جست‌وجوی استاد و پژوهشگر

Erik Knudsenمشاهده پروفایل

دانشیار

• Quantitative Media Research
• Political Communication
• Selective Exposure
• +۱۰ مورد دیگر

Dr. Erik Knudsen is an Associate Professor at the Department of Information and Media Studies, University of Bergen , specializing in media use and audience research . He leads the NEWSREC project on news recommenders' democratic impact and co-leads a work package at the MediaFutures Research Centre . His research focuses on quantitative audience analysis , media polarization , and trust in journalism . Education : PhD from UiB, Postdoc at DIGSSCORE Research Interests : Selective exposure, affective polarization, media effects, conjoint analysis, political communication, and framing theory Awards : Recipient of the Research Council of Norway's Research projects for young talents fellowship He serves as editor-in-chief of Norsk medietidsskrift and chairs NordMedia23 . His work appears in Journal of Communication , Political Communication , and Digital Journalism , with 2022's Modeling news recommender systems being highlighted. He founded the Norwegian Journalism Panel and leads DIGSSCORE's Political Behavior and Communication Research Group . The summary includes his Ted-talk, PechaKucha presentation, and Forsker Grand Prix participation.

Mariel Vazquezمشاهده پروفایل

استاد

• DNA Topology
• Molecular Biology
• Polymer Physics
• +۵ مورد دیگر

Mariel Vázquez is a Professor of Mathematics and Professor of Microbiology and Molecular Genetics at the University of California, Davis. Her research integrates topological methods, polymer physics, and molecular biology to study DNA structure, viral evolution, and chromosomal organization. She holds joint appointments in the Departments of Mathematics and Microbiology & Molecular Genetics, and leads the Topological Molecular Biology Lab. Education: B.Sc. in Mathematics (1994, National Autonomous University of Mexico); Ph.D. in Mathematical Biology (2000, Florida State University). Awards include AAAS Fellow (2024) and NSF CAREER Award (2015). Her work focuses on DNA topology, R-loop formation, and coronavirus evolution using tools like knot theory, Monte Carlo simulations, and topological data analysis. Key research areas include: DNA packing in bacteriophages, chromosomal aberrations in cancer, and computational models of viral evolution. She collaborates on projects like predicting R-loop formation via formal grammars and analyzing SARS-CoV-2 mutation landscapes. Her lab also develops novel knot tables and symmetry-driven nomenclature for topological studies. Advocacy: Committed to diversity in STEM, she co-founded initiatives like the Center for the Advancement of Multicultural Perspectives on Science (CAMPOS) and published on challenges faced by early-career Latinas in academia. Her work bridges interdisciplinary research with educational outreach to underrepresented groups.

Maxim Buzdalovمشاهده پروفایل

مدرس

• Algorithms
• Evolutionary Algorithms
• Genetic Algorithms
• +۳ مورد دیگر

Maxim Buzdalov is a Lecturer in Computer Science at the Department of Computer Science, Aberystwyth University. His research focuses on evolutionary algorithms, optimization, and theoretical computer science, with particular emphasis on algorithm design, runtime analysis, and parameter control. He has contributed to advancements in genetic algorithms, crossover operators, and non-dominated sorting techniques. His work addresses challenges in computational complexity, including fixed-target optimization, black-box complexity, and efficiency improvements through novel storage mechanisms like minimum spanning trees. Buzdalov's research also explores parameter tuning strategies, such as lazy parameter control, and their applications in improving algorithm performance. His publications span conferences like GECCO, FOGA, and IEEE Congress on Evolutionary Computation, reflecting a strong track record in evolutionary computation theory and practice. Key themes in his articles include runtime analysis, algorithmic efficiency, and the theoretical foundations of evolutionary methods. His contributions often bridge algorithm design with practical applications, such as optimizing robotic systems and testing algorithms for programming challenges.

