جست‌وجوی استاد و پژوهشگر

Christos Nomikosمشاهده پروفایل

دانشیار

• Algorithm Design and Analysis
• Approximation Algorithms
• Computational Complexity
• +۳ مورد دیگر

Christos Nomikos is an Associate Professor at the Department of Computer Science and Engineering, University of Ioannina. He holds a PhD from the National Technical University of Athens (NTUA) and has been a faculty member since 2003. His roles include teaching and research supervision in computer science disciplines. Education: Diploma in Electrical Engineering, NTUA, 1992 PhD in Computer Science, NTUA, 1997 Research Interests: Christos Nomikos focuses on theoretical computer science and logic, including algorithm design and analysis, computational complexity, formal languages, logic programming, and graph theory. His work explores foundational aspects of programming languages and their semantics, as well as applications in knowledge representation and automated reasoning. Teaching: In the 2024-25 academic year, he teaches Discrete Mathematics II , Principles of Programming Languages , Computational Complexity , and Approximation Algorithms . He emphasizes structured learning approaches and maintains a dedicated ecourse page for students. Advising & Grants: He supervises undergraduate thesis projects in computer science, with a focus on theoretical topics like Turing machine transformations and Boolean grammar algorithms. No explicit grant information is provided in the texts. His advising process involves rigorous academic evaluation and prefers candidates with strong performance in core computer science courses. Labs/Teams: While no specific lab or team affiliations are mentioned, his research collaborations include work with international conferences and journals, indicating active participation in academic networks.

M. Schumannمشاهده پروفایل

استادیار

• Econometrics
• Nonlinear Panel Data Models
• Fixed Effects Estimation
• +۴ مورد دیگر

M. Schumann is an Assistant Professor of Econometrics at Maastricht University's School of Business and Economics , specializing in quantitative economics. His research bridges statistical theory and applied economic modeling. Key research areas include Nonlinear panel data models with fixed effects Second-order bias reduction techniques Integrated likelihood inference Difference-in-differences methodologies Applications in innovation systems and flood statistics Recent publications demonstrate interdisciplinary trends, combining econometric theory with hydrology-inspired statistical approaches. His work appears in top-tier journals like Journal of Econometrics and Journal of Business & Economic Statistics . Collaborative efforts with researchers like T.A. Severini, G. Tripathi, and M.F. Arroyabe highlight his methodological and applied contributions to both economic and environmental sciences.

• Crystallography
• Materials Science
• Geometry
• +۵ مورد دیگر

Professor Alan MacKay is a distinguished academic at Birkbeck College, University of London, renowned for his groundbreaking work in crystallography and quasicrystals. A former student and lecturer under J.D. Bernal, he pioneered the concept of quasicrystals through his 1981 paper on pentagonal snowflakes, leading to the discovery of the MacKay icosahedron. His research spans geometry in materials, interdisciplinary science, and the social dimensions of scientific inquiry. Educated at Trinity College, Cambridge, MacKay has held posts globally and contributed to science policy through the Science Policy Foundation. His writings, including translations of Haeckel and a poetry collection Floating World , reflect his belief in bridging cultures and disciplines. Education: Wolverhampton Grammar School, Trinity College, Cambridge Key Contributions: Quasicrystal theory, flexi-crystallography, science policy advocacy Research interests include geometric principles in materials, historical context of science, and the societal role of scientific knowledge. His interdisciplinary approach integrates mathematics, physics, and humanities, exemplified by his anthology of scientific quotations and translations.

