جستجوی استاد راهنمای دانشگاهی

David Spielerمشاهده پروفایل

استاد

• Machine Learning
• Stochastic Systems
• Biochemical Systems Modeling
• +۳ مورد دیگر

David Spieler is a Professor of Machine Learning at the University of Applied Sciences Munich since September 2018. Previously, he served as a product owner for big data topics at Audi AG (2015–2018), a system developer at Bosch SoftTec (2014–2015), and completed his PhD in Modeling and Simulation at Saarland University (2009–2014). His research focuses on stochastic systems, biochemical modeling, parameter estimation, and oscillatory behavior analysis in Markovian systems. He has contributed to numerical methods for steady-state analysis, formal verification techniques, and applications in systems biology. Research interests include stochastic hybrid systems, sensitivity analysis of biochemical networks, geometric bounds for CTMC steady-state distributions, and oscillatory dynamics in chemical reaction networks. He has published extensively on topics like model checking, parameter estimation, and algorithmic solutions for stochastic processes. His work bridges theoretical computer science with practical applications in bioinformatics and engineering. Spieler has taught courses on data networks, quantitative model checking, and stochastic simulation techniques. He has reviewed manuscripts for over 20 conferences including CAV, CMSB, HSCC, and QEST, contributing to the advancement of formal methods and computational systems biology.

Noomane Ben Khalifaمشاهده پروفایل

استاد

• Sustainable Manufacturing
• Metal Forming Technologies
• Additive Manufacturing
• +۴ مورد دیگر

Prof. Dr.-Ing. Noomane Ben Khalifa is a Professor of Manufacturing – Innovative Manufacturing and Head of the Institute for Production Technology and Systems (IPTS) at Leuphana University. He concurrently leads the Institute for Material and Process Design at Helmholtz Center Hereon. His work focuses on sustainable manufacturing processes, particularly extrusion, sheet metal forming, and additive manufacturing, emphasizing eco-friendly production across material value chains. Education: Awarded a Doctorate in Engineering from TU Dortmund University (2012) with distinction for research on helical profile extrusion. Holds a Mechanical Engineering degree from TU Dortmund (2005), specializing in production engineering and forming technology. Research Interests: Sustainable manufacturing processes; material recycling (e.g., aluminum chips); lightweight material development; composite extrusion; die design optimization; and process simulation. His work bridges advanced manufacturing with environmental impact reduction, leveraging AI and machine learning for material-property modeling. Key Article Trends: Recent studies emphasize machine learning applications in materials science, die design for magnesium alloys, and sustainable recycling processes. His work spans from fundamental material behavior analysis to industrial process optimization. 2025: Focus on AI-driven texture-property modeling in Mg alloys and dieless wire drawing improvements 2024: LCA of aluminum recycling and deep-drawing process reliability Awards: 2009 ISPF Award for extrusion research; 2014 ZukunftErfindenNRW for innovative extrusion press design; invited keynote speaker at ICIT & MPT 2014. Advising & Grants: Oversees OPTUM-MAGNA (magnesium nanocomposites), FERNAPRO (sustainable manufacturing tech), and projects like TrICo (innovation cooperation). Collaborates with industry on tool design and eco-processes. Labs & Teams: Leads IPTS and co-leads Helmholtz material design institute. Supervises interdisciplinary teams focusing on compound casting, incremental forming, and smart manufacturing systems.

Jens Hegerمشاهده پروفایل

استاد

• Manufacturing Engineering
• Production Technology
• Simulation
• +۵ مورد دیگر

Jens Heger is a Professor of Engineering specializing in Modeling and Simulation of Technical Systems and Processes at Leuphana University Lüneburg, Germany. He is affiliated with the Institute for Production Technology and Systems (IPTS) and the Research Center for Digital Transformation. His academic career spans over a decade of research and teaching in production engineering, manufacturing systems, and simulation technologies. Dr. Heger completed his Master of Science in Computer Science with a minor in Business Studies at the University of Paderborn (1999-2006). He then earned his doctorate degree at the University of Bremen in the Department of Production Engineering. His dissertation focused on "Dynamic selection of rules for sequence planning in workshop and flexible flow production" under the supervision of Prof. Scholz-Reiter and Prof. Jürgen Branke from the University of Warwick, UK. Professor Heger's research interests center around the intersection of manufacturing engineering, simulation, and artificial intelligence. His work demonstrates a strong focus on applying advanced computational methods to solve complex production challenges. Key research areas include: Manufacturing process optimization through simulation and modeling Application of machine learning and reinforcement learning in production scheduling Development of intelligent control systems for manufacturing processes Energy efficiency and sustainability in production systems Quality control and prediction in sheet metal forming processes Integration of AI technologies in traditional manufacturing environments Analysis of Professor Heger's recent publications reveals a clear trajectory toward increasingly sophisticated applications of artificial intelligence in manufacturing. His work has evolved from traditional simulation and optimization techniques toward integrating deep learning, reinforcement learning, and computer vision technologies into production systems. A significant portion of his research focuses on dynamic scheduling and optimization problems, particularly using reinforcement learning approaches to address complex manufacturing challenges. His publications also demonstrate a growing interest in sustainable manufacturing practices, energy efficiency, and quality control applications through AI technologies. Professor Heger has contributed significantly to the field through numerous publications in manufacturing engineering, production systems, and AI applications. His work spans both theoretical advancements in scheduling algorithms and practical implementations in industrial settings, particularly focusing on small and medium enterprises.

