Find Academic Supervisors

• Empirical Software Engineering
• Software Process Improvement
• Requirements Engineering
• +7 more

Andreas Jedlitschka is a researcher at Fraunhofer IESE in Kaiserslautern, Germany, affiliated with the University of Kaiserslautern. He has made significant contributions to empirical software engineering, software process improvement, and experience management. His research focuses on evidence-based decision support, technology transfer, and quality in agile and AI-driven software development. PhD, University of Kaiserslautern (2009) His research interests include empirical software engineering, requirements engineering, software quality, experience management, and AI in software development. He investigates how to improve software processes through data-driven methods, empirical validation, and knowledge reuse. His work often bridges industry and academia, emphasizing practical applicability. The recent articles highlight a growing focus on AI-enabled systems, sustainability in software engineering, and data-driven technical debt management. His work spans both theoretical frameworks and industrial case studies, particularly in agile environments and safety-critical systems. He has co-organized workshops and edited conference proceedings, indicating active participation in the research community. He mentors researchers through collaborative projects and has contributed to doctoral research frameworks. His work is supported by collaborations across Europe, particularly in EU-funded projects related to software quality and AI. He is involved in initiatives like the Q-Rapids project, which supports decision-makers in managing quality in rapid software development, and contributes to building AI innovation labs with industry partners.

Veronika CheplyginaView profile

Professor

• Machine Learning
• Medical Image Analysis
• Pattern Recognition
• +4 more

Veronika Cheplygina is a Professor at the IT University of Copenhagen and leads the Data-intensive Systems and Applications research group within the Pattern Recognition Revisited lab. Her work focuses on Machine Learning , Medical Image Analysis , and Pattern Recognition , with particular emphasis on Open Science practices and Bias in Medical AI . Primary Affiliation: Department of Data Science and Artificial Intelligence Research Metrics: 20 publications, 2 projects (2021-2025), 2 datasets Her research addresses critical challenges in medical imaging datasets, including annotation bias , data reuse , and fairness in AI through active collaboration with institutions across 5 countries. She has contributed to major conferences like NeurIPS and MICCAI. Scientific Funding & Recognition: Principal Investigator (PI) for Making Metadata Count (MMC) (Danmarks Frie Forskningsfond, 2022-2025) PI for Choosing a Transfer Source for Medical Image Classification (CATS) (Novo Nordisk Foundation, 2021-2025) She maintains an active role in academic governance as head of an assessment committee for faculty recruitment (2023) and has been featured in media discussions about medical AI practices.

Mojtaba NayyeriView profile

Researcher

• Knowledge Graph Embedding
• Machine Learning
• Artificial Intelligence
• +3 more

Mojtaba Nayyeri is a Researcher at the University of Stuttgart, working within the Analytic Computing group under the KI institute. His office is located at Universitätsstraße 32, Room 2.302, 70569 Stuttgart, Germany, with contactable phone number +49 711 685 88105. His research centers on Knowledge Graph Embeddings, where he pioneers mathematical frameworks including hypercomplex spaces, stochastic processes, and differential equations to model structured knowledge. This work bridges Artificial Intelligence, Machine Learning, and Natural Language Processing, with significant contributions to semantic web technologies and knowledge representation systems. Nayyeri's publication record (2020-2023) reveals a consistent trajectory toward geometric and topological innovations in knowledge graph representation. He has developed ultrahyperbolic embeddings for heterogeneous structures, neural Itô processes for stochastic trajectory modeling, and hypercomplex space integrations for temporal knowledge graphs, demonstrating how non-Euclidean geometries outperform traditional methods in capturing hierarchical and relational complexities. He actively contributes to the Analytic Computing research group at the University of Stuttgart, which specializes in advanced computational methodologies for knowledge-intensive systems and large-scale data analysis.

Naim Abdul Khalek GharzeddineView profile

Research Fellow

• Mass Spectrometry
• Bioinformatics
• Biotechnology
• +3 more

Naim Abdul Khalek Gharzeddine is a Postdoctoral Research Fellow at the Department of Chemistry and Life Sciences within the Faculty of Engineering and Science at Aalborg University , Denmark. His research focuses on applying deep learning to enhance peptide analysis and mass spectrometry predictability.

