Dynamic graph algorithms Professors and Supervisors in Germany

Emmanouil Athanasiadisمشاهده پروفایل

پژوهشگر ارشد

Leibniz Institute for Zoo and Wildlife ResearchGermany

Dr. Emmanouil Athanasiadis is a bioinformatician at the University of Cambridge's Department of Haematology, with dual affiliations at the Wellcome Trust Sanger Institute and Medical Research Council (MRC) Stem Cell Institute. His research integrates computational biology with medical applications, focusing on single-cell genomics, cancer biology, and cardiovascular disease mechanisms since 2016. His educational foundation includes: BSc in Biomedical Engineering from Technological Educational Institution of Athens (2004) MSc in Medical Physics from University of Patras (2006), funded by State Scholarships Foundation of Greece PhD in Medical Physics from University of Patras (2010), supported by National State Scholarship Foundation Athanasiadis specializes in developing computational frameworks for biological data interpretation. His work spans single-cell RNA sequencing analysis in haematopoiesis, medical imaging algorithms for cancer diagnostics, and drug repurposing pipelines. Key contributions include SPNsim for pulmonary nodule simulation and ChemBioServer for chemical compound analysis, demonstrating his dual expertise in algorithm development and biomedical application. Publication analysis reveals a trajectory from medical imaging (2007-2012) to genomic network analysis (2013-2016), culminating in current single-cell and spatial transcriptomics research. His work consistently bridges computational innovation with clinical questions in oncology, haematology, and cardiovascular disease, with strong emphasis on open-source tool development. His scientific recognition includes: Computational award from Greek Research and Technology Network for 'GRAND' project Multiple presentation prizes at national medical conferences Continuous academic distinctions from State Scholarships Foundation of Greece K. Karatheodoris research scholarship He has secured EU funding through FP7 projects including 'PIK3CA Oncogenic Mutations' and 'NOISEPLUS', and maintains active collaborations across Cambridge, UCL, and Greek research institutions. His teaching contributions include lecturing in biomedical engineering programs at Technological Educational Institution of Athens (2010-2016). Current work centers on single-cell RNA sequencing analysis within Cambridge's haematology research ecosystem, particularly investigating transcriptional dynamics in blood cell development and cancer evolution through collaborative projects with Sanger Institute.

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Niki Kilbertusمشاهده پروفایل

استاد

Max Planck Institute for Intelligent SystemsGermany

Niki Kilbertus is a Professor in the Department of Informatics at the Technical University of Munich and a group leader at Helmholtz AI (Helmholtz Munich). They are also affiliated with MCML, the Konrad Zuse School relAI, and the Munich Unit of ELLIS. Since 2024, they have been a member of the Junge Akademie and received the Leopoldina Prize for Young Scientists. In 2025, they were awarded an ERC Starting Grant and achieved tenure at TUM. Professor Kilbertus's research focuses on causal machine learning, mechanistic ML, dynamical systems, and AI for science. Their work spans theoretical foundations of causal inference and practical applications across scientific domains. They have made significant contributions to causal effect estimation, causal discovery in stochastic processes, learning differential equations, and fair machine learning. Their research often bridges computer science with physics, biology, and climate science, demonstrating the interdisciplinary nature of their work. Professor Kilbertus has published extensively in top machine learning venues including NeurIPS, ICML, and ICLR, with numerous publications in 2024-2025. Their recent work shows a strong trend toward causal discovery in continuous-time systems, intervention modeling, and physics-informed machine learning applications. Scientific Awards: Leopoldina Prize for Young Scientists (2024) ERC Starting Grant (2025) Professor Kilbertus actively supervises multiple PhD students and collaborates with researchers across institutions including Max Planck Institutes and Helmholtz centers. They serve as an Action Editor for TMLR and regularly review for major ML conferences. The research group is well-funded through the ERC grant and institutional support from TUM and Helmholtz AI, enabling active recruitment of new PhD students and postdocs. Based at Technical University of Munich and Helmholtz AI, Professor Kilbertus's team works at the intersection of theoretical machine learning and scientific applications, with particular strengths in causal reasoning for complex dynamical systems.