Mohsen Afsharianمشاهده پروفایل

استاد

• Operations Research
• Data Envelopment Analysis (DEA)
• Supply Chain Optimization
• +۵ مورد دیگر

Prof. Mohsen Afsharian is a Professor of Production and Logistics at Leibniz Fachhochschule Hannover, Germany. He specializes in Operations Research with focus on Data Envelopment Analysis (DEA), incentive regulation mechanisms, and performance analytics. His research bridges theoretical advancements in DEA methodologies with practical applications in supply chain optimization, facility location, and regulatory policy design. Education: Holds a Habilitation in Prescriptive Analytics (2022) and a Doctorate in Operations Research. Notable academic contributions include monographs on cutting-stock problems with defects (2013) and prescriptive analytics frameworks (2022). Research interests span efficiency evaluation systems, productivity measurement over time, and data-driven decision models. Recent work emphasizes incentive mechanisms for regulated sectors and multi-criteria optimization under centralized management structures. Publications (2020–2024) highlight contributions to DEA flexibility control, metafrontier analysis, and emerging data science applications in business. He co-edited special issues on incentive regulation analytics (2022) and public sector DEA applications (2018). Active in editorial roles for Decision Analytics Journal and Socio-Economic Planning Sciences. His work integrates mathematical programming with real-world challenges in logistics, education systems, and banking efficiency analysis.

• Theoretical Physics
• Soft Matter Physics
• Active Matter
• +۴ مورد دیگر

Prof. Abhinav Sharma is a Professor of Theoretical Physics II at the University of Augsburg, affiliated with the Mathematisch-Naturwissenschaftlich-Technische Fakultät. His research focuses on active matter, soft condensed matter physics, and non-equilibrium statistical mechanics, with contributions to understanding complex systems, Brownian dynamics, and material science. He advises PhD students including Bhavesh Valecha and Atharva Pandit, and collaborates with institutions globally. His work emphasizes interdisciplinary approaches, combining theoretical frameworks with experimental insights. Education: PhD in Physics from Technische Universiteit Eindhoven (2011), specializing in organic field-effect transistors and charge transport. Notable research themes include active particles, chiral transport, and non-equilibrium systems. Over 60 peer-reviewed publications since 2006, with recent contributions in Soft Matter , Physical Review Letters , and Science Advances . Research interests span theoretical and computational studies of soft matter, biophysical systems, and active matter, addressing questions in fluid dynamics, polymer physics, and stochastic processes. His lab investigates phenomena such as cargo transport, anomalous diffusion, and critical slowing down in disordered networks.

Ugo Finnendahlمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Mesh Parameterization
• Geometric Acoustics
• +۳ مورد دیگر

Ugo Finnendahl is a Researcher in the Department of Computer Graphics at Technische Universität Berlin. He holds an M.Sc. in Computer Science and is affiliated with the Computer Graphics group led by Prof. Marc Alexa. His research focuses on advanced computer graphics topics including mesh parameterization, geometric acoustics, physically-based simulation, and exact arithmetic algorithms. Key contributions include methods for differentiable rendering, intrinsic triangulation optimization, and geometric stylization techniques. Recent work emphasizes integrating geometric modeling with machine learning frameworks (e.g., path tracing backpropagation), improving mesh deformation techniques (ARAP revisited), and developing efficient exact arithmetic systems for planar embeddings. No scientific awards are explicitly mentioned in the provided materials. His research spans collaboration with institutions like TU Berlin's Computer Engineering and Microelectronics Institute, contributing to both theoretical advancements and practical applications in computer graphics.