Francisco Perez-Recheمشاهده پروفایل

مدرس ارشد

• Mathematical Epidemiology
• Infectious Disease Modelling
• Machine Learning
• +۷ مورد دیگر

Dr. Francisco Perez-Reche is a Reader in the School of Natural and Computing Sciences at the University of Aberdeen, where he has been a faculty member since 2012. He is actively involved in research, teaching, and supervising PhD students. His work bridges applied mathematics, data science, and public health, with a focus on modeling infectious diseases and food safety risks. His research interests include: Mathematical and computational modeling of infectious disease transmission Machine learning for source attribution in epidemiology Quantitative risk assessment using Monte Carlo simulations Network-based immunization strategies (e.g., explosive immunisation) Dose-response modeling for pathogens like E. coli O157 and Campylobacter His recent publications reveal a strong focus on public health challenges, including the role of untested individuals in COVID-19 spread, post-COVID health outcomes like diabetes and mental illness, and the origins of zoonotic pathogens. He applies interdisciplinary methods combining statistics, machine learning, and mathematical physics to real-world health problems. Dr. Perez-Reche has contributed to high-impact studies involving large datasets and collaborations with public health agencies across Europe. His work has been covered by major media outlets including The Times, BBC, and international science news platforms. He teaches courses in mathematical methods, machine learning, and physics, and serves as Director of Undergraduate Pathways in Physics at Aberdeen. He is a member of the Aberdeen Group for the Mathematics of Infectious Diseases (AGMID) and the Centre for Bacteria in Health and Disease (CBHD).

Ahmet Karagozogluمشاهده پروفایل

استاد

• Algorithmic Trading
• Financial Modeling
• Credit Risk
• +۷ مورد دیگر

Ahmet Karagozoglu is the C.V. Starr Distinguished Professor of Finance and Investment Banking at the Zarb School of Business, Hofstra University. He has been a key figure in academic leadership, serving as the founding Academic Director of the Martin B. Greenberg Trading Room (2005–2021) and Coordinator of the Hofstra-Erasmus Summer Program (2003–2005). His educational background includes a B.S. in Industrial Engineering from Bogazici University, an M.B.A. from the University of Wisconsin-Oshkosh, and an M.Phil. and Ph.D. in Finance from Baruch College, CUNY, where he received the Oscar Lasdon Best Dissertation Award. Dr. Karagozoglu's research spans algorithmic trading, financial modeling, credit risk, derivatives, and risk management. His work integrates quantitative methods with real-world financial applications, particularly in market microstructure and risk modeling. He has published extensively in journals such as the Review of Finance , Journal of Futures Markets , and International Review of Financial Analysis . His recent publications reflect a strong focus on machine learning in credit risk, high-frequency trading dynamics, volatility modeling, and robust hedging strategies. These works demonstrate a consistent trend toward integrating advanced computational techniques with traditional financial theory. C.V. Starr Distinguished Professor of Finance and Investment Banking (2016) Oscar Lasdon Best Dissertation Award Dean’s Research Award (2000, 2012) Service Award (2007) Distinguished Teacher of the Year (2009) Dr. Karagozoglu has been actively involved in academic service, including conference presentations at FMA, EFA, MFA, and invitations to the Chicago Board of Trade Research Seminar. He has also contributed to curriculum development and international academic programs. He does not currently list named advisees, but his leadership in academic programs suggests mentorship at institutional levels. He led the Martin B. Greenberg Trading Room, a state-of-the-art financial lab used for experiential learning, research, and student training in trading and analytics, enhancing practical finance education at Zarb School of Business.

Marc HOFFMANNمشاهده پروفایل

استاد

• Statistics
• Nonparametric Statistics
• Stochastic Processes
• +۳ مورد دیگر

Marc HOFFMANN is a Professor at Université Paris Dauphine-PSL and holds the Fundamental Chair at the Institut Universitaire de France since 2024. His academic journey includes roles at institutions such as INRIA (2020-2022), École Polytechnique (2007-2015), and Université Gustave Eiffel (2003-2012). Research Focus: Statistics of random processes, nonparametric statistics, and applications in financial modeling and population biology. Key Contributions: Adaptive estimation, confidence bands, inverse problems, rough volatility modeling, and growth-fragmentation processes. Advising: Supervised 19 PhD students with topics spanning statistical inference, Hawkes processes, and stochastic volatility. Collaborations include CIFRE grants with EDF, SCOR, and Banque de France. Scientific Awards : Fundamental Chair at Institut Universitaire de France (2024).

