جستجوی استاد راهنمای دانشگاهی

• Resilience Research
• Psychiatry
• Psychotherapy
• +۴ مورد دیگر

Prof. Dr. Klaus Lieb serves as Scientific Director of the Leibniz Institute for Resilience Research (LIR) and Director of the Department for Psychiatry and Psychotherapy at Johannes Gutenberg University Mainz. He holds a W3 Professorship in Psychiatry and Psychotherapy and leads one of Germany's premier research centers focused on psychological resilience. His academic leadership extends to his role as Editor of the Cochrane Library's section on personality disorders and as an official member of the Drug Commission of the German Medical Association since 2009. Dr. Lieb's educational background includes: Medical studies at the University of Ulm and Tübingen, Germany, and Los Angeles, USA (1985-1992) Dr. med. degree from University of Ulm (1992, summa cum laude) Residency in Psychiatry and Psychotherapy at University of Freiburg (1994-1999) Habilitation in Psychiatry from University of Freiburg (1999) Board certification in Psychiatry and Psychotherapy (1999) His research focuses on psychological resilience mechanisms, mental health interventions, and personality disorders. Dr. Lieb's work bridges clinical practice and scientific research, with particular emphasis on systematic reviews and meta-analyses of resilience-promoting interventions. His group examines trajectories of mental health in response to major disruptions like the COVID-19 pandemic and develops evidence-based interventions for resilience promotion in various populations. His research has significant implications for public health policy and clinical practice in mental healthcare. Analysis of his recent publications reveals a strong focus on digital interventions for resilience promotion, pandemic mental health impacts, and evidence-based approaches to mental health interventions. His work demonstrates a consistent trajectory toward translating resilience research into practical applications, with increasing emphasis on digital health solutions and population-level mental health strategies. The publications span high-impact journals across psychiatry, psychology, and public health domains. Dr. Lieb has received several prestigious awards recognizing his contributions: Hochschullehrer des Jahres (German University Lecturer of the Year, 2013) Hamburger Preis Persönlichkeitsstörungen (Hamburg Prize for Personality Disorders, 2013) Golden Helix Award (2004) Hormosan-Forschungspreis Psychiatrie (2003) As a mentor, Dr. Lieb leads a research group comprising multiple PhD students and postdoctoral researchers including Jan Broll, Clara Burmeister, and Sarah Schäfer. His research program has secured substantial funding exceeding 5 million euros from diverse sources including the German Research Foundation (DFG), Federal Ministry of Education and Research (BMFTR), Hertie Foundation, Volkswagen Foundation, and European Union. His research group at LIR focuses on three main areas: systematic reviews of resilience mechanisms, examination of resilience trajectories in different populations, and implementation of resilience-promoting interventions. Dr. Lieb directs the Leibniz Institute for Resilience Research, a major center dedicated to understanding how individuals cope with stress and adversity. His research group collaborates with numerous international partners across Europe and North America and works on cutting-edge projects including the development of digital resilience interventions, optimization of resilience assessment using machine learning, and implementation of evidence-based practices in real-world settings.

• Computational Neuroscience
• Resilience Research
• Systems Biology
• +۳ مورد دیگر

