysis. Education: PhD in Electrical and Electronic Engineering, University of Bristol (2009) Diplom in Mathematics and Theoretical Computer Science, University of Sarajevo (2006) Sejdinovic's work emphasizes bridging theoretical foundations with practical applications, such as cloud type classification using vision transformers and machine learning-driven quantum device optimization. His recent publications explore topics like kernel-based causal inference, Bayesian neural networks, and uncertainty quantification in statistical models. Advising and grants: Eligible to supervise Masters and PhD students in machine learning and statistics, though specific grants or student advisees are not explicitly listed in the provided texts.

Luyang Zhaoمشاهده پروفایل

استادیار

• Robotics
• Artificial Intelligence
• Soft Robotics
• +۹ مورد دیگر

Luyang Zhao is an incoming tenure-track Assistant Professor in the Department of Electrical and Computer Engineering at Clemson University (starting August 2025). He earned his PhD in Computer Science and double undergraduate degrees in Computer Science and Mathematics from Dartmouth College and the University of Minnesota respectively. Academic Affiliation : Clemson University (Assistant Professor) Education : PhD in Computer Science (Dartmouth College), BS in Computer Science & Mathematics (University of Minnesota) His research focuses on Robotics , particularly soft robotics, modular systems, and bio-inspired designs. Key areas include: Large Language Models for robotic design automation Modular tensegrity systems for self-assembling structures Swarm coordination strategies Multi-environment adaptability (land/aquatic/aerial) Simulation tool integration for design optimization Recent publications highlight his work on SoftSnap modular platforms, LLM-driven swarm intelligence, and bioinspired dolphin robots. He received the Neukom Outstanding Graduate Research Prize for his contributions. Industry Experience : Research internships at Amazon Robotics and TuSimple Mentorship : Advised 6+ graduate/undergraduate researchers Open-Source Contributions : Developed SoftSnap platform for rapid prototyping Academic Service : Workshop co-organization (IROS 2023), peer reviewing (RA-L, ICRA, IROS, RoboSoft, BioRob)

Etienne Méminمشاهده پروفایل

استاد

• Stochastic Modeling
• Geophysical Flows
• Fluid Mechanics
• +۳ مورد دیگر

Etienne Mémin is a Research Director (Full Professor status) at Inria and leads the Odyssey research group, which is affiliated with multiple institutions including University of Rennes, IRMAR, Ifremer, LOPS, UBO, IMT Atlantique, and Lab-STICC. He serves as a Visiting Professor at the Department of Mathematics, Imperial College London (2020–2026) and is the Principal Investigator of the ERC STUOD grant. His research spans the intersection of geophysical sciences, fluid mechanics, computational sciences, and applied mathematics, focusing on stochastic modeling of fluid flows, data assimilation, and uncertainty quantification. He has developed frameworks for stochastic geophysical flows, coarse-scale simulations, and robust motion estimation techniques. Recent publications highlight his work on stochastic Navier-Stokes equations, ensemble forecasting, and data assimilation for ocean and atmospheric models. He has applied these methods to numerical weather prediction, turbulence analysis, and real-time flow reconstruction using sparse measurements. Scientific Awards: ERC STUOD grant PhD Students: Francesco Tucciarone (ERC STUOD, NEMO code) Benjamin Dufée (Ensemble Kalman filters, ATER position) Berenger Hug (Stochastic Navier-Stokes analysis, teaching) Antoine Moneyron (Stochastic ocean models) Collaborations: Imperial College London (D. Crisan, S. Laizet), Zhejiang University (S. Cai, C. Xu), MétéoFrance (P. Arbogast, O. Pannekoucke), Ifremer (B. Chapron), IRSTEA Lyon (L. Pénard), IRMAR (R. Lewandovsky), University of Buenos Aires (G. Artana), ISSI Beijing (T. Corpetti).

