جست‌وجوی استاد و پژوهشگر

Martina Maggioمشاهده پروفایل

استاد

• Control Theory
• Cyber-Physical Systems
• Real-Time Systems
• +۴ مورد دیگر

Martina Maggio is a Professor at the Department of Computer Science , Saarland University (since March 2020) and holds a 20% position at the Department of Automatic Control , Lund University (since 2023). Her research bridges control theory with software engineering to address predictability in computing systems. PhD : Politecnico di Milano (completed with work on control-theoretical tools for computing systems) Visiting Graduate Student : MIT CSAIL (collaborated on Self-Aware Computing project) Her research focuses on: Control theory for resource allocation in cloud infrastructures and embedded systems Formal verification of event-triggered systems and deadline misses Robustness of cyber-physical systems under computational faults Automated methodologies for runtime adaptation in software Timing requirements in big data applications Security vulnerabilities in control systems Key article trends include: control-theoretic approaches to computing systems, stochastic modeling of deadline misses, formal verification of timing constraints, and security-aware control algorithms . Scientific recognition: Co-authored paper selected as Most Influential Paper at SEAMS 2025 Best Paper Award at ECRTS 2021 ACM SIGSOFT Distinguished Paper Award (FSE 2020) Outstanding Reviewer (ICPE 2020) Mentorship includes supervising PhD candidates (e.g., Clara Rubeck, Robert Pietsch) and serving as an academic advisor for industry collaborations (e.g., with Bosch Corporate Research). She also contributes to open-source tool development (WeaklyHard.jl) and has held leadership roles like Program Co-Chair for ICCPS 2020.

Florian Röselمشاهده پروفایل

پژوهشگر

• Optimization under Uncertainty
• Data Science
• Operations Research
• +۴ مورد دیگر

Dr. Florian Rösel is a researcher affiliated with the Department of Data Science at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He works under the Professorship of Optimization under Uncertainty & Data Analysis led by Prof. Dr. Frauke Liers, focusing on data-driven optimization techniques and their applications in aviation logistics and complex system modeling.

Henrik Leopoldمشاهده پروفایل

استاد

• Business Process Management
• Process Mining
• Artificial Intelligence
• +۵ مورد دیگر

Professor Henrik Leopold is a Professor for Data Science and Business Intelligence and Head of Department of Operations and Technology at Kühne Logistics University (KLU) in Hamburg, Germany. He joined KLU in February 2019 and has held progressive academic positions there, advancing from Assistant Professor (2018-2020) to Associate Professor (2020-2023) and ultimately to full Professor (since 2023). Prior to KLU, he served as Assistant Professor at Vrije Universiteit Amsterdam (2015-2019) and Vienna University of Economics and Business (2014-2015). Henrik Leopold received his PhD (Dr. rer. pol.) in Information Systems from Humboldt University of Berlin in 2013, where he also completed his M.Sc. (2010) and B.Sc. (2008) in Information Systems. His doctoral work earned him the prestigious TARGION Dissertation Award 2014 for the best doctoral thesis in Information Management and recognition as runner-up for the McKinsey Business Technology Award 2013. Leopold's research primarily focuses on the intersection of information systems and business processes, with particular emphasis on leveraging artificial intelligence technologies—including machine learning and natural language processing—to develop innovative techniques for process analysis, mining, and automation. His work bridges theoretical advancements with practical applications across various industries, particularly in logistics and healthcare. He has published over 100 peer-reviewed articles in top-tier journals such as IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Software Engineering, ACM Transactions on Management Information Systems, Decision Support Systems, and Information Systems. His recent research demonstrates a clear trajectory toward increasingly sophisticated applications of AI in business process management, with growing emphasis on semantic analysis, explainability, and the integration of large language models. This evolution reflects both technological advancements in AI and the growing complexity of business process challenges in the digital era. Among his notable achievements are the TARGION Dissertation Award 2014 and being named runner-up for the McKinsey Business Technology Award 2013. His research has secured significant funding and has been implemented in practical settings through collaborations with industry partners. As an academic leader, Leopold has supervised numerous PhD and Master's students, contributing to the next generation of researchers in business process management and data science. He actively participates in major research projects focused on automated process weakness identification and semantic process discovery from user interaction logs. His leadership extends to heading the Department of Operations and Technology at KLU, where he shapes academic programs and research directions. Leopold maintains an active presence in the academic community through his personal website (www.henrikleopold.com), Google Scholar profile, and DBLP page, making his research widely accessible to scholars and practitioners worldwide.

