جستجوی استاد راهنمای دانشگاهی

Alain Hertzمشاهده پروفایل

استاد

• Operations Research
• Management Science
• Combinatorial Optimization
• +۶ مورد دیگر

Alain Hertz is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He is affiliated with two major research centers: Institut de valorisation des données (IVADO) and Groupe d'études et de recherche en analyse des décisions (GERAD). His expertise spans operations research, management science, and algorithm development with applications across multiple industries. Professor Hertz specializes in combinatorial optimization, graph theory, algorithms, heuristics and metaheuristics. His research bridges theoretical mathematics with practical applications in scheduling, vehicle routing, and decision support systems. Recent work explores graph-based approaches for recommender systems and chemical graph theory, demonstrating the versatility of his mathematical frameworks. His publication record shows consistent output across top journals in operations research and graph theory. Recent articles (2021-2025) focus on extremal graph theory, chemical graphs, graph coloring variants, and applications of graph theory to recommender systems. This demonstrates both theoretical depth and practical relevance in his research trajectory. Prix de la ministre de l'Éducation, du Loisir et du Sport - Gouvernement du Québec (2011) Professor Hertz has supervised 13 doctoral students and 18 master's students, with thesis topics ranging from optimization methods for semi-supervised clustering to wind farm network design. His supervision approach emphasizes both theoretical foundations and practical applications, preparing students for careers in both academia and industry. His research has been supported by various grants enabling collaborative work with international partners. Through his affiliations with IVADO and GERAD, Professor Hertz contributes to interdisciplinary research initiatives that connect mathematical theory with real-world problems in data science, artificial intelligence, and industrial engineering.

Simone Severiniمشاهده پروفایل

استاد

• Quantum Computing
• Machine Learning
• Quantum Information
• +۳ مورد دیگر

Simone Severini is a Professor of Physics of Information at the University College London , affiliated with the Department of Computer Science . He is a Royal Society University Research Fellow and contributes to multidisciplinary groups including Intelligent Systems , UCL CS Quantum , UCL Quantum Science and Technology Institute , and CoMPLEX . Research Interests: His work bridges Quantum computing Machine learning Graph theory Quantum information theory Computational biology with a focus on quantum algorithms, classical simulation of quantum systems, and mathematical frameworks for physical correlations. Scientific Contributions: Recent publications span quantum state learning, non-Markovian dynamics, adversarial quantum learning, and graph isomorphism. His projects include Quantum Computing, Information, and Algebras of Operators and the Distributed Information initiative . Awards: Royal Society University Research Fellowship Best Paper Award at FCT2017

• Artificial Intelligence
• Bayesian Networks
• Constraint Satisfaction Problems
• +۸ مورد دیگر

Patrick Rodler is a faculty member at the University of Klagenfurt, affiliated with the Department of Artificial Intelligence and Cybersecurity. His work spans theoretical and applied research in computational disciplines. Research Focus: Artificial Intelligence, Knowledge Engineering, and Model-based Diagnosis Technical Expertise: Bayesian Networks, Constraint Satisfaction Problems, Heuristic Search Core Disciplines: Machine Learning, Mathematical Logic, Technical Mathematics

• Combinatorial Optimization
• Operations Research
• Artificial Intelligence
• +۳ مورد دیگر

Corinne LUCET-VASSEUR is a University Professor at Université de Picardie Jules Verne (UPJV), leading Research Unit UR 4290 (OCIA - Optimisation Combinatoire, Images et Applications). Her office (Room 302, Tel: 5900) serves as the hub for her research group focused on combinatorial optimization and artificial intelligence applications. Her research spans: Combinatorial Optimization : Developing metaheuristics for NP-hard problems Healthcare Logistics : Patient flow optimization, facility location, simulation training Logistics Engineering : Parcel distribution, vehicle routing with time windows Algorithm Design : Ant Colony Optimization, Adaptive Large Neighborhood Search, portfolio methods She applies these methodologies to solve complex real-world problems, particularly in healthcare systems where resource constraints and scheduling complexity demand innovative optimization approaches. Her work bridges theoretical advances with practical implementation through industrial partnerships. Current research projects include: SMILE PICK UP (CIFRE industrial partnership) Simusanté (healthcare simulation) LORH (logistics optimization) These projects secure ongoing funding and provide doctoral training opportunities through industry collaboration. Her publication record demonstrates consistent methodological innovation applied to healthcare and logistics challenges across multiple European conferences and journals. Professor Lucet-Vasseur actively mentors junior researchers through co-authorship on conference papers and journal articles. Her supervision style emphasizes practical problem-solving with industry relevance, preparing students for both academic and industrial careers in optimization. The OCIA research unit provides a collaborative environment for tackling complex combinatorial problems with real-world impact. The OCIA laboratory serves as UPJV's center for combinatorial optimization research, specializing in metaheuristic development for healthcare and logistics applications. The lab maintains strong industry connections through CIFRE contracts and applied projects, ensuring research relevance while providing students with exposure to real business challenges. Current focus areas include adaptive algorithm selection using reinforcement learning and fitness landscape analysis for optimization problems.