Timothy S. Vaughanمشاهده پروفایل

استاد

• Operations Management
• Analytics/Quantitative Methods
• Decision Making
• +۶ مورد دیگر

Timothy S. Vaughan, Ph.D., is a Professor in the Marketing and Supply Chain Management Department at the University of Wisconsin-Eau Claire's College of Business. His academic career spans decades, focusing on operations management, inventory systems, and statistical pedagogy. He holds a Ph.D. from the University of Iowa and a B.A. from the University of Northern Iowa. Ph.D., University of Iowa B.A., University of Northern Iowa Research interests include operations management, analytics, simulation, and statistical quality control. Recent work explores quality control applications in daily habits, workload variability in capacity planning, and dynamic inventory policies for spare parts. His publications span journals like Decision Sciences Journal of Innovative Education and International Journal of Production Research . Scientific awards include the 2021 College of Business Creativity and Innovation Award. He has presented extensively at conferences such as the Winter Simulation Conference and Decision Sciences Institute National Meetings, covering topics from beer game implementations to cyclical scheduling systems.

Alfredo Pulvirentiمشاهده پروفایل

استاد

• Bioinformatics
• Biomedicine
• RNA Interference
• +۹ مورد دیگر

Alfredo Pulvirenti is a Full Professor of Computer Science at the University of Catania, Italy. He holds a joint appointment with the Department of Clinical and Experimental Medicine and the Department of Mathematics and Computer Science. Born in 1974, he earned his Laurea (1999, summa cum laude), PhD (2003), and post-doc (2004) in Computer Science from the University of Catania. His academic career includes roles as Assistant Professor (2005-2014), Associate Professor (2014-2023), and Full Professor since 2023. His research bridges Bioinformatics and Biomedicine , focusing on RNA interference (microRNAs, long non-coding RNAs) via stochastic and network-based inference methods. Key contributions include subgraph matching , motif finding , and multiple network alignment using Monte Carlo techniques for analyzing biological networks . He also applies time series analysis to seismic and infrasonic signals through collaborations with Italy's National Institute of Geophysics and Volcanology (INGV). Recent publications highlight advancements in graph algorithms (e.g., MultiGraphMatch, ArcMatch) and systems biology tools like MITHrIL and SPECifIC. He co-directs the Jacob T. Schawartz International School for Scientific Research and leads national/regional projects on Bioinformatics and Big Data in Cancer . His group includes 3 researchers, 1 post-doc, and 3 PhD students in Complex Systems. Awards and Leadership : Best Paper Award at CBS 2009 Guest Editor for BMC Bioinformatics, Briefings in Bioinformatics, Elsevier Program Committee Member for major international conferences International Collaborations span institutions like NYU, Ohio State University Wexner Medical Center, Brown University, Tel Aviv University, Toronto University, and Università di Ancona.

Robert L. Carrawayمشاهده پروفایل

استاد

• Data Analytics
• Decision Sciences
• Optimization
• +۴ مورد دیگر

Robert L. Carraway is a Professor at the University of Virginia's Darden School of Business, where he has been a faculty member since 1984. He currently serves as the Quantitative Analysis (QA) area head and previously held the position of Senior Associate Dean for MBA Programs from 2006-2011. Carraway teaches Data Analytics and Decision Sciences courses in Darden's MBA program, including First Year Decision Analysis, Managerial Quantitative Analysis, Optimization Models, and Darden Business Projects: Venturing. His global executive education work spans Europe, Asia, and Latin America. Education: B.S., East Carolina University M.B.A., East Carolina University Ph.D., Purdue University Carraway's research spans multiple areas of quantitative business analysis. His original work focused on Optimization, but he has expanded his focus to the broader role of analytics in decision making. He examines the relationship between intuition and rational analysis, exploring how these approaches complement one another in business contexts. His work investigates how analytical methods enhance innovation processes, making them more effective and efficient. A significant portion of his recent research addresses 'Big Data' applications in business. He has authored articles such as 'Big Data, Small Bets' and 'Meeting the Big Data Challenge: DON'T Be Objective,' which explore practical approaches to leveraging large datasets for competitive advantage. His work emphasizes combining analytical rigor with business judgment rather than relying solely on data-driven approaches. Carraway's publication record demonstrates a consistent focus on quantitative methods for business decision-making. His earlier work centered on optimization models and mathematical programming, while more recent publications address analytics, probability distributions, and risk analysis. A notable trend is his exploration of how decision-makers integrate quantitative analysis with intuitive judgment in complex, uncertain environments. Award Recognition: Multiple teaching honors at Darden School of Business Carraway has significantly influenced business education through his case method teaching, with numerous cases used in Darden classrooms worldwide. His global executive education work extends his impact to practicing managers across continents. While specific doctoral students aren't listed, his long tenure suggests substantial mentoring experience. As QA area head, he oversees faculty specializing in quantitative business analysis and collaborates with colleagues like Sam Bodily, Phillip Pfeifer, and Sherwood Frey on research teams focused on decision sciences.

