جست‌وجوی استاد و پژوهشگر

Ainara Novaمشاهده پروفایل

پژوهشگر

• Computational Chemistry
• Catalysis
• Reaction Mechanisms
• +۴ مورد دیگر

Ainara Nova is a Senior Researcher at the Centre for Materials Science and Nanotechnology (SMN) and an associate member of the Hylleraas Centre for Quantum Molecular Sciences, both within the Department of Chemistry at the University of Oslo. Her research combines computational methods with experimental techniques to study homogeneous and heterogeneous reaction mechanisms involving organometallic molecular catalysts and metal-organic frameworks (MOFs), focusing on CO2 and methane conversion into fuels and chemicals. PhD in Chemistry (University of Barcelona, 2008) Postdoctoral work (Montpellier, France; ICIQ, Spain, 2012) Moved to Norway in 2012 Nova has over 50 publications (H-index 26) and supervised 1 MSc and 5 PhD students. She leads a national project (FRIPRO-YT) and two international initiatives (MSCA-ITN, Nordic University Hub), fostering computational-experimental collaborations. Her work bridges computational modeling with practical catalytic applications, emphasizing sustainability and carbon capture strategies. Recent publications highlight her integration of AI and microkinetic modeling to decode catalytic mechanisms, particularly in MOF-based systems and CO2 hydrogenation. She explores ligand design, single-atom catalysts, and multiobjective optimization for efficient catalysis at scale. Nova’s expertise spans organometallic reaction mechanisms, MOF functionalization, and solvent dynamics in catalysis. She is actively involved in supervising computational chemistry projects related to metal-organic frameworks and CO2 hydrogenation, reflecting her commitment to advancing sustainable chemical transformations.

• Health Systems Optimization
• Medical Decision Making
• Cancer Care
• +۶ مورد دیگر

Dr. Maryam Eghbalizarch serves as Assistant Professor in the Department of Industrial and Systems Engineering at Kennesaw State University's Southern Polytechnic College of Engineering and Engineering Technology. Her institutional affiliations include: Current: Assistant Professor, Kennesaw State University Previous: Data Scientist, The University of Texas MD Anderson Cancer Center Previous: Postdoctoral Fellow, Wayne State University (2022-2024) Previous: Assistant Professor, Alzahra University (2021-2022) Visiting Researcher, University of Pittsburgh (2016-2017) Member, Cancer Intervention and Surveillance Modeling Network (CISNET) Member, Society for Medical Decision Making (SMDM) Member, INFORMS and IISE Her educational background features a Ph.D. in Industrial Engineering from the University of Tehran (2018), with M.Sc. and B.Sc. degrees from K.N. Toosi University of Technology. As director of the Health Systems Optimization Lab, she leads research at the intersection of advanced modeling techniques and healthcare applications. Dr. Eghbalizarch's work integrates Markov decision processes , multi-objective optimization , and machine learning to solve critical problems in cancer care (ovarian/lung cancer modeling) and diabetes management . Her methodology-driven approach focuses on sequential decision-making under uncertainty to improve healthcare delivery and patient outcomes through cost-effective interventions. Her recent publications (2024-2025) demonstrate strong momentum in oncology modeling and healthcare optimization, with multiple papers in high-impact journals including American Journal of Obstetrics and Gynecology and Lung Cancer. These works reveal consistent themes in histology-specific cancer modeling, natural history validation, and diabetes treatment optimization using advanced analytics. Key recognitions include: Institute for Data Science in Oncology Fellowship at MDACC Consecutive Postdoctoral Trainee Research Awards (Wayne State, 2023-2024) National Elites Foundation Award (Iran) International Affairs Department Research Grant Dr. Eghbalizarch maintains active grant funding through CISNET and currently recruits fully funded graduate students for Fall 2026. Her mentorship approach emphasizes hands-on research experience in healthcare analytics, with students contributing to high-impact projects in cancer screening optimization and diabetes management systems. The HSOpt Lab provides a collaborative environment focused on translating engineering methodologies into practical healthcare solutions, with strong connections to cancer research networks and clinical partners. Current projects emphasize data-driven decision support systems for medical practitioners and health policy makers.

