Find Academic Supervisors

Alyson van RaalteView profile

Researcher

• Mortality Disparities
• Lifespan Inequality
• Population Health
• +7 more

Alyson van Raalte is a Research Scientist at the Max Planck Institute for Demographic Research (MPIDR) in Rostock, Germany, where she is affiliated with the Laboratory of Population Health and the MaxHel Center. Her research focuses on mortality disparities, lifespan inequality, and socioeconomic determinants of population health. She earned her PhD in public health from Erasmus Medical Center, Rotterdam, and is an alumnus of the European Doctoral School of Demography. Her work integrates advanced demographic methods with public health applications to understand how inequalities in mortality evolve across populations and over time. Her research interests include lifespan inequality, healthy longevity, cohort analysis, demographic methods for measuring mortality variation, and the social determinants of health. She investigates how factors such as education, region, and childhood adversity contribute to disparities in life expectancy and lifespan variation. Her methodological contributions include perturbation analysis of lifespan variability and decomposition techniques for mortality inequalities. The trends in her recent publications reveal a strong focus on lifespan inequality, cohort mortality profiling, educational and regional disparities in mortality, and the impact of structural factors like smoking and reunification on population health. She frequently employs decomposition methods, multistate models, and innovative visualization techniques to analyze complex demographic data. European Demographer Award (mid-career) from Population Europe European Research Council (ERC) Starting Grant (LIFEINEQ) Max Planck Society STG Extension Award Alyson van Raalte has been supported by significant grants, including an ERC Starting Grant and Max Planck Society funding. She collaborates extensively with international researchers and contributes to large-scale projects such as COVerAGE-DB, a global database on age-structured COVID-19 cases and deaths. While no formal students are listed, she likely mentors junior researchers through her collaborative work and leadership in methodological innovation. She is actively involved in research groups focused on population health and contributes to software development, including R and Stata packages for Markov chain analysis. Her work bridges demographic theory, statistical methodology, and real-world public health challenges.

• History of Chinese Medicine
• Daoism and Religion
• Digital Humanities
• +5 more

Michael Stanley-Baker (徐源) is an Associate Professor in the History Department at Nanyang Technological University, specializing in the history of Chinese medicine and religion, particularly Daoism. He also serves as an associate of the UCL China Centre for Health and Humanities and as treasurer on the council of the International Association for the Study of Traditional Asian Medicines. Stanley-Baker received his PhD from University College London and holds a clinical degree in Chinese medicine from Ruseto College, Boulder CO. His academic journey includes work and study at Indiana University, Bloomington; Academia Sinica, Taipei; and the Needham Institute, Cambridge. He has conducted extensive fieldwork with healers in Taiwan and China, bridging scholarly and clinical perspectives. His research focuses on the intersection of religion, medicine, and science in Chinese history, with particular emphasis on Daoist healing practices and the circulation of medical knowledge. He employs innovative digital humanities methods to analyze historical medical texts, creating tools like the Polyglot Asian Medicine Knowledge Graph System and the DAOBUDMED6D corpus for studying Six Dynasties medical, Daoist, and Buddhist texts. His recent publications reveal a growing emphasis on transnational medical exchanges, the historical geography of drugs, and the political dimensions of medical knowledge. Stanley-Baker's work challenges conventional boundaries between 'medicine' and 'religion,' arguing for more nuanced understandings of how healing knowledge was organized and transmitted in pre-modern contexts. International Association for the Study of Traditional Asian Medicines Council Member Lead developer of Polyglot Asian Medicine digital project Co-editor of the Routledge Handbook of Chinese Medicine Stanley-Baker directs the 'Drugs across Asia' digital humanities project, which creates new tools for analyzing pharmacological knowledge in Asian historical texts. His work combines traditional philological approaches with cutting-edge digital methods, facilitating large-scale analysis of drug references across religious and medical canons. His research has important implications for understanding how medical knowledge circulated across cultural and political boundaries in pre-modern Asia.

