Find Academic Supervisors

• Data Science
• Internet of Things (IoT)
• Visual Analytics
• +10 more

Professor Dirk J. Lehmann is a Professor of Data Science in IoT at Ostfalia University of Applied Sciences, Faculty of Computer Science, where he has been employed since May 2022. He holds significant leadership roles including Deputy Head of the Institute for Information Engineering (since 2024), Research Officer of the Faculty of Computer Science (since 2023), and membership in multiple committees including the Admissions Committee for Digital Technologies and the Digital Technologies Examination Board. Professor Lehmann's extensive academic journey includes: Part-time professorship in Data Science in IoT at Ostfalia University (2020-2022) Senior Specialist for Digitalization, AI, and Visual Analysis at IAV GmbH (2018-2023) Assistant Professor of Visual Data Analysis at Nazarbayev University, Kazakhstan (2017) Visiting professorships at TU Graz, Austria and Universidad Rey Juan Carlos, Spain (2016-2017) Researcher at Otto-von-Guericke University Magdeburg (2009-2017) His research expertise centers on Visual Analytics and Data Science, with particular emphasis on high-dimensional data visualization, categorical data analysis, and IoT applications. Professor Lehmann leads the Data Science in IoT working group, conducting research across three main areas: visual data analysis, distributed data analysis using AI methods, and applied data analysis in geology, climate data, medicine, and industrial processes. His methodological contributions include innovative visualization techniques for complex datasets across multiple domains. Analysis of Professor Lehmann's 15 most recent publications (2017-2025) reveals a consistent focus on advancing visualization techniques for complex data analysis. His work spans categorical data visualization (CatNetVis), biological data analysis (D. Melanogaster research), optimization of star coordinate systems, and interactive exploration methods for large datasets. These publications appear in top venues including IEEE Transactions on Visualization and Computer Graphics and EuroVis, demonstrating both theoretical rigor and practical application across diverse domains from healthcare to environmental science. As an educator, Professor Lehmann teaches a comprehensive range of courses from foundational mathematics to advanced machine learning and visualization techniques. He actively supervises student projects and theses, emphasizing clear project definitions with measurable acceptance criteria. His international collaborations span institutions in Israel, Saudi Arabia, China, Austria, and Spain, reflecting a global research perspective that bridges academic theory with industry applications, particularly through his previous role at IAV GmbH, a Volkswagen subsidiary.

• E-learning
• Multimedia Technologies
• Computerized Testing
• +3 more

Yoana Minkova is an Assistant Professor in the Department of Informatics at New Bulgarian University, where she has taught since 1999. Her academic responsibilities include directing the 'Computer Media Technologies' program and previously leading the Multimedia and Computer Visualization Studio (2000-2006) and Center for Electronic Materials (2006-2007). Her research specializes in: E-learning systems and multimedia educational tools Automated knowledge assessment methodologies Computerized testing frameworks and optimization Educational software development and authoring tools She has participated in international collaborations including SOCRATES ERASMUS teaching mobility (2001-2012), STELLA (2008-2009), and STENCIL (2011-2013) projects, with annual lectures at Germany's University of Applied Science in Wurzburg (2001-2014). She has supervised bachelor's students on multimedia application development projects and developed educational products including interactive atlases, multimedia catalogs, and automated testing systems.

Adam PererView profile

Assistant Professor

• Data Visualization
• Human-Computer Interaction
• Machine Learning
• +3 more

Adam Perer is an Assistant Professor at Carnegie Mellon University within the Human-Computer Interaction Institute . His research focuses on data visualization , machine learning , and human-AI collaboration systems, particularly in healthcare contexts. Co-directs the Data Interaction Group Area Papers Chair at IEEE VIS PhD in Computer Science from University of Maryland Former Research Scientist at IBM Research His work integrates visual analytics with machine learning to create interactive systems for clinical data analysis , AI interpretability , and exploratory data science . Recent publications demonstrate applications in epidemiology , ICU medicine , and child welfare decision-making . Key research themes: Designing collaborative AI interfaces Visualizing embedding spaces for ML Interactive clinical decision support tools Explainable AI systems evaluation Temporal data exploration techniques Awards include Best Paper Honorable Mentions at CHI and EuroVis, plus Most Reproducible Paper recognition. He advises multiple PhD candidates and teaches courses in Data Visualization and Interactive Data Science .