Janina Hesse serves as Junior Professor at the University Medical Center of Johannes Gutenberg University Mainz and leads the Computational Resilience Research group at the Leibniz Institute for Resilience Research (LIR) in Mainz. Her interdisciplinary work bridges mathematical modeling, machine learning, and clinical medicine to investigate resilience mechanisms across biological scales. Her educational background includes: PhD in Computational Neurosciences/Theoretical Biology (2011-2017), Humboldt-University Berlin Diploma in Physics & Cognitive Sciences, École Normale Supérieure Paris (2007-2011) Master of Science in Theoretical Physics of Complex Systems, University Pierre-et-Marie-Curie Paris (2007-2011) Bachelor of Science in Physics, École Normale Supérieure Paris (2007-2011) Vordiplom in Mathematics, Goethe University Frankfurt (2005-2007) Her research centers on computational resilience dynamics , employing dynamical systems theory to model phase transitions in neuronal networks, circadian impacts on cancer therapy, and stress-response mechanisms. The group develops integrative frameworks combining machine learning with experimental data to identify resilience biomarkers and optimize treatment timing, particularly in oncology and neuroscience contexts. Publication trends (2014-2023) reveal a consistent focus on nonlinear dynamics in biological systems , with increasing clinical translation. Early work established theoretical foundations in neural criticality and bifurcation theory, while recent studies apply these principles to colorectal cancer chronotherapy and temperature-dependent neural encoding, demonstrating strong interdisciplinary collaboration with medical institutions. Key recognitions include: Portuguese Pneumology Society First Prize (2020) Berlin Neuroscience Forum Poster Prize (2012) Michael-Loulakis Study Achievement Award (2007) She mentors four doctoral students and a Master's candidate in her LIR research group, with funding primarily from the Boehringer Ingelheim Foundation. Her advisory scope extends to co-initiating ScientistsForFuture (2019) and serving as neuroscience specialist for Germany's Federal Agency for Civic Education. Current collaborations span MSH Hamburg, Charité Berlin, and Oldenburg's Helmholtz Institute. The Computational Resilience Research group operates through six core projects examining neuronal phase transitions, mouse behavioral resilience, cancer gene expression dynamics, and seasonal stress variations. This work integrates theoretical modeling with experimental validation through close partnerships with clinical and basic science teams at Johannes Gutenberg University Mainz, emphasizing translational applications for stress-related disorders.

• Systems Immunology
• Genomics
• Artificial Intelligence
• +۴ مورد دیگر

Prof. Dr. Joachim L. Schultze is Director of Systems Medicine at the German Center for Neurodegenerative Diseases (DZNE) and leads the Genomics & Immunoregulation group at the University of Bonn's LIMES Institute. His research integrates immunology, neurobiology, genomics, and artificial intelligence, with a focus on single-cell technologies and systems medicine approaches. Key research areas include: Swarm Learning : Development of decentralized AI frameworks for medical research NEUROCOV : Investigation of neurological complications in COVID-19 PRECISE Platform : Single-cell genomics and epigenomics technology development Systems immunology approaches for chronic inflammatory diseases Myeloid cell biology and trained immunity mechanisms His recent publications (2019-2022) demonstrate strong focus on: COVID-19 immunopathology and myeloid cell dysregulation Neuroimmunology and neurodegenerative disorders AI/blockchain applications in healthcare Single-cell transcriptomics in inflammation Metabolic regulation of immune cells Prof. Schultze coordinates multiple international consortia including the DFG-funded NGS Competence Network and Immunosensation2 Cluster of Excellence. His laboratory combines experimental genomics with computational biology at the PRECISE joint platform between DZNE and University of Bonn.

Hamed Alaviمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Smart Environments
• Urban Computing
• +۵ مورد دیگر

Hamed Alavi is a tenured Assistant Professor at the University of Amsterdam and founding member of the Digital Interaction Lab. His work bridges computer science, architecture, and urban design to develop inclusive intelligent spaces that reflect human values across architectural and urban dimensions. His educational background includes a PhD in Computer Science from the Swiss Federal Institute of Technology (EPFL) and post-doctoral research at University College London (UCL). Prior to academia, he led an educational technology start-up for eight years with global impact. Alavi's research focuses on Human-Building Interaction (HBI), exploring how intelligent environments can co-create inclusive spaces through biophilic design, physiological sensing, and human-centered methodologies. His work emphasizes socio-technical integration in smart buildings and cities, addressing neurodiversity, environmental comfort, and ethical data practices. His recent publications reveal strong trends in multisensory interaction design, urban computing, and inclusive technology development. Key themes include physiological-environmental sensing integration, AI ethics in urban contexts, and human experience frameworks for smart buildings, with growing emphasis on neurodiverse populations and biophilic elements. Scientific recognition includes: Inclusive City-Making Grant (PI, €3.8M, NWO) Best Paper Award at CHI 2025 As Principal Investigator for the NWO-funded Inclusive City-Making project, Alavi leads large-scale interdisciplinary research translating academic insights into urban solutions. His entrepreneurial background informs practical technology deployment strategies and industry-academia collaboration models. The Digital Interaction Lab, which he co-founded, serves as a research hub for intelligent environment design, bringing together computer scientists, architects, and urban planners to prototype responsive built environments through living lab methodologies and wild deployment studies.

