Dark-Box Optimization Professors and Supervisors

Krzysztof Walkowiakمشاهده پروفایل

استاد

Wrocław University of Science and TechnologyPoland

Krzysztof Walkowiak is a Professor at the Faculty of Computer Science and Telecommunications, Wrocław University of Science and Technology (PWr), where he also serves as the Dean of the Doctoral School. He leads the Computer Networks Team (ZSK) and is actively involved in multiple research projects, including Dark-Box Optimization and evolutionary methods for multi-criteria network design. A Senior Member of IEEE and IEEE ComSoc, he contributes to academic governance as a member of the Polish Academy of Sciences’ Committee on Electronics and Telecommunications. PhD in Computer Science (2000, with distinction) Habilitation in Computer Science (2008) Professor of Technical Sciences (2017) Dean of the Doctoral School, PWr (since 2020) Senior Member, IEEE and IEEE Communications Society Member, Committee on Electronics and Telecommunications, Polish Academy of Sciences His research spans computer network optimization, machine learning in networks, evolutionary algorithms, survivable optical networks, and intelligent computational techniques . He has pioneered the integration of AI methods in teleinformatics and leads initiatives in intent-based and cognitive networking. His work emphasizes multi-layer, application-aware network design and distributed processing systems. The 15 most recent publications reflect a strong trend in optimization under uncertainty, AI-driven networking, and metaheuristic algorithm development . There is a clear focus on survivability, scalability, and automation in modern network architectures, with increasing attention to edge intelligence via TinyML and cognitive systems. His recent work also addresses internationalization and pedagogical innovation in doctoral education. Fabio Neri Best Paper Award 2014 (Elsevier Journal of Optical Switching and Networking) Best Paper Award, DRCN2009 (Washington, USA) Best Paper Award, RNDM2015 (Munich, Germany) Medal of the National Education Commission (2011) Scientific Scholarship, Wrocław University of Science and Technology (2017) Star of Internationalization 2024 – Teaching Star Professor Walkowiak has supervised 10 completed PhD theses and currently mentors 5 doctoral students. He has led or managed 13 research projects funded by NCN (including 4 OPUS grants), EU, NAWA, and national agencies. His grant leadership includes the NAWA InterDocSchool project (2021–2023) and the Unite! Doctoral School curriculum development. He has reviewed over 300 journal submissions and served as session chair at 22 international conferences. He leads several research teams: Computer Networks Team (ZSK), Machine Learning Team, Advanced Data Analysis Methods Team, Metaheuristics Team, and Teaching Team . He has developed innovative educational programs such as the Research Skills course and Recent Research Trends , promoting international collaboration and doctoral training. He championed the transition to English-language instruction in the Doctoral School and promotes internationalization as a 'team sport' across institutional levels.

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Agata Kirjanów-Błażejمشاهده پروفایل

پژوهشگر

Wrocław University of Science and TechnologyPoland

Dr. Eng. Agata Kirjanów-Błażej is an active researcher at Wrocław University of Science and Technology, affiliated with the Advanced Data Analysis Methods Team (ZZMAD) and involved in interdisciplinary research connecting computer science, optimization, and industrial engineering. Her research interests include: Conveyor transport in underground and open-pit mines Non-invasive diagnostics of conveyor belts Production and project management Multi-criteria and evolutionary optimization Machine learning and classifier training Application-aware network optimization Her recent work focuses on developing advanced optimization methods, including dark-box optimization and gene-linkage techniques for high-dimensional problems. She also contributes to industrial innovation, particularly in mining technology diagnostics. Dr. Kirjanów-Błażej has been recognized with multiple awards, including the Rector’s Award (2017, 2018), Lower Silesian Master of Technology, an innovation distinction in mining technology, and the prestigious Secundus Award in 2023 for early-career researchers under 40 without habilitation, based on high-impact publications. Lower Silesian Master of Technology First degree award "For outstanding achievements in the field of technology" Rector's Award for scientific achievements 2017 Rector's Award for scientific achievements 2018 Mining success of the year - Distinction in the innovation category for the "Integrated system for assessing the condition of DiagBelt conveyor belts" Secundus Award (5th edition) - 2023 She serves as a project manager in a research initiative funded by the National Centre for Research and Development (NCBR) under the LEADER program, focusing on innovative diagnostic devices for measuring conveyor belt thickness during operation. She supervises diploma theses and is actively involved in academic mentoring. Dr. Kirjanów-Błažej is a member of several research teams, including the Machine Learning Team, Computer Networks Team, Advanced Data Analysis Methods Team, and Metaheuristics Team, reflecting her interdisciplinary approach to solving complex engineering and computational problems.