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.

Carlos A. Coello Coelloمشاهده پروفایل

استاد مهمان

• Evolutionary Computation
• Multi-objective Optimization
• Genetic Algorithms
• +۵ مورد دیگر

Dr. Carlos A. Coello Coello is a distinguished professor (CINVESTAV 3F Researcher) at the Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN) in Mexico City. He also serves as a Distinguished Visiting Professor in Computer Science and Computational Intelligence at Tecnológico de Monterrey's School of Engineering and Science, and as a visiting professor at the Basque Center for Applied Mathematics (BCAM) in Spain. Elected to El Colegio Nacional in 2023, he is the first computer scientist to join this prestigious Mexican institution, which groups the country's most important scientists, artists, and intellectuals. Dr. Coello's research focuses on evolutionary computation, particularly multi-objective optimization algorithms. His work spans genetic algorithms, evolutionary strategies, and bio-inspired optimization techniques with applications in engineering. He has made pioneering contributions to evolutionary multi-objective optimization, including developing the first genetic microalgorithm for multi-objective optimization, which has been applied in real-world contexts such as aerospace design. His research bridges computer science, applied mathematics, and operations research. Dr. Coello has published extensively since the 1990s, with recent publications focusing on advanced evolutionary algorithms, surrogate modeling for expensive optimization, and dynamic multi-objective optimization. His work shows a clear progression from foundational evolutionary computation techniques to sophisticated multi-objective and many-objective optimization approaches with practical engineering applications. MCDM Edgeworth-Pareto Prize (2024) Crónica Prize in Science and Technology (2023) SIGEVO Outstanding Contribution Award (2023) IEEE Computational Intelligence Society Evolutionary Computation Pioneer Award (2021) 2013 IEEE Kiyo Tomiyasu Award 2012 National Medal of Science and Arts IEEE Fellow (since 2011) Dr. Coello has successfully secured significant research funding through Mexico's CONACyT, with projects ranging from foundational algorithm development to advanced multi-objective optimization techniques. He has supervised PhD students, including Dr. Luis Vicente Santana-Quintero who received the Arturo Rosenblueth PhD Thesis Award. As Associate Editor for prestigious journals including IEEE Transactions on Evolutionary Computation, Dr. Coello plays an active role in shaping the field. His work has earned him international recognition, including being ranked 289th worldwide and 1st in Mexico among the 1000 most outstanding computer scientists in the 2022 Guide2Research Ranking.

E.C. van Berkumمشاهده پروفایل

استاد

• Transport Modelling
• Traffic Management
• Network Design
• +۳ مورد دیگر

Eric C. van Berkum is a Professor in Transport Engineering and Management at the University of Twente's Faculty of Engineering Technology and Civil Engineering and Management (ET-CEM) department. He serves as head of the Centre for Transport Studies and previously led the Department of Civil Engineering (2016-2021). His career spans academia and industry, including roles at Goudappel Coffeng and Cap Gemini. PhD (1993) in Civil Engineering from Delft University of Technology MSc (1984) and BSc (1982) in Applied Mathematics from University of Twente His research focuses on transport modeling, traffic management, network design, and travel behavior, integrating computer science models with social science perspectives. He contributes to multidisciplinary transport studies and heads the Centre for Transport Studies at UT. Van Berkum is actively involved in academic leadership as chair of KIVI's transport section and through his role in the Digital Society Institute. His work emphasizes dynamic traffic network design and multiobjective optimization.