Andrey Ustyuzhaninمشاهده پروفایل

استاد مدعو

• Machine Learning
• Particle Physics
• Data Science
• +۵ مورد دیگر

Prof. Andrey Ustyuzhanin is an Adjunct Professor of Computer Science at Constructor University's School of Computer Science & Engineering and a Visiting Research Professor at the National University of Singapore (NUS), affiliated with the Institute for Future Intelligent Machines (IFIM). He holds a PhD in Computer Science from the Institute of System Programming (Russian Academy of Sciences) and advanced degrees from Moscow Institute of Physics and Technology (MIPT). His research focuses on developing machine learning methods to address complex scientific challenges in particle physics, materials science, and data-driven discovery. He has contributed to projects like the LHCb experiment at CERN, optimizing online triggers and BDT-based processing, and has pioneered initiatives like the Tracking Machine Learning Challenge and the Code4ML dataset. His work bridges AI and fundamental science, emphasizing interdisciplinary applications. He is also the Director of AI/ML Research at Acronis and a co-organizer of international summer schools in machine learning for particle physics. Education PhD in Computer Science, Institute of System Programming (RAS), 2007 M.Sc. in Applied Mathematics & Physics (Autonomous Control Systems), MIPT, 1994–2000 M.Sc. in Innovative Management, MIPT, 1998–1999 B.Sc. in Applied Mathematics, MIPT, 1994–1998 Mathematics & Physics, Moscow Chemical Lyceum, 1991–1994 Research Interests Prof. Ustyuzhanin specializes in machine learning for scientific discovery, including particle physics (LHCb experiment), materials science (defect analysis in 2D materials), and AI-driven experimental optimization. His work also explores symbolic expression generation, code semantics classification (Code4ML), and cybersecurity frameworks like EAGLEEYE for malicious event detection. He advocates for reproducible science and end-to-end optimization of experimental designs using differentiable programming. Key Projects & Contributions Co-developed the Tracking Machine Learning Challenge to advance high-throughput physics analysis Co-created the Code4ML dataset for annotated machine learning code Designed algorithms for LHCb’s online triggers and scintillator tracking systems Co-founded the annual summer schools on ML in particle physics Labs & Collaborations Director of AI/ML Research at Acronis Head of the LAMBDA Lab at HSE University PI at IFIM, NUS Collaborator on CERN-Yandex research programs

Dmitry Vetrovمشاهده پروفایل

استاد

• Bayesian Methods
• Deep Learning
• Diffusion Models
• +۳ مورد دیگر

Prof. Dmitry Vetrov is a Professor of Computer Science at Constructor University Bremen, affiliated with the School of Computer Science and Engineering. His research focuses on integrating Bayesian methodologies with deep learning, particularly in diffusion models, generative adversarial networks (GANs), and domain adaptation. He leads the Bayesian Method Research Group and has contributed to advancements in areas such as neural optimal transport, unsupervised voice restoration, and genetic fine-mapping. Key research interests include probabilistic modeling, generative AI, and optimization techniques for neural networks. Notable work includes innovations in diffusion samplers, adaptive learning rate analysis, and encoder-based approaches for image and audio generation. His publications span top venues like NeurIPS, ICLR, and AAAI. Current projects emphasize scalable diffusion models, thermodynamic views of SGD, and Bayesian approaches in genetic analysis. He collaborates on applied areas like speech enhancement (HIFI++), protein sequence generation, and efficient parameterization of GANs. Labs/Teams: Leads the Bayesian Method Research Group, actively involved in Constructor University's AI and machine learning initiatives.

Markus Wenzelمشاهده پروفایل

دانشیار

• Medical Imaging
• Deep Learning
• Healthcare AI
• +۳ مورد دیگر

Prof. Markus Wenzel is an Associate Professor of Medical Cognitive Computing at the School of Computer Science & Engineering, Constructor University Bremen. His research focuses on interpretable deep learning and computer-aided diagnosis systems for healthcare applications, particularly in medical imaging. He holds a PhD in 'Complexity Reduction in Image-Based Breast Care' (2014) from University Bremen and a BSc in Computer Science (With Distinction) from the Distance Learning University, Hagen. Wenzel has extensive industry experience, including roles at Fraunhofer MEVIS as a Key Scientist in Computational Breast Care (since 2021) and Senior Scientist (2005–2021). His work emphasizes clinical practicality, such as breast MRI segmentation and AI-driven diagnostic tools. Research highlights include developing the QuantMed platform for translational research and exploring generative adversarial networks (GANs) for medical data synthesis. He has authored over 50 peer-reviewed publications, with recent work focusing on CNN-based lesion detection, swarm learning, and uncertainty quantification in medical imaging. Awards include a prestigious scholarship from the Studienstiftung des Deutschen Volkes and certification as a Scientific Trainer (2022). Wenzel collaborates with clinicians to bridge AI research and clinical practice, addressing challenges like model interpretability and data scarcity in healthcare. His contributions span medical imaging analysis, AI-driven decision support systems, and robust deep learning frameworks for heterogeneous medical data.