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Michał Kucharzakمشاهده پروفایل

پژوهشگر

Wrocław University of Science and TechnologyPoland

Michał Kucharzak is an active researcher engaged in multiple interdisciplinary research teams including the Machine Learning Team, Computer Networks Team, Advanced Data Analysis Methods Team, and Metaheuristics Team. He contributes to significant research projects such as Dark-Box Optimization and the development of evolutionary methods for multi-criteria optimization. His work focuses on optimization techniques applied to complex systems, particularly in network design and decision-making models. His research interests span a wide range of topics in computational optimization and network technologies. These include metaheuristics, linear and integer programming, constraint programming, modeling and simulation, and advanced data analysis methods. He also investigates computer network architectures such as P2P, EON, FA, FCA, and NB-IoT, with a focus on application-aware and multi-layer network optimization. The trends in his research, though not supported by explicit publications in the provided text, indicate a strong emphasis on algorithmic innovation for solving high-dimensional, multi-objective problems in both theoretical and applied domains. His work bridges operations research, computer science, and engineering applications. Involved in the AEON research project Active contributor to interdisciplinary research initiatives There is no available information on formal advising roles or grant funding in the provided text. However, his involvement in multiple research teams suggests collaborative engagement across projects. He maintains a scientific presence through profiles such as ORCID, indicating ongoing scholarly activity. Michał Kucharzak is part of dynamic research groups focused on cutting-edge computational methods. These teams work on developing novel optimizers, improving classifier training via multi-criteria approaches, and advancing evolutionary computation techniques for real-world applications in networking and decision support systems.

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Piotr Lechowiczمشاهده پروفایل

پژوهشگر ارشد

Wrocław University of Science and TechnologyPoland

Piotr Lechowicz is a postdoctoral researcher currently affiliated with Chalmers University of Technology, Department of Computer Science and Engineering, within the College of Engineering. Previously associated with the Department of Computer Systems and Networks (ZSK) at Wrocław University of Science and Technology, he is an active member of the Computer Networks Team and contributes to interdisciplinary research in network optimization and data analysis. His research focuses on advanced optimization techniques in computer networks, particularly in spectrally-spatially flexible optical networks. Key areas include routing, space and spectrum allocation (RSA), migration planning, and fragmentation issues. He applies multi-criteria optimization and evolutionary methods to solve complex network problems and classifier training tasks. Dr. Lechowicz is involved in significant research projects such as Dark-Box Optimization and the development of evolutionary methods for large-scale multi-criteria optimization. His work integrates machine learning, metaheuristics, and application-aware network modeling. He has been recognized with the Secundus Award for academic excellence. His scientific contributions are documented through publications and profiles on Google Scholar and ORCID. Dr. Lechowicz supervises diploma theses and contributes to academic teaching, particularly in research methodologies. He also co-organized the 'Networked Music Performance' concert in 2023, showcasing interdisciplinary collaboration. He is currently conducting a two-year postdoctoral fellowship under Professor Paolo Monti at Chalmers University of Technology, focusing on computer network optimization and automation. His research teams include the Machine Learning Team, Advanced Data Analysis Methods Team, and Metaheuristics Team.