Thomas BorschView profile

Professor

• Evolution of flowering plants
• Phylogenetics and phylogenomics
• Biogeography
• +3 more

Prof. Dr. Thomas Borsch is a renowned academic at the Freie Universität Berlin, serving as Professor of Systematic Botany and Geography of Plants and Director of the Botanical Garden Berlin. He holds a W3 professorship and leads the Borsch Group, focusing on plant evolution, phylogenetics, and biodiversity conservation. His career includes roles at the University of Oldenburg and the Nees Institute, with expertise in molecular systematics and integrative taxonomy. Education: PhD (2000) and Habilitation (2005) from the University of Bonn. His research spans plant phylogenomics, biogeography, and conservation strategies, with active projects in Colombia, the South Caucasus, and Vietnam. He co-leads initiatives like ColBioDiv and EvoBoGa, emphasizing biodiversity management and genetic resource utilization. Key achievements include over 110 peer-reviewed articles, contributions to global flora projects (e.g., World Flora Online), and leadership roles in scientific bodies like the Consortium of European Taxonomic Facilities. Awards include the Heisenberg Fellowship and Jugend forscht Prize. His lab facilities specialize in DNA analysis, genome sequencing, and microscopy. Research interests emphasize workflow development for museum collections, sustainable biodiversity use, and international collaboration. His team includes postdocs and PhD students working on projects ranging from Campanulaceae phylogeny to Andean forest dynamics.

• Databases
• Distributed Systems
• Big Data
• +1 more

Svein Erik Bratsberg is a Professor in databases and distributed systems at the Norwegian University of Science and Technology (NTNU). His work focuses on managing and processing high-speed big data, contributing to advancements in real-time data handling and system scalability. Bratsberg’s research interests lie at the intersection of database systems , distributed computing , and big data technologies . He explores methodologies to optimize data ingestion, storage, and analysis in environments characterized by rapid data flows and complex system architectures.

Francisco José Cuesta ValeroView profile

Researcher

• Climate Change
• Earth's Energy Imbalance
• Remote Sensing
• +3 more

Francisco José Cuesta Valero is a researcher at the Helmholtz Centre for Environmental Research - UFZ in the Department of Remote Sensing . His work focuses on climate change, Earth's energy imbalance, and subsurface thermal dynamics using remote sensing and climate models. Education: Ph.D. in Environmental Sciences, Memorial University of Newfoundland (2020) Master in Earth Sciences, St. Francis Xavier University (2016) BSc in Physics, University of Murcia (2013) Research Interests include: Earth's heat inventory quantification Satellite-based ground heat flux estimation Permafrost and climate feedbacks Borehole climatology methods Thermodynamic modeling of soil-bedrock systems Scientific Contributions reveal trends in climate modeling, heat storage metric development, and land-air temperature coupling analysis. His publications span 2016-2024 with 20+ peer-reviewed works. Awards: Alexander von Humboldt Research Fellowship (2021) He contributes to major projects like G-Flux (global ground heat flux from satellite data) and AI4GHEObs (machine learning for heat flux estimation). Collaborates with institutions including Memorial University, St. Francis Xavier University, and international climate research centers.

• Soil Science
• Agricultural Engineering
• Materials Science
• +3 more

Rui Machado is an academic affiliated with the University of Algarve, University of Évora, and Embrapa. His research spans diverse fields including soil science, materials science, biotechnology, and environmental science. He has published extensively on topics such as soil salinity management, FPGA-based hardware design, and hydrogel-based cancer therapies. His work on tomato root distribution and irrigation regimes has contributed to agricultural sustainability. Machado's recent studies focus on combating skin cancer using advanced biomaterials and analyzing agroforestry systems in Portugal. He has developed computational tools like StemChecker for stem cell research and databases for molecular interaction networks. His interdisciplinary approach bridges engineering, biology, and environmental science. Research Highlights: Leadership in hydrogel applications for medical treatments Pioneer in FPGA-based time-to-digital converter optimization Expertise in soil management and crop physiology Creator of UniHI database for human molecular interactions