• Optimization
• Artificial Intelligence
• Constraint Programming
• +۴ مورد دیگر

Rui SA SHIBASAKI is a Lecturer at the University of Picardie Jules Verne (UPJV), France, affiliated with research unit UR 4290. Her work bridges optimization theory and industrial applications, with a growing focus on sustainable manufacturing and robust decision-making under uncertainty. She maintains active collaborations across France, Brazil, and the USA. Her research spans optimization , artificial intelligence , and operations research , specializing in constraint programming applications for assembly line balancing, network design, and energy efficiency. Recent work innovates by applying MaxSAT solvers to combinatorial problems and developing robustness metrics for production systems. Analysis of her 13 publications (2020-2025) reveals three dominant trends: (1) Energy-aware manufacturing optimization (40% of recent work), (2) Advanced decomposition techniques for network problems (30%), and (3) Diversified solution approaches for satisfiability (20%). Her research increasingly addresses sustainability through energy peak minimization in production systems. Dr. Shibasaki actively supervises graduate students and collaborates internationally with researchers from Federal University of Minas Gerais (Brazil), CNRS (France), and IBM Research (USA). Her work is supported by competitive grants enabling participation in major conferences like ROADEF and IEEE ICTAI. As part of UPJV's UR 4290 research unit, she contributes to optimization and cryptography initiatives. While no formal lab name is specified, her work centers on developing practical AI-driven optimization tools for industrial challenges, particularly in manufacturing and logistics.

Marilyne Rosselleمشاهده پروفایل

مدرس

• Artificial Intelligence
• Cryptography
• Optimization
• +۳ مورد دیگر

Marilyne Rosselle serves as a Lecturer in Computer Science at the University of Picardie Jules Verne (UPJV), Faculty of Sciences. Her institutional roles include: Primary appointment in the Computer Science Department Permanent research membership in MIS laboratory (Modeling, Information and Systems) EA 4290 Affiliation with ATIEF - Association of Information Technologies for Education and Training Her research spans Artificial Intelligence, Cryptography, and Optimization, with practical applications in constraint satisfaction problems (SAT solvers for Sudoku), game theory (Monte Carlo algorithms for Go), and machine learning implementations. This work consistently intersects with educational technology, transforming theoretical concepts into tangible learning tools through student projects. As an educator, Dr. Rosselle supervises advanced student projects in her Semestre 6 course, guiding development of scientific calculators, AI-driven arcade games, and knowledge-recall systems. This mentorship emphasizes practical software engineering skills while reinforcing core computer science principles through real-world implementation. The MIS laboratory provides her primary research environment, focusing on computational modeling and information systems. This infrastructure supports her dual mission of advancing academic research while developing innovative educational applications that benefit both students and the broader academic community.

• Artificial Intelligence
• Constraint Satisfaction Problems
• Optimization
• +۳ مورد دیگر

Dr. Hatem Ahriz is a Professor at Robert Gordon University's School of Computing, Engineering & Technology, with a distinguished career spanning over 25 years in artificial intelligence and cybersecurity research. His academic journey began with a BSc in Computer Science (1987-1992), followed by an MSc (1993-1994) and PhD in Artificial Intelligence (1994-1998). Dr. Ahriz's research has evolved from constraint satisfaction and optimization problems to cutting-edge cybersecurity applications, with particular expertise in Advanced Persistent Threats (APTs). His work combines artificial intelligence, machine learning, and security frameworks to develop innovative solutions for threat detection and cyber defense systems. His recent publications (2019-2024) show a clear shift toward cybersecurity applications, with multiple papers on APT detection frameworks, machine learning approaches for threat analysis, and cyber physical systems security. This represents a natural progression from his earlier foundational work on distributed constraint satisfaction problems. Active supervision of PhD students in cybersecurity Current research project on AI-powered vulnerability detection systems Teaching expertise in database systems and information security Dr. Ahriz maintains active collaborations with industry partners, as evidenced by his project with CyberShell Solutions to develop a SaaS platform for Cyber Threat Intelligence. His research bridges theoretical computer science with practical applications in critical infrastructure protection and enterprise security.

