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Thanasis FokasView profile

Professor

• Nonlinear Mathematical Science
• Boundary Value Problems
• Integrable Systems
• +7 more

Thanasis Fokas is a Professor of Nonlinear Mathematical Science at the University of Cambridge's Faculty of Mathematics, holding the Chair in Nonlinear Mathematical Science since 2002. Previously, he held chairs at Imperial College London (1996) and Clarkson University (1986). His research focuses on integrable systems, boundary value problems for PDEs, inverse problems in medical imaging, and novel complex analysis techniques. He is affiliated with the Cantab Capital Institute for the Mathematics of Information and the Centre for Mathematical Imaging in Healthcare. Education: PhD in Applied Mathematics (Clarkson University) Doctorate in Physics (Caltech, assumed based on career timeline) Research Interests: Prof. Fokas develops analytical methods for solving linear and nonlinear PDEs, with applications to water waves, medical imaging (EEG/MEG/PET), and mathematical biology. His work bridges pure and applied mathematics, contributing to fields like quantum gravity and cognitive neuroscience through innovative inverse problem solutions and imaging algorithms. Key Contributions: Unified Transform Method (Fokas Method) for PDEs Mathematical models for SARS-CoV-2 transmission dynamics Neuronal current reconstruction via EEG/MEG Labs/Teams: He leads research groups in the Centre for Mathematical Imaging in Healthcare and collaborates with the DAMTP Applied Mathematics and Theoretical Physics division.

Masoud FakhimiView profile

Senior Lecturer

• Modelling and Simulation
• Sustainable Development
• Operations Research
• +4 more

Dr. Masoud Fakhimi is a Senior Lecturer in Operational Research at the University of Surrey's Surrey Business School, affiliated with the Surrey Institute for People-Centred AI, Institute for Sustainability, and Centre for Business Analytics in Practice. He holds a PhD, MSc, and BSc in relevant fields. His research focuses on hybrid modeling and simulation methodologies applied to healthcare, sustainability, supply chain management, and Industry 4.0. He has received awards for teaching excellence and doctoral supervision, notably Surrey Business School's 'Teacher of the Year' (2021) and 'Postgraduate Research Supervisor of the Year' (2022). His academic background includes degrees in operational research and analytics. Research interests span hybrid simulation frameworks, sustainable operations management, and data-driven decision-making in healthcare and renewable energy sectors. He collaborates with industry and academic stakeholders on projects addressing circular economy challenges and AI-driven solutions. Key publications include a Springer monograph on hybrid modeling and simulation (2024) and peer-reviewed articles on healthcare system analysis, machine learning applications in biomarker detection, and disruptive technologies in agriculture. His work emphasizes cross-disciplinary approaches to complex decision-making in dynamic environments. Masoud supervises doctoral students in hybrid modeling and simulation methodologies, particularly in healthcare contexts. He actively contributes to research centers focused on AI ethics, sustainability, and business analytics innovation.

Jayanth RaghothamaView profile

Associate Professor

• Healthcare Informatics
• Complex Systems Modeling
• Simulation and Gaming in Policy Design
• +3 more

Jayanth Raghothama is an Associate Professor at KTH Royal Institute of Technology, affiliated with the School of Computer Science and Communication (CBH) and the Digital Futures Faculty. He is part of the Division of Health Informatics and Logistics, focusing on healthcare systems, complex systems modeling, and simulation-based decision-making. His academic role includes co-leading the Digitalization Platform and serving as a Vice-Director. Education: PhD in Complex Systems from KTH (2017). Prior experience includes software industry roles and work with a think tank in Bangalore. Research Interests: Healthcare logistics, complex systems analysis, participatory simulations, AI/ML ethics, and the philosophy of games and simulations. His work bridges computational models with real-world applications in healthcare, urban planning, and disaster management. Recent Projects: Co-Principal Investigator of the 'Data-driven Improvement of Work-Flows at the Karolinska University Hospital' project. Active in AI-based colorectal cancer detection in primary care (AIDCCIP project). Teaching: Courses on Applied Machine Learning, Simulation Methods, and Healthcare Logistics at Bachelor, Master, and PhD levels. Modules include AI/ML ethics and Agent-Based Modeling. Publications: Over 20 peer-reviewed articles focusing on healthcare simulation models, real-world data analysis in oncology, and hybrid modeling techniques. Notable works include patient flow optimization algorithms and frameworks for risk stratification in lung cancer studies.

