جستجوی استاد راهنمای دانشگاهی

• Evolutionary Computation
• Multi-Objective Optimization
• Artificial Intelligence
• +۷ مورد دیگر

Dr. Ciprian Zavoianu is an academic researcher at Robert Gordon University (RGU) in the School of Computing, Engineering & Technology. He leads the Net Zero Operations research programme at the National Subsea Centre and is affiliated with the Complex Optimisation Research Group. His work focuses on applying artificial intelligence, particularly evolutionary computation algorithms, to solve complex real-world optimization problems with practical engineering applications. Dr. Zavoianu earned his academic qualifications from West University of Timisoara, Romania (BSc and MSc in Computer Science) and Johannes Kepler University Linz, Austria (PhD in Computer Science, 2015). His doctoral research focused on enhancing multi-objective evolutionary algorithms for computationally-intensive optimization problems. His primary research interests include: Evolutionary Computation Multi-Objective Optimization Data Mining & Machine Learning (particularly for surrogate modeling) Timetabling and Rostering Parallel/Distributed Computing Dr. Zavoianu's recent publications (2021-2025) demonstrate a strong focus on applying optimization techniques to transportation systems, electrical machine design, and sustainable energy solutions. His work consistently addresses the challenge of computationally expensive optimization through innovative surrogate modeling approaches, enabling practical applications of evolutionary algorithms to real-world engineering problems. He has secured multiple research grants including 'Data For Net Zero' and 'Ferry Passenger and Freight Modelling for Shetland,' demonstrating the practical relevance and industry applicability of his research. Dr. Zavoianu actively supervises five PhD/EngD students across diverse topics including predictive analytics for subsea installations, optimization of electrical machines, and operations optimization for harbor operations. His supervision approach emphasizes bridging theoretical algorithm development with practical implementation in engineering contexts. His laboratory work is centered around the National Subsea Centre, where he leads the Net Zero Operations research programme, focusing on sustainable solutions for the energy transition through advanced computational methods.

• Computational Social Science
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Dr. Dachun Sun serves as an Assistant Teaching Professor in the Department of Computer Science at Worcester Polytechnic Institute (WPI), with additional affiliations in the Data Science and Artificial Intelligence programs. Based in Unity Hall 452, he contributes to WPI's academic mission through both teaching innovation and computational social science research. His educational foundation was built entirely at the University of Illinois Urbana-Champaign: Ph.D. in Computer Science M.S. in Computer Science B.A. in Music Technology B.S. in Computer Science Dr. Sun's research sits at the critical intersection of artificial intelligence and societal impact , specializing in computational social science through graph neural networks , large language models , and retrieval-augmented generation . His work analyzes polarization in online communities to detect ideological positions and model responses to real-world events, with direct applications in influence campaign detection and crisis response systems. He simultaneously pioneers the integration of AI tools into computer science education while fostering critical awareness of their ethical implications. His 2023-2024 publications reveal a strong trajectory in temporal reasoning, polarized social graph analysis, and human activity recognition—appearing in top venues like ACM SIGIR and ICCCN. These works demonstrate consistent innovation in knowledge graph reasoning, multimodal representation, and practical AI applications for societal challenges. Dr. Sun actively mentors students through hands-on research opportunities in his computational social science group, emphasizing collaborative problem-solving for real-world issues. His funded projects directly translate research into tools for crisis response planning and misinformation mitigation. Though no formal lab name is specified, his research collective focuses on bridging AI methodology with social analysis, maintaining strong industry and academic connections as evidenced by his multi-institutional publications and upcoming CIKM 2024 tutorial on temporal graph learning.

