جست‌وجوی استاد و پژوهشگر

Pengfei Liمشاهده پروفایل

استادیار

• Online optimization
• Trustworthy machine learning
• Sustainable AI
• +۷ مورد دیگر

Pengfei Li is an Assistant Professor in the School of Information at the Rochester Institute of Technology (RIT), where he leads research at the intersection of machine learning, sustainability, and social equity. Previously, he completed his Ph.D. in Computer Science at the University of California, Riverside under Prof. Shaolei Ren, with additional collaborations at Caltech with Adam Wierman and an internship at Nokia Bell Labs. His educational background includes an M.S.E. in Robotics from Johns Hopkins University and a B.E. in Electrical Engineering from Zhejiang University. Dr. Li's research focuses on three interconnected pillars: developing trustworthy online algorithms with strict robustness guarantees, creating sustainable AI systems that minimize environmental impact, and addressing environmental and social inequities through algorithmic solutions. Analysis of his recent publications reveals a strong emphasis on the environmental consequences of AI systems, particularly water consumption ('Making AI Less 'Thirsty'') and geographical distribution of environmental burdens ('Towards Environmentally Equitable AI'). His work bridges theoretical computer science with practical sustainability challenges, often incorporating learning-augmented approaches to traditional online optimization problems. Scientific Recognition: Dissertation Completion Fellowship Award (DCFA) from the Graduate Program in Computer Science (February 2025) 'Making AI Less 'Thirsty'' in Communications of the ACM has received 15 citations and over 17,000 downloads Organizer of the workshop on learning-augmented algorithms at SIGMETRICS 2025 Dr. Li actively seeks to build a research group focused on societal fairness, reliable generative AI, and decision-focused learning. His work has established important connections between theoretical computer science and critical societal challenges, particularly around AI's environmental footprint and equitable resource distribution. Current research directions include developing algorithmic solutions for environmental and social fairness, with applications in water infrastructure, energy systems, and equitable AI deployment.

• Simulation-Based Optimization
• Industry 4.0
• Production Engineering
• +۴ مورد دیگر

Amos H. C. Ng is a Professor at the School of Engineering Science, University of Skövde, specializing in simulation-based optimization and Industry 4.0 technologies. His research bridges production engineering with human-robot collaboration, ergonomics evaluation, and cloud-based cyber-physical systems for manufacturing efficiency. Key Affiliations: University of Skövde (School of Engineering Science), Uppsala University (Industrial Engineering and Management) Research Themes: Multi-objective optimization, Digital Twin frameworks, Human-centric production systems, Reconfigurable manufacturing, Throughput bottleneck analysis Projects: ACCURATE 4.0 (Knowledge Foundation), VF-KDO (Virtual Factories with Knowledge-Driven Optimization), EWASS (Wire Harness Assembly Optimization) His recent publications demonstrate expertise in applying evolutionary algorithms, machine learning models, and digital human modeling tools to solve complex manufacturing problems ranging from crankshaft machining to wood supply chain robustness. Current work integrates motion capture technology with DHM tools for objective ergonomic assessments in assembly stations. Amos collaborates extensively with industrial partners like Volvo Penta and academic institutions, utilizing simulation-based approaches to enhance decision-making in production systems. His methodological focus includes non-dominated sorting genetic algorithms, surrogate modeling, and parallel computing architectures for optimization tasks.

Amy Ellen Mainville Cohnمشاهده پروفایل

استاد

• Combinatorial Optimization
• Healthcare Systems
• Aviation Systems
• +۳ مورد دیگر

Amy Ellen Mainville Cohn is a Professor in the Department of Industrial and Operations Engineering at the University of Michigan and holds a secondary appointment in the Department of Health Management and Policy in the School of Public Health. She serves as the Faculty Director of the Center for Healthcare Engineering and Patient Safety (CHEPS) and Chief Transformation Officer at Michigan Medicine, where she collaborates on process re-design and operational improvements. Ph.D. in Operations Research from MIT (2002) A.B. magna cum laude in Applied Mathematics from Harvard (1991) Her research focuses on applying combinatorial optimization to complex systems like healthcare and aviation, emphasizing multi-criteria objective functions and uncertainty management. She is known for bridging methodologies across domains (transportation, healthcare) to develop scalable solutions for resource allocation, scheduling, and decision-making under conflicting objectives. As an educator, she teaches optimization techniques to undergraduate and graduate students and mentors experiential learners. She values fostering a diverse academic community and has led initiatives to enhance teaching and research collaboration. Alfred F. Thurnau Professor (recognizing excellence in undergraduate education) Leadership roles at CHEPS and Michigan Medicine Amy is actively involved in healthcare engineering, co-directing projects to improve patient safety and provider scheduling systems. She collaborates with institutions like the Department of Veterans Affairs and previously worked on radiation treatment optimization models. Her work integrates simulation, data science, and stakeholder engagement to address real-world constraints.