Suhail YousafView profile

Lecturer

• Distributed Computing
• Machine Learning
• Environmental Monitoring
• +4 more

Suhail Yousaf is a Lecturer in Computer Science at the School of Computer Science & Engineering, Constructor University Bremen gGmbH. His academic career spans roles at the University of Engineering and Technology Peshawar, Pakistan, where he served as an Assistant Professor (2018-2023) and Lecturer (2013-2018, 2005-2007). He earned his PhD and MSc in Distributed and Parallel Computer Systems from Vrije Universiteit Amsterdam (2009-2014, 2007-2009), and an MSc in Computer Science from Quaid-i-Azam University, Islamabad (2002-2004). Education PhD in Distributed Computer Systems, Vrije Universiteit Amsterdam (2009-2014) MSc in Parallel and Distributed Computer Systems, Vrije Universiteit Amsterdam (2007-2009) MSc in Computer Science, Quaid-i-Azam University (2002-2004) His research focuses on distributed computing, machine learning, and environmental monitoring. Key areas include earthquake phase classification, landslide detection, and PM2.5 forecasting using neural networks and time-series analysis. He has also contributed to GPU acceleration in optimization algorithms and fake news detection via ensemble methods. Recent publications highlight his interdisciplinary work in environmental science and high-performance computing. For instance, the 2024 paper 'ConvEQ' applies convolutional neural networks to seismic signal processing, while 2020 studies explore crop monitoring, air quality prediction, and GPU-based simplex method improvements. Teaching Roles MDE-CS-02 Parallel and Distributed Computing (Spring 2025, Spring 2024) SDT-104 Scientific Programming with Python (Fall 2024) CH-250 Programming in Python and C++ (Fall 2023) ACS-201 Databases and Web Services (Fall 2024, Fall 2023) ACS-106 Distributed Development (Spring 2024, Fall 2023)

Eirini NtoutsiView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Data Mining
• +6 more

Eirini Ntoutsi is an Associate Professor at the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover and a member of the L3S Research Center. Her academic journey includes a post-doctoral position at LMU Munich with an Alexander von Humboldt Foundation fellowship, and she earned her PhD from the University of Piraeus, Athens under the supervision of Y. Theodoridis. She holds a diploma and M.Sc. in Computer Engineering & Informatics from the University of Patras, Greece. Her research lies at the intersection of Artificial Intelligence and Machine Learning, focusing on two main pillars: learning over complex data and data streams (covering adaptive learning, change detection, and model stability), and responsible Artificial Intelligence (covering fairness-aware learning, data quality, and proper evaluation of AI/ML methods). Her work addresses critical societal challenges related to bias in algorithmic decision-making systems. Dr. Ntoutsi's recent publications demonstrate a strong focus on drift-aware learning for imbalanced data streams and fairness in AI systems. Her research shows a clear progression from foundational work in pattern management during her PhD to cutting-edge applications addressing real-world challenges in social streams, sensor data, and recommendation systems. Alexander von Humboldt fellowship for postdocs Best-student paper award at ICBK 2018 1st place in the 2006 innovation competition in Greece Dr. Ntoutsi actively mentors PhD and master's students while leading major research initiatives including NoBIAS (EU-funded, as Network coordinator), BIAS (Volkswagen Stiftung-funded), and OSCAR (DFG-funded). Her interdisciplinary approach combines technical AI solutions with philosophical and legal considerations to develop more equitable algorithmic systems.