Mauro PrevostiniView profile

Lecturer

• Wireless Sensor Networks
• Precision Agriculture
• Embedded Systems
• +4 more

Mauro Prevostini is the Program Manager of the Faculty of Informatics at the Università della Svizzera italiana (USI) since 2004 and holds the academic rank of Lecturer. He previously managed the creation of the Faculty of Informatics from 2001 to 2004 and has been a staff member of the ALaRI institute until 2018. His roles include coordinating academic-industry collaborations and promoting computer science education in local schools. Education: MSc in Electrical Engineering (ETH Zürich, 1994), Thesis: "On-Line Recognition of Masticatory Muscles Activity with Long-Time EMG Recorder" Secondary Education: Liceo Cantonale Lugano 1 (1984–1988) Research Interests: Focuses on Wireless Sensor Networks applied to Precision Agriculture, particularly in pest monitoring systems like PreDiVine DSS. His work integrates UML-based design methodologies for embedded systems and cyber-physical systems. Collaborations include ALaRI and Agroscope research center. Academic Contributions: Over 15 key publications from 2003–2022 emphasize system-level design, hardware/software co-design, and sensor network optimization. Recent articles address deep learning strategies for pest detection and adaptive decision support systems. Professional Activities: Co-founder of Dolphin Engineering Sagl (2012–present), a Precision Agriculture startup Member of the academic senate (2017–2019; 2023–present) Former coordinator of the Ticino branch for the informatica08 initiative (2008) Labs & Projects: Leads projects on wireless sensor networks for agricultural monitoring and collaborates with ALaRI on embedded system design tools and methodologies.

Robert PuckettView profile

Associate Professor

• Urban Entomology
• Invasive Species Ecology
• Biological Control
• +2 more

Robert Puckett serves as Associate Professor and Extension Entomologist in the Department of Entomology at Texas A&M University's College of Agriculture & Life Sciences. His research program focuses on urban entomology, invasive species ecology, and biological control methods. Puckett specializes in the biology and management of social insects including fire ants and termites, with applied research benefiting agricultural and urban environments. Research interests include: Behavioral ecology of invasive ant species Biocontrol strategies for urban pests Integrated pest management systems Landscape-scale invasion dynamics Socio-economic impacts of pest species Puckett's interdisciplinary publications span entomology, epidemiology, and petroleum engineering, demonstrating diverse methodological approaches. Recent works primarily examine HIV transmission modeling and geological formations, indicating broad scientific collaboration. As Extension entomologist, Puckett develops outreach programs translating research into practical pest management solutions for Texas communities.

Konrad Paul KordingView profile

Professor

• Neuroscience
• Machine Learning
• Computational Neuroscience
• +6 more

Konrad Paul Kording is the Nathan Francis Mossell University Professor at the University of Pennsylvania's School of Engineering and Applied Science, holding primary appointments in Bioengineering and Neuroscience, and a secondary appointment in Computer and Information Science. His research focuses on computational neuroscience, machine learning, and their applications to understanding neural systems and human behavior. He leads a research group located in Room 404 Richards and maintains an active research website and personal academic page. His work explores topics such as neural decoding, causal inference, and the alignment of artificial neural networks with biological systems. He has pioneered methods for analyzing neural activity in macaques, developing large-scale facial analysis resources like PrimateFace. His interdisciplinary approach integrates computer vision, medical informatics, and robotics to address challenges in neurology and developmental disorders. Recent research highlights include advancing techniques for early prediction of cerebral palsy using motion tracking and automated segmentation of synchrotron-scanned fossils. His contributions to causal discovery methodologies and neural network interpretability have been influential in both neuroscience and machine learning communities.