Salvatore Vitaleمشاهده پروفایل

دانشیار

• Gravitational Waves Data Analysis
• Astrophysics Observation
• Instrumentation and Experiment
• +۳ مورد دیگر

Salvatore Vitale is an Associate Professor of Physics at the Massachusetts Institute of Technology (MIT), affiliated with the MIT LIGO Lab and the MIT Kavli Institute for Astrophysics and Space Research. His research focuses on gravitational wave data analysis, particularly using LIGO instruments to study compact objects like binary black holes and neutron stars. He has contributed to foundational work on parameter estimation, tests of general relativity, and next-generation observatory design like Cosmic Explorer. Education: BSc/MSc in Physics from University of Bologna, PhD from Pierre et Marie Curie University (Paris). Postdoctoral roles at Nikhef Amsterdam and MIT LIGO Lab before faculty appointment in 2017. Research emphasizes gravitational wave signal analysis, including unmodeled searches, compact object population studies, and calibration effects. Key publications address measuring the Hubble constant, binary black hole formation channels, and spin dynamics. Awards include the 2021 NSF CAREER Award and 2016 Gruber Cosmology Prize (LIGO Team). Lab affiliations: MIT LIGO Group and Advanced LIGO at MIT. Current group includes students (e.g., Cailin Plunkett) and postdocs (e.g., Matthew Mould). Active in open-source data releases and collaborations like the LIGO Scientific Collaboration.

• Genetics
• Molecular Biology
• Yeast Evolution
• +۳ مورد دیگر

Bryce Taylor is an Assistant Professor of Biology at Loras College. He holds a BS from Georgia Institute of Technology (2011), PhD in Biology from University of Southern California (2015), and completed a postdoctoral fellowship at University of Washington (2016-2021). His teaching focuses on Microbiology, Genetics, and Principles of Biology. Research interests center on the genetics of Saccharomyces cerevisiae domestication, particularly mechanisms of drug resistance and evolutionary pathways. He co-founded the yEvo lab, developing high school curricula integrating yeast evolution experiments (yEvo.org). His work bridges laboratory research with educational innovation, emphasizing hands-on learning in K-12 settings. Education: Bachelor of Science, Georgia Tech, 2011 PhD in Biology, USC, 2015 Postdoctoral Research, University of Washington, 2016-2021 Research explores multidrug resistance pathways in yeast, focusing on caffeine and clotrimazole tolerance. His experimental evolution studies reveal how genetic architectures shape phenotypic traits. The yEvo initiative extends this work into classrooms, enabling students to conduct authentic research on yeast evolution. Recent publications highlight novel mutations and epistatic interactions influencing drug resistance, while educational papers emphasize modular lab design and historical case studies in teaching molecular biology. Professional contributions include developing curricula that integrate cutting-edge genetics with classroom experiments. His work on T-cell memory in HLH pathogenesis (2016) demonstrates broader immunological interests. Though no scientific awards are listed, creative ventures like award-winning jalapeno cheddar cornbread showcase interdisciplinary passions. Labs/Teams: Active member of yEvo lab coordinating nationwide high school research initiatives. Collaborates with institutions on yeast evolution and K-12 STEM outreach.

Richard Addo-Tenkorangمشاهده پروفایل

مدرس

• Enterprise Supply-Chain Management
• Concurrent Engineering
• Digital Manufacturing
• +۳ مورد دیگر

Richard Addo-Tenkorang is a Lecturer at the University of Hertfordshire, affiliated with the School of Physics, Engineering & Computer Science and the Department of Engineering and Technology. He holds a DSc.-Tech/PhD from the University of Vaasa (2014), an MSc in Digital Enterprise Management from the University of Westminster (2008), and a BEng (Hons) in Mechanical Engineering from the University of Wolverhampton (2004). His research focuses on Enterprise Supply-Chain Management, Digital Manufacturing, Industry 4.0, IoT, and Big Data Value-chain Management. He is a Chartered Engineer (CEng MIET) and a Fellow of the Higher Education Academy (FHEA). Research Interests: Enterprise Supply-Chain Management Concurrent Engineering Digital Transformation in Manufacturing IOT and Smart Production Systems Lean Manufacturing and Sustainability Engineering Pedagogy Awards and Recognition: Fellow of the Higher Education Academy (FHEA), Advance HE (2022) Chartered Engineer (CEng MIET), IET (2021) Key Research Contributions: Developed frameworks for tracking and tracing in global supply chains Explored digital transformation roadmaps for SMEs in smart factories Advanced IoT integration in supply-chain networks Investigated data-driven approaches in engineer-to-order systems Collaborations: Active in Nordic and African manufacturing sectors, focusing on SME digitization and sustainable practices.