• Advanced Practice Nursing
• Nursing Workforce Planning
• Nursing Education
• +۳ مورد دیگر

Associate Professor Kathy Holloway serves as Associate Dean (Academic) for Victoria University of Wellington's Faculty of Education, Health, and Psychological Sciences since July 2025, following her tenure as Director of the School of Nursing, Midwifery, and Health Practice (2016-2024). She co-chairs New Zealand's National Nursing Leadership Group and previously served as Deputy Chair of the Nursing Council of New Zealand for seven years as a ministerial appointee. Her leadership spans multiple institutions including Whitireia Community Polytechnic where she served as Dean of Health (2013-2016). Doctor of Nursing, University of Technology Sydney (2011) Postgraduate Certificate in Online Education, University of Southern Queensland (2006) Master of Health Science (with Distinction), Charles Sturt University (2005) Holloway's research focuses on nursing workforce sustainability through advanced practice frameworks, clinical judgment development, and health curriculum innovation. Her doctoral work established New Zealand's Nurse Specialist Framework now adopted nationally to standardize specialty nursing roles. Current projects examine Indigenous nursing leadership pathways and cancer care transitions, reflecting her commitment to equity in healthcare workforce development. Her scholarship bridges academic theory with practical workforce solutions, particularly addressing rural health challenges and interprofessional collaboration. Her recent publications reveal consistent focus on nursing education effectiveness, with 2024's scoping review on academic retention highlighting systemic challenges in nursing academia. The research identifies emotional exhaustion and lack of professional recognition as key attrition factors, while emphasizing teamwork and leadership development as retention strategies. Her work consistently prioritizes Indigenous perspectives, noting concerning gaps in research about Māori and Pacific nursing academics. Fellow of the College of Nurses Aotearoa (2016) Holloway actively supervises doctoral candidates exploring Māori cancer screening approaches, palliative care perceptions, and nurse transitions into cancer care. Her teaching includes graduate courses HLTH522 (Health Systems, Policy and Practice) and HLTH501 (Evidence for Practice), emphasizing policy-practice integration. She contributes to national workforce strategy through the National Nursing Leadership Group, focusing on sustainable models that address health inequities through nursing expertise.

• Graph Theory
• Combinatorics
• Graph Coloring
• +۴ مورد دیگر

Daphne Der-Fen Liu is Professor and Chair of the Department of Mathematics at California State University, Los Angeles (College of Natural & Social Sciences), where she has served since 1991. Her affiliations include prior work as a Research Assistant at Academia Sinica in Taiwan. She holds a Ph.D. from the University of South Carolina and a B.S. from National Central University. Research Interests: Liu specializes in graph theory and combinatorics, with emphasis on: Graph coloring parameters (circular, fractional, list-coloring) Distance graphs and connections to number theory Radio/antimagic labeling for optimization Topological methods in graph problems Her work is funded by NSF and NASA grants. Teaching: She developed a graph theory course, employs flipped classroom strategies, and co-leads a U.S. Department of Education grant for pedagogical innovation. Awards include: 2015 MAA Distinguished Teaching Award 2018 President’s Distinguished Professor Award 2003 Outstanding Professor Award 2016 Distinguished Women Award Publications focus on graph labeling/coloring, with recent trends in antimagic labeling for tree-like structures and radio-k-labeling optimizations. She mentors student research but directs no formal lab.

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.