Stefan Canzarمشاهده پروفایل

استاد پژوهشی

• Computational Biology
• Bioinformatics
• Transcriptomics
• +۴ مورد دیگر

Dr. Stefan Canzar leads the Algorithmic Bioinformatics research group at Ludwig-Maximilians-Universität München's Gene Center since 2016, where he develops computational methods for genomic data analysis. His lab focuses on reconstructing biological patterns from fragmented sequencing data using combinatorial optimization and machine learning techniques. Research interests span: Transcriptome reconstruction through methods like CIDANE and Ladder-seq that improve RNA-seq accuracy Single-cell genomics including Sphetcher for cellular heterogeneity analysis and multimodal integration Algorithm engineering for high-dimensional biological data processing Combinatorial optimization applied to omics data integration and network analysis Canzar's publications demonstrate consistent focus on developing scalable algorithms for genomic data challenges, particularly in RNA reconstruction, single-cell analysis, and multimodal integration. His recent work emphasizes dimensionality reduction techniques and transcriptome quantification improvements. Currently funded projects include BAYHOST and DFG collaborative research center LETSIMMUN. As group leader, he mentors doctoral researchers including Alice Descoeudres and Shuang Li. The Canzar Lab develops open-source software tools that have contributed to discoveries in neural development, leukemia research, and immune system regulation through collaborations with medical institutions.

Eva Darulovaمشاهده پروفایل

دانشیار

• Programming Languages
• Software Verification
• Program Synthesis
• +۳ مورد دیگر

Eva Darulova is an Associate Professor at the Department of Information Technology, Uppsala University, and Adjunct Faculty at Max Planck Institute for Software Systems (MPI-SWS). Previously held tenure-track faculty at MPI-SWS and completed her PhD at École Polytechnique Fédérale de Lausanne (EPFL) under Viktor Kuncak. Current research: Applications in numerical/embedded domains, program synthesis, software verification, approximate computing Key projects: Blossom (fuzzing), Lassie (HOL4 tactics), Pine (floating-point loops), LTLTalk (robot instruction), Incarnate (3D printing), Icing (compiler semantics), Flipper (NLP interface), FloVer (error checking), Daisy (accuracy analysis), Rosa (real compiler) Teaching: Program Design (2021/22), Program Synthesis (2021), Program Analysis (multiple years), Advanced Program Analysis (2019), Static Program Analysis (2017), Approximate Computing seminar (2016) Service: Artifact evaluation chair (ASPLOS'22), PC member (ECOOP'22, ESEC/FSE'22, EMSOFT'21, PLDI'21) Research Trends: Focus on numerical computing verification, approximate computing frameworks, and program synthesis techniques. Publications span TACAS, EMSOFT, ISSTA, POPL, CAV, FMCAD, TOPLAS, with emphasis on floating-point error analysis, mixed-precision optimization, and compiler verification. Scientific Recognition: Nominated for EAPLS best paper award (TACAS'21). Co-advisor for Anastasiia Izycheva and mentor for multiple MSc/BSc students including Mustafa Hafidi, Joachim Bard, Nikita Zyuzin, Youcef Merah, Safya Alzayat, Anastasiia Izycheva. Collaborations: Works with researchers at EPFL, MPI-SWS, University of Saarland, University of Washington, and Saarbrücken/Kaiserslautern institutions.

