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Alexander BrodskyView profile

Professor

• Decision Support Systems
• Decision Guidance
• Optimization
• +6 more

Alexander Brodsky is a Professor in the Department of Computer Science at George Mason University's Volgenau School of Engineering, where he has conducted research since 1993. His work focuses on Decision Support, Guidance, and Optimization (DSGO) systems with applications spanning sustainable manufacturing, energy systems, power networks, and supply chain management. He teaches core database courses and specialized decision-guidance systems at both graduate and undergraduate levels. Brodsky earned his educational credentials from The Hebrew University of Jerusalem, completing a BS and MS in Mathematics and Computer Science followed by a PhD in Computer Science. His academic foundation supports his interdisciplinary research bridging theoretical computer science with practical industrial applications. Current research centers on developing DSGO frameworks for real-world optimization challenges, particularly in sustainable manufacturing and energy infrastructure. His work integrates multi-objective optimization, stochastic modeling, and decision-guidance query languages to create systems that balance competing objectives like cost, efficiency, and environmental impact. Prior research established foundational contributions to constraint databases and secure information disclosure mechanisms. Analysis of his 2022-2025 publications reveals a decisive shift toward virtual manufacturing ecosystems, carbon-neutral investment modeling, and healthcare decision support. Key trends include the development of reusable model repositories for virtual things, Pareto-optimal solutions for energy infrastructure, and interactive recommender systems that incorporate user preference learning and voting mechanisms for group decision-making. Scientific recognition includes: NSF CAREER Award NSF Research Initiation Award Six Best Paper Awards across major conferences Brodsky has mentored 15 PhD graduates (as of 2016) and currently supervises four doctoral candidates, with former students holding professorships at institutions worldwide and research positions at organizations like NIST and Mozilla. His research program is sustained by substantial funding from NSF, ONR, NASA, NIST, and Dominion Virginia Power, supporting projects like the Process Analytics Language for Sustainable Manufacturing and Decision Guidance for Power Optimization. Though not formally named in the source, his research group operates as a hub for developing decision-guidance frameworks and reusable analytics models, collaborating with industry partners on smart manufacturing and energy infrastructure projects while maintaining strong ties to international conferences like ICEIS and ICTAI.

Fazleena BadurdeenView profile

Professor

• Sustainable Manufacturing
• Lean Manufacturing
• Manufacturing Systems Modeling
• +2 more

Fazleena Badurdeen is a Professor of Mechanical Engineering and Director of Graduate Studies for the Manufacturing Systems Engineering Program at the University of Kentucky , where she leads research in sustainable manufacturing and supply chain systems. She holds a Ph.D. and M.S. in Industrial Engineering from Ohio University, an M.B.A. from the Post Graduate Institute of Management, and a B.Sc. in Engineering from the University of Peradeniya. Research Interests: sustainable manufacturing, lean systems, supply chain modeling, and sustainable product design. Awards: Earl Parker Robinson Chair Professor in Mechanical Engineering. Notable Contributions: development of frameworks for product sustainability, circular economy integration, and risk assessment tools using Bayesian networks and simulation. Her recent Google Scholar publications focus on additive manufacturing , digital product passports , and real-time defect detection , with keywords spanning sustainable manufacturing, systems engineering, and environmental metrics. She has also contributed to education through curriculum development in systems thinking and graduate program leadership.

Azim AhmadzadehView profile

Assistant Professor

• Machine Learning
• Space Weather Forecasting
• Solar Physics
• +4 more

Azim Ahmadzadeh serves as an Assistant Professor in the Department of Computer Science at the University of Missouri–St. Louis within the College of Arts and Sciences. Holding a Ph.D. in Computer Science from Georgia State University (2021), he maintains office hours in 329 ESH on Mondays and Wednesdays from 5:00-6:30 PM via Zoom or in person, with flexible scheduling options for students. Education: Ph.D. Computer Science, Georgia State University, 2021 Dr. Ahmadzadeh's research pioneers machine learning applications for space weather forecasting, specializing in solar flare prediction through innovative time series analysis and computer vision techniques. His work systematically addresses critical challenges including extreme class imbalance in solar datasets, temporal coherence requirements, and data scarcity through novel algorithmic developments. Key contributions include advanced similarity metrics for multivariate time series, synthetic data generation frameworks, and anomaly detection systems specifically designed for solar observations. His publication trajectory (2022-2025) reveals a strategic focus on operationalizing machine learning for heliophysics, with significant output in solar filament detection systems (Elements, MAGFILO), time series analysis innovations (Multiscale Dubuc, Ts-miou), and practical guides for scientific data annotation. This work bridges theoretical machine learning with real-world space weather forecasting needs, evidenced by roadmap documents for SEP monitoring and solar research cyberinfrastructure. Dr. Ahmadzadeh actively develops open resources for the solar physics community including manually annotated datasets, standardized annotation protocols, and ML-ready data pipelines. His research program demonstrates strong interdisciplinary collaboration potential with space agencies and observatories, particularly through contributions to white papers on solar research infrastructure and operational forecasting requirements.