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Stefan Wirtensohnمشاهده پروفایل

پژوهشگر

University of Applied Sciences KonstanzGermany

Stefan Wirtensohn is a Researcher at the Institute of System Dynamics, Konstanz University of Applied Sciences, where he has served as a research associate since 2012. His work bridges theoretical control engineering with practical marine robotics applications, focusing on autonomous surface vehicle development and advanced control systems. His academic credentials include: Bachelor of Engineering in Mechanical Engineering, HTWG Konstanz (2010) Master of Engineering in Mechatronics, HTWG Konstanz (2012) Wirtensohn's research centers on Control Engineering, Mechatronic Modeling, and Nonlinear Control systems for marine environments. He specializes in parameter identification, motion estimation, and trajectory optimization for autonomous vessels, with significant contributions to docking control, wave filtering, and extended object tracking. His methodology integrates unscented Kalman filters, model predictive control, and sensor fusion techniques to address real-world navigation challenges. Analysis of his 15 most recent publications (2013-2022) reveals consistent focus on marine robotics control systems. Key trends include the application of random matrices for lidar-based target tracking, model predictive path integral control for vessel stabilization, and advanced disturbance estimation techniques. His work demonstrates strong emphasis on experimental validation using autonomous surface vehicles across diverse maritime scenarios. He actively contributes to teaching through Control Engineering 1 exercises and laboratory supervision, emphasizing hands-on implementation of control algorithms. Current projects under his involvement include: Integrated autonomous measuring system for maritime surveying tasks Automated parking systems for truck trailers Collision prevention procedures for inland waterway vessels Wirtensohn operates within the Institute of System Dynamics research team specializing in signal processing, control engineering, and robotics, maintaining close collaboration with Johannes Reuter and other institute members on marine automation challenges.

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Yang Liمشاهده پروفایل

دانشیار

ESMT BerlinGermany

Yang Li serves as Associate Professor of Marketing and Associate Dean for the MBA Program at Cheung Kong Graduate School of Business (CKGSB). Holding a PhD in Marketing from Columbia Business School alongside dual master's and bachelor's degrees from Columbia and Peking University respectively, he bridges advanced statistical methodologies with practical business applications. His research centers on statistical machine learning and Bayesian nonparametrics applied to consumer behavior analysis, with specialization in online personalization, text mining, and choice modeling. Recent work demonstrates significant focus on fragmented attention economies, ethical AI frameworks, and NFT network dynamics, reflecting contemporary digital market challenges. Management Science Marketing Science Journal of Marketing Research Journal of Consumer Research Harvard Business Review Professor Li's publications reveal evolving expertise from foundational pricing elasticity studies toward cutting-edge AI applications in consumer contexts. His work increasingly integrates generative models and graph neural networks to decode complex consumer collection behaviors and digital ecosystem dynamics. Scientific recognition includes being a Finalist for the 2021 Paul E. Green Best Paper Award. Industry impact is demonstrated through executive education programs and strategic consultancies with Tencent, Haier, and Tmall. As Associate Dean for MBA Programs, he oversees curriculum development while maintaining active corporate governance roles on boards of publicly traded companies across China and Hong Kong, directly applying his research insights to strategic decision-making in digital transformation initiatives.

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Smita Krishnaswamyمشاهده پروفایل