• Artificial Neural Networks
• Traction Systems
• Building Automation
• +4 more

Dr. Eng. Marek Daniel Dudzik serves as a Lecturer in the Department of Infotronics and Cybersecurity at the Faculty of Electrical and Computer Engineering, Cracow University of Technology. His academic profile demonstrates a strong research focus on artificial intelligence applications across diverse engineering domains, with particular expertise in neural network modeling for complex physical systems. Dr. Dudzik's research interests span multiple engineering disciplines, with primary focus on applying neural networks to solve challenging problems in materials science, particularly regarding aluminum foams and cellular materials under compression. His work bridges theoretical neural network development with practical applications in building automation systems, environmental monitoring, and mining engineering. The research portfolio reveals a consistent pattern of interdisciplinary work connecting electrical engineering principles with computational intelligence techniques. The publication record shows a clear trend toward increasingly sophisticated neural network applications across multiple domains. Early work focused on traction systems and electrical engineering applications, while more recent publications demonstrate expansion into building automation, environmental monitoring, and materials science. The research methodology consistently employs neural networks to model complex physical phenomena that are difficult to characterize through traditional analytical approaches, with particular emphasis on practical implementation and verification of results. Dr. Dudzik's work demonstrates strong connections between theoretical neural network development and practical engineering applications across diverse fields. His collaborations span multiple departments and institutions, reflecting the interdisciplinary nature of his research. The publication record indicates active participation in both theoretical development and practical implementation of neural network solutions for complex engineering problems.

Fang ZhaoView profile

Associate Professor

• Multimedia Learning
• Cognitive Psychology
• Educational Psychology
• +4 more

Fang Zhao serves as Junior Research Group Leader of the Multimedia group at FernUniversität Hagen's Center of Advanced Technology Assisted Learning and Predictive Analytics (CATALPA) since April 2021. As a cognitive and educational psychologist, she conducts interdisciplinary research on multimedia learning and multitasking within this university-funded research center, collaborating with partners across psychological and educational domains. Her academic credentials include a 2024 Habilitation from FernUniversität Hagen, a Ph.D. in Psychology from University of Koblenz Landau (2012-2015), an M.A. in Linguistics and Translation Studies from Durham University, U.K. (2010-2012), and a B.A. in Linguistics and English Language from Henan University, China (2006-2010). Dr. Zhao's research examines how individuals process multimedia information through text-picture integration, interactive simulations, and video-based instruction, with particular focus on exploration-exploitation dynamics in learning environments. Her sequence learning investigations extend to multitasking scenarios involving basic sequences, timing patterns, and complex hierarchical structures like Origami folding. This work bridges cognitive theory with practical educational technology applications through rigorous experimental methodologies. Recent publication analysis reveals consistent emphasis on optimizing visual representations for data comparison, measuring cognitive load in online learning, and evaluating guidance structures in asynchronous courses. Her research demonstrates that while interactive elements are generally preferred by learners, their educational benefits remain context-dependent with significant individual differences in effectiveness. Leading the Multimedia junior research group at CATALPA, Dr. Zhao directs investigations into how interactive and multimedia components influence learning processes across diverse student populations. The team operates within an interdisciplinary framework that integrates psychological theory with technological innovation to develop predictive analytics for learning enhancement.

• Causal Machine Learning
• Mechanistic Machine Learning
• Dynamical Systems
• +6 more

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.