• Embedded computer systems
• Real-time systems
• Technical cybernetics
• +5 more

Kristian Muri Knausgård is a Lecturer at the Department of Engineering Sciences , University of Agder , Norway. He teaches courses in embedded systems, software development, and robotics. Current courses: MAS245 (Embedded Computer Systems), MAS417 (Software Development), MAS418 (Robotics Programming) Previous courses: MAS218 (Electrical Circuits), MAS234 (Embedded Systems) His research focuses on embedded systems , real-time systems , and technical cybernetics , with applications in artificial intelligence , computer vision , and systems engineering . He contributes to the university's research groups on Robotics and Automation and Systems Engineering and Modeling . Recent publications show a strong emphasis on: Autonomous systems (robotics, docking algorithms) Deep learning applications in marine ecology 3D reconstruction and computer vision techniques Fish detection/classification using neural networks Industrial automation for aquaponic systems His work bridges theoretical research with practical implementations in mechatronic systems and environmental monitoring.

Markus TatzgernView profile

Professor

• Augmented Reality
• Virtual Reality
• Mixed Reality
• +3 more

Dr. Markus Tatzgern serves as Professor for Mixed Reality and Game Development and Head of Research at the Department for Creative Technologies, Salzburg University of Applied Sciences. He leads the Digital Realities Lab, a multi-disciplinary research group focused on human-centered solutions at the intersection of software, design, and creative engineering for technology interaction. His academic foundation includes a Master of Science and Doctorate with highest distinction from Graz University of Technology. He furthered his expertise through post-doctoral research at the Christian Doppler Laboratory for Handheld Augmented Reality, recognized as a world-leading facility in mobile AR. Tatzgern's research spans Augmented Reality, Virtual Reality, Mixed Reality, and Human-Computer Interaction with emphasis on practical applications. His work addresses critical challenges in view management, haptic feedback systems, industrial procedure visualization, medical imaging interfaces, and game development. Key contributions include novel techniques for eye-perspective rendering, breathing-based VR interaction, and latency compensation in medical robotics. His publication trajectory reveals evolving focus from foundational AR visualization (2010-2016) toward applied XR solutions in healthcare, industrial training, and user experience optimization (2017-2024). Recent work demonstrates increasing integration of physiological inputs, real-world safety applications, and enterprise-grade XR prototyping. Scientific recognition includes: Honorable Mention for Best Paper at CHI 2022 for 'AirRes Mask' breathing interface Honorable Mention for Best Paper at IEEE 3DUI 2017 for adaptive rendering techniques As Head of Research, Tatzgern actively shapes the field through program committee roles and peer review for top venues including ACM CHI and IEEE VR. His four patents in AR visualization demonstrate translational impact from academic research to practical applications. The Digital Realities Lab operates as an innovation hub where software engineers, designers, and creative technologists collaborate on next-generation interaction paradigms. Current projects emphasize medical XR applications, industrial training systems, and accessibility-focused AR solutions for vulnerable populations.