• Internet of Things
• Low Power Wide Area Networks
• Wireless Sensor Networks
• +7 more

Teresa Olivares Montes is a faculty member at the University of Castilla-La Mancha, where she serves as a Profesor Contratado Doctor in the Department of Systems Informatics. She has been actively contributing to research and education since completing her PhD in Computer Engineering at the same institution. Her academic career includes international research experiences in Hong Kong, Ireland, and the UK, reflecting a strong global engagement. PhD in Computer Engineering, University of Castilla-La Mancha (2003–present) Licentiate in Computer Science, University of Granada Diploma in Computer Science, University of Granada Her research focuses on Internet of Things (IoT), low-power wireless communication protocols, energy-efficient networking, and smart systems for applications in healthcare, agriculture, education, and Industry 4.0. She has published extensively in high-impact journals such as IEEE Transactions on Instrumentation and Measurement, IEEE Internet of Things Journal, and Computer Networks. Her recent work emphasizes practical, low-cost IoT solutions for environmental monitoring (e.g., glyphosate detection, agrochemical drift) and digital twin development for smart campuses. The analysis of her recent publications (2020–2025) reveals a consistent trend toward applied IoT systems integrating sensor networks, machine learning, and human-centered design. Her work spans domains including public health (MosquIoT), environmental protection (DriftGLY, SpectroGLY), smart education (WIoTED), and industrial efficiency (OperaBLE). These projects demonstrate a strong interdisciplinary approach, combining computer science fundamentals with real-world societal and industrial challenges. No formal scientific awards are listed in the provided text. Teresa Olivares Montes has contributed to academic service, notably as a member of the Equality Commission at her department since 2017. While no specific grants are detailed beyond a 1996 training contract, her sustained publication record suggests ongoing research funding. She collaborates with a broad network of researchers across Spain and internationally. She has also contributed to educational initiatives, including mentoring program coordination across multiple degrees. She is involved in several research initiatives related to smart environments, including smart homes, smart campuses, and smart worker systems. Her lab or team likely focuses on IoT protocol design, wireless sensor networks, and embedded systems for sustainable and socially responsible applications.

• Identification Technologies
• Computer Science
• Engineering
• +3 more

Andres Queralto Pereira is a Professor at Carlos III University of Madrid . He is associated with the University Group of Identification Technologies (GUTI) . Contact details: andres.queralto@uc3m.es . Research Focus: His work centers on identification technologies , with implications in computer science , engineering , and information systems .

Amaranta Membrillo SolisView profile

Lecturer

• Geometric and Topological Data Analysis
• Algebraic Topology
• Metric Geometry
• +7 more

Dr. Amaranta Membrillo Solis is a Lecturer in Mathematical Data Science at the School of Mathematical Sciences, Queen Mary University of London. Her research focuses on advanced mathematical methodologies intersecting topology, geometry, and data science. She holds a prominent position in geometric and topological data analysis, with expertise in soft materials modeling, persistence theory, and spectral geometry of singular spaces. Her work integrates algebraic topology with applications in materials science, including the analysis of metasurfaces and soft matter systems. Notable contributions include studies on Hodge spectra differentiation between orbifolds and manifolds, topological characterization of nanoparticle networks, and homotopy theory of gauge groups. She actively collaborates with interdisciplinary teams to bridge theoretical mathematics and practical applications in nanotechnology and molecular conformational analysis. Her publications span high-impact journals such as the Journal of Applied and Computational Topology , Michigan Mathematical Journal , and ACS Nano . She maintains an active research blog ( https://amarantamembrillosolis.wordpress.com ) and is engaged in computational projects leveraging topological methods for material science innovation.

Richard M. GoldenView profile

Professor

• Cognitive Science
• Statistical Machine Learning
• Computational Psychometrics
• +4 more

Richard M. Golden is a Professor of Cognitive Science and Participating Faculty in the Erik Jonsson School of Engineering and Computer Science at the University of Texas at Dallas (UTD). He directs the COgnitive INformatics and Statistics (COINS) Lab, focusing on computational psychometrics and statistical machine learning applied to educational technologies. His research integrates cognitive science, machine learning, and usability engineering to improve personalized instruction. He authored the textbook Statistical Machine Learning: A Unified Framework and hosts the Learning Machines 101 podcast. Golden leads UTD's Undergraduate Cognitive Science and Graduate Applied Cognition and Neuroscience programs. Education : Ph.D. in Experimental Psychology (Brown, 1987), M.S. in Electrical Engineering (Brown, 1986), B.S. in Electrical Engineering & Psychology (UC San Diego, 1982). Research : Prior work includes neural network models of text comprehension, model misspecification analysis, and Bayesian inference. Current projects include diagnostic assessment tools and AI-driven educational systems. Awards : Secretary-Treasurer Service Award (2017), IEEE Senior Member (1999), multiple invited speaker roles. Labs/Teams : COINS Lab develops tools like the ARCADE system for automated reading assessment. Collaborates with organizations like the Veteran's Administration and National Science Foundation.