Shing-Chi Cheungمشاهده پروفایل

استاد

• Software Engineering
• Program Analysis
• Software Testing
• +۷ مورد دیگر

Shing-Chi Cheung is a Professor of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST), School of Engineering. He founded the CASTLE research group and co-founded the International Workshop on Automation of Software Testing (AST) in 2006. His leadership includes serving as General Chair of FSE 2014 and chairing multiple APSEC conferences. His research focuses on software quality enhancement through program analysis, testing, debugging, and AI techniques, targeting Android apps, open-source software, deep learning systems, smart contracts, and spreadsheets. Current projects include metamorphic testing frameworks, binary analysis tools, and vulnerability detection systems for emerging technologies. His publication portfolio demonstrates consistent contributions to software engineering since 2016, with recent work emphasizing AI-integrated testing methodologies, smart contract security, and deep learning system reliability. Key trends show increasing focus on cross-language analysis, data visualization quality, and compiler-level verification for modern software stacks. Distinguished Member of the ACM Fellow of the British Computer Society Editorial board member: Science of Computer Programming (SCP), Journal of Computer Science and Technology (JCST) Former editorial board member: IEEE Transactions on Software Engineering (2006-2009), Information and Software Technology (2012-2015) Four patents in China and the United States Cheung actively mentors through the CASTLE research group and serves on program committees for major conferences including ICSE, ESEC/FSE, and ISSTA. His work bridges academic research with practical applications through industry collaborations and tool development. He has contributed to numerous workshops and symposia as steering committee member and program chair.

Hui Liuمشاهده پروفایل

استاد

• Software Engineering
• AI-based Software Development
• LLM-based Program Generation
• +۵ مورد دیگر

Hui Liu is a Professor in the School of Computer Science and Technology at Beijing Institute of Technology, where he leads research in AI-based software development with a focus on LLM applications. His work spans software refactoring, quality improvement, and maintenance, funded by the National Natural Science Foundation of China and the National Key Research and Development Program of China. PhD from Peking University (2008) Former graduate student at Software Engineering Institute, Peking University Distinguished member of China Computer Federation Secretary-General of CCF Technical Committee on Software Engineering Professor Liu's research centers on LLM-based program generation, evaluation and testing of large language models, software refactoring techniques, and automatic construction of software engineering datasets. His work bridges artificial intelligence and software engineering, with particular emphasis on improving code quality through empirical studies and machine learning techniques. Current projects include code contamination detection, context-aware naming recommendations, and refactoring validation using LLMs. His research has evolved from traditional code smell detection to cutting-edge applications of large language models in software development. Liu's publication record shows a strong trend toward LLM applications in software engineering, with recent work focusing on code review generation, commit message generation, and refactoring validation using large language models. His research combines empirical methods with machine learning approaches, often analyzing large code corpora from open-source projects. The work spans both theoretical foundations and practical tool development, with several contributions merged into Eclipse as part of the open-source community. ACM Distinguished Paper Award (ESEC/FSE 2023) ACM Distinguished Paper Award (ICSE 2022) RE'2021 Best Research Paper Award IET Software Premium Award (2018) New Century Excellent Talents in University (2013) Beijing Higher Education Young Elite Teacher (2013) Professor Liu actively mentors PhD and Master's students, with recent graduates including Waseem Akram (awarded Outstanding Graduate) and several students publishing at top venues. His research is supported by major Chinese funding agencies, and he serves on program committees for leading software engineering conferences including ASE, ICSE, and FSE. He maintains strong industry connections through contributions to Eclipse and studies of open-source ecosystems like Rust. Liu leads a research group focused on AI for software engineering, with active projects on code generation, refactoring, and quality improvement. The group collaborates extensively with international researchers and contributes directly to open-source tools, particularly in the Eclipse ecosystem where multiple refactoring improvements have been merged.