• Bayesian Networks
• Probabilistic Graphical Models
• Machine Learning
• +5 more

Antonio Salmerón Cerdán is a Professor in the Mathematics Department at the University of Almería, where he has established himself as a leading researcher in probabilistic artificial intelligence and Bayesian networks. With over 25 years of academic experience, he leads the 'Análisis de datos' research group and serves as Principal Investigator for multiple nationally and internationally funded projects, including the current 'Hacia una Inteligencia Artificial Probabilística Confiable (TOPAI-UAL)' project (2023-2026). His research expertise spans theoretical and applied aspects of probabilistic graphical models, with particular focus on Bayesian networks, causal inference, and their applications across diverse domains. His work demonstrates a consistent trajectory from foundational theoretical contributions to practical implementations in software engineering, genomics, sports analytics, and trustworthy autonomous systems. Professor Salmerón's publication portfolio reveals a strong emphasis on methodological innovations in probabilistic reasoning, with recent work exploring divide-and-conquer approaches for causal computation, noise-robust classification methods, and the integration of observational and randomized data sources. His research shows increasing interdisciplinary reach, connecting computer science methodologies with applications in plant genomics, software maintenance, and healthcare. Journal Publications: 105 articles in high-impact venues including Ecological Informatics (Q1), International Journal of Approximate Reasoning (Q2), and ACM Transactions Research Funding: Principal Investigator for 9 major projects since 2001 totaling over €800,000 in funding Thesis Supervision: Director of 7 doctoral theses on probabilistic graphical models and their applications Metrics: h-index 22 (Web of Science), i10 index 59 His research program demonstrates a unique combination of theoretical rigor in probabilistic reasoning with practical applications across diverse scientific domains, positioning him at the forefront of reliable probabilistic AI development.

Bret JacksonView profile

Associate Professor

• Human-Computer Interaction
• Data Visualization
• Computer Graphics
• +3 more

Bret Jackson is an Associate Professor of Computer Science in the Mathematics, Statistics, and Computer Science department at Macalester College. He holds a BA from Carleton College (2008), and both a Masters and PhD from the University of Minnesota (2013 and 2014) in Computer Science. His educational background: BA: Carleton College, Computer Science, 2008 Masters: University of Minnesota, Computer Science, 2013 PhD: University of Minnesota, Computer Science, 2014 Professor Jackson's research focuses on 3D human-computer interaction, data visualization, and computer graphics. He investigates how to interact with spatial data more effectively using new computer interfaces such as virtual reality displays, multi-touch input, and 3D body tracking, with emphasis on improving spatial data analysis through innovative hardware interfaces. During his doctoral studies, he was a member of the Interactive Visualization Lab at the University of Minnesota under Daniel Keefe. Prior to academia, he worked as a software developer for Ultralingua, inc., gaining industry experience before transitioning to teaching and research. No scientific awards are documented in the provided text. Information regarding advised students, grants, or funding sources is not mentioned. His professional activities include maintaining a research website (http://bret-jackson.com/) and participating in departmental operations at Macalester's Olin-Rice Science Center (Room 122), where he teaches and conducts research.

• Social Insects
• Honey Bee Health
• Varroa Mite Resistance
• +5 more

Professor Stephen Martin holds a Chair in Social Entomology in the School of Science, Engineering & Environment at the University of Salford, Manchester. With a career spanning over three decades studying social insects, he is a leading expert in honey bee health, Varroa mite resistance, and viral pathogens affecting pollinators. His research bridges fundamental entomology with practical apiculture, seeking sustainable solutions to honey bee health challenges. His educational background includes: DSc from Bangor University (2006) PhD from Bangor University (1988-1989) Bachelor's degree in Zoology from Bangor University (1979-1983) MRes (1985-1987) Professor Martin's research focuses on the complex relationship between honey bees and their parasites and pathogens, particularly the Varroa mite and Deformed Wing Virus. He has developed innovative tools like the 'Varroa calculator' to help beekeepers manage mite infestations and created a graphic computer model of hornet colony growth used in teaching. His expertise spans hornet ecology, pest and diseases of honeybees, and chemical ecology of ants. Analysis of Professor Martin's recent publications (2020-2024) reveals a strong focus on understanding natural resistance mechanisms to Varroa destructor across different geographical regions. His work examines viral dynamics, particularly Deformed Wing Virus variants, and how they interact with host resistance behaviors like recapping. This research spans field studies in the UK, Cuba, South Africa, Brazil, and Hawaii, demonstrating an international collaborative approach to honey bee health challenges. Professor Martin has received significant recognition for his contributions: Honorary Fellow of the British Beekeepers Association (2024) Vice-chancellors Research Excellence award (2019) Japanese Society for the Promotion of Science 'Furosato award' (2008) With 13 active or recent research projects, Professor Martin maintains a robust research program directly engaging with beekeeping communities. His current major projects focus on helping UK beekeepers transition to Varroa treatment-free beekeeping (2022-2027) and understanding the evolution of natural Varroa tolerance mechanisms. His research is funded in part by beekeepers themselves, demonstrating practical relevance to the apiculture industry. Professor Martin leads the Varroa Resistant research group, which maintains an online presence at www.varroaresistant.uk. His team investigates natural resistance mechanisms in honey bees, viral pathogen dynamics, and sustainable approaches to honey bee health management, collaborating internationally while working closely with beekeeping associations to ensure research outcomes are practically applicable.