Balázs VassView profile

Associate Professor

• Network Resilience
• Programmable Packet Scheduling
• Discrete Optimization
• +3 more

Assoc. Prof. Balázs Vass is an Associate Professor at University Babeș-Bolyai (Cluj-Napoca, Romania) and holds a Marie Skłodowska-Curie Postdoctoral Fellowship for his QoSeRM project. Previously, he conducted postdoctoral research at Budapest University of Technology and Economics (BME) under Gábor Rétvári and visited institutions in Vienna, Jerusalem, and Bucharest. His research focuses on network resiliency, programmable packet scheduling, and discrete optimization in backbone networks. Education: BSc/MSc in Mathematics/Applied Mathematics (ELTE University), PhD in Future Internet Research at BME under János Tapolcai. Postdoctoral work included collaborations with Stefan Schmid (TU Berlin), David Hay (Hebrew University), and Costin Raiciu (UPB). Research priorities include disaster-aware network design, SRLG-disjoint path algorithms, and compiling packet programs for reconfigurable switches. Recent grants include a Romanian Young Teams grant (2025) and a Marie Curie fellowship (2024-2026). Teaching responsibilities at UBB include courses on algorithms, data structures, and communication networks. Supervises PhD students Zoltán Tasnádi and Nándor Bándi, with student advisees winning multiple awards at national informatics competitions. Key projects: SMR-A-DRT (disaster-resilient routing), QoSeRM (QoS optimization), and contributions to frameworks like eFRADIR for network resilience. Active in conference organizing (INFOCOM TPC since 2023) and journal reviewing (ToN, TNSM).

Vardges MelkonianView profile

Associate Professor

• Combinatorial Optimization
• Network Design Problems
• Approximation Algorithms
• +7 more

Vardges Melkonian is an Associate Professor in the Department of Mathematics at Ohio University, part of the College of Arts and Sciences. His academic work bridges theoretical and applied mathematics, with a strong emphasis on optimization and algorithmic problem-solving. Research Interests: Dr. Melkonian specializes in combinatorial optimization, network design, approximation algorithms, and applications of operations research. His research integrates mathematical programming and discrete modeling to solve complex real-world scheduling and allocation problems. These interests are reflected in his extensive publication record spanning sports leagues, hybrid work, exercise routines, and social partitioning. Publication Trends: Over the past decade, his scholarly output has consistently focused on developing integer programming and optimization models for diverse domains—from recreational puzzles like KenKen to large-scale logistical challenges in manufacturing and public policy. His work demonstrates a unifying theme: transforming practical problems into formal mathematical frameworks for efficient solution. Scientific Awards: No awards are mentioned in the provided text. Advising and Grants: The available information does not list any students or grant funding. However, his research contributions suggest active engagement in academic mentorship and potential involvement in funded projects, though specifics are not disclosed. Labs and Research Teams: There is no mention of specific labs, research groups, or collaborative teams associated with Dr. Melkonian in the provided content.

• Applied Computer Science
• Software Engineering
• Domain-Specific Languages
• +3 more

Prof. Igor Dejanović is a Regular Professor at the Department of Informatics, Faculty of Technical Sciences, University of Novi Sad. He holds a BSc (2000) and MSc (2008) in Computer Science from the same institution, followed by a PhD in 2012 with a thesis on 'Contributions to Methods of Rapid Software Development Based on Extensible Domain-Specific Specifications'. Positions Held: 2000–2004: Technical Associate at Faculty Computing Center 2004: Selected as Assistant Professor 2008: Promoted to Assistant Professor 2012: Promoted to Associate Professor 2017: Promoted to Full Professor Research focuses on domain-specific languages, software patterns, model-driven development, and software configuration management. He has contributed extensively to software engineering methodologies and education in technical informatics.