• Multiobjective Optimization
• Evolutionary Algorithms
• Combinatorial Optimization
• +۳ مورد دیگر

Professor Andrzej Jaszkiewicz is a faculty member at the Faculty of Computer Science and Telecommunications, Poznań University of Technology, where he works in the Institute of Informatics. He holds the position of full professor with extensive experience in multiobjective optimization and evolutionary algorithms, as evidenced by his habilitation completed in 2001 and continuous research output through 2025. Professor Jaszkiewicz's research focuses on theoretical and practical aspects of multiobjective optimization, particularly addressing the challenges of many-objective problems. His work spans quality indicator development (hypervolume, R2), efficient data structures (ND-Trees), evolutionary algorithm design, and applications to combinatorial optimization problems. His publications demonstrate consistent theoretical rigor combined with practical implementation considerations. Recent publications (2022-2025) reveal continued productivity with multiple articles in top journals like IEEE Transactions on Evolutionary Computation and Physics of Fluids. These works explore theoretical properties of quality indicators, efficient calculation methods, and novel applications of optimization techniques to complex problems including fluid dynamics. Professor Jaszkiewicz has supervised doctoral research including Tarek Alkhaeir's 2021 dissertation on software quality metrics and Marek Kubiak's 2009 work on memetic algorithms. He has also reviewed numerous doctoral dissertations in related fields, demonstrating his standing in the academic community and commitment to graduate education. His scholarly output includes journal articles, conference papers, book chapters, and two books. The 2005 edited volume 'Advanced OR and AI methods in transportation' reflects his interest in practical applications of optimization techniques. His research demonstrates sustained scholarly activity from the 1990s to the present, with particular emphasis on advancing the state-of-the-art in multiobjective optimization theory and practice.

Karim Tamssaouetمشاهده پروفایل

دانشیار

• Scheduling
• Inventory-Transportation Integration
• Operations Research
• +۳ مورد دیگر

Karim Tamssaouet is an Associate Professor at BI Norwegian Business School's Department of Accounting, Auditing and Business Economics. His academic trajectory includes positions as Assistant Professor at BI (2020-2024), Postdoctoral Researcher at École des Mines de Saint-Étienne (2019-2020), and R&D Engineer at STMicroelectronics (2016-2019). He holds a PhD from École des Mines de Saint-Étienne, with additional graduate degrees from Université Paris Dauphine and Ecole Nationale Polytechnique. His research specializes in optimization methodologies for industrial challenges, with core interests in: Scheduling algorithms for job-shop and flexible manufacturing systems Integrated inventory-transportation modeling Metaheuristic design (scatter search, neighborhood structures) Dynamic lot-sizing with logistics constraints Bayesian network learning for decision optimization Publication analysis reveals consistent focus on operational efficiency in manufacturing and supply chains, with recent expansions into machine learning applications for optimization. Methodological contributions include novel neighborhood frameworks for scheduling problems and multi-objective approaches for semiconductor production. He currently teaches Business Optimization and Supply Chain Analytics, bridging theoretical operations research with business applications. No research lab affiliations, scientific awards, or supervised students are documented.

Yichi Zhangمشاهده پروفایل

استادیار

• Causal Mechanism and Causal Learning
• High-dimensional Models
• Large-scale Computations
• +۲ مورد دیگر

Yichi Zhang is an Assistant Professor in the Department of Statistics at Indiana University Bloomington. Previously, he was a Postdoctoral Scholar at Duke University with dual appointments in the Fuqua School of Business and Department of Biostatistics & Bioinformatics. His academic credentials include a Ph.D. in Statistics from North Carolina State University (2023) and a B.S. in Mathematics and Applied Mathematics from Sichuan University (2018). Professor Zhang's research centers on methodological innovation in statistical learning, with primary focus areas: Causal Mechanism and Causal Learning High-dimensional Models Large-scale Computations Randomized Algorithms Uncertainty Quantification His publication trajectory (2023-2025) reveals rapid advancement in theoretical statistics and machine learning, with increasing contributions to causal inference frameworks and computational methodologies. Recent work demonstrates particular strength in adapting statistical theory to AI challenges, including large language model evaluation and network analysis. His scholarly recognition includes: ASA Nonparametric Statistics Student Paper Award Finalist (2023) IMS Hannan Graduate Student Travel Award (2021) ENAR Distinguished Student Paper Award (2021) Mu Sigma Rho National Statistics Honor Society (2018) As an early-career faculty member, Professor Zhang is establishing his research program while teaching core statistics courses. His active publication pipeline suggests robust research momentum, though specific grant details aren't provided. Prospective students interested in theoretical-computational statistics should contact him directly regarding advising opportunities and current projects.