• Population Genomics
• Evolutionary Bioinformatics
• Microbial Evolution
• +3 more

Weigang Qiu is a Professor in the Department of Biological Sciences at Hunter College, part of the City University of New York (CUNY). His research focuses on the population genomics of microbial pathogens, particularly the Lyme disease bacterium Borrelia burgdorferi . Dr. Qiu received his educational training from: BS in Biochemistry from Fudan University, China (1986) PhD in Ecology and Evolution from State University of New York at Stony Brook (1999) Postdoctoral training in Bioinformatics at University of Maryland Biotechnology Institute (2002) Dr. Qiu's research interests center on evolutionary bioinformatics and pathogen variability. His lab specializes in comparative analysis of multiple genomes of the Lyme disease pathogen, reconstructing the history of worldwide diversification of Lyme disease bacteria, understanding mechanisms of genome evolution (particularly roles of recombination and natural selection), and inference of gene and genome functions. His approach is primarily bioinformatics-based, focusing on computational and statistical testing of evolutionary hypotheses. Students in his lab learn modern computational tools including relational databases/SQL, Perl/BioPerl, and statistical computing with R. Analysis of Dr. Qiu's publication record shows a consistent focus on Lyme disease pathogens and bacterial evolution, with recent work expanding into cancer biology and thyroid cancer research. His research demonstrates interdisciplinary connections between evolutionary biology, microbiology, and medical applications, highlighting how evolutionary principles can inform our understanding of disease mechanisms. Dr. Qiu teaches several courses including Molecular Evolution (BIOL 375), Computational Molecular Biology (BIOL 425), Bioinformatics Summer Workshop (BIOL 470), and Microbiology (BIOL 230). He is also involved in the QuBi Project for curricular improvement in quantitative biology. Dr. Qiu leads the Qiu Lab, which focuses on bioinformatics tools development for bacterial genomics research. The lab has developed several specialized tools including: Borrelia Ortholog Retriever: For obtaining ortholog alignments from 23 Borrelia spp genomes SimBac: A package for simulating bacterial genome evolution DNATweezer: A Perl wrapper of BioPerl modules The Bio::Align::Graphics module

• Statistical methods for high-dimensional data
• Cancer genomics
• Biomarker identification
• +9 more

Dr. Yuan Huang is an Assistant Professor in the Department of Biostatistics at Yale School of Public Health. She holds multiple affiliations across Yale University, including the Yale Cancer Center, Yale Center for Analytical Sciences (YCAS), Cancer Prevention and Control program, and the Center for Brain & Mind Health. Her research focuses on developing statistical methods for high-dimensional data, addressing challenges in cancer genomics such as low reproducibility, nonlinearity, and heterogeneity. Dr. Huang specializes in biomarker identification, large-scale network structure estimation, and gene-environment analysis, with recent expansion into neurodegenerative diseases including Alzheimer's, Huntington's, and Parkinson's diseases. Dr. Huang's publication record demonstrates expertise in advanced statistical methodologies including Bayesian mixture models, precision matrix estimation, functional data analysis, and stagewise algorithms for nonconvex optimization. Her work consistently bridges theoretical statistical development with practical biomedical applications. As an active collaborator, Dr. Huang works on clinical trials, genetics, epidemiology, and other biomedical research projects, applying her methodological expertise to solve complex data analysis challenges in these fields.