• Machine Learning
• Causal Inference
• Signal Processing
• +۴ مورد دیگر

Yongqiang Chen is a Professor at the Chinese Academy of Sciences in the School of Engineering, Department of Computer Science and Engineering. With over 15 years of continuous research contributions spanning from 2005 to 2025, Dr. Chen has established himself as a versatile scholar working at the intersection of theoretical mathematics, machine learning, and practical engineering applications. Dr. Chen's research expertise encompasses: Machine Learning and Causal Inference, with recent focus on large language models and out-of-distribution generalization Signal and Image Processing, particularly in keyword spotting and watermarking applications Optimization Algorithms for engineering and management problems Theoretical Mathematics, including partition functions and combinatorial identities Engineering Management and Project Governance in construction contexts His recent publication trajectory (2023-2025) reveals a strategic evolution toward integrating causal reasoning with modern neural architectures, while maintaining strong connections to practical applications. Dr. Chen's work demonstrates exceptional interdisciplinary range, bridging pure mathematics with cutting-edge AI development and engineering solutions. His publications in venues like AAAI, NeurIPS, and IEEE Transactions reflect both theoretical rigor and practical relevance across multiple domains including renewable energy, neuroscience, and construction management. Dr. Chen maintains active collaborations with researchers across institutions, particularly with Bo Han on causal language models, James Cheng and Yiping Ke on brain network analysis, and various colleagues in engineering management. His research group appears to focus on developing theoretically sound yet practically applicable computational frameworks that address real-world challenges while advancing fundamental knowledge in machine learning and optimization.

• Analog Circuit Design
• Data Converters
• Mixed-Signal IC Design
• +۵ مورد دیگر

Franco Maloberti is a distinguished Professor of Electrical and Computer Engineering at the University of Macau's Faculty of Science and Technology. With a prolific research career spanning over three decades, he has authored over 315 publications in top-tier IEEE journals and conferences, demonstrating sustained scholarly productivity and significant contributions to the field of analog and mixed-signal circuit design. His educational background, though not explicitly detailed in the provided text, is inferred from his research trajectory to include advanced degrees in Electrical Engineering, likely from Italian institutions given his early career patterns. His research focuses on analog circuit design, data converters, low-power systems, biomedical circuits, and power management solutions for modern electronic applications. Professor Maloberti's publication record shows remarkable consistency and impact, with recent work (2020-2024) demonstrating continued innovation in high-speed data converters, biomedical instrumentation, and energy-efficient circuit design. His work bridges theoretical advances with practical implementations, often addressing challenges in emerging applications like implantable medical devices, high-speed communication systems, and edge computing architectures. His scientific contributions include pioneering work in data converter architectures, low-voltage circuit techniques, and power management solutions. The breadth of his research is evident in publications spanning IEEE Journal of Solid-State Circuits, IEEE Transactions on Circuits and Systems, and major conferences like ISCAS, ISSCC, and ESSCIRC. Professor Maloberti has mentored numerous researchers who have become prominent in the field, including Edoardo Bonizzoni, Rui Paulo Martins, and Sai-Weng Sin. His collaborative network spans institutions worldwide, with particularly strong ties to the University of Macau where his recent work is primarily conducted. His laboratory focuses on cutting-edge research in analog and mixed-signal integrated circuits, with current projects addressing challenges in biomedical instrumentation, high-speed data conversion, and energy-efficient circuit design for next-generation electronic systems.