Diego Patiñoمشاهده پروفایل

استادیار

• Machine Learning
• Geometric Computer Vision
• 3D Reconstruction
• +۷ مورد دیگر

Diego Patiño is an Assistant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington (UTA), a position he began in September 2024. He earned his Ph.D. in Computer Engineering from the National University of Colombia in 2020, following M.S. and B.S. degrees from the same institution. Prior to joining UTA, he served as a Postdoctoral Fellow at Drexel University and a Postdoctoral Researcher at the GRASP Laboratory, University of Pennsylvania. B.S. in Computer Engineering, National University of Colombia, 2010 M.S. in Computer Engineering, National University of Colombia, 2012 Ph.D. in Computer Engineering, National University of Colombia, 2020 Dr. Patiño's research centers on geometric computer vision and machine learning, with applications in robotics and 3D vision. His primary interests include 3D reconstruction, graph neural networks, symmetry detection, physics-informed machine learning, and reinforcement learning. He develops algorithms that integrate geometric priors and physical constraints into deep learning models to improve robustness and generalization in real-world robotic systems. His recent publications demonstrate a strong trend in leveraging implicit neural representations for 3D shape reconstruction, applying graph neural networks to swarm robotics, and enhancing computer vision tasks with self-supervised and physics-informed learning. Work spans high-impact venues such as IEEE RA-L, ICRA, ICPR, and MICCAI, showing a consistent focus on geometric reasoning, robotic perception, and medical imaging applications. His scientific contributions have been recognized with awards from the UTA Division of Student Affairs for exceptional dedication and positive impact (2024 and 2025). He is actively involved in securing research funding, with multiple grants under review from NSF, Air Force SBIR, and industry partners like Sony. Exceptional dedication and positive impact recognition, UTA Division of Student Affairs (December 9, 2024) Exceptional dedication and positive impact recognition, UTA Division of Student Affairs (April 30, 2025) Dr. Patiño advises and serves on committees for multiple graduate students in computer science and engineering, including doctoral and master’s candidates. He is also leading or co-leading several research grants under review, covering topics such as aerial swarm navigation, neuromorphic sensing, and industrial computer vision. He teaches graduate courses in computer vision and is involved in service roles including PhD admissions and faculty appointments committees. He is affiliated with research initiatives at UTA, including the UTARI Research Institute, where he has presented on geometric modeling and physics-informed learning. His lab focuses on developing next-generation computer vision algorithms for robotics, industrial inspection, and safety-critical systems.

• Rhizosphere Biology
• Soil Science
• Environmental Modeling
• +۷ مورد دیگر

Professor Tiina Roose is a leading academic in biological and environmental modeling at the University of Southampton. She holds a faculty position as Professor in the Bioengineering Group and is affiliated with interdisciplinary research groups including the Nature-Based Ocean Solutions and Southampton Marine and Maritime Institute. Her work integrates engineering principles with environmental and agricultural sciences to address challenges in soil science, plant-microbe interactions, and sustainable agriculture. Research focuses on rhizosphere dynamics, soil mechanics, and biofilm modeling. Active in collaborative projects with industry and international partners, funded by BBSRC, EPSRC, and EU Horizon programs. Develops advanced imaging techniques like synchrotron X-ray CT and microdialysis for in situ soil analysis. Her research interests emphasize agricultural sustainability, environmental adaptation, and precision agriculture technologies. Recent work includes studies on Xylella fastidiosa plant pathogens, nutrient dynamics in soil, and digital twins for fertilizer management. She has led projects such as 'Rhizosphere by Design' and 'fertilAIser', contributing to innovative solutions for food security and soil health. Key collaborations span biomaterials engineering, environmental microbiology, and computational modeling, with outputs published in journals like Plant and Soil , Soil Biology and Biochemistry , and New Phytologist .

Ronald Ting Tai Chanمشاهده پروفایل

مدرس ارشد

• VR/AR/MR development for engineering applications
• Gamification in engineering courses
• Innovative product design using VDI-2221 Framework and Axiomatic Design Methodology
• +۳ مورد دیگر

Dr Ronald Ting Tai Chan is a Senior Lecturer and Education Focused Academic in the School of Mechanical and Manufacturing Engineering at the University of New South Wales (UNSW). He holds a pivotal leadership role as Postgraduate Coursework Coordinator, focusing on curricula development and accreditation processes, including leading the 2021 Engineers Australia (EA) accreditation of the Master of Engineering program. Education: PhD in Manufacturing and Management Engineering (UNSW), BE (Hons I) in Aerospace Engineering (UNSW), and BSc in Mathematics and Statistics (UNSW) His research interests center on leveraging educational technology to enhance learning experiences, particularly through VR/AR/MR integration, gamification, and data-driven methodologies like PLS-PM for industrial systems analysis. He advocates for critical thinking and methodological approaches in solving open-ended engineering challenges. Dr Chan teaches advanced courses such as Engineering Management, Design and Analysis of Product Process Systems, and Reliability Engineering. His academic contributions span statistical process improvement methodologies applied in industries like healthcare and beverage manufacturing. He actively participates in international conferences, presenting work on PLS regression models and team performance analysis at the 8th International Conference on Partial Least Squares (Paris, 2014).