Eduardo De Souza Netoمشاهده پروفایل

استاد

• Computational Mechanics
• Multi-scale Modelling
• Solid Mechanics
• +۵ مورد دیگر

Professor Eduardo De Souza Neto is Chair of Civil Engineering at Swansea University's School of Aerospace, Civil, Electrical and Mechanical Engineering. His office is located in the Engineering Central Building at Bay Campus. He is available for postgraduate supervision and maintains an active research profile in computational mechanics. Research Focus: Professor De Souza Neto specializes in multi-scale modeling of solids using computational homogenization methods. His work investigates complex material behaviors across physical scales, with applications including: Constitutive modeling of biological tissues (e.g., human arteries) Polycrystalline metal behavior prediction Heat conduction problems Microstructural optimization via topological derivatives Computational efficiency enhancements for multi-scale frameworks Current research explores sensitivity of macroscopic material properties to microstructural changes and fracture modeling in heterogeneous materials. Publication Trends: His recent articles (2016-2025) demonstrate strong focus on multi-scale computational frameworks, particularly: Development of virtual power methods for homogenization Advanced fracture and damage modeling techniques Computational efficiency in material failure analysis Applications in biomechanics, composites, and geomechanics Teaching & Supervision: He teaches undergraduate and postgraduate modules including Finite Elements for Civil Engineers (EG-3067/EG-M92), Construction Methods in Infrastructure Works (EG-M345), and Construction Methods in Building Works (EG-M347). He currently supervises PhD students in computational mechanics topics including boundary element methods, machine learning-aided material modeling, and computer vision-based microstructure reconstruction.

Nicola Byromمشاهده پروفایل

عضو هیئت علمی

• Mental Health in Emerging Adulthood
• Peer Support in Higher Education
• Public Health
• +۳ مورد دیگر

Dr. Nicola Byrom is a Reader in Psychology at King's College London, affiliated with the School of Mental Health & Psychological Sciences and the Department of Psychology. She specializes in mental health and wellbeing in emerging adulthood, focusing on institutional, social, and cultural factors influencing mental health in higher education settings. Her work informed the University Mental Health Charter and Advance HE's Education for Mental Health Toolkit. Byrom founded Student Minds (UK's student mental health charity) and led the UKRI-funded SMaRteN network, supporting early-career researchers in mental health research. She advocates interdisciplinary collaboration and has published widely on peer support, curriculum design, and mental health policy. Education: DPhil in Experimental Psychology from the University of Oxford (supervised by Prof. Robin Murphy), with an MRC Centenary Award. Her research employs mixed methods, blending quantitative and qualitative approaches. Research Interests: Mental health in emerging adulthood, peer support systems, lived experience perspectives, educational practices in higher education, and clinical applications of learning theory. Notable Projects: U-Belong initiative (toolkit for student belonging), Nurture-U (multi-university mental health collaboration), and the Education for Mental Health Toolkit. She also led the development of an online resource for universities to support student mental health. Awards: MRC Centenary Award. Active roles include trustee of Student Minds and organizing committee member for the International Association of Youth Mental Health conference. Grants and Funding: £4.4 million UKRI MRC funding for IoPPN research projects (2022). SMaRteN supported 19 research projects and over 50 researchers. Labs/Teams: SMaRteN network, EASY Research Group (Experimental And Social Psychology).