Huaming Chenمشاهده پروفایل

مدرس ارشد

• Software Engineering
• Artificial Intelligence
• Software Security
• +۳ مورد دیگر

Dr. Huaming Chen is a Senior Lecturer in the School of Electrical and Computer Engineering at The University of Sydney, Australia. His work focuses on trustworthy machine learning systems, software engineering, and software security. With numerous publications in top-tier conferences and journals, Dr. Chen has established himself as a significant contributor to the fields of AI security and software engineering. Dr. Chen's primary research interests lie at the intersection of software engineering and artificial intelligence, with a strong emphasis on trustworthy AI systems. His work spans several key areas including: Software Security for AI-enabled systems Trustworthy and Responsible AI development Computational biology applications Industrial 4.0 implementations Federated learning and privacy-preserving techniques Large language model verification and uncertainty analysis His research addresses critical challenges in ensuring AI systems are secure, reliable, and ethically sound. Dr. Chen's recent publications demonstrate a strong trend toward addressing security and trustworthiness challenges in AI systems. His work spans multiple domains including software security (particularly for AI systems), trustworthy AI development, and applications in computational biology. A significant portion of his recent work focuses on large language models, examining their vulnerabilities, verification methods, and uncertainty analysis. He also maintains active research in federated learning, adversarial machine learning, and software security techniques. Dr. Chen has received several notable awards and recognitions: 2020 IEEE CIS Student Grant for IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE (WCCI) 2017 Student and Early Career Travel Fellowship for The 16th International Conference on Bioinformatics (InCoB 2017) 2017 Student Travel Award for 2017 IEEE World Congress on Services Dr. Chen actively supervises multiple research students working on cutting-edge projects related to trustworthy AI and software security. His current students are exploring topics ranging from blockchain-based governance frameworks to open-source AI security and digital twin platforms. He also serves in numerous committee roles at top conferences including area chair for ACM MM, and PC member for ACM CCS, IJCAI, KDD, and many others. His service as a Guest Editor for journals like Computers & Security and as a Grant Reviewer for UKRI demonstrates his standing in the research community. Dr. Chen organizes workshops focused on Trustworthy and Responsible AI, reflecting his commitment to advancing the field. His research group appears to focus on practical applications of AI security techniques, with projects spanning multiple domains including healthcare, finance, and industrial systems. He maintains active collaborations with researchers across multiple institutions, as evidenced by his co-authorship on diverse publications.

Christoph Treudeمشاهده پروفایل

دانشیار

• Software Engineering
• Empirical Studies
• Natural Language Processing in Software Development
• +۵ مورد دیگر

Christoph Treude is an Associate Professor of Computer Science at Singapore Management University. His academic journey includes senior lecturer positions at the University of Melbourne and the University of Adelaide, and postdoctoral research at McGill University, the University of São Paulo, and the Federal University of Rio Grande do Norte. Treude's research focuses on improving software quality and developer efficiency through better access to relevant information. His methodology combines empirical studies with tool development that considers natural language artifacts in software repositories. His work spans several key areas of software engineering: Empirical studies of developer behavior and practices Integration of large language models in software development Human-AI collaboration frameworks Software documentation quality and maintenance Reproducibility in scientific software Security practices in code reviews His recent publications demonstrate a strong focus on the intersection of artificial intelligence and software engineering, examining how AI technologies can enhance developer productivity while maintaining code quality and security. His research often involves large-scale empirical studies across various software ecosystems and developer communities. Treude has received significant recognition for his contributions: ARC Discovery Early Career Research Award (2018-2020) Four best paper awards, including two ACM SIGSOFT Distinguished Paper Awards He serves on the Editorial Boards of IEEE Transactions on Software Engineering and Springer's Empirical Software Engineering journal, and holds the role of Open Science Editor for Elsevier's Journal of Systems and Software. Treude has chaired major conferences including ICSME 2020, ICPC 2023, and TechDebt 2023, and regularly participates in software engineering conference program committees. His research has been funded by industry leaders including Google, Facebook, and DST, and he has authored over 150 scientific articles with more than 250 co-authors.