Pieter Vansteenwegenمشاهده پروفایل

استاد

• Public Transportation Optimization
• Metaheuristics
• Demand-Responsive Transport Systems
• +۴ مورد دیگر

Prof. Pieter Vansteenwegen is a full professor at KU Leuven's Faculty of Engineering Science, leading the Centre for Industrial Management/Traffic and Infrastructure (CIB) and chairing the KU Leuven Institute for Mobility (LIM). With a PhD in Operations Research (2008), he works on robust public transport systems, metaheuristics for logistics, and demand-responsive mobility solutions. Chair, KU Leuven Institute for Mobility Program Director, Master of Mobility and Supply Chain Engineering Board member, International Association of Railway Operations Research His research integrates transportation engineering , logistics optimization , and smart mobility systems , focusing on: Demand-responsive public transportation Metaheuristic algorithm development Inventory routing problems Smart waste collection strategies Multi-robot scheduling Recent publications demonstrate expertise in variable neighborhood search , Benders decomposition , and genetic algorithms applied to public transport, satellite scheduling, and circular economy logistics. His work has been cited over 9,400 times (Google Scholar H-index 51). Scientific recognition includes: Multiple IAROR best paper awards Best Reviewer European Journal of Operational Research BIVEC-GIBET PhD awards (2009, 2021, 2023) RASIG-INFORMS Student Paper Prizes He directs KU Leuven's Planning and Operational Research subdivision , serves on faculty councils, and founded spin-off dyNAVic for electronic tourist guides. His teaching portfolio includes courses in Public Transportation Design , Distribution Logistics , and Operational Management .

• Electrical engineering
• Electronics, sensors and digital hardware
• Distributed computing and systems software
• +۶ مورد دیگر

Masood Parvania is the Roger P. Webb Endowed Professor at the University of Utah in Electrical and Computer Engineering. He serves as Director of the Utah Smart Energy Laboratory (U-Smart) and Co-Director of the NSF WIRED Global Center . His research focuses on mathematical optimization , control theory , and machine learning applications in power system operation, resilience, and interdependent infrastructure modeling. His work addresses critical challenges including: Equity-aware grid restoration Climate-resilient energy systems Cyber-physical security analysis Hybrid energy storage coordination Extreme heat event mitigation strategies Key research trends across his 15 most recent publications reveal deep integration of: Renewable energy with storage systems Machine learning in real-time grid operation Cybersecurity frameworks for critical infrastructure Equity metrics in energy distribution Honors include: IEEE Outstanding Associate Editor Award (2022) University of Utah Presidential Scholar (2020) IEEE Utah Section Outstanding Educator Award (2017) Multiple Best Reviewer and Distinguished Service Awards As Associate Editor for IEEE Transactions on Power Systems , he actively shapes energy research discourse while leading NSF-funded initiatives like: U.S.-Canada Climate-Resilient Grid Center ($90M+ Western EV Infrastructure Scale-Up Wasatch Multi-Modal Corridor Electrification

• Process Systems Engineering
• Chemical Engineering
• Optimization
• +۶ مورد دیگر

Alexander Mitsos is a Professor at Forschungszentrum Jülich in Germany, where he leads research at the intersection of process systems engineering, chemical engineering, and computational methods. His work spans optimization theory, machine learning applications, and energy systems, with a focus on developing novel methodologies for complex engineering problems across multiple domains. Dr. Mitsos's research interests center on the application of advanced optimization techniques to chemical engineering problems. His primary areas of focus include: Process systems engineering and optimization Machine learning applications in chemical engineering Energy systems and hydrogen technologies Bioprocess engineering and control systems Ammonia energy storage and carbon capture His recent publications reveal a strong trend toward integrating machine learning with traditional chemical engineering approaches. He has pioneered work on graph neural networks for molecular property prediction, reinforcement learning for control systems, and bilevel optimization for energy systems. His research demonstrates a consistent focus on developing computationally efficient methods that bridge theoretical advances with practical engineering applications, particularly in sustainability-focused domains like hydrogen technologies and carbon emission reduction. The analysis of his 15 most recent publications shows a balanced portfolio between theoretical method development (e.g., optimization algorithms) and practical applications (e.g., cement production, hydrogen compression). Dr. Mitsos has mentored numerous graduate students and postdoctoral researchers, as evidenced by his extensive publication record with junior authors. His research has been supported by various grants focused on energy transition, process optimization, and sustainable chemical engineering solutions, with significant collaborations across European institutions. The funding landscape for his work appears to emphasize sustainability transitions and industrial decarbonization, particularly in energy-intensive sectors. His work appears to be conducted within a research group focused on process systems engineering, with strong connections to both computational mathematics and practical chemical engineering applications. The group maintains laboratories for experimental validation of computational models, particularly in bioprocess engineering and hydrogen technologies, while maintaining strong theoretical foundations in optimization and control theory.