Madhav MaratheView profile

Professor

• Network Science
• Computational Epidemiology
• Artificial Intelligence
• +15 more

Madhav Marathe is a tenured Professor of Computer Science and the Distinguished Professor in Biocomplexity at the University of Virginia, where he also serves as Executive Director of the Biocomplexity Institute. He has held leadership roles at Virginia Tech and Los Alamos National Laboratory, and his work is deeply rooted in transdisciplinary team science. His research spans a wide range of domains including network science, artificial intelligence, computational epidemiology, high-performance computing, and complex systems. He develops foundational methods to model, analyze, and control large-scale biological, information, social, and technical (BIST) systems. His work integrates theoretical computer science with practical applications in public health, disaster response, and infrastructure resilience. The recent publications reflect a strong trend toward data-driven modeling of societal challenges—especially in pandemic response, forced migration, and energy systems. His team leverages agent-based simulations, machine learning, and high-performance computing to create scalable, policy-relevant models that support real-world decision-making. Fellow, American Association for the Advancement of Science (AAAS) Fellow, Association for Computing Machinery (ACM) Fellow, Institute of Electrical and Electronics Engineers (IEEE) Fellow, Society for Industrial and Applied Mathematics (SIAM) Distinguished Researcher Award, University of Virginia (2023) Honorary Doctoral Degree, Chalmers University (2023) Best Paper Award, SIGKDD 2021 (Applied Data Science) Endowed Distinguished Professor of Biocomplexity (2019) Dean’s Award for Excellence in Research, Virginia Tech (2018) Constellation Group’s Supernova Award (2016) Dr. Marathe has mentored over 30 doctoral students, 20+ MS students, and 15 postdoctoral fellows, and has led major federally funded projects including those related to computational epidemiology and national security. His lab, the Biocomplexity Institute, develops high-performance computing services and data analytics platforms for policymakers and emergency planners. He is also involved in initiatives such as the National Security Data and Policy Institute and the Expeditions in Global Pervasive Computational Epidemiology.

• Smart Wireless Systems
• Internet of Things (IoT)
• Information Security
• +8 more

Mohammad Derawi is a Professor in the Department of Electronic Systems at the Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU), Gjøvik campus. He leads the Smart Wireless Systems (SWS) research group and serves as the scientific leader of the IoT Lab at NTNU Gjøvik. Educational Background: PhD in Information Security from NISLab (Norway) and CASED (Germany) BSc and MSc in Informatics from DTU (Denmark) His research interests span smart wireless systems, Internet of Things (IoT), information security with a focus on biometric authentication, digital electronics, applied machine learning for activity recognition, and e-learning technologies. His work integrates cybersecurity, embedded systems, and data science to develop secure and intelligent IoT solutions for real-world applications. The recent publications highlight a strong trend in mmWave-based sensing for unmanned aerial systems, RF fingerprinting for secure identification, IoT security frameworks, and machine learning applications in education and human resource analytics. His research bridges theoretical innovation with practical implementation, particularly in smart cities, healthcare, and transportation. Scientific Awards and Recognition: Invitation to the Crown Prince and Princess's 50th birthday celebration, 2023 Study Quality Award, NTNU, 2017 Norway’s Youngest Professor Award, 2016 Denmark’s youngest M.Sc. engineering award, 2009 IEEE Commendation for Young Professionals Volunteer, 2011 Multiple best paper awards from IEEE, ACM, and Springer Mohammad Derawi has been involved in several funded research and development projects, including IoT Safetraffic (RFF Inland), Ambulance Drone (NTNU Vice-Rector), Wireless ECG (Innovation Norway), biometric handgun security (RFF Innlandet), and the EU Framework 7 TURBINE project. He mentors students and collaborates with international researchers, contributing significantly to both academic and applied domains. His leadership in the SWS group and IoT Lab fosters innovation in wireless and secure embedded systems. He is actively engaged in laboratory and team-based research, particularly through the Smart Wireless Systems group and the IoT Lab, focusing on developing secure, intelligent, and scalable solutions for next-generation wireless applications.