• Statistical Power Analysis
• Optimal Experimental Design
• Multilevel Data Analysis
• +4 more

Mirjam Moerbeek is an Associate Professor at the Department of Methodology and Statistics , Faculty of Social and Behavioural Sciences , Utrecht University , Netherlands. She holds a PhD in Applied Statistics (Maastricht University, 2000) and a MSc in Biometrics (Wageningen Agricultural University, 1996). Her work focuses on statistical power analysis and optimal experimental design for hierarchical and survival data , with over 100 peer-reviewed publications. Education MSc in Biometrics (cum laude), Wageningen Agricultural University (1996) PhD in Statistics, Maastricht University (2000) Research Focus Statistical power analysis Optimal design theory Multilevel modeling Survival analysis Cluster randomized trials Stepped-wedge trials Recent Publications Bayesian sample size determination for longitudinal studies Optimal group sizes in Bayes factor analysis Robustness in cluster randomized trials Cost-efficient designs for social and biomedical sciences Scientific Awards NWO Veni Grant (2003) NWO Vidi Grant (2008) NWO Open Competition Grants (2006, 2022) Ius Promovendi (2023) Organizational Roles President, European Association of Methodology (2023–2025) Vice President, European Association of Methodology (2021–2023) Co-Organizer, International Conference on Multilevel Analysis (2009–2022) Chair, VI European Congress of Methodology (2014) Editorial & Review Activities Editorial Board, PLOS ONE (2014–2017) Peer Reviewer, Tijdschrift voor Psychiatrie Scientific Committee, European Congress of Methodology Consultant for multilevel analysis software

• Computational Genetics
• Genetic Risk Modeling
• Bioinformatics
• +5 more

Albert Smith is a computational geneticist and biostatistician at the University of Michigan School of Public Health , specializing in genetics and genomics research. Education : PhD in Biology, Massachusetts Institute of Technology (1993) BA in Biochemistry and Cell Biology, University of California-San Diego (1985) Research Focus : With over 30 years of experience, Dr. Smith develops and applies analytical methods in genetics, including genetic risk modeling and bioinformatics. His work emphasizes large-scale data management and the creation of data-intensive web applications and databases. He has contributed to major initiatives like the International HapMap Project, the CHARGE Consortium, and the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program. Scientific Contributions : Recent publications highlight his role in analyzing expression/splicing quantitative trait loci, investigating persistent opioid use genetics, studying clonal hematopoiesis, and integrating multi-omics data across diverse populations. His expertise spans gene-environment interactions, rare variant analysis, and polygenic risk scoring. Methodologies : Dr. Smith utilizes computational tools including R , Perl , and SQL , and is recognized for leading interdisciplinary teams in tackling complex statistical challenges in genomics.

Johanna SchwarzView profile

Associate Professor

• Sleep Science
• Stress and Recovery
• Cognitive Neuroscience
• +5 more

Johanna Schwarz is an Associate Professor at Stockholm University's Department of Psychology, specializing in sleep science and stress-related recovery mechanisms. Her research focuses on sleep's role in emotional regulation, cognitive performance, and aging through experimental and population-based studies. Stockholm University, Department of Psychology Division for Sleep and Alertness Research Research interests center on sleep's interaction with stress load, emotional processing, and cognitive function across adult lifespan. Key methodologies include: Magnetoencephalography (MEG) for neurophysiological measurements Polysomnography in real-life contexts Experience sampling method (ESM) for longitudinal tracking fMRI and EMG for brain-behavior relationships Recent publications examine age-related differences in sleep deprivation impacts, subjective sleep quality interpretation, and emotional regulation neuroimaging. While specific awards aren't detailed in current data, her collaborative studies with Stress Research Institute demonstrate significant contributions to sleep neuroscience. Key research trends show cross-disciplinary integration of: Cognitive neuroscience with gerontology Clinical sleep medicine with epidemiology Neuroimaging with behavioral economics Population health with neurophysiological biomarkers Stress response modeling with affective regulation Subjective sleep experience with objective measurement