• Multimodal Generative AI
• Computer Vision
• Machine Learning
• +۳ مورد دیگر

Vicky Kalogeiton is a Professor in AI at École Polytechnique's Computer Science Laboratory (LIX) and leads the VISTA team. As an ELLIS member, her research focuses on multimodal generative AI with applications in medical imaging, efficient generation, and structured output modeling. She actively publishes in top venues like CVPR, ICCV, and IJCV, and supports Slow/ Open Science principles. PhD from University of Edinburgh and INRIA Grenoble Habilitation (HDR) from École Polytechnique 2024 Hi!Paris Chaire and multiple grants (ANR, Microsoft, DIM RFSI) Her recent work explores diffusion models for visual geolocation (Around the World in 80 Timesteps), camera motion control (AKiRa & E.T. dataset), and multimodal humor detection (FunnyNet-W). She pioneered coherence-aware training frameworks and cinematic trajectory analysis methods. Scientific recognition includes CVPR 2024 Highlight paper ACCV 2022 Student Honorable Mention ICCV-W 2021 Best Paper Award Outstanding Reviewer Awards (CVPR, ICCV, ECCV) She supervises current PhD candidates and has mentored numerous students across institutions like MBZUAI, Inria, and Telecom Paris. Her teaching includes Advanced Deep Learning and Computer Vision courses at École Polytechnique.

• Performability Evaluation
• Stochastic Modeling
• Learning Analytics
• +۴ مورد دیگر

Gabriel Alves is a Brazilian computer science researcher affiliated with the University of Pernambuco and collaborating with institutions like Federal Higher Education Institutions and Pontifical Catholic University of Minas Gerais. His work focuses on performability evaluation, stochastic modeling, and learning analytics, particularly applied to education technology and transport logistics systems. Research Interests : Performability analysis, stochastic modeling, learning analytics, cyber-physical systems, cloud computing, and green supply chain optimization Key Contributions : Development of Tutoria platform for educational feedback, performability models for BRT systems, and frameworks for AI-driven instructor support tools His publications span journals like IEEE Transactions on Learning Technology and conferences including SMC and EC-TEL, with recent work exploring natural language processing for educational assessment. Current collaborations include researchers from Chile (Dragan Gasevic), Brazil (Rafael Ferreira Mello, Paulo Maciel), and Portugal (Taciana Falcão).

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +۵ مورد دیگر

Ying Cai is a prolific researcher with significant contributions across diverse domains of computer science, mathematics, and biomedical applications. Their work spans artificial intelligence, medical imaging, cybersecurity, and computational methods, as evidenced by recent publications in journals like Engineering Applications of Artificial Intelligence and IEEE Transactions on Medical Imaging . Key research areas include lung cancer detection algorithms, distributed filtering under cyber-attacks, and cryptographic protocols. 2025: 9 publications 2024: 18 publications 2023: 8 publications Notable collaborations include work with Yang Zhao, Zeyu Zhang, and Daji Ergu on medical AI applications and computational techniques. Their recent articles demonstrate expertise in: Medical imaging and diagnostic automation Deep learning optimization Secure communication protocols Computational mathematical models Ying Cai's research bridges theoretical rigor with practical implementation across domains like health informatics, network security, and educational technology.

Daniel Nygaمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Probability Theory
• Probabilistic Knowledge Processing
• +۵ مورد دیگر

Daniel Nyga is a postdoctoral researcher at the Institute for Artificial Intelligence (IAI) , University of Bremen. He holds a PhD in computational science (summa cum laude) from the University of Bremen (2017), a master's (2014) and bachelor's (2012) in computer science from the Technical University of Munich (TUM) with a focus on AI and Machine Learning. Visited Bio-intelligence Laboratory (Prof. Byoung-Tak Zhang), Seoul National University (2014) Visited Robust Robotics Group (Prof. Nicholas Roy), MIT CSAIL (2014) Lead developer of open-source projects: pracmln , PRAC , and pyrap Recipient of Best Service Robotics Paper Award (ICRA 2015) His research focuses on probabilistic knowledge representation and reasoning for natural language interpretation in robotics, including: Markov logic networks for ambiguous instruction resolution Semantic analogical reasoning for task completion Statistical relational learning for web-enabled knowledge acquisition Probabilistic modeling of symbolic concepts Cloud-based knowledge services for robotic systems Notable publications span ICRA, IROS, ISRR, and Arxiv , with recent work on joint probability trees (2023). He has supervised multiple theses on probabilistic models for robot manipulation and instruction understanding.