دانشیار

Max Planck Institute of Molecular Cell Biology and GeneticsGermany

Smita Krishnaswamy is an Associate Professor of Genetics and Computer Science at Yale University with joint appointments in both departments. She is affiliated with multiple interdisciplinary programs including the Applied Mathematics Program, Computational Biology and Bioinformatics Program, Yale Center for Biomedical Data Science, Yale Cancer Center, and the Wu Tsai Institute. Her research bridges computational methods development with biomedical applications, focusing on unsupervised machine learning approaches for high-dimensional data analysis. Associate Professor of Genetics, Yale School of Medicine Associate Professor of Computer Science, Yale University Affiliated Faculty, Applied Mathematics Program Affiliated Faculty, Computational Biology and Bioinformatics Member, Yale Center for Biomedical Data Science Member, Yale Cancer Center Member, Wu Tsai Institute Dr. Krishnaswamy's research focuses on developing unsupervised machine learning techniques, particularly manifold learning and deep learning methods, to analyze high-dimensional biomedical data. Her lab creates algorithms for non-linear dimensionality reduction, data geometry learning, denoising, imputation, and inference of multi-granular structures from complex datasets. These methods are applied to diverse data types including single-cell RNA-sequencing, mass cytometry, electronic health records, and connectomic data across multiple biological systems. Her work spans several key application areas including immunology and immunotherapy, cancer research, neuroscience, developmental biology, and health outcomes analysis. The lab employs approaches from geometric deep learning, multiscale graph signal processing, and topological data analysis to extract meaningful biological insights from complex datasets. Recent publications demonstrate the lab's leadership in developing methods for spatial transcriptomics, brain-state trajectory modeling, and organ donation prediction. Excellence in Science Early-Career Investigator Award from FASEB (2022) Yale Cancer Center Class of '61 Cancer Research Award (2025) Dr. Krishnaswamy maintains active collaborations across Yale and secures research funding supporting her work in computational biomedicine. She advises students through multiple programs including Genetics, Computer Science, and the Biological and Biomedical Sciences Graduate Program, fostering interdisciplinary training at the intersection of computation and biomedicine. The Krishnaswamy Lab operates at the forefront of computational biomedicine, developing mathematical approaches that enable new biological discoveries from complex datasets.

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Angela Radulescuمشاهده پروفایل

استادیار

Max Planck Institute for Human DevelopmentGermany

Dr. Angela Radulescu is an Assistant Professor at the Icahn School of Medicine at Mount Sinai in the Department of Psychiatry. She holds faculty positions at the Mt. Sinai Center for Computational Psychiatry and the Lipschultz Center for Cognitive Neuroscience, with additional affiliation at the Friedman Brain Institute. Her research program focuses on cognitive computational neuroscience, specifically investigating how patterns of symptoms that cut across psychiatric diagnoses arise from the interplay between cognitive and affective processes. Dr. Radulescu employs a diverse methodological toolkit including reinforcement learning, Bayesian inference, behavioral experiments, and virtual reality to model how the mind generates behavior and to uncover how the algorithms supporting behavior are implemented in the brain. Dr. Radulescu's recent publications demonstrate expertise in attention mechanisms, emotional processing, and reinforcement learning within mental health contexts. Her work bridges computational approaches with clinical psychiatry, contributing to the growing field that aims to develop more precise models of mental health conditions through quantitative methods. She actively participates in the academic community as evidenced by her involvement in the New York Computational Psychiatry Workshop (which she co-organizes), presentations at international conferences including CCN in Amsterdam and UCL, and collaborations with researchers across institutions. Her lab recently secured funding through a BD2 Foundation Discovery Research Grant. As Principal Investigator of the Dynamics of Cognition and Affect Lab, Dr. Radulescu provides research opportunities for students and collaborators interested in computational approaches to understanding mental health. She maintains an active online presence where she shares updates about her research and relevant developments in her field.

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Xin Zhangمشاهده پروفایل

استادیار

Max Planck Institute for Security and PrivacyGermany

Xin Zhang serves as an Assistant Professor in the Department of Computer Science and Technology within the School of Electronics Engineering and Computer Science at Peking University. His academic profile demonstrates deep engagement with programming languages and software engineering research communities through active participation in major conferences including ASE, SPLASH/OOPSLA, PLDI, and ICSE. Dr. Zhang's research focuses on the synergistic relationship between program analysis and machine learning. He investigates how ML/AI techniques can enhance traditional program analysis methods while simultaneously developing program analysis approaches to improve the interpretability, fairness, robustness, and safety of AI systems. His work spans probabilistic program analysis, abstraction refinement techniques, Bayesian modeling for program semantics, and applications of graph neural networks to static analysis problems. His publication record shows consistent contributions to top venues from 2016 through 2025, with recent work emphasizing Bayesian program analysis, abstraction refinement methods, and the intersection of formal methods with machine learning. The trajectory of his research demonstrates increasing sophistication in combining traditional program analysis techniques with modern AI approaches. Dr. Zhang actively contributes to the academic community as a program committee member for major conferences including ASE, SAS, PLDI, and SPLASH. His service includes reviewing, session chairing, and committee participation across multiple venues, reflecting his standing in the programming languages and software engineering communities.