Sébastien Le Digabelمشاهده پروفایل

استاد

• Operations Research
• Mathematical Optimization
• Black-box Optimization
• +۵ مورد دیگر

Sébastien Le Digabel is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He holds affiliations with GERAD (Groupe d'études et de recherche en analyse des décisions) and IVADO (Institut de valorisation des données), where he contributes to research in optimization and artificial intelligence. Professor Le Digabel's research focuses on black-box optimization and derivative-free methods, particularly the Mesh Adaptive Direct Search (MADS) algorithm. His work spans three main areas: development of optimization methods, application to engineering problems, and development of optimization software. He has made significant contributions to extending MADS for constrained optimization, mixed-variable problems, multiobjective optimization, and stochastic optimization. His recent publications (2023-2025) demonstrate continued innovation in handling complex optimization challenges including categorical variables, hierarchical constraints, and multi-fidelity approaches. These works appear in top journals like Computational Optimization and Applications, SIAM Journal on Optimization, and Optimization and Engineering. Professor Le Digabel actively supervises graduate students, having completed 8 PhD theses and 10 Master's theses with topics spanning multiobjective optimization, hyperparameter tuning, smart grid applications, and power systems optimization. His research is supported by diverse funding sources including NSERC, FRQNT, InnovÉÉ, IVADO, Hydro-Québec, Rio Tinto, and Huawei-Canada, reflecting the broad applicability of his work across academic and industrial domains. Professor Le Digabel leads the NOMAD Research Group and is the primary developer of the NOMAD optimization software, which implements the MADS algorithm and serves as a standard tool in the derivative-free optimization community. The software is available at www.gerad.ca/nomad and continues to evolve with new capabilities for challenging optimization problems.

• Forest Ecology
• Forest Management
• Ecophysiology
• +۵ مورد دیگر

Sergio Roberto Donoso Calderon is an Associate Professor at the Department of Forestry and Nature Conservation within the Faculty of Forestry Sciences and Nature Conservation at the University of Chile. He serves as Director of the Department of Forestry and Nature Conservation from 2024 to 2026 and teaches undergraduate courses including Territorial Planning, Forestry Policy, and Forest Dynamics. With a work schedule of 44 hours per week, he maintains an active academic and research profile. His educational background includes: Doctor from University of Cordoba (1999) Forestry Engineer from University of Chile (1990) Donoso's research focuses on forest ecology, ecophysiology, and forest management, with particular emphasis on water stress responses in native Chilean tree species. His work investigates physiological adaptations of species like Peumus boldus (boldo), Cryptocarya alba (peumo), and Nothofagus species to drought conditions, which is increasingly relevant in the context of Chile's megadrought. He also studies forest dynamics, silvicultural practices, and the conservation of native ecosystems including the Pewén (Araucaria araucana) biocultural ecosystem. His recent publications (2022-2024) demonstrate a strong focus on global forest patterns, tree reproduction mechanisms, and responses to climate change. These works often involve large international collaborations examining water stress responses, seed production dynamics, and the relationship between tree size and reproductive strategies across different climate zones. His research combines field experimentation with analysis of global datasets to understand fundamental forest ecological processes. Donoso has supervised numerous undergraduate and graduate theses, primarily focused on native Chilean species and their responses to environmental stressors. His students have investigated topics including water stress in boldo trees, forest management practices, and the physiology of native species under drought conditions. His academic leadership extends to directing the Department of Forestry and Nature Conservation, where he shapes curriculum and research directions for the faculty.