• Nuclear Energy and Engineering
• Nuclear Fusion
• Radioactive Wastes
• +۲ مورد دیگر

Raffaella Testoni is an Associate Professor at the Department of Energy (DENERG) within Politecnico di Torino. She serves as a member of the College of Electrical and Energy Engineering and contributes to teaching in both PhD and Master's programs. Research interests: Nuclear energy and engineering, nuclear fusion, radioactive waste management Key skills: Energy processes engineering (ERC PE8_6), climate action (SDG 13), clean energy (SDG 7) Her recent publications focus on ARC-class fusion reactor design, including neutron activation studies, thermal modeling, and safety assessments. She leads commercial research contracts with NewCleo SpA and collaborates on international projects like the Inter-University Center for Peace Studies (CISP). PhD students under her supervision include Gabriele Iob (multiphysics modeling), Danilo Caterino, Serena Crobu, Alessandro Morandi, Antonio Zurzolo, Davide Pettinari, Gabriele Ferrero, Ciro Alberghi (MHD and tritium transport), and Samuele Meschini (tokamak safety assessment).

François-Xavier Coudertمشاهده پروفایل

استاد

• Metal-Organic Frameworks (MOFs)
• Nanoporous Materials
• Machine Learning in Materials Science
• +۵ مورد دیگر

François-Xavier Coudert is a Senior Researcher at CNRS and Professor at PSL University. His work focuses on computational and experimental studies of metal-organic frameworks (MOFs) , nanoporous materials , and machine learning applications in material discovery. He leads an active research group and collaborates internationally, including with Shinshu University on graphene oxide nanosheets. Academic Roles: Professor (PSL University), Senior Researcher (CNRS) Key Research Areas: MOFs, Adsorption/Diffusion in Nanopores, Elastic Properties, Topology Analysis His team develops open-source software like CrystalNets for topology identification and ELATE for elastic tensor analysis. Recent publications highlight AI-driven MOF discovery, hybrid glasses, and diffusion coefficient prediction. He was recognized as a pioneer in hybrid glass research via the 2025 Dalton Horizon Prize . Former students include PhD graduates Nicolas Castel , Emmanuel Ren , and Lionel Zoubritzky .

• Applied Physics
• Particle Physics
• Quantum Chromodynamics (QCD)
• +۳ مورد دیگر

Jorge Segovia González is a Professor at Universidad Pablo de Olavide, affiliated with the Department of Physical, Chemical and Natural Systems within the College of Engineering. His research focuses on Applied Physics, Particle Physics, and Quantum Chromodynamics, particularly in the study of quark models, meson spectroscopy, and multiquark systems. He earned his PhD in 2012 from the Universidad de Salamanca, where he studied spectroscopy and decay mechanisms of heavy quark mesons under the supervision of Dr. David Rodríguez Entem. His work spans topics like Constituent and chiral quark models Tetraquark and pentaquark systems Generalized Parton Distributions Strong decay formalisms Proton-antiproton scattering Electromagnetic form factors of hadrons . Recent publications (2024–2025) emphasize theoretical advancements in tetraquark systems, Monte Carlo simulations, and applications of QCD to meson structure. His research contributes to understanding hadron stability, quark interactions, and experimental signatures at facilities like the Jefferson Lab and Electron-Ion Collider (EIC).