Nemanja RakićView profile

Associate Professor

• Astronomy
• Astrophysics
• Active Galactic Nuclei Research
• +4 more

Nemanja Rakić serves as an Associate Professor in the Department of General Physics at the Faculty of Science and Mathematics, University of Banja Luka. His academic appointment, confirmed by election on January 26, 2023, focuses on astronomy and astrophysics within the Serbian academic system where his title 'доцент' corresponds to Associate Professor rank. He teaches across multiple physics and astronomy programs including undergraduate courses in Physics 1 & 2, Basics of Astronomy, Fundamentals of Astrophysics, and graduate-level courses such as Astronomical Content in Physics Lessons and Computer Simulations in Physics Teaching. Dr. Rakić's research centers on active galactic nuclei (AGN) with particular emphasis on spectroscopic analysis of broad-line regions, the Baldwin effect in various galaxy types, and detection methods for supermassive binary black holes. His work combines observational data from surveys like SDSS with theoretical modeling to understand galactic nuclei kinematics. He has developed innovative approaches to physics education, integrating astronomical content and computer simulations into teaching methodology. His research spans observational astronomy, spectroscopy, and astrophysical modeling with applications in understanding black hole dynamics and galaxy evolution. His publication record shows consistent output in high-impact astronomy journals including Monthly Notices of the Royal Astronomical Society, Astronomy and Astrophysics, and Classical and Quantum Gravity. Recent publications (2020-2022) demonstrate expansion into science communication and educational methodologies, reflecting his dual commitment to research excellence and pedagogical innovation in astronomy education. Dr. Rakić has led significant research projects including 'Study of binary supermassive black holes in active galaxies in the optical and X-ray domains' (2016-2017) as principal investigator, and participated as a team member in 'Elastic electron scattering on a bismuth atom and a triethyl phosphate molecule' (2018-2020). His collaborative work involves researchers from multiple institutions across Serbia and internationally, with notable collaborations on gravitational wave research and AGN monitoring projects.

İBRAHİM RIZA HALLAÇView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Parallel and Distributed Systems
• +3 more

İbrahim Rıza Hallaç is an Assistant Professor at RAFET KAYIŞ FACULTY OF ENGINEERING, Department of Computer Engineering. He holds a PhD (2021), Master's (2014), and Bachelor's (2011) in Computer Engineering from Firat University, with doctoral research on social media analysis using deep learning methods under advisor Galip Aydın. His research focuses on: Artificial Intelligence and language models Machine learning for distributed systems Big data analytics and cloud computing Natural language processing applications Social media sentiment analysis Radar-based object detection systems His publications (2015-2024) demonstrate consistent focus on machine learning applications across diverse domains including social network analysis, distributed computing, Turkish NLP, and sensor data processing. Recent works emphasize radar-based multi-target detection (2024), credit scoring models (2023), and wind speed estimation (2023), reflecting expertise in scaling AI solutions for real-world problems. He has led four national R&D projects: Deep Learning-Based Face Recognition Authorization System (2017-2019) DEĞİRMEN Big Data Analysis Platform for Turkish Armed Forces (2017-2021) KOSGEB Innovation Program for Face Recognition Systems (2017-2019) Cloud-Based Social Media Reputation Analysis System (2015-2017) No students, awards, or contact emails are documented in available records.

• Organic Computing
• Evolutionary Algorithms
• Learning Classifier Systems
• +6 more

Professor Jörg Hähner holds the Chair of Organic Computing at the University of Augsburg's Faculty of Applied Computer Science within the Institute of Computer Science. He leads a research team focused on evolutionary computation, self-organizing systems, and intelligent computing approaches. His educational background includes computer science studies at TU Darmstadt. His academic career progression shows steady advancement in the field of organic and self-organizing computing systems. Prof. Hähner's research spans multiple interconnected domains in computational intelligence. His primary focus is on Organic Computing, which involves developing systems that can adapt and self-organize in complex environments. Within this framework, he has made significant contributions to Evolutionary Algorithms, particularly Cartesian Genetic Programming and Learning Classifier Systems. His work explores how these techniques can be applied to real-world problems such as predictive maintenance, energy systems optimization, and industrial automation. The research demonstrates a strong emphasis on both theoretical foundations and practical applications of self-adaptive systems. An analysis of his recent publications reveals a strong concentration on evolutionary computation techniques, particularly Cartesian Genetic Programming variants and Learning Classifier Systems. His research group has been actively developing frameworks like CRust_GP and GRAHF to advance modular construction of evolutionary algorithms. There's a clear trend toward applying these techniques to industrial problems including predictive maintenance, resource allocation in networks, and energy management systems. The publications show consistent exploration of fundamental questions about algorithm behavior while maintaining strong connections to practical applications. Prof. Hähner leads an active research group with numerous PhD students and collaborators, including Karen Poloczek, Henning Cui, Victor Gerling, Dr. Michael Heider, Marco Hüller, Neele Kemper, Helena Stegherr, Jonathan Wurth, and Roman Sraj. His team regularly publishes in top-tier conferences and journals in evolutionary computation, intelligent systems, and industrial applications. The Organic Computing research group maintains a strong presence in both theoretical and applied research, with projects spanning from foundational algorithm development to industrial applications in manufacturing, energy systems, and network optimization. The group's work demonstrates a cohesive research vision centered on creating adaptive, self-organizing computational systems that can operate effectively in complex real-world environments.