• Dataspaces
• Semantic Web Technologies
• Ontologies
• +6 more

Johannes Theissen-Lipp is a senior scientific researcher at the Fraunhofer FIT Office Aachen with significant academic affiliations at RWTH Aachen University. He operates within the Department of Computer Science (Informatik 5) under the Faculty of Mathematics, Computer Science and Natural Sciences, working from the Databases and Information Systems (DBIS) group led by Professor Stefan Decker. His research focuses on semantic foundations of dataspaces with practical applications in manufacturing and mobility sectors. He earned his Computer Science degree from RWTH Aachen University, where he later conducted research at the Institute for Information Management in Mechanical Engineering (IMA) for nearly two years before joining the "Knowledge Pipelines" research group in December 2019. He successfully defended his PhD thesis titled "Semantic Foundations of Dataspaces" on September 25, 2024, completing his doctoral studies at RWTH Aachen. Theissen-Lipp's research spans semantic web technologies, ontologies, and data interoperability with particular expertise in RDF validation (SHACL), semantic mappings (RML), and FAIR data implementation within dataspaces. His work bridges theoretical foundations with industrial applications, especially in manufacturing contexts where data integration across organizational boundaries presents significant challenges. He has pioneered approaches for enhancing dataspaces through large language models and developing scalable validation techniques for distributed data environments. His publication record demonstrates consistent contributions to major semantic web venues including ESWC, SEMANTiCS, and dedicated workshops on dataspaces. As both author and editor (notably for the Second International Workshop on Semantics in Dataspaces proceedings), he has helped shape research directions in this emerging field. His recent work increasingly explores the intersection of traditional semantic technologies with contemporary AI approaches. Theissen-Lipp actively supervises multiple thesis projects including "FAIRification of Data Models in Manufacturing," "Leveraging Solid Pods for Sovereign Data Sharing in the Cultural Sector," and "Identification Techniques for Data-driven Feedback Loops in Manufacturing." He has taught the "Dataspaces Proseminar" in the summer semester of 2025, demonstrating his commitment to academic education alongside his research activities. As part of the Cluster of Excellence Internet of Production, he contributes to advancing data interoperability in industrial contexts. His professional presence extends to GitHub (JohannesLipp), LinkedIn, and ResearchGate, with a personal website providing additional details about his research portfolio and professional activities.

• Data Stream Processing
• In-Network Computing
• Industrial Cyber-Physical Systems
• +7 more

Liam Tirpitz is a researcher and PhD candidate at the Department of Computer Science, Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University. He is affiliated with the Chair of Computer Science i5 (Information Systems and Databases) and the Data Stream Management and Analysis Group, where he has been working since February 2022. Education : Master's degree in Computer Science from RWTH Aachen University (2021). His research focuses on data stream processing, in-network computing, and FAIR data ecosystems, with applications in industrial and cyber-physical systems. He explores methods for efficient data aggregation, stability detection, and cross-organizational data sharing, often leveraging technologies like WebAssembly and knowledge graphs. His work integrates distributed systems principles with database technologies to enable scalable and secure data processing in industrial environments. The recent publications of Liam Tirpitz reflect a strong focus on industrial data management, with trends toward in-network computing, edge-based stream processing, and FAIR principles. His work spans both theoretical architectures (e.g., GALOIS) and applied implementations in domains like manufacturing and health data, demonstrating interdisciplinary collaboration and real-world impact. Liam Tirpitz is actively involved in teaching and academic supervision. He has taught courses such as Data Ecosystems Lab and Implementation of Databases, and is currently supervising multiple thesis projects on topics including WebAssembly-based query functions, anomaly detection, and hybrid stream processing. He participates in research projects like StreamFröst and the Cluster of Excellence Internet of Production, contributing to the development of digital infrastructure for sustainable industrial systems. He is based at the Chair of Computer Science i5, a leading research group in information systems and databases at RWTH Aachen, which supports his work in data-intensive applications and distributed computing environments.

Michael MumaView profile

Professor

• Robust Data Science
• Signal Processing
• Machine Learning
• +5 more

Michael Muma is a Professor in the Department of Electrical Engineering and Information Technology at Technische Universität Darmstadt. His research focuses on robust data science theory and methods applied to signal processing and machine learning in biomedicine and engineering. He leads the ERC Starting Grant ScReeningData project, developing methods for reproducible information discovery in biomedical databases, and is a Principal Investigator in the LOEWE center emergenCITY and BMBF cluster curATime. Prior roles include Independent Junior Research Group Leader (Athene Young Investigator) and Lecturer at TU Darmstadt from 2017 to 2022, and Research Associate (Post-Doc since 2014) from 2009 to 2017. His research interests span robust statistical methods, high-dimensional data analysis, emergency response systems, and biomedical signal processing. Notable projects include FDR-controlled portfolio optimization, ECG delineation algorithms, and radar-based vital sign estimation. Muma has contributed to distributed sensor networks, robust clustering, and sparse regression techniques. His work addresses challenges in multi-source detection, financial data analysis, and genomics through interdisciplinary approaches combining signal processing, machine learning, and robust statistics. Recent publications emphasize scalable solutions for high-dimensional problems, including applications in robotics, cardiology, and financial index tracking.