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Xin Xiaمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Xin Xia is a Qiushi Distinguished Professor at the College of Computer Science and Technology, Zhejiang University. Previously, he served as the Chief Expert and Director of the Software Engineering Application Technology Lab at Huawei Technologies, China from 2021 to 2025. His academic career spans software engineering research with a focus on AI applications in the field. Ph.D. from Zhejiang University (2014) Supervised by Prof. Xiaohu Yang and Prof. Jianling Sun Visiting student at Singapore Management University (2012-2014) under Prof. David Lo Xin Xia's research primarily focuses on applying data science techniques to software engineering problems. His work spans AI for Software Engineering, Mining Software Repositories, Empirical Software Engineering, and Large Language Models for code understanding and generation. He employs data mining, information retrieval, natural language processing, search-based algorithms, and program analysis to transform software engineering data into automated tools and insights. His recent publications show a strong trend toward leveraging Large Language Models for various software engineering tasks, including code generation, vulnerability detection, and test generation. He has been exploring how to make these models more effective, reliable, and practical for real-world software development scenarios, with a particular focus on Java and Python ecosystems. ACM SIGSOFT Early Career Researcher Award (2022) ACM Distinguished Member 16 best or distinguished paper awards, including nine ACM SIGSOFT Distinguished Paper Awards Recipient of the IEEE Transactions on Software Engineering 2021 Best Paper Award Runner-Up Xin Xia has advised numerous students who have gone on to publish in top software engineering venues. His research has been supported by grants from both academic institutions and industry partners, particularly during his time at Huawei. He actively collaborates with researchers worldwide, especially with David Lo at Singapore Management University. At Zhejiang University, Professor Xia leads research in the intersection of AI and Software Engineering. His work has practical applications in improving developer productivity through automated tools that analyze software repositories and provide actionable insights.

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Jie Luمشاهده پروفایل

دانشیار

Max Planck Institute for Security and PrivacyGermany

Jie Lu is an Associate Professor at the Institute of Computing Technology of the Chinese Academy of Sciences (ICT, CAS), where he leads research in software security and program analysis. His work focuses on developing advanced program analysis techniques to improve software reliability and security, with applications in cloud systems, distributed environments, and modern web applications. Dr. Lu's research interests include: Software Security: Focusing on vulnerability detection and prevention in open-source software Program Analysis: Specializing in static/dynamic analysis techniques and context-sensitive pointer analysis Cloud Systems: Researching distributed system security, crash-recovery, and concurrency bug detection His recent publications demonstrate a strong focus on practical security solutions for real-world systems. The research spans Kubernetes ecosystems, PHP applications, Linux kernel security, Java web applications, and Windows IPC systems. A notable trend is the development of precise static analysis techniques that balance efficiency with accuracy, addressing the longstanding challenge in program analysis. His work often bridges theoretical advances with practical implementations that have been adopted by industry. Dr. Lu has received several prestigious awards: ACM SIGSOFT Distinguished Paper Award 2025 Best Paper Honorable Mention at CCS 2022 Chinese Academy of Sciences Outstanding Doctoral Dissertation 2021 Chinese Academy of Sciences President's Special Award 2020 ICT New Hundred Stars 2020 Dr. Lu actively mentors students and researchers, recruiting PhD candidates, Master students, and research interns interested in software security and program analysis. His research has been supported by the National Natural Science Foundation of China, CCF-Huawei Innovation Research Plan, and CCF-Ant Research Fund. The Program Analysis Group (ICT-PAG) at the National Key Laboratory of Processor has successfully identified numerous errors and vulnerabilities in popular open-source applications, with over 200 severe bugs confirmed by the open-source community and assigned more than 100 CVE numbers. His research group, the Program Analysis Group (ICT-PAG), is based in the National Key Laboratory of Processor at ICT, CAS. The group has achieved significant impact through both academic publications in top venues (SOSP, CCS, USENIX Security, NDSS, OOPSLA, ISSTA, FSE, ASE, TSE) and practical applications in leading IT companies and government organizations.