• Artificial Intelligence
• Evolutionary Algorithms
• Automatic Machine Learning
• +4 more

Shengcai Liu is an Assistant Professor in the Department of Computer Science and Engineering at the Southern University of Science and Technology (SUSTech). Holding a PhD in Engineering, he has established himself as a researcher with over 10 publications as first or corresponding author in top artificial intelligence conferences and journals. His academic journey includes research positions at Singapore's Agency for Science, Technology and Research (A*STAR) before joining SUSTech's faculty. Education: PhD in Computer Science and Engineering, University of Science and Technology of China (2014-2020) Bachelor's in Computer Science and Engineering, University of Science and Technology of China (2010-2014) Dr. Liu's research centers on automatic algorithm design and evolutionary approaches to machine learning. He investigates how AI systems can autonomously construct efficient algorithms for complex optimization problems while maintaining robustness and reliability. His work bridges theoretical foundations with practical applications in combinatorial optimization, adversarial machine learning, and large language model development, with particular focus on making algorithm design processes more systematic and effective. His publication record demonstrates a clear trajectory from foundational work on algorithm portfolios to sophisticated integration of evolutionary methods with large language models. Recent work shows increasing focus on textual adversarial attacks, combinatorial optimization, and leveraging large language models as optimization tools themselves. This progression reflects both theoretical depth and practical relevance to current AI challenges. Professional Activities: Project Leader for Huawei-funded research on combinatorial optimization solvers (CNY 400,000) Member of IEEE, IEEE CIS, and AAAI Reviewer for top journals including TPAMI, TEVC, TCYB Conference reviewer for NeurIPS, ICML, AAAI, IJCAI Dr. Liu maintains active research collaborations through the Artificial Intelligence RAMS Technology Innovation Laboratory at SUSTech, where his team focuses on developing next-generation automatic machine learning systems with applications in optimization and security domains.

• Boolean Optimization
• Constraint Reasoning
• Satisfiability (SAT) Solving
• +3 more

Matti Järvisalo is a Professor in the Department of Computer Science at the University of Helsinki, Finland, holding the title of Docent and serving as Supervisor for the Doctoral Programme in Computer Science. He is affiliated with the Helsinki Institute for Information Technology (HIIT), a leading collaborative research institute between the University of Helsinki and Aalto University. His research centers on computational logic and constraint-based reasoning, with core expertise in Boolean optimization, SAT/MaxSAT solving, and argumentation frameworks. He develops declarative approaches for combinatorial problems in computational social choice, judgment aggregation, and fair division, emphasizing certified algorithms and preprocessing techniques. His work bridges theoretical computer science with practical implementations in optimization and AI. Recent publications (2024-2025) reveal a strong focus on multi-objective optimization, certified reasoning, and argumentation under incomplete information. Key trends include symmetry-aware core learning for Pseudo-Boolean optimization, Pareto-optimality certification in MaxSAT, and novel algorithms for manipulation analysis in judgment aggregation, demonstrating his leadership in advancing SAT-based AI methods. Professor Järvisalo's significant contributions have been recognized by prestigious awards: IJCAI-JAIR Best Paper Prize (2019) Best Researcher Award from University of Helsinki Department of Computer Science (2011) CP 2017 Distinguished Paper Award ECAI 2016 Runner-Up Best Student Paper Award Honorary Mention at ICCMA 2015 He actively mentors the next generation of researchers and secures major research funding: Supervision: 6 doctoral theses and 6 Master's/Licentiate theses Active Grant: Academy of Finland project 'Next-generation Unsatisfiability-based Declarative Optimization' (2023-2027) Past Projects: 'Symbolic Techniques for Formally Verified and Explainable AI' (2020-2022), 'Declarative Boolean Optimization: Pushing the Envelope' (2019-2023) As a core member of HIIT, he collaborates within Finland's premier information technology research ecosystem, contributing to the institute's mission of advancing fundamental and applied IT research through interdisciplinary teamwork and international partnerships.