Andrzej Indrzejczakمشاهده پروفایل

استاد

• Proof Theory
• Sequent Calculus
• Modal Logics
• +۷ مورد دیگر

Professor Andrzej Indrzejczak is a distinguished academic at the University of Lodz, where he serves as Head of the Department of Logic and Methodology of Sciences within the Faculty of Philosophy and History. With a career spanning over three decades, he has established himself as a leading expert in proof theory, sequent calculus, and non-classical logics. His educational background includes a Master's degree in Cultural Studies (1989), a Master of Philosophy (1993), a PhD in Philosophy (1997) with dissertation on "Sequential and assumption formalizations of propositional modal logics," and habilitation in 2007 with thesis "Hybrid deductive systems in modal logics." He was awarded the title of Professor of Humanities in 2015. Professor Indrzejczak's research focuses on proof theory for non-classical logics, particularly practical applications of proof methods and their combinations (hybrid systems), natural proof search methods, and algorithms. His work encompasses sequent calculus methodology, cut elimination techniques extended to non-classical logics, and anti-realist approaches to the problem of meaning of logical constants. He also investigates applications of non-classical logics in formal analysis of traditional philosophical problems, argumentation theory, and methodology of social sciences. His publications reveal deep expertise in modal logics, free logics, and the theoretical foundations of logical systems. His extensive publication record shows a strong trend toward developing proof-theoretic approaches to definite descriptions and complex terms, with recent work focusing on bisequent calculi, cut elimination theorems, and formalization of multi-valued logics. His research bridges philosophical questions with rigorous logical formalization, particularly in the areas of hybrid logic, temporal reasoning, and logical ontology. The progression from modal logics to definite descriptions demonstrates his expanding research scope while maintaining technical rigor. Professor Indrzejczak has received significant research funding, including an ERC Advanced Grant for his "Coming to Terms: Proof Theory Extended to Definite Descriptions and other Terms" project (2022-2027), valued at 1.6 million EUR, along with multiple National Science Center grants totaling over 470,000 PLN. This ERC project represents a major advancement in extending proof theory to handle complex terms. He currently supervises doctoral students and has served as a promoter for at least one doctoral thesis. His research team includes several collaborators including Dr. Nils Kürbis, Dr. Przemysław Wałęga, and Dr. Michał Zawidzki. He has led multiple research projects including "80 years of research on natural deduction and sequent calculi" and "Sequent and tableau deductive systems for non-classical logics." Professor Indrzejczak actively contributes to the academic community as Editor-in-Chief of Bulletin of the Section of Logic and Chairman of the Editorial Board for Studia Logica. He has organized numerous conferences including the "Non-Classical Logics. Theory and Applications" series spanning over 15 years, demonstrating his leadership in the international logic community.

• Optimal Control
• Partial Differential Equations
• Optimization
• +۵ مورد دیگر

Hannes Meinlschmidt is an Assistant Professor and Senior Scientist at the Chair for Dynamics, Control, Machine Learning and Numerics (Alexander von Humboldt Professorship) at Friedrich-Alexander University Erlangen-Nuremberg (FAU) since March 2021. He previously held a PostDoc position at the Radon Institute for Computational and Applied Mathematics (RICAM) in Linz, Austria, from September 2017. He obtained his Diploma (2011) and PhD (2017) in Mathematics from TU Darmstadt, supervised by Stefan Ulbrich. His research intersects optimal control, optimization, and PDE analysis with emphasis on: Regularity theory for elliptic/parabolic equations Control of nonlinear PDE systems Applications with nonsmooth data Variational methods for dynamic models His publications demonstrate expertise in mathematical analysis of physical systems, including semiconductor equations, thermistor control, and biological models. Work frequently addresses mixed boundary conditions, nonsmooth domains, and Hölder regularity. Teaching includes advanced mathematics courses such as: Optimization with PDEs Interpolation theory Partielle Differentialgleichungen I/II He leads research at the FAU DCN-AvH chair, contributing to PDE numerics and control theory.