Jan Meinkeمشاهده پروفایل

پژوهشگر

• High-Performance Computing
• GPU Programming
• Performance Portability
• +۵ مورد دیگر

Dr. Jan Meinke is a Researcher at the Jülich Supercomputing Centre (JSC) within Forschungszentrum Jülich, Germany. His work focuses on high-performance computing, GPU programming, and performance portability across different hardware platforms. He contributes to the development of exascale computing applications and benchmarks, particularly through the JUPITER benchmark suite. Based in Building 14.14, Room 4012 at the Jülich research campus, he maintains active research collaborations across computational science domains. Dr. Meinke's research spans two major domains: high-performance computing and computational epidemiology. In HPC, he investigates GPU programming models, performance portability across vendors, and scalable computational fluid dynamics. His work on the JUPITER benchmark suite aims to address challenges in application-driven exascale computing. In computational epidemiology, he has developed forecasting models for COVID-19 spread across European nations, focusing on ensemble approaches and short-term prediction. His earlier work includes protein folding simulations and Monte Carlo methods, demonstrating a long-standing interest in computational methods across scientific domains. Analysis of Dr. Meinke's publication history reveals a strategic evolution from computational biophysics to high-performance computing infrastructure. His recent work (2023-2025) shows a strong emphasis on performance portability across GPU architectures, particularly for scientific computing applications like the N-body problem and computational fluid dynamics. The JUPITER benchmark suite represents a significant contribution to exascale computing evaluation, bridging theoretical computer science with practical applications. His dual focus on HPC infrastructure and epidemiological modeling demonstrates versatility in applying computational methods to diverse scientific challenges. Dr. Meinke has been actively involved in both teaching and research aspects of high-performance computing, authoring educational materials on GPU programming with CUDA and advanced GPU techniques. His work demonstrates a commitment to advancing both the theoretical foundations and practical applications of high-performance computing, with implications for scientific discovery across multiple domains including physics, engineering, and public health.

Esmae Woodsمشاهده پروفایل

پژوهشگر ارشد

• Computational Physics
• Theoretical Physics
• Algorithm Development
• +۷ مورد دیگر

Esmae Woods is a Research Fellow in the Department of Engineering, specializing in theoretical and computational physics with a focus on algorithmic and software development. She completed her PhD at the University of Cambridge, where she studied dynamical processes on energy landscapes, particularly in large ill-conditioned systems. Currently, she contributes to the MaThRad project, advancing Monte Carlo simulations for radiation transport applications such as criticality safety calculations. Education: PhD in Physics, University of Cambridge Her research bridges computational physics, numerical analysis, and biophysics. She develops algorithms to address challenges in energy landscape analysis, rare event simulations, and Markov chain modeling. Recent work emphasizes Monte Carlo methods for radiation transport and multiscale modeling of chromatin organization. Publications highlight her contributions to fields including energy transfer in photosynthetic systems, polaritonic rate suppression, and the structural dynamics of chromatin. Her methods are particularly impactful for systems where numerical instability complicates direct computation. Woods collaborates with researchers like Eugene Shwageraus and David J. Wales, advancing mathematical and computational frameworks in interdisciplinary contexts.

Nima Rabieiمشاهده پروفایل

استادیار

• Computational Mathematics
• Numerical Analysis
• Optimal Control
• +۴ مورد دیگر

Nima Rabiei is an Assistant Professor at the International University of Sarajevo in the Department of Applied Mathematics . His research focuses on computational mathematics, numerical analysis, and their applications to engineering and environmental systems. Education: PhD in Applied Mathematics (2014) from Universitat Politècnica de Catalunya-BarcelonaTech (UPC) His work spans population balance modeling , non-linear optimal control , integro-differential equations , and limit analysis decomposition methods . He has developed numerical schemes for crystallizers and explored statistical methods for hyper-ellipsoids. Recent publications highlight his contributions to adjoint methods , Monte Carlo techniques , and meshless computational approaches .