Luciano Baresiمشاهده پروفایل

استاد

• Software Engineering
• Self-Adaptive Systems
• Edge Computing
• +۵ مورد دیگر

Luciano Baresi is a Full Professor at the Polytechnic University of Milan (Politecnico di Milano), Italy, affiliated with the Department of Electronics, Information and Bioengineering. He earned his laurea (MSc) and PhD in Computer Science from the same institution and has held visiting positions at the University of Oregon (USA), Tongji University (China), and the University of Paderborn (Germany). His research spans software engineering, with current focuses on self-adaptive systems, edge computing, and AI/ML-based software. His work integrates formal methods with practical applications, emphasizing autonomous systems, cloud-edge continuum, and federated learning. Recent publications highlight AI-driven advancements in software testing, resource optimization, and educational tools. Key research themes include: AI/ML for autonomous driving testing and data augmentation Serverless computing at the edge Federated learning system architectures Containerization and cloud resource management Awarded for impactful contributions: RE 2020 Most Influential Paper ICSOC 2020 Best Paper SEAMS 2022 Best Paper He advises 14+ PhD students and leads projects like Ketonet (health app), WHO's Essential Items Estimator, and dynaSpark. As Editor-in-Chief of Proceedings of the ACM on Software Engineering and senior editor for multiple journals, he shapes academic discourse in adaptive systems and software engineering.

Tom Zimmermannمشاهده پروفایل

استاد

• Financial Stability
• Empirical Macroeconomics
• Finance
• +۳ مورد دیگر

Tom Zimmermann serves as Professor in Economics at the University of Cologne and has been a Cluster member at the Center for Financial Research Cologne since 2019. Previously, he worked at the Federal Reserve Board in Washington, bringing central banking experience to his academic role. His educational background includes: PhD and MA in Economics from Harvard University Studies in Economics at the University of Bonn Zimmermann's research centers on Financial Stability through empirical analysis of macroeconomics, finance, and data science applications. His work examines historical economic phenomena like hyperinflation, develops open-source asset pricing models, and investigates fiscal policy impacts using U.S. procurement data. He integrates machine learning techniques with traditional economic theory to address complex financial system questions, particularly focusing on debt-inflation dynamics and market regulation. Recent publications reveal consistent methodological rigor across macroeconomics and finance, with increasing emphasis on data-driven approaches since 2020. His 2025 American Economic Review paper extends historical analysis to contemporary debt management challenges, while earlier work establishes frameworks for cross-sectional asset pricing and government stimulus effects. No scientific awards were documented in the source material. As an educator, Zimmermann emphasizes the value of student interactions in generating "good ideas" and finds teaching "very enriching." His doctoral advice stresses timely thesis completion and maintaining perspective during academic challenges, reflecting hands-on mentoring experience though specific students aren't listed. Grant details remain unspecified in available texts. His primary research affiliation is with the Center for Financial Research Cologne, where he contributes to financial stability initiatives as part of ongoing cluster activities.

• Cybersecurity
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Prof. Dr. Dieter Landes serves as a Professor in the Faculty of Electrical Engineering and Computer Science at Coburg University of Applied Sciences, Germany. His research bridges theoretical AI advancements with practical cybersecurity and energy solutions, particularly targeting small and medium enterprises (SMEs) through federally funded initiatives. Landes' core research interests include: Cybersecurity applications of Large Language Models for Blue/Red Teaming operations AI-driven predictive maintenance in photovoltaic energy systems Realistic data generation for security system training and testing Intelligent educational technologies for personalized learning navigation High Performance Computing infrastructure for applied AI workloads His current grant portfolio demonstrates sustained research leadership: Blue-and Red-Agent-based Cyber Security (2025-2028): Federal Office for Information Security project developing LLM-powered defenses against SME-targeted cyberattacks Kick-PV (2023-2026): AI characterization methods for remote photovoltaic system diagnostics GENESIS (2022-2025): Framework for generating realistic data flows to train insider threat detection systems VoLL-AI (2021-2025): Multi-university collaboration (Erlangen/Bamberg) on AI-driven learning navigation HPC4AAI (2021-2024): Infrastructure development for applied artificial intelligence computing Landes operates within interdisciplinary research collectives at Coburg University, frequently partnering with Friedrich-Alexander University of Erlangen-Nuremberg and Otto-Friedrich University of Bamberg. His project pipeline extending to 2028 confirms active research trajectory in applied computer science.