جستجوی استاد راهنمای دانشگاهی

Markus Schafferمشاهده پروفایل

پژوهشگر

• Energy Engineering
• Smart Meter
• Building Performance Simulation
• +۱۲ مورد دیگر

Markus Schaffer is a Researcher at Aalborg University, affiliated with the Department of Construction, Urban and Environmental Engineering under the Faculty of Engineering and Science. His work focuses on leveraging commercial smart heat and water meter data to drive building-specific renovation strategies, integrating these datasets with existing building information to create actionable insights for the scientific community. PhD-level contributions to energy data integration Automated disaggregation of space heating and domestic hot water demands at city scale Non-intrusive large-scale occupancy detection via water meter analytics Advanced clustering for energy data distillation His research portfolio extends to school renovation concepts, where he explores double-skin facades with diffuse ceiling ventilation through parametric simulations. He applies machine learning to Indoor Environmental Quality sensor data collaboration, and teaches master's courses in IT System Development and Sensitivity/Uncertainty Analysis. 2025: Residential Household Dataset publication 2024: 4 peer-reviewed journal articles across Energy, Data in Brief, and REHVA Journal 2023: 2 datasets and 3 conference participations 2022: District Heating data time-series publication Markus' scientific contributions align with UN Sustainable Development Goals 7 (Affordable Energy) and 11 (Sustainable Cities), with ongoing roles in IEA EBC Annex 84 (2020-2025) and FOREFRONT (2021-2024) projects.

Yong Chenمشاهده پروفایل

استادیار

• statistical methods for electronic health record data
• meta-analysis
• mathematical statistics
• +۳ مورد دیگر

Yong Chen, PhD, is an Assistant Professor of Biostatistics specializing in advanced statistical methodologies for healthcare data analysis. His research bridges biostatistics, machine learning, and real-world evidence generation with significant contributions to federated learning frameworks and meta-analytic techniques. His core research interests include: Statistical methods for electronic health record data Meta-analysis and network meta-analysis Mathematical statistics with healthcare applications Federated learning for decentralized research networks Causal inference methods for observational data Real-world evidence generation in rare diseases Analysis of Dr. Chen's 2025 publications reveals a dominant focus on privacy-preserving distributed analytics for multi-site healthcare studies, particularly through one-shot lossless algorithms (e.g., COLA-GLM). His work demonstrates methodological innovation in addressing unmeasured confounding through negative control calibration and advancing multitask learning frameworks. Key application areas include SARS-CoV-2 sequelae analysis, neuropsychiatric outcomes, and disparities in obesity treatment access, consistently leveraging electronic health records for real-world evidence generation. Scientific recognition: FACMI (Fellow of the American College of Medical Informatics)

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.

• Artificial Intelligence
• Machine Learning
• Data-Driven Science and Engineering
• +۷ مورد دیگر

Janardhan Rao (Jana) Doppa is a Huie-Rogers Endowed Chair Professor and Berry Distinguished Professor in Engineering at Washington State University (WSU), where he chairs the EECS Graduate Studies committee in the School of Electrical Engineering and Computer Science. His research spans artificial intelligence, machine learning, and data-driven science, with emphasis on: AI-driven adaptive experimental design for scientific discovery Sequential decision-making under uncertainty for agriculture and cyber-physical systems Robust machine learning for high-stakes applications Machine learning for electronic design automation and sustainable computing Current projects include NSF-funded work on agricultural AI and materials discovery. Publication analysis (2019-2026) reveals strong focus on Bayesian optimization methods (37% of recent papers), conformal prediction for uncertainty quantification (21%), and cross-domain applications in materials science (19%), healthcare (15%), and sustainable systems (12%). Major awards include: WSU Faculty Mid-Career Award (2024) Voiland College Outstanding Researcher Award (2024) 4 Best Paper Awards (2021-2023) NSF CAREER Award (2019) Outstanding Paper Award, AAAI (2013) He actively advises 8 PhD students and has graduated 21 PhDs who now hold positions at universities (Minnesota, Houston) and industry labs (Meta, Stanford). Research is supported by NSF, USDA, and industry grants exceeding $5M. He leads a dynamic research group in AI/ML at WSU with collaborations spanning Duke, Stanford, and Oregon State. Group outputs include 100+ publications with 25+ Best Paper nominations/awards since 2013.