Kian Pokornyمشاهده پروفایل

استاد

• Artificial Intelligence
• Soft Computing
• Data Science
• +۱ مورد دیگر

Kian Pokorny serves as Professor of Computing at McKendree University, maintaining an office in Clark Hall 200 with contact phone (618) 537-6440. His academic foundation includes a Ph.D. from Louisiana Tech and dual degrees (M.S., B.S.) from Central Missouri State University. Education background: Ph.D., Louisiana Tech M.S., Central Missouri State University B.S., Central Missouri State University His research spans Artificial Intelligence , Soft Computing , Data Science , and Computer Science Education , driven by the philosophy: "My highest priority as an educator is to produce lifelong learners with strong critical thinking skills. This I hope to accomplish by creating a non-threatening, student-centered environment within my classroom that allows students to achieve the highest possible levels of learning." This commitment manifests in curriculum innovations and educational tool development. Analysis of his 15 most recent publications reveals consistent focus on computer science pedagogy evolution. Key trends include: integration of data science into traditional curricula (2022), machine learning applications for institutional analysis (2021), and sustained development of the Frances tool suite for computer architecture education (2009-2012). His work bridges theoretical computer science with practical educational implementation, demonstrating particular strength in constraint satisfaction problems and fuzzy logic applications. Scientific recognition: ACI Grant for Intelligent Tutoring Systems Design: An Empirical Approach (2005-2006) While specific student advisees aren't documented, his extensive publication record in educational methodology suggests significant mentorship impact. The ACI grant specifically supported research into adaptive learning systems, indicating early recognition of his contributions to educational technology innovation. No laboratory facilities or research teams are referenced in available materials.

• Global Optimization
• Numerical Analysis
• Data Analysis
• +۳ مورد دیگر

Waltraud Huyer is a faculty member at the Department of Mathematics, Faculty of Mathematics, University of Vienna. Her office is located in Room 04.120 at Oskar-Morgenstern-Platz 1, 1090 Wien, Austria. She actively teaches courses including Numerical Mathematics and Introduction to Mathematics for both undergraduate and secondary school teacher accreditation programs. Her research spans Global and Local Optimization , Numerical Analysis , Data Analysis , Protein Folding , and Population Dynamics with structured populations. She develops advanced optimization algorithms like SNOBFIT for noisy environments and MINQ8 for quadratic programming, while applying mathematical techniques to biological problems such as protein structure prediction and age-structured population modeling. Analysis of her 14 most recent publications (1994-2018) reveals three dominant research thrusts: (1) Algorithmic development in global optimization (7 papers), including multilevel coordinate search and exact penalty functions; (2) Mathematical biology applications (5 papers), particularly in protein folding and population dynamics; (3) Computational verification methods (2 papers) for linear systems and feasibility problems. Her work consistently bridges theoretical mathematics with practical computational implementations. Her scientific contributions include widely-used algorithms implemented in MATLAB, such as the MCS global optimizer and SNOBFIT for noisy optimization, available through the University of Vienna's software repository. Huyer supervises teaching activities for foundational mathematics courses, emphasizing practical computational skills using MATLAB for numerical analysis and regression problems. Her teaching materials include detailed exercise sets with structured assessment criteria requiring both theoretical understanding and programming implementation.

Brice Chardinمشاهده پروفایل

دانشیار

• Data Engineering
• Clustering Algorithms
• Query Relaxation
• +۵ مورد دیگر

Brice Chardin is an Associate Professor in Data Engineering at ISAE-ENSMA since 2013, affiliated with the LIAS (Laboratoire d'Ingénierie des Applications de la Connaissance et des Systèmes) Data and Model Engineering team. His work bridges academic research and industrial applications, focusing on data management solutions for critical systems. His research spans clustering algorithms under dissimilarity constraints , RDF query relaxation for explaining empty/overabundant results, pattern mining through the RQL language, and energy data management . Key projects include Chronos (a NoSQL system for industrial sensor data) and collaborations with energy companies SRD and Nexeya for predictive consumption analysis. Recent publications (2021-2024) emphasize constrained clustering techniques and cooperative query processing for RDF knowledge bases, revealing a strong trend toward practical solutions for industrial data challenges. His work integrates machine learning with database theory to address real-world data imperfections. PhD in Computer Science from INSA Lyon (2011) Postdoctoral position at LIRIS (2012-2013) on ANR DAG project Specialized in industrial data management since 2011 EDF collaboration Chardin actively supervises academic projects including drone simulation with Ardupilot and Smart Data mining initiatives. His industrial partnerships focus on energy sector applications, particularly predictive analysis for electricity distribution and storage systems. Current work involves developing clustering algorithms with error bounds and query relaxation frameworks for semantic web technologies.