Mohammad SadoghiView profile

Professor

• Database Systems
• Distributed Systems
• Blockchain Technology
• +5 more

Mohammad Sadoghi is a Professor in the Department of Computer Science at University of California, Davis, where he leads the Exploratory Systems Lab. His research spans database systems, distributed computing, and blockchain technologies with over 54 publications from 2007-2025 and more than 900 citations. His primary research domains include: Distributed database transactions Byzantine fault tolerance Consensus protocols Event processing systems Blockchain applications Database indexing techniques Prof. Sadoghi's publication trajectory shows evolution from foundational work on boolean expression indexing and event processing to cutting-edge research on blockchain consensus mechanisms. His recent work (2023-2025) demonstrates significant contributions to understanding BFT protocols, with publications in top venues like VLDB, EuroSys, and IEEE TKDE. His research bridges theoretical analysis with practical implementations, particularly focusing on performance optimization and security in distributed environments. His notable recognition includes: ACM Senior Member (2020) Prof. Sadoghi has advised multiple doctoral students who have become active researchers in distributed systems, including Suyash Gupta and Thamir M. Qadah. His lab has secured research funding for projects spanning database engines, consensus protocols, and blockchain infrastructure. The Exploratory Systems Lab maintains strong industry and academic collaborations worldwide, with recent work focusing on edge-cloud consensus applications and high-performance data management systems.

• Computer Science
• Distributed Systems
• Blockchain Technology
• +3 more

Hans-Arno Jacobsen is a Professor at the Faculty of Computer Science (Technische Universität München, TU Munich) and affiliated with the Department of Electrical and Computer Engineering at the University of Toronto. His work spans Computer Science , Distributed Systems , and Artificial Intelligence . Research interests include Blockchain Technology , Consensus Algorithms , Graph Neural Networks , and Quantum Computing . Recent projects focus on decentralized consensus , energy-efficient databases , and federated learning in edge environments. His 15 most recent articles (2024–2025) explore topics such as dynamic resource orchestration , CRDT-based blockchains , and multimodal depression recognition . Collaborates with researchers like Ruben Mayer , Gengrui Zhang , and Shiqiang Wang on systems for federated computing , blockchain benchmarking , and distributed GNN training .

Liangmin WangView profile

Professor

• Cybersecurity
• Smart Contract Vulnerability Detection
• Internet of Things Security
• +3 more

Liangmin Wang is a Professor at the School of Cyber Science and Engineering, Southeast University, Nanjing, China. His research spans cybersecurity, smart contract vulnerability detection, internet of things security, data deduplication, and blockchain technology. He actively contributes to advancements in secure computation, privacy preservation, and network threat analysis. Current affiliation: Southeast University Research focus: Cybersecurity, blockchain, IoT security, federated learning, and malware detection His recent publications demonstrate expertise in applying machine learning and cryptographic methods to security challenges in blockchain systems, fog-assisted cloud storage, and vehicular networks. He collaborates extensively with researchers like Xiaoyu Zhang, Xia Feng, and Wenjin Wang.

Wolfgang MüllerView profile

Associate Professor

• Scientific Databases
• Visualization
• Systems Biology
• +5 more

Dr. Wolfgang Müller is a Privatdozent (Associate Professor) at the University of Bamberg's Faculty of Business Informatics and Applied Informatics. Since 2008, he has led the Scientific Databases and Visualization (SDBV) group at the Heidelberg Institute for Theoretical Studies (HITS), focusing on data management and visualization in systems biology.