Karl Hollausمشاهده پروفایل

پژوهشگر

• Computational Electromagnetics
• Multiscale Finite Element Methods
• Eddy Current Problems
• +۳ مورد دیگر

Prof. Karl Hollaus is a researcher at TU Wien's Computational Mathematics in Engineering Research Group within the Faculty of Mathematics and Geoinformation . His work focuses on computational electromagnetics , multiscale finite element methods (MSFEM) , and eddy current problem simulations . He leads projects such as Effektive Materialtransformation für ferromagnetische Bleche and Magnetik für Filter , addressing challenges in laminated iron cores and magnetic materials. His research emphasizes high-performance computational techniques for engineering applications, including nonlinear systems and material modeling. Key research interests include domain decomposition methods , vector hysteresis modeling , and effective interface conditions . He has developed innovative approaches for simulating eddy current losses, magnetic flux routing in electrical machines, and ventilation panel thermal behavior using multiscale FEM. Collaborators include Markus Schöbinger and Valentin Hanser, with whom he co-authored numerous conference papers and journal articles. Education: Dipl.-Ing. (Master's) and Dr.techn. (PhD) in engineering. Awards: None explicitly listed. Recent publications (2022–2023) highlight advancements in harmonic balance methods combined with model order reduction for nonlinear systems, magnetic microwire materials for flux guidance, and effective material modeling for laminated cores. His work bridges theoretical computational mathematics with practical engineering solutions, particularly in electromechanical systems and metamaterials. Advising: Supervised PhD candidates Valentin Hanser (2021 thesis on eddy current computations) and Markus Schöbinger (multiple co-authored presentations). Active in TU Wien's Network Lab and contributes to the Scientific Computing and Modelling Research Division .

Ingo Scholtesمشاهده پروفایل

استاد

• Machine Learning
• Network Science
• Graph Mining
• +۵ مورد دیگر

Prof. Ingo Scholtes is a Full Professor of Machine Learning for Complex Networks at Julius-Maximilians-Universität Würzburg's Center for Artificial Intelligence and Data Science (CAIDAS). He holds a doctorate in computer science and mathematics from the University of Trier and has held roles including SNSF Professor at the University of Zurich, Full Professor at Bergische Universität Wuppertal, and Senior Assistant at ETH Zürich. His research focuses on higher-order graph analytics for temporal networks, machine learning, and computational social science. Education: PhD in Computer Science (University of Trier, Germany), Postdoctoral Research at ETH Zürich (2011–2016), and prior roles at Karlsruhe Institute of Technology and CERN. Research Interests: Machine learning on graphs, temporal network analysis, higher-order network models, and their applications in software engineering and social systems. He develops open-source tools like pathpy and git2net for network analysis. Recent Work: Focuses on causality-aware graph neural networks, temporal graph isomorphism, and network science applications in AI. Recent articles include studies on temporal network dynamics, path prediction, and Bayesian inference of network transitions. Awards: SNSF Professorship (2018), Junior-Fellowship (2014), and German Academic Scholarship Foundation (2004-2005). Active in editorial roles for EPJ Data Science and leadership in GI's Computational Social Science working group.

Ginestra Bianconiمشاهده پروفایل

استاد

• Statistical Mechanics
• Network Theory
• Mathematical Physics
• +۷ مورد دیگر

Professor Ginestra Bianconi holds the position of Professor of Applied Mathematics at Queen Mary University of London (QMUL) within the School of Mathematical Sciences. She is also an Alan Turing Fellow at the Alan Turing Institute. Her research focuses on statistical mechanics, network theory, and their applications to complex systems. Notably, she formulated the Bianconi-Barabási model describing Bose-Einstein condensation in networks and pioneered studies on higher-order networks, including simplicial complexes and multilayer networks. Her work bridges statistical mechanics with topology, geometry, and dynamical systems. Education and Career: Ginestra Bianconi earned her PhD in Physics from the University of Notre Dame under Albert-László Barabási. She has held postdoctoral positions at the International Center for Theoretical Physics (ICTP) and the University of Fribourg. She joined QMUL in 2013, progressing from Lecturer to Reader before becoming a full Professor in 2019. Research Interests: Her research spans statistical mechanics of networks, higher-order networks (simplicial complexes), multilayer networks, topological signals, and quantum phenomena on networks. She explores applications in biological networks, neuroscience, and condensed matter systems. Awards and Recognition: Awards include the APS Fellow distinction, Network Science Society Fellowship, and the 2023 Franqui Chair. She is Chief Editor of JPhys Complexity and serves on editorial boards of Scientific Reports and PLOS ONE . Grants and Funding: She has secured funding from EPSRC, the Royal Society, the Simons Foundation, and the Alan Turing Institute for projects on network science applications and theoretical challenges in complex systems. Group and Alumni: Her research group includes current PhD students and alumni who have contributed to fields like simplicial synchronization, network geometry, and multilayer dynamics. Notable alumni include Dr. Arda Halu (Harvard Medical School) and Dr. Jacopo Iacovacci (Imperial College). Labs and Collaborations: She leads research at QMUL’s School of Mathematical Sciences and collaborates with institutions like the Alan Turing Institute and the Francis Crick Institute on interdisciplinary projects.