• Artificial Intelligence
• Probabilistic Graphical Models
• Deep Generative Models
• +۴ مورد دیگر

Ewa Szczurek is a Professor at the University of Warsaw's Faculty of Mathematics, Informatics and Mechanics and Co-director of the Institute for AI for Health, leading joint labs at Helmholtz Munich and the University of Warsaw. Education: Master degree in Computer Science from Uppsala University, Sweden (2005) Master degree in Computer Science from University of Warsaw, Poland (2006) Doctoral degree from Max Planck Institute for Molecular Genetics, Berlin (2011) Postdoctoral fellowship at ETH Zurich, Switzerland (2011) Habilitation at University of Warsaw, Poland (2020) Visiting associate professor at Northwestern University, USA (2023) Research Focus: Her work centers on probabilistic graphical models and deep generative models applied to computational medicine, with emphasis on oncology (tumor microenvironment modeling and evolution), pulmonology, and AI-driven antimicrobial peptide design to combat antibiotic resistance. She develops specialized deep learning frameworks for spatial transcriptomics analysis, tumor evolution reconstruction, and synthetic antimicrobial compound generation. Publication Trends: Recent publications (2022-2023) demonstrate consistent innovation in AI-driven biomedical solutions, bridging machine learning with oncology and infectious disease research. Her work shows increasing focus on generative models for drug design and spatial data analysis, with strong industry-academia collaborations (e.g., Merck, IMMUcan consortium). Awards: ERC Consolidator Grant (2023) for DOG-AMP project Scientific and didactic award from Rector of University of Warsaw ETH Zurich and IMPRS fellowship awards Grants & Leadership: Leads the ERC Consolidator Grant project DOG-AMP for antimicrobial peptide design. Serves as Associate Editor for Genome Biology and participates in program committees for ISMB and RECOMB-CCB conferences. Collaborates with international consortia including IMMUcan and Merck's Oncology Bioinformatics department. Labs & Networks: Co-directs AI for Health Institute with joint labs at Helmholtz Munich and University of Warsaw. Active in ELLIS (pan-European AI network) and Polish Bioinformatics Society, driving interdisciplinary research at the AI-medicine interface.

• Transportation Systems
• Smart Cities
• Traffic Simulation
• +۳ مورد دیگر

Maria Paz Linares Herreros is a Lecturer at the Universitat Politècnica de Catalunya (UPC), affiliated with the School of Mathematics and Statistics (FME) and the Department of Statistics and Operations Research. She is a member of the IMP (Information Modeling and Processing) research group and collaborates with inLab FIB on intelligent transportation systems. Research interests: Transportation systems, smart cities, traffic simulation, data-driven modeling, environmental impact assessment Specializes in applying machine learning and simulation to urban mobility challenges Her recent publications focus on: Parking availability prediction using deep learning Traffic emission modeling linked to urban policies Dynamic ride-sharing system optimization Integration of IoT data in transportation planning Scientific recognition: Recipient of the IV International Award on Transport Infrastructure Management Research (2018) Active contributor to projects like CitScale and Virtual Mobility Lab Collaborator in European initiatives like KIC Urban Mobility

Elin Nymanمشاهده پروفایل

دانشیار

• Systems Biology
• Biomedical Engineering
• Drug Development
• +۳ مورد دیگر

Elin Nyman is the Head of the Department of Biomedical Engineering (IMT) and an Associate Professor at Linköping University. She leads the department, fostering an environment of trust and collaboration. Her research focuses on systems biology and e-health, integrating mathematical models with experimental data to advance drug development and clinical tools. She supervises students in both the Faculty of Science and Engineering and the Faculty of Medicine and Health Sciences, examining courses like TMBI28 and 8BKG45. Her research interests include systems biology, particularly in drug development and AI applications in healthcare. Key projects involve robust metabolic measurements, AI-driven diagnostic tools (M4-health), and knowledge-driven drug development with AstraZeneca. Recent publications highlight liver steatosis dynamics, IL-10 feedback mechanisms, and insulin resistance modeling. Her work bridges interdisciplinary collaboration, such as a course combining medical and engineering students to develop digital health solutions. She contributed to a SEK 13 million grant for AI-based crime-solving using detailed analyses and AI. Current affiliations include the Division of Biomedical Engineering (MT) and IMT department.