Patric Eichelbergerمشاهده پروفایل

استاد

• Movement biomechanics of the lower extremity
• Movement analysis
• Biomechanics in injury prevention and rehabilitation
• +۳ مورد دیگر

Prof. Dr. Patric Eichelberger is a Professor and Head of the Bern Movement Lab at the Bern University of Applied Sciences, School of Health Professions, Department of Physiotherapy. He leads the Foot Biomechanics and Technology Research Group, focusing on quantitative assessment of human movement and biomechanics in injury prevention and rehabilitation. His work bridges clinical practice with technological innovation, particularly in foot biomechanics and orthopedic technology applications. Dr. Eichelberger's research interests center on movement biomechanics of the lower extremity, with special emphasis on foot biomechanics, movement analysis techniques, and biomechanics applications in injury prevention and rehabilitation. His work explores how current technologies can be applied to transfer objective assessment of movement biomechanics from laboratory settings into clinical routine. Specific areas of investigation include footwear and orthoses for running-related injuries, the relationship between running biomechanics and injury, and the development of innovative measurement techniques for dynamic postural stability. His recent publication trends show a strong focus on clinical biomechanics applications, with particular emphasis on ankle and foot biomechanics, movement analysis in injury contexts, and innovative measurement techniques. His work frequently appears in journals related to biomechanics, physiotherapy, orthopedics, and sports medicine, demonstrating interdisciplinary collaboration across these fields. The research consistently applies quantitative methods to address clinically relevant questions in movement science. Prof. Eichelberger actively supervises master's thesis projects through the Bern Movement Lab and teaches across multiple health profession programs. His teaching portfolio includes Quantitative Research Methods and Applied Statistics, Movement Biomechanics, Gait Analysis, and Biomechanical Models. He is also involved in the Center Health Technologies as Co-Head and serves on the Committee for 'Human Digital Transformation' at BFH. His laboratory infrastructure includes the Bern Movement Lab, Bern Mobility Centre, and Bern Pain & Stress Lab, which provide comprehensive facilities for biomechanical assessment, movement analysis, and clinical testing. Through partnerships with institutions like Ortho-Team AG, Praxisklinik Rennbahn AG, and Bern University Hospital, his research maintains strong clinical relevance while advancing methodological approaches in movement science.

• Machine Learning
• Reinforcement Learning
• Plasma Physics Simulations
• +۱ مورد دیگر

Fabian Paischer is a Researcher at the Institute for Machine Learning, Johannes Kepler University Linz (JKU), holding a Doctorate and Master of Science degree. His work bridges machine learning with high-impact scientific domains including plasma physics for fusion energy and reinforcement learning systems. Education: Doctorate (Dr.) Master of Science (MSc) His research focuses on developing neural surrogate models for plasma turbulence simulations and advancing reinforcement learning through pre-trained model modulation and human-readable memory architectures. This interdisciplinary work integrates deep learning with computational physics and autonomous decision-making, targeting applications in sustainable energy and intelligent systems where interpretability and data efficiency are critical. Analysis of his 2023-2025 publications reveals two dominant research thrusts: (1) neural operator applications for plasma edge simulations requiring long-term predictive accuracy in fusion environments, and (2) modular reinforcement learning frameworks enabling knowledge transfer between pre-trained models. These directions highlight his commitment to solving complex scientific computing challenges through novel AI methodologies. Dr. Paischer actively participates in academic communities, including the ELLIS Doctoral Symposium (2021), and maintains ongoing research output through preprints and conference publications at venues like NeurIPS and ICLR workshops.