Torben Bach PedersenView profile

Professor

• Data Engineering
• Artificial Intelligence
• Machine Learning
• +5 more

Professor Torben Bach Pedersen at Aalborg University's Department of Computer Science within The Technical Faculty of IT and Design is a leading expert in Data Engineering, Artificial Intelligence, and Energy Systems. With over 394 publications and 19 completed projects, he directs research at the Daisy – Center for Data-intensive Systems and leads innovations in energy flexibility and smart grid technologies. Key research areas: Data Warehousing, AI/ML, Energy Systems, Smart Grids Major projects: domOS (Smart Building OS), FEVER (Virtual Power Plants), DiCyPS (Cyber-Physical Systems) His research spans data-intensive systems, AI applications in energy management, and smart infrastructure development. Recent work focuses on transformer-based network AI and energy flexibility metrics. Scientific recognition includes: Æresdoktor (Honorary Doctor) at TU Dresden (2021) Best Paper Award Runner-Up (2019) WWW 2017 Best Demo Award Best Poster Award World Smart Grid Forum (2013) Member of Danish Academy of Technical Sciences (2013) As principal investigator and supervisor in 17 PhD projects, he advances AI-driven solutions for 6G wireless systems, smart buildings, and energy market optimization.

D.H.J. EpemaView profile

Professor

• Distributed Systems
• Resource Management
• Scheduling Algorithms
• +3 more

D.H.J. Epema is a full Professor at the Faculty of Electrical Engineering, Mathematics, and Computer Science at Delft University of Technology (TU Delft), specializing in Data-Intensive Systems . His research focuses on resource management, scheduling algorithms, performance analysis, trust/reputation systems, and blockchain applications in distributed systems. Research Interests: Distributed Systems, Blockchain, IoT, and Trust Management Notable Awards: Best Paper Awards at CCGrid (2010, 2014), MASCOTS (2013), and LCN (2021) Recent Publications emphasize blockchain integration in IoT, energy trading platforms, and decentralized identity systems. His work bridges theoretical frameworks with practical implementations in large-scale systems. Editorial Roles: Editor for ACM (2013–present) and Lecture Notes in Computer Science (2009–present)

Daniel HummerView profile

Associate Professor

• Mineralogy
• Geochemistry
• Crystallography
• +4 more

Daniel Hummer serves as an Associate Professor in the Department of Geology at Southern Illinois University, teaching courses including Mineralogy, Ore Deposits, and Earth's Deep Interior. His research program investigates fundamental thermodynamics and kinetics of mineral formation across diverse geological conditions, with emphasis on mineral stability, crystal structure evolution, and Earth's chemical history. Education: B.S. in Chemistry, Iowa State University, 2004 B.S. in Geology, Iowa State University, 2004 Ph.D. in Geoscience, Penn State University, 2010 Dr. Hummer's research integrates experimental, computational, and data-driven approaches across four primary domains: (1) Predictive mineral discovery through the Carbon Mineral Challenge, which identified 31+ new carbon-bearing species; (2) High-pressure mineral compression modeling using ion-based compressibility frameworks; (3) Development of MinKin software for quantifying mineral-fluid reaction kinetics; and (4) Reconstruction of Earth's redox evolution via transition-metal mineral occurrence databases. His work bridges mineral physics, geochemistry, and data science to address planetary-scale questions. Analysis of his publication record reveals strong thematic continuity in mineral ecology and data-intensive methodologies, with significant contributions to carbon mineral systems, manganese redox chemistry, and computational mineralogy. Key trends include network analysis of mineral associations, fractal distributions in crystallography, and planetary habitability assessments through hydrothermal mineral studies. Scientific Awards: No major scientific awards listed in available information Dr. Hummer leads active research projects supported by collaborations with Penn State, Oak Ridge National Lab, Brookhaven National Lab, Carnegie Institution, and international partners. While specific grant details aren't provided, his work on mineral discovery and kinetics modeling demonstrates sustained external funding. His Carbon Mineral Challenge initiative exemplifies large-scale collaborative science involving global mineralogical networks. His research teams include geoscientists from Johns Hopkins, Rensselaer Polytechnic Institute, and University of Arizona, focusing on mineral occurrence databases and redox evolution studies. Current fieldwork targets thallium-arsenic deposits in North Macedonia for novel mineral discovery, while computational efforts expand MinKin's capabilities for complex mineral reaction systems.