Stéphane Bessyمشاهده پروفایل

استاد

• Graph theory
• Directed graphs
• Algorithms
• +۱ مورد دیگر

Stéphane Bessy is a Professor of Computer Science at the University of Montpellier , serving as Director of the Computer Science Department within the Faculty of Sciences . He is a member of the Graph Algorithms and Combinatorics (AlGCo) team at LIRMM (Laboratory of Informatics, Robotics, and Microelectronics of Montpellier). His research focuses on graph theory, directed graphs, algorithms, and combinatorial optimization. Current teachings include Algorithmic 3, System, Algebra and Geometry, Probability and Statistics, Graphs and Structures, Graphs, Algorithms, Complexity, and Oral Preparation courses. His publications explore topics such as cycle packing in tournaments , exponential domination in graphs , Wiener index optimization , and degree-constrained graph partitions . His work often intersects algorithmic complexity and structural graph theory. Contact: Stephane.Bessy@lirmm.fr

Fuyuki Ishikawaمشاهده پروفایل

دانشیار

• Software Engineering
• Testing
• Formal Methods
• +۴ مورد دیگر

Fuyuki Ishikawa is an Associate Professor at the Information Systems Architecture Science Research Division of the National Institute of Informatics (NII) in Tokyo, Japan, where he also serves as Director of the GRACE Center. Additionally, he holds positions as Associate Professor at Sokendai (The Graduate University for Advanced Studies) and Visiting Associate Professor at The University of Electro-Communications. His research program, which he describes as 'Trustworthy & Smart Software Engineering,' focuses on dependability in Cyber-Physical Systems and Machine Learning Systems through techniques of formal methods and automated test generation. He investigates verification, reasoning, optimization, automated test generation, and self-adaptation by utilizing various models for requirements, specifications, and designs. His group promotes international and industry-academia collaborations with members from different organizations. Ishikawa's recent work has increasingly concentrated on quantum program testing, autonomous driving systems verification, and quality assurance frameworks for AI systems. His publications demonstrate significant contributions to software engineering, particularly in applying formal methods to emerging domains like quantum computing and machine learning. His research often bridges theoretical foundations with practical applications in safety-critical contexts, particularly for autonomous systems. Awards for Science and Technology (Research Category) from the Minister of Education, Culture, Sports, Science and Technology (April 2024) IPSJ/IEEE Computer Society Young Researcher Award for 'Research on Intelligence-driven Engineering of Dependable Smart Systems' (March 2020) Multiple Best Paper Awards at conferences including ICFEM 2020, SEKE 2020, ICECCS 2019, and ISSRE 2019 Industry Track Best Artifact Award at ISSTA 2015 Ishikawa actively supervises research projects and interns through the ERATO-MMSD Project and MIRAI-eAI Project. He has served on numerous program committees for major software engineering conferences including ASE, ICSE, and ICST, and has organized workshops on machine learning systems engineering (SIG-MLSE) and quality assurance for AI systems (QA4AI). His laboratory provides two main research directions: generative AI for trustworthy software engineering and testing/trust exploration for AI systems, with specific focus on autonomous driving and deep learning applications.

• Documentary Filmmaking
• Film Directing
• Social Issues
• +۴ مورد دیگر

Mirosław Dembiński (born February 21, 1959 in Bydgoszcz) is a Professor of Art at the National Film School in Łódź, where he serves as a lecturer at the Film and Television Directing Department. He holds a PhD in Film Arts (2007) and a habilitated doctorate in film arts (2011), both from the National Film School in Łódź. His educational background includes a degree in Mathematics, Physics and Chemistry from Nicolaus Copernicus University in Toruń (1983) and a degree in Directing from the National Film School in Łódź (1990). Professor Dembiński's research focuses on documentary filmmaking, social issues, cultural representation, and Polish cinema. His work often explores human conditions, social dynamics, and cultural identity through the lens of documentary and educational film. He has developed a distinctive approach to film education, mentoring numerous students through their film projects while maintaining his own active production schedule. His methodology bridges theoretical knowledge with practical filmmaking experience, emphasizing both technical proficiency and thematic depth. His recent filmography demonstrates a continued engagement with diverse social themes, from mental health and communication disorders to urban life and economic challenges. His work shows a consistent interest in both Polish cultural identity and international perspectives, particularly evident in his 'Świat od świtu do zmierzchu' (The World from Dawn till Dusk) series that documented cities across multiple countries. The Greatest Optimist of the Decade Award at the 10th Optimistic Film Festival 'Multimedia Happy End' in Rzeszów (2012) Flisak Tofifest for an outstanding filmmaker associated with the Kuyavian-Pomeranian region at the 12th International Film Festival 'Tofifest' in Toruń (2014) Congratulatory letter from the President of the Republic of Poland (2006) Professor Dembiński has supervised numerous student film projects throughout his academic career, providing pedagogical guidance while encouraging creative exploration. His mentorship extends beyond technical film skills to encompass thematic depth and social relevance. He has also been involved in significant documentary projects both in Poland and internationally, including the 'Świat od świtu do zmierzchu' series that featured cities across multiple countries. His work demonstrates a commitment to both the art of filmmaking and its educational transmission, bridging professional practice with academic instruction in a way that enriches both domains.