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Đorđe Žikelićمشاهده پروفایل

استادیار

Max Planck Institute for Security and PrivacyGermany

Đorđe Žikelić is an Assistant Professor of Computer Science at the School of Computing and Information Systems at Singapore Management University (SMU) in Singapore. He completed his PhD in 2023 at the Institute of Science and Technology Austria (ISTA) under Krishnendu Chatterjee and Petr Novotný, receiving both Outstanding PhD Thesis and Outstanding Scientific Achievement awards. Prior to his doctorate, he earned bachelor's and master's degrees in mathematics from the University of Cambridge. His educational background includes: PhD in Computer Science, Institute of Science and Technology Austria (ISTA), 2023 Bachelor's and Master's in Mathematics, University of Cambridge Dr. Žikelić's research focuses on advancing formal methods to ensure software and AI systems are correct, safe, and trustworthy. His work bridges theoretical aspects of formal reasoning about probabilistic systems with practical automated verification methods. His primary research interests span three interconnected areas: Program Analysis and Verification: He develops techniques for analyzing probabilistic programs, numerical programs, and efficient quantifier elimination methods, addressing fundamental challenges in verifying complex software systems. Trustworthy AI and Safe Autonomy: He creates formal verification frameworks for learning-enabled control systems and neural networks, ensuring AI operates safely in uncertain environments through methods like runtime monitoring and certificate repair. Probabilistic System Verification: He explores broader applications including bidding games on graphs and blockchain protocol analysis, extending formal methods to novel domains beyond traditional finite-state verification. His publication trajectory shows a consistent progression from theoretical foundations to practical applications, with recent work increasingly focused on integrating formal verification with machine learning. His 2024-2025 publications demonstrate growing expertise in verifying learning-based systems and developing practical tools like PolyQEnt for quantified entailment solving. His scientific achievements have been recognized with: Outstanding PhD Thesis Award at ISTA Outstanding Scientific Achievement Award at ISTA Distinguished Paper Award at FM 2024 Dr. Žikelić serves on program committees for major conferences including TACAS, PLDI, AAAI, and CAV. He actively mentors through the Programming Languages Mentoring Workshop (PLMW) at PLDI 2025. His research group at SMU focuses on developing novel algorithms for verifying correctness of programs and AI systems, with current projects spanning formal methods, artificial intelligence, and programming languages.

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Stephan Doerfelمشاهده پروفایل

استاد

Kiel University of Applied SciencesGermany

Professor Stephan Doerfel holds a professorship for Data Science at Kiel University of Applied Sciences since 2021, where he is assigned to the Department of Media and serves as head of the Institute for Data Science (IfDS). He teaches Mathematics and Multivariate Statistics, Machine and Deep Learning modules in the Master's Program Data Science (MADS). His primary research interests span Data Science, Machine Learning, and Deep Learning, with significant contributions to recommender systems, social bookmarking systems (particularly BibSonomy), formal concept analysis, and publication analysis. His recent work shows a shift toward applied industrial problems, especially in injection molding quality prediction and agricultural technology. Analysis of his publication trends reveals an evolution from foundational work in formal concept analysis and social tagging systems (2010-2016) to more applied machine learning research in manufacturing and agriculture (2020-2024). His work consistently bridges theoretical computer science with practical applications across diverse domains. Professor Doerfel has been actively involved in numerous academic service roles including peer reviewing for major journals and serving on program committees for prestigious conferences including ECML/PKDD, WebSci, and ICFCA. His extensive conference participation spans over a decade with consistent contributions to the machine learning and data science community. He has supervised various research projects including AVAPS (focusing on injection molding data), PUMA (Academic Publication Management), and Info 2.0 (Informational self-determination in Web 2.0). As a core contributor to the BibSonomy open-source project, he has helped create a scholarly social bookmarking system that serves as a testbed for recommendation algorithms and user behavior studies.

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