Chiara Fusar Bassiniمشاهده پروفایل

پژوهشگر

• Machine Learning
• Energy Markets
• Renewable Energy Transition
• +۳ مورد دیگر

Chiara Fusar Bassini is a PhD candidate and research associate at the Hertie School’s Center for Sustainability and Data Science Lab in Berlin, Germany. Her research focuses on applying machine learning methods to analyze European energy data, particularly time-series data from grid operators to identify bottlenecks in the renewable energy transition. She holds a Master of Science in Scientific Computing from the Technical University of Berlin and a Bachelor of Science in Economics, Management, and Computer Science from Bocconi University. Under the supervision of Prof. Lynn Kaack and collaborating with Prof. Priya Donti at MIT’s Laboratory for Information and Decision Systems (LIDS), she explores optimizing energy systems through robust machine learning approaches. Chiara’s academic background combines economics, computer science, and applied mathematics, supported by professional experience in the energy sector. Her work addresses challenges in energy market designs, aiming to enhance sustainability and resilience. She is currently engaged in a research stay at MIT, where she investigates how machine learning can improve electricity market incentives and operational efficiency. Her research interests span data-driven solutions for energy systems, renewable integration, and future market design innovations. Chiara is actively involved in various academic events, including the 2025 DACH+ Conference on Energy Informatics and the Summer School on Sustainable Energy Markets & Optimization. Chiara’s interdisciplinary approach bridges technical and economic aspects of energy systems, leveraging her expertise in machine learning and policy-relevant analysis. She contributes to initiatives like the Hertie School’s Policy Lab and collaborates with institutions such as MIT’s LIDS, fostering international research networks to advance sustainable energy solutions.

Heike Trautmannمشاهده پروفایل

استاد

• Multi-criteria optimization
• Evolutionary optimization
• Automated algorithm selection and configuration
• +۶ مورد دیگر

Prof. Dr. Heike Trautmann is a leading researcher in statistics and optimization at the University of Twente (2021-2026) and former Professor at WWU Münster (2013-2016). Her work bridges computational statistics, evolutionary optimization, and social media analytics. She has held visiting positions at TU Dortmund, Leiden University, and RWTH Aachen. Current affiliation: University of Twente (Data Science: Statistics and Optimization) Previous roles: WWU Münster (Professor for Information Systems and Statistics), TU Dortmund (Postdoctoral researcher) Research Focus: Multi-criteria optimization, automated algorithm selection, data stream mining, and disinformation detection in social media. Her methodological innovations in exploratory landscape analysis and evolutionary computation have transformed algorithm configuration practices. Developed COSEAL consortium for algorithm selection Co-founder of Benchmarking Network (2019) Principal investigator in projects like PropStop and MODERAT! Academic Contributions: Over 150 publications in top venues like GECCO, PPSN, and Evolutionary Computation journal. Pioneered feature-based landscape analysis tools (flacco, pflacco) and stream clustering frameworks.

• Distributed Systems
• Wireless Communication
• Disruption-Tolerant Networking
• +۴ مورد دیگر

Bernd Freisleben is a Professor at Philipps University of Marburg in the Department of Mathematics and Computer Science, leading the Distributed Systems research group. His work focuses on distributed systems, wireless communication, and emergency communication networks. He is affiliated with the Verteilte Systeme (Distributed Systems) group and the emergenCITY project, emphasizing resilient disaster communication and smart city technologies. Research interests include disruption-tolerant networking (DTN), machine learning applications in multimedia analysis, and energy-efficient wireless protocols. His group develops tools like ProgDTN for programmable DTN and the BatRack device for wildlife monitoring. Students under his supervision include Hicham Bellafkir (PhD since 2021), Markus Sommer (PhD since 2020), and Daniel Schneider (PhD since 2020). Key projects include Nature 4.0, a networked sensor system for biodiversity monitoring, and the ForestEdge initiative for unobtrusive environmental monitoring. Recent publications highlight work on resilient networks, deep learning for multimedia, and bio-inspired data storage solutions.

• Network Security
• Wireless Communication
• Blockchain Technology
• +۵ مورد دیگر

Dimitrios Makrakis is a Professor affiliated with the University of Ottawa, Canada. His research focuses on interdisciplinary areas at the intersection of computer networks, cybersecurity, and biomedical engineering. Key research interests include blockchain technology applications in healthcare and finance, molecular communication for nanonetworks, vehicular networks security, and optogenetic-based communication systems. He has collaborated extensively with researchers like Abdelhakim Hafid and Binod Vaidya, producing impactful work in IEEE journals and conferences. His work spans theoretical and applied domains, such as developing secure authentication protocols, analyzing blockchain protocols in quantum contexts, and designing biomedical sensor systems. Notable contributions include frameworks for federated learning in healthcare, privacy-enhanced authentication systems, and bio-inspired nanogenerators for medical applications. Makrakis has also contributed to network protocols optimization in software-defined data centers and vehicular communication networks. Publications highlight his expertise in interdisciplinary fields: from quantum-resistant blockchain strategies to machine learning-driven price prediction in cryptocurrency markets. His research often bridges fundamental science with real-world applications, addressing challenges in privacy, security, and efficiency across distributed systems.