Matteo Marsiliمشاهده پروفایل

پژوهشگر

• Statistical Physics
• Non-Equilibrium Statistical Mechanics
• Critical Phenomena
• +۷ مورد دیگر

Matteo Marsili is a Senior Research Scientist at the Quantitative Life Sciences Section of the Abdus Salam International Centre for Theoretical Physics (ICTP) in Trieste, Italy. He holds a PhD from SISSA, Trieste (1994) and has held postdoctoral positions at Manchester University, Fribourg University (Switzerland), and SISSA. He joined ICTP in 2002, initially in the Condensed Matter and Statistical Physics (CMSP) Section before transitioning to the Quantitative Life Sciences Section. His research spans interdisciplinary domains, applying statistical physics to complex systems. Key areas include non-equilibrium statistical mechanics, critical phenomena, quantitative finance, statistical inference, machine learning, systems biology, and neuroscience. He investigates how collective behaviors emerge from interactions among simple units—such as particles, neurons, or financial traders—using tools from probability, information theory, and thermodynamics. His recent publications (2020–2025) show a strong focus on information-theoretic approaches to learning, relevance quantification, deep learning, and optimal inference. Themes include Bayesian modeling, minimal complexity, self-organized criticality in neural networks, and thermodynamics of information in financial markets. His work frequently appears in journals like Physical Review E , Journal of Statistical Mechanics , Physics Reports , and PLoS ONE . Matteo Marsili has collaborated extensively with researchers such as Y. Roudi, R.J. Cubero, J. Song, and R. Xie, suggesting active mentorship and team leadership. While no formal awards are listed, his sustained publication record in high-impact journals reflects significant scientific contributions. His lectures at institutions like the Kavli Institute and IHÉS further highlight his academic influence.

Tiago de Paula Peixotoمشاهده پروفایل

استاد

• Network Science
• Complex Systems
• Statistical Physics
• +۱۳ مورد دیگر

Tiago de Paula Peixoto is a Professor of Complex Systems and Network Science at the Institute of Science and Technology Austria (IT:U), where he leads the Inverse Complexity Lab. He has previously held faculty positions at Central European University (2019–2024) and the University of Bath (2016–2019), and conducted postdoctoral research at the University of Bremen and Technical University of Darmstadt. He holds a PhD in Physics from the University of São Paulo (2008) and a habilitation in Theoretical Physics from the University of Bremen (2017). His research lies at the intersection of statistical physics, computational statistics, information theory, Bayesian inference, and machine learning , with a central focus on inverse problems in network science . His group develops principled mathematical and computational models to infer the local interaction rules of complex systems from observed macroscopic behavior. Key research themes include statistically sound pattern detection in networks, network reconstruction from indirect data, uncertainty quantification, generative modeling of modular hierarchies and latent spaces, and scalable inference algorithms. His recent publications (2020–2025) reflect a consistent focus on advancing the theoretical and algorithmic foundations of network inference. A major theme is the development of Bayesian and information-theoretic frameworks for robust network reconstruction, moving beyond simplistic heuristics like correlation thresholding. He has pioneered methods for posterior sampling to quantify uncertainty and for minimum description length to prevent overfitting. His work also addresses scalability, with algorithms achieving subquadratic time complexity. Applications span diverse domains, including social systems (migration flows), political networks, and biological systems. Erdős–Rényi Prize from the Network Science Society (2019) Alexander von Humboldt Foundation Fellowship (2008) Karate Club Club Prize (6th recipient) Peixoto advises a vibrant group of PhD students and postdoctoral researchers, including Thomas Robiglio, Sebastian Kusch, Martina Contisciani, and Bukyoung Jhun. His former students include Felipe Vaca, Lizhi Zhang, and Silvia Guerrini. He has not received any specific grant mentions in the text, but his group’s sustained activity suggests successful funding. His lab is strongly committed to open science, with most of their methods implemented in the widely used graph-tool library, which is extensively documented and freely available. The lab organizes events like the annual Inverse Complexity Retreat and participates in major conferences such as NetSci and STATPHYS. The group is actively recruiting new PhD candidates and postdocs, indicating ongoing expansion and research momentum.