• Distributed Systems
• Cloud Computing
• Edge Computing
• +7 more

Nishant Saurabh is a tenured Assistant Professor at the Department of Information and Computing Sciences , Utrecht University , Netherlands. His research focuses on resource and data management , application scheduling , performance modeling , and optimization in large-scale hybrid distributed systems including Cloud, Edge, and Quantum computing. Recently, he has expanded into ML workload scheduling and causal performance attribution across the Cloud-Edge continuum. Research Expertise : Distributed Systems and Cloud Computing Quantum-HPC Middleware Systems Performance Modeling and Optimization ML/AI Workload Scheduling Cloud-Edge Continuum Architectures Observability Frameworks Scientific Leadership : Associate Editor, Springer's Journal of Cloud Computing Editorial Board, Frontiers of AI book series IBM HPC-Quantum Working Committee member Workshop Chair, IEEE IPDPS 2025 Supervision : He has supervised 16+ Master/Bachelor students on projects including container migration, reinforcement learning for serverless computing, and quantum middleware evaluation. Current PhD students include Diogo Landau (Performance Observability), Nathan Keyaerts (LLM Methods), and Negar Alizadeh (Energy Efficiency).

Hu CaoView profile

Researcher

• Autonomous Driving
• Robotic Grasping
• Medical Image Analysis
• +5 more

Hu Cao is a postdoctoral research associate at the Chair of Robotics, Artificial Intelligence and Real-Time Systems (Prof. Alois Knoll) at the Technical University of Munich (TUM) . Holding a Ph.D. from TUM, his research bridges autonomous driving , robotic grasping , medical image analysis , and dense prediction (classification, detection, segmentation). Education : Ph.D. from TUM Hu's work explores: Autonomous Driving : Perception under adverse conditions, multi-sensor fusion, and risk-based safety models Robotic Grasping : Vision-language integration for 6D pose estimation Medical Imaging : Transformer-based segmentation techniques (e.g., Swin-Unet) His recent publications include 15+ works at top venues like CVPR , ICCV , IEEE TPAMI , and IEEE TIV , with 6052+ Google Scholar citations . Notably, Swin-Unet ranks among the top 3 most cited ECCV papers in 5 years, and his work on event-based autonomous driving perception was featured in IEEE Xplore Innovation Spotlight . Editorial roles include: Associate Editor for Visual Intelligence and Frontiers in Neurorobotics Editorial Board member of Artificial Intelligence and Autonomous Systems (AIAS) Topic Editor for Frontiers in Robotics and AI and Frontiers in Neuroscience He has reviewed for 20+ top journals (e.g., Nature Computational Science , IEEE TRO ) and served on program committees for NeurIPS , CVPR , ICCV , and MICCAI .

• Resilience in Healthcare
• Healthcare Quality and Safety
• Dementia Caregiving
• +2 more

Heidi Janne Dombestein serves as a Postdoctoral Fellow in the Department of Quality and Health Technology at the University of Stavanger's Faculty of Health Sciences, where she conducts critical research through the SHARE - Center for Resilience in Healthcare. Her work bridges theoretical resilience frameworks with practical healthcare implementation, focusing on systemic safety and caregiver dynamics. Her research expertise centers on resilience engineering in healthcare systems , with specialized investigations into risk perception within homecare settings and motivational sustainability for dementia caregivers. Through mixed-methods approaches, she examines how healthcare professionals and families navigate complex care environments, developing tools for translating resilience theory into actionable practice. Recent work emphasizes stakeholder mapping and patient-family involvement as core components of adaptive healthcare systems. Analysis of her publication trajectory (2015-2025) reveals a strategic evolution from foundational studies on caregiver motivation toward sophisticated systemic resilience frameworks. Current projects demonstrate increasing sophistication in operationalizing resilience through learning tools, policy translation, and multi-organizational stakeholder engagement, reflecting growing influence in healthcare quality improvement. Dombestein actively contributes to the SHARE Center's mission through annual reports, workshop facilitation (including Resilience in Healthcare Society meetings), and knowledge dissemination initiatives. Her collaborative approach spans Norwegian homecare services, international conferences, and national caregiver strategy development, positioning her at the forefront of resilience implementation research.