Philipp Baumeisterمشاهده پروفایل

پژوهشگر

• Exoplanet Interior Structure
• Planetary Geodynamics
• Habitability Modeling
• +۳ مورد دیگر

Dr. Philipp Baumeister is a Postdoctoral Researcher in Planetary Geodynamics at the Institute of Geological Sciences, Department of Earth Sciences, Freie Universität Berlin. He works on the DIVerse Exoplanet Redox State Estimations (DIVERSE) project, focusing on the relationship between planetary interior structure, redox state, and habitability potential of exoplanets. His research combines geophysical modeling with machine learning techniques to rapidly characterize exoplanet interiors. Dr. Baumeister completed his academic training at Technische Universität Berlin, earning a B.Sc. in Physics (2011-2015), M.Sc. in Physics (2015-2017) with thesis on heat-piping effects on Earth's interior evolution, and a PhD in Physics (2018-2023) with dissertation on interior structure, mantle-atmosphere co-evolution, and habitability of low-mass exoplanets. His primary research interests include exoplanet interior structure modeling, planetary geodynamics, habitability assessment of stagnant-lid planets, redox state analysis, and machine learning applications in planetary science. He has developed innovative approaches like ExoMDN (Exoplanet Mixture Density Networks) to rapidly characterize exoplanet interiors using probabilistic machine learning. Analysis of his publication record shows a strong focus on the connection between planetary interior structure and atmospheric properties, with particular attention to stagnant-lid planets (like Venus and many exoplanets). His work increasingly incorporates machine learning methods to address computational challenges in exoplanet characterization, while maintaining rigorous physical modeling of planetary interiors and atmospheres. Dr. Baumeister actively collaborates with researchers across multiple institutions, as evidenced by his co-authorship on studies involving atmospheric modeling, exoplanet system characterization, and Venus research. His work on the DIVERSE project represents a significant contribution to understanding how planetary redox states influence long-term habitability potential.

• Stochastics
• Financial Mathematics
• Probability Theory
• +۱ مورد دیگر

Alessandro Sgarabottolo is a Researcher at the Department of Mathematics within the Faculty of Mathematics, Computer Science and Statistics at Ludwig Maximilian University of Munich. His work focuses on Stochastics and Financial Mathematics , contributing to probabilistic modeling and quantitative finance. Contact: Phone: +49 (0)89 2180-4697 Office: Room 232 Email: sgarabottolo@math.lmu.de Address: Theresienstr. 39, D-80333 Munich.

Kai Chengمشاهده پروفایل

پژوهشگر ارشد

• Stochastic dynamics
• Surrogate models
• Machine learning
• +۴ مورد دیگر

Kai Cheng is a Humboldt Research Fellow at the Engineering Risk Analysis Group, Technical University of Munich (TUM), since November 2023. His work focuses on developing computational methods for reliability assessment and uncertainty quantification in engineering systems. Education: Ph.D. in Flight Vehicle Design, Northwestern Polytechnical University (2018–2021) M.Sc. in Flight Vehicle Design, Northwestern Polytechnical University (2015–2018) B.Sc. in Mechanics, China University of Petroleum (East China) (2011–2015) Research Interests: Dr. Cheng's research integrates stochastic dynamics, machine learning, and model reduction techniques to address challenges in reliability analysis. Key areas include surrogate modeling for high-dimensional systems, rare event probability estimation, and adaptive algorithms for uncertainty quantification in mechanical and structural applications. Publication Trends: His recent articles emphasize machine learning-enhanced reliability methods, including gradient-based Kriging, Bayesian regression, and Monte Carlo variants. Dominant themes are computational efficiency in high-dimensional problems and adaptive algorithms for structural safety. Awards: Humboldt Research Fellowship (2023–present) Teaching: At TUM, he assists in graduate courses on Stochastic Finite Element Methods and Estimation of Rare Events and Failure Probabilities . Team Affiliation: He collaborates with the Engineering Risk Analysis Group at TUM, which specializes in probabilistic risk assessment for infrastructure and dynamical systems.