Malka Gorfine OrgadView profile

Professor

• Survival Data
• Non-parametric Statistics
• Biostatistics
• +5 more

Professor Malka Gorfine Orgad is a distinguished faculty member in the Department of Statistics and Operations Research at the School of Mathematical Sciences, Tel Aviv University. She has held the position of Professor since 2016, following her promotion from Associate Professor (2014-2016). Additionally, she maintains a long-standing affiliation as an Affiliate Investigator at the Division of Public Health Sciences, Fred Hutchinson Cancer Research Center in Seattle since 2003. Prof. Gorfine Orgad earned her MA (1994) and PhD (1999) in Statistics from the Hebrew University of Jerusalem under the supervision of Professors Banjamin Yakir and David Zucker. Her academic journey includes previous appointments as Associate Professor at Harvard School of Public Health (2011-2012) and at the Technion - Israel Institute of Technology (2010-2014), where she served as Senior Lecturer prior to that. Her research focuses on survival data analysis, non-parametric statistics, biostatistics, and machine learning. Specifically, she investigates inference based on Survival Deep Learning, natural experiment methods for causal inference, and various challenges of causal inference with survival outcomes. She has developed several innovative methodologies for survival analysis, particularly in the areas of competing risks, semi-competing risks, and illness-death models. Analysis of her recent publications reveals a strong emphasis on practical applications of survival analysis methodology to real-world health problems, particularly related to cancer and infectious diseases like COVID-19. Her work frequently bridges theoretical statistical development with practical implementation through software packages, demonstrating her commitment to making advanced methods accessible to applied researchers. Associate Editor of JASA Applications and Case Studies (2022 - present) Associate Editor of EJS (2022 - present) Associate Editor of Scandinavian Journal of Statistics (2021 - 2024) Co-Editor of Biometrics (2017 - 2019) Associate Editor of Biometrics (2009 - 2016) Member of Editorial Board of Lifetime Data Analysis (2013 - 2016) Prof. Gorfine Orgad actively mentors students at all levels, currently supervising one postdoc, two PhD students, and four MSc students, with numerous former students who have completed their degrees under her guidance. She serves as co-chair of the STRATOS TG8 (Survival Analysis Topic Group), which provides guidance on survival analysis methods for observational studies. She has also developed multiple software packages including PyMSM, PyDTS, frailty-LTRC, HHG, and others that implement her methodological contributions for the broader research community.

Ekaterina GilmanView profile

Researcher

• Data Analytics
• Context Modeling
• Machine Learning
• +6 more

Ekaterina Gilman, D.Sc. (Tech), is a Researcher at the University of Oulu's Center for Ubiquitous Computing, Faculty of Information Technology and Electrical Engineering. She is supported by the Academy of Finland and has collaborated with Northern Finland Biobank Borealis and the Centre for Health and Technology. Her research spans Data Analytics Ubiquitous Computing Machine Learning Internet of Things Smart Environments with applications in intelligent urban environments, stream reasoning, and wireless communications. Recent publications highlight trends in Edge computing architectures Concept drift detection Social distancing monitoring Well-being measurement Smart city data challenges . She has participated in projects funded by the European Regional Development Fund, Business Finland, Academy of Finland, and EU Horizon 2020. She serves as a reviewer and organizer in scientific communities and has over 30 publications.

Gintautas GrigasView profile

Associate Professor

• Programming teaching methodology
• Software localization
• Computer terminology
• +3 more