Matjaz Percمشاهده پروفایل

استاد

• Complex Systems
• Nonlinear Dynamics
• Network Science
• +۱۲ مورد دیگر

Matjaz Perc is a Professor of Physics at the University of Maribor and Vice Dean of Natural Sciences at the European Academy of Sciences and Arts. He is a member of Academia Europaea and the European Academy of Sciences and Arts, and recognized among the top 1% most cited physicists by Clarivate Analytics in 2020. His research interests span Complex Systems , Network Science , Sociophysics , Game Theory , Evolution of Cooperation , Neural Networks , and Epidemic Modeling . He applies physics-based approaches to social, biological, and economic systems, often integrating AI and data analytics. The recent trend in his publications shows a strong focus on higher-order interactions in networks , cooperation dynamics , synchronization in neural systems , epidemic modeling on simplicial complexes , and AI-driven decision-making . His work bridges physics with applications in healthcare, economics, and sustainability. Young Scientist Award for Socio and Econophysics, German Physical Society, 2015 USERN Laureate, 2017 Zois Award, 2018 Fellow of the American Physical Society, 2019 Top 1% Most Cited Physicist, Clarivate Analytics, 2020 Matjaz Perc has not publicly listed his advisees, but he leads a prolific research group with extensive international collaborations. He has contributed to major interdisciplinary projects, including pan-European responses to the COVID-19 pandemic. His work is supported by high-impact publications and recognition from leading scientific academies. He is actively involved in promoting network science and complex systems research through editorial roles and collaborative platforms.

Baltasar Enrique Beferull Lozanoمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Signal Processing
• +۳ مورد دیگر

Baltasar Enrique Beferull Lozano is a tenured Professor at the University of Agder , leading the Center Intelligent Signal Processing and Wireless Networks (WISENET) since 2015. With a PhD in Electrical Engineering from USC (2002) and prior roles at EPFL, AT&T Shannon Labs, and University of Valencia, his career spans 20+ years of academic and industrial research in signal processing, wireless systems, and AI. Education: PhD (USC), MSc (USC), MSc (University of Valencia) Expertise: Data Science, Machine Learning, Graph Signal Processing, Cyber-Physical Systems His research focuses on AI-driven wireless networks and in-network collective intelligence , addressing fundamental and applied challenges in smart water systems , energy management , and next-gen 5G/6G . He has secured 20+ international projects including 10 EU-funded initiatives (HYDROBIONETS, SENDORA) and 5 RCN-funded projects. Recent publications emphasize dynamic graph learning from time series data, quantized graph filters , and multi-agent reinforcement learning for networked environments. Awards include IEEE Best Paper Awards (2012, 2021), TOPPFORSK Grant (2015), and Ramón y Cajal Program Rank #1 (2005). As a Senior IEEE Member , he serves as Area Editor for IEEE Transactions on Signal Processing and evaluates research proposals for the European Commission , NSF , and Qatar National Research Fund . His lab has produced 15 PhD graduates and collaborates with 12+ industry partners including Telenor, IBM, and SINTEF.