Charles Louis Feffermanمشاهده پروفایل

استاد

• Mathematical Analysis
• Harmonic Analysis
• Partial Differential Equations
• +۷ مورد دیگر

Charles Louis Fefferman is the Herbert E. Jones, Jr. '43 University Professor of Mathematics at Princeton University, where he has held a faculty position since 1977. Previously, he served as a full professor at the University of Chicago from 1971 to 1977, becoming the youngest full professor in U.S. history at age 22. His academic journey began at the University of Maryland, College Park, where he earned his undergraduate degree at 17 before completing his PhD at Princeton under Elias Stein at age 20. Fefferman's research spans mathematical analysis with particular emphasis on harmonic analysis, partial differential equations, and complex analysis. His groundbreaking work on singular integrals, Hardy spaces, and the Bergman kernel revolutionized these fields, leading to his Fields Medal in 1978. More recently, he has made significant contributions to Whitney extension problems, fluid dynamics singularity formation, and mathematical aspects of topological materials. His publication record shows remarkable consistency over five decades, with recent work (2019-2023) focusing on manifold learning, smooth function interpolation, and quantum systems. These publications demonstrate both theoretical depth and increasing connections to data science applications, maintaining his position at the forefront of mathematical research. Fefferman's scientific honors form an exceptional constellation of recognition: Fields Medal (1978) Alan T. Waterman Award (1976, inaugural recipient) Salem Prize (1971) Bergman Prize (1992) Bôcher Memorial Prize (2008) Wolf Prize in Mathematics (2017) BBVA Foundation Frontiers of Knowledge Award (2021) As an advisor, Fefferman has mentored numerous doctoral students who have become leaders in their fields, including Matei Machedon, Luis Seco, and Michael Christ. His research group continues to explore fundamental questions in analysis while developing mathematical frameworks for emerging applications in data science and quantum physics. Fefferman remains actively engaged in research, with publications through 2023 demonstrating his continued intellectual vitality and mathematical creativity.

Stephan Doerfelمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Professor Stephan Doerfel holds a professorship for Data Science at Kiel University of Applied Sciences since 2021, where he is assigned to the Department of Media and serves as head of the Institute for Data Science (IfDS). He teaches Mathematics and Multivariate Statistics, Machine and Deep Learning modules in the Master's Program Data Science (MADS). His primary research interests span Data Science, Machine Learning, and Deep Learning, with significant contributions to recommender systems, social bookmarking systems (particularly BibSonomy), formal concept analysis, and publication analysis. His recent work shows a shift toward applied industrial problems, especially in injection molding quality prediction and agricultural technology. Analysis of his publication trends reveals an evolution from foundational work in formal concept analysis and social tagging systems (2010-2016) to more applied machine learning research in manufacturing and agriculture (2020-2024). His work consistently bridges theoretical computer science with practical applications across diverse domains. Professor Doerfel has been actively involved in numerous academic service roles including peer reviewing for major journals and serving on program committees for prestigious conferences including ECML/PKDD, WebSci, and ICFCA. His extensive conference participation spans over a decade with consistent contributions to the machine learning and data science community. He has supervised various research projects including AVAPS (focusing on injection molding data), PUMA (Academic Publication Management), and Info 2.0 (Informational self-determination in Web 2.0). As a core contributor to the BibSonomy open-source project, he has helped create a scholarly social bookmarking system that serves as a testbed for recommendation algorithms and user behavior studies.

Gatot Tri Pranotoمشاهده پروفایل

مدرس

• Information Systems
• Decision Support Systems
• Data Mining
• +۴ مورد دیگر

Gatot Tri Pranoto is a Faculty Member in the Information Systems department at Trilogi University, Jakarta, Indonesia. His academic work focuses on the application of information technology and data science methods to solve practical problems across various domains including business, healthcare, government, and education. His research interests span Information Systems , Decision Support Systems , Data Mining , and Artificial Intelligence applications. Dr. Pranoto's work demonstrates particular expertise in developing practical solutions using methods such as K-Means clustering, Particle Swarm Optimization, Analytical Hierarchy Process (AHP), and various machine learning algorithms. His research often addresses real-world challenges in Indonesian contexts, including village development classification, flood prediction, customer segmentation, and educational management. Analysis of his publication trends from 2020-2022 reveals a consistent focus on developing decision support systems and applying data mining techniques to diverse problems. His work shows particular strength in comparative analysis of different algorithms and methodologies, often evaluating multiple approaches to determine optimal solutions for specific contexts. The geographical focus of his research predominantly centers on Indonesian regions including Aceh Province, West Java, and Palangka Raya City.