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Paweł Majewskiمشاهده پروفایل

پژوهشگر

Wrocław University of Science and TechnologyPoland

Paweł Majewski is a researcher at the Department of Computer Systems and Networks within the Faculty of Computer Science and Telecommunications at Wrocław University of Science and Technology. He is an active member of the Advanced Data Analysis Methods Team (ZZMAD) and conducts research at the intersection of machine learning, computer vision, and precision agriculture applications. Dr. Majewski completed his doctoral studies with a dissertation titled "Machine Learning Methods for Phenotyping Insect Biosystems, as Exemplified by Honeybees and Mealworms," supervised by Professor Robert Burduk and Professor Jacek Reiner. His doctoral research addressed significant challenges in biological system analysis including time-varying data acquisition conditions, dense scenes, and limited representative datasets. His research spans several key technical domains: Computer vision techniques for biological system analysis Semi-supervised and unsupervised anomaly detection methods Instance and semantic segmentation applications Precision agriculture and precision breeding technologies Machine learning approaches for phenotyping insect biosystems Dr. Majewski has received recognition for his interdisciplinary work: Scholarship of the Mayor of Wrocław (2023), awarded to 24 outstanding doctoral students in Wrocław Jan Mozrzymas Scholarship for outstanding achievements in interdisciplinary research Visegrad Fund scholarship enabling research at National Taiwan University's Biophotonics and Bioimaging Laboratory His practical contributions include developing solutions in collaboration with the Machine Vision Laboratory (MVLab) that are already supporting agricultural growers and increasing production efficiency. During his doctoral studies, he completed an international internship at National Taiwan University under Professor Ta-Te Lin, working with one of the world's leading teams in precision agriculture research. As a member of ZZMAD, Dr. Majewski contributes to multiple research initiatives including "Dark-Box Optimization" and evolutionary methods for multi-criteria optimization. His work exemplifies the translation of theoretical machine learning advancements into practical agricultural applications with real-world impact.

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Herbie Leeمشاهده پروفایل

استاد

University of California, Santa CruzUnited States

Herbie Lee is a Professor in the Department of Statistics at the Jack Baskin School of Engineering, University of California, Santa Cruz. He also serves as the Interim Dean of Social Sciences. His faculty office is located in 537A Baskin Engineering, and he can be reached at 831-459-1655 (though email is preferred). Lee completed his educational journey with a strong foundation in mathematics and statistics: B.S. in Mathematics from Yale University with a specialty in statistics Ph.D. in Statistics from Carnegie Mellon University (1998), advised by Larry Wasserman Post-doctoral work at Duke University's ISDS department Professor Lee's research focuses on Bayesian statistics, particularly in the areas of computer models, spatial inverse problems, simulator emulation, adaptive experimental design, and optimization. His work bridges theoretical statistics with practical applications across diverse domains including cosmology (dark energy modeling), aircraft damage detection, electronic health records analysis, and environmental monitoring. He has made significant contributions to the understanding of neural networks within a statistical framework. An analysis of his recent publications reveals a strong emphasis on Gaussian process modeling, Bayesian optimization, and nonstationary spatial statistics. His work increasingly addresses complex real-world problems requiring sophisticated statistical approaches for mixed data types, constrained optimization, and real-time decision making. The interdisciplinary nature of his research is evident in collaborations with institutions like Lawrence Livermore National Laboratory and the Naval Postgraduate School. Among his notable achievements: Supervised PhD student Bobby Gramacy won the prestigious Savage Award Authored two books on Bayesian optimization and multiscale modeling Published over 50 peer-reviewed articles in top statistics and machine learning journals Professor Lee has supervised numerous graduate students, with his most recent PhD student graduating in 2024. His research has been supported by various grants, though specific funding sources aren't detailed in the provided text. His teaching portfolio includes courses on Bayesian statistics, design and analysis of computer simulation experiments, and statistical methods for biological, environmental, and health sciences. He is actively involved in several professional statistical societies including the International Society for Bayesian Analysis (ISBA), American Statistical Association (ASA), and Institute of Mathematical Statistics (IMS).

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