Lea Friedliمشاهده پروفایل

پژوهشگر

• Bayesian Methods
• Inverse Problems
• Value of Information
• +۷ مورد دیگر

Lea Friedli is a Postdoctoral Researcher at the Engineering Risk Analysis Group of the Technical University of Munich (TUM) since March 2025, funded by the Swiss National Science Foundation. She previously served as a Postdoctoral Researcher at the University of Bern's Institute of Mathematical Statistics and Actuarial Science in 2024. PhD in Environmental Sciences (2019–2023), University of Lausanne MSc in Statistics (2016–2019), University of Bern BA in German Language and Literature (2013–2016), University of Bern Her research focuses on Bayesian methods , inverse problems , and rare event probability estimation , with applications in environmental systems and risk analysis. Key methodologies include Gaussian process modeling, sequential design, and pseudo-marginal inference. Lea's publications demonstrate expertise in uncertainty quantification for geophysical and hydrological systems. Recent work includes energy-based rare event estimation (2023), groundwater inverse problems (2024), and lithological tomography using Bayesian frameworks (2022). Teaching: Estimation of rare events (TA, SS25, TUM); Combinatorics and Probability (Lecturer, HS24, University of Bern); Uncertainty quantification in action (Lecturer, HS24, University of Bern) She is affiliated with the TUM Engineering Risk Analysis Group, contributing to projects involving digital twins, wildfire risk, and probabilistic ground models for offshore wind farms. No scientific awards were mentioned in the provided text.

Calvin Chauمشاهده پروفایل

پژوهشگر

• Algebraic Foundations
• Computational Logic
• Computer Science
• +۳ مورد دیگر

Calvin Chau is a researcher affiliated with the Faculty of Computer Science at Technische University Dresden , specializing in the International Center for Computational Logic (ICCL) . His work focuses on algebraic and logical foundations of computer science, with contributions to formal verification and neural network abstraction. His research interests include: Algebraic Foundations Computational Logic Formal Methods Artificial Intelligence Quantitative Verification Calvin has published in prestigious venues such as QEST+FORMATS and ATVA , addressing challenges in Markov decision processes and neural network verification. He is involved in the SEMECO-Q1 project, focusing on secure medical microsystems and communication architectures. His recent publications emphasize: Multi-objective optimization in probabilistic systems Abstraction-refinement techniques for neural networks Semantic vs. syntactic approaches to formal analysis Calvin can be reached via email at calvin.chau@tu-dresden.de or in person at APB 3004.

• Mathematics
• Stochastics
• Analysis
• +۳ مورد دیگر

Martin Nils Bischoff is affiliated with the Faculty of Mathematics at the University of Bielefeld and contributes to the Collaborative Research Center (SFB) 1283, "Taming uncertainty and profiting from randomness and low regularity in analysis, stochastics and their applications." As an EDV (electronic data processing) supervisor, he supports research initiatives focused on uncertainty quantification, probabilistic modeling, and mathematical analysis under low regularity conditions. His work intersects with the university's strategic research area "The Mathematical World," which emphasizes interdisciplinary applications of fundamental mathematical theories. Key research themes include stochastic processes, analytical methods for irregular systems, and computational approaches to randomness. Based in room UHG V3-208 , he can be reached via phone at +49 521 106-2472. Further contact details require authentication due to institutional access policies.