Richard Martin LusbyView profile

Associate Professor

• Operations Research
• Mathematical Optimization
• Logistics and Supply Chain Management
• +5 more

Richard Martin Lusby is an Associate Professor in the Department of Technology, Management and Economics at DTU Management, Technical University of Denmark. His academic work focuses on Operations Research with specialization in mathematical optimization applied to transportation and energy systems. His research spans railway scheduling, container logistics, maritime transport, and energy storage optimization. Department: Department of Technology, Management and Economics Section: Section for Operations Research, Division for Management Science Location: Building 358, Room 145A, Academy Road, 2800 Kgs. Lyngby, Denmark Contact: rmlu@dtu.dk, Tel: 45253084 ORCID: 0000-0002-8652-6219 Professor Lusby's research interests center around mathematical optimization methods applied to complex transportation and energy systems. His work spans railway scheduling problems, container logistics optimization, maritime transport operations, and energy storage scheduling. He develops advanced algorithms including Lagrangian Relaxation, Benders decomposition, and stochastic programming approaches to solve challenging real-world problems in these domains. His research has practical applications in improving efficiency and sustainability in transportation networks and energy systems. His recent publications demonstrate a strong focus on optimization techniques for transportation systems, particularly in railway operations and container logistics, as well as energy storage scheduling. The research shows a consistent pattern of developing sophisticated mathematical methods to address complex operational challenges in transportation networks, with increasing attention to sustainability considerations in maritime shipping and energy systems. Professor Lusby has been involved in significant research projects including the SWITCH project (Sustainable Network Design - Electrifying Container Shipping), which received funding from Den Danske Maritime Fond. This project explores how battery-powered ships could replace fossil fuels in global shipping through mathematical modeling and AI-driven optimization. He also supervises PhD students, including Georgios Vassos who completed a thesis on "Procurement Policy Analysis in Intermodal Container Logistics". His work has practical implications for improving efficiency in transportation networks, reducing environmental impact in maritime shipping, and optimizing energy systems operations. Through his research, Professor Lusby contributes to both theoretical advances in optimization methods and their practical applications in critical infrastructure sectors.

Francesca VocaturoView profile

Associate Professor

• Operations Research
• Logistics
• Optimization
• +4 more

Francesca Vocaturo serves as Associate Professor of Operations Research at the University of Calabria's Department of Economics, Statistics and Finance (DESF), where she has maintained continuous academic affiliation since 2003. Her teaching portfolio includes Operations Research for Master's programs in Business Administration and Management, and Mathematical Methods for Economics for undergraduate Economics students, with documented teaching loads of 63+ hours annually through the 2024/25 academic year. Her research expertise centers on Logistics Optimization with specialized focus on stochastic vehicle routing, urban transportation systems, and crowdshipping applications. Key methodological contributions span combinatorial optimization, dynamic programming, and simulation-based approaches to solve complex logistics challenges under uncertainty. Current projects integrate machine learning with optimization frameworks for sustainable mobility solutions. Recent publication trends (2021-2025) reveal concentrated advancement in dynamic logistics systems , particularly crowdshipping models, real-time inventory routing, and risk-aware transportation planning. Her work bridges theoretical algorithm development with practical applications in postal services, hazardous material transport, and public transit optimization. Notable recognition includes: Editors' Choice Article selection by the European Journal of Operational Research (2020) She has supervised 26 Master's theses in Business Administration covering topics from inventory management to green logistics, plus the PhD dissertation of Sara Stoia on crowdshipping. As Local Coordinator for the national PRIN2020 project ULTRA OPTYMAL , she leads research on optimization under uncertainty for urban logistics, while previously contributing to projects like SOLIDO (industrial logistics) and GREENPLAN (workforce optimization). Vocaturo actively collaborates with the TESEO laboratory (Tecnologie di Simulazione e Ottimizzazione) and the Quantitative Methods for Economics, Finance and Management research group within DESF, maintaining international partnerships through research stays at the University of Montreal, University of Valencia, and teaching engagements in Portugal.