Gintautas Grigas is an Associate Professor and Affiliated Scientist at the Institute of Mathematics and Informatics , Vilnius University, Lithuania, serving within the Educational Systems Group . Since joining the Institute in 1965, he has become one of Lithuania’s most prolific authors in computer science education and software localization. Education: 1959 – Graduated, Faculty of Electrical Engineering, Kaunas Polytechnic Institute (now Kaunas University of Technology) 1970 – Candidate of Technical Sciences (post-graduate), nostrified 1992 to Doctor of Mathematical Sciences Continuing education courses in programming languages, data types and programming technology at Vytautas Magnus University, Vilnius University and Vilnius Pedagogical University Research Interests Grigas’ scientific work centres on four tightly interwoven themes: abstract data types , programming teaching methodology , computer terminology , and software localization . His contributions range from theoretical analyses of data structures to practical guidelines for localizing major software suites into Lithuanian. He has also pioneered studies on how interface defects influence national language culture and on letter-frequency analyses supporting cryptography and linguistic research. Publication Trends Across more than 150 scholarly and professional works, a clear progression is evident: early focus on programming fundamentals and Lithuanian computing history, followed by intensive investigation of localization pedagogy, and more recently, mobile-device text input and cultural impacts of internationalization errors. Collaborative volumes such as the Encyclopedic Dictionary of Computing and the textbook Fundamentals of Software Localization have become standard references in Lithuanian computer science curricula. Awards & Recognition No specific competitive awards are listed; however, his sustained output—40 books, 100+ scientific articles, 250+ popular/professional articles—is itself a testament to national academic recognition. Doctoral Advising & Grants While individual doctoral students are not named, Grigas has long contributed to doctoral education through the Institute’s doctoral committees and study guides. His research has been supported by national projects on software localization and educational technology, although explicit grant numbers are not provided in the text. Labs & Teams He carries out his work within the Educational Systems Group , a subdivision of the Institute of Mathematics and Informatics, located at Akademijos St. 4, Vilnius. The group investigates learning technologies, curriculum design, and the societal impact of digital systems, providing a collaborative environment for his ongoing projects.

Jeremy M. MikeczView profile

Researcher

• Digital Humanities
• Spatial History
• Data Visualization
• +5 more

Jeremy M. Mikecz is a Researcher at Dartmouth College's Research Data Services , where he supports computational and data-intensive research in the arts, humanities, and social sciences. His work bridges digital humanities, spatial history, and Indigenous studies, with a focus on recovering marginalized histories through data visualization and cartographic techniques. Interdisciplinary expertise in history, geography, and digital methods Specializes in GIS, narrative cartography, and critical data analysis Current research centers on early colonial Latin America and place-name studies His recent publications in Hispanic American Historical Review (2020) and International Journal for Arts and Humanities Computing (2017) examine how digital tools can reconstruct Indigenous agency during the Spanish conquest. He teaches courses on digital history, spatial analysis, and Indigenous narratives at Dartmouth and previously at USC. Scientific recognition includes the Andrew W. Mellon Postdoctoral Fellowship in Digital Humanities.

Angela P. MurilloView profile

Associate Professor

• Data Curation
• Data Management
• Data Sharing and Reuse
• +3 more

Angela P. Murillo serves as an Associate Professor at Indiana University's Luddy School of Informatics, Computing, and Engineering in Indianapolis, where she directs the undergraduate Data Science program. She concurrently holds roles as Co-Principal Investigator and Student Fellowship Director for CI Compass, a National Science Foundation-funded initiative (Grant #2127548) supporting cyberinfrastructure advancement. Her academic foundation includes an undergraduate degree in geoscience and a Ph.D. in Information Science from the University of North Carolina at Chapel Hill. Her doctoral research examined data sharing and reuse within scientific cyberinfrastructures, establishing her trajectory in data-intensive scholarship. Murillo's research program critically examines data curation, preservation, and cyberinfrastructure through lenses of workforce development and educational innovation. She develops competency frameworks for data professionals while investigating diversity in LIS education and virtual reality applications for learning. Her work bridges theoretical data management principles with practical implementations across scientific domains, emphasizing FAIR data practices and cross-cultural educational frameworks. Analysis of her recent publications reveals concentrated exploration of data curation competencies (2023-2025), workforce dynamics in data fields, and cyberinfrastructure fellowship impacts. These works demonstrate evolving focus from foundational data sharing studies toward systemic educational frameworks and workforce development strategies, with increasing attention to equity and community-driven standards in data science. As Co-PI for the NSF-supported CI Compass project, Murillo secures critical infrastructure funding while directing student fellowships that cultivate next-generation cyberinfrastructure professionals. Her leadership in developing Indiana University's Data Science undergraduate program since 2017 demonstrates sustained commitment to building academic pathways, though specific student advisee details remain unpublicized. Murillo collaborates within the CI Compass project team and leverages industry experience from her tenure as Research and Development Information Scientist at Novozymes. Her current initiatives focus on expanding data science education through community-curated competency frameworks and addressing workforce gaps via innovative fellowship models, positioning her at the intersection of data infrastructure and educational transformation.