Jing MaView profile

Associate Professor

• Network analysis
• High-dimensional data analysis
• Microbiome data analysis
• +3 more

Jing Ma is an Associate Professor in the Biostatistics Program within the Public Health Sciences Division at Fred Hutchinson Cancer Center. She also holds affiliate appointments as an Assistant Professor in the Department of Biostatistics at the University of Washington and the Department of Statistics at Texas A&M University. As a member of the Translational Data Science Integrated Research Center at Fred Hutch, Dr. Ma leads research at the intersection of statistics, machine learning, and biological applications. Dr. Ma received her PhD in Statistics from the University of Michigan in 2015 and her BS in Mathematics from Fudan University in 2010. Her academic journey has positioned her as an expert in developing novel statistical methodologies for complex biological data. Dr. Ma's research focuses on network analysis, high-dimensional data analysis, and microbiome data analysis. Her lab specializes in statistical and computational methods for genomic data, particularly microbiome data. The Ma Lab employs various statistical learning methods including dimensionality reduction, graphical models, and high-dimensional inference to address analytical challenges in interpreting omics data. Key research areas include: Statistical methods for microbiome data analysis Statistical data integration Network-based pathway enrichment analysis High-dimensional graphical models Her current projects include "Statistical Methods for Network-based Integrative Analysis of Microbiome Data" funded by an R01 award from the National Institute of General Medical Sciences, and a pilot project on "Systems biology analysis of the immunomodulatory influence of circulating gut microbe-derived metabolites after transplantation" funded by the Translational Data Science Integrated Research Center. Dr. Ma's publications reveal a strong focus on applying statistical methodology to solve problems in cancer biology, aging research, and microbiome studies. Her work frequently involves collaborations with researchers across multiple institutions and leverages large datasets like those from the Dog Aging Project. She has made significant contributions to methods for differential network analysis, microbial community structure analysis, and integrative analysis of multi-omics data. Among her notable scientific achievements are: R01 award from the National Institute of General Medical Sciences Translational Data Science Pilot Award Microbiome Research Pilot Award Jayne Koskinas Ted Giovanis Foundation Award Dr. Ma actively mentors students and postdocs, with several former trainees now holding positions at academic institutions and tech companies. She also serves as Chair of the ASA Section on Statistics in Genomics and Genetics, where she organizes the Distinguished Student Paper Award Competition. Her work bridges statistical methodology development with impactful biological applications, particularly in understanding the role of microbiomes in human health and disease.

Zhao PengView profile

Assistant Professor

• Data Visualization
• Online Privacy Literacy
• Computational Journalism
• +4 more

Dr. Zhao Peng is an assistant professor in the Department of Journalism at Emerson College, Boston, specializing in data visualization, online privacy literacy, computational journalism, and topic modeling. Her research addresses critical questions about interactive data presentation, DEI applications in visual communication, audience interactions with algorithmic media, and privacy literacy campaigns for families. She employs mixed-method approaches to bridge theoretical communication frameworks with practical journalistic applications. Education: B.S., Yunnan University of Finance and Economics M.S., Michigan State University Ph.D., Michigan State University Dr. Peng's research integrates technical communication methods with societal challenges, particularly through her work on the Design Justice Framework for DEI-compliant data visualizations and algorithmic journalism. Her sharenting studies examine parental behavior through privacy calculus and multiparty perspectives, revealing tensions between self-presentation and privacy self-efficacy. She actively investigates how news organizations leverage AI for content production while maintaining journalistic integrity, with implications for news gathering, distribution, and audience engagement in digital ecosystems. Her publication trajectory (2017-2024) demonstrates evolving expertise from foundational journalistic interviewing competencies to advanced computational methods. Recent work emphasizes practical DEI applications in data visualization during crises like the COVID-19 pandemic, while her sharenting research identifies cultural and psychological drivers of parental sharing behaviors. The corpus shows consistent focus on communication methodology, with increasing attention to ethical AI implementation and privacy frameworks in digital journalism. Dr. Peng teaches Algorithmic News, Data Journalism, Interactive News, Digital Journalism, and Multimedia Capstone courses, equipping students with technical skills in R (95%), Python (70%), SPSS (90%), and data storytelling. Her research projects include developing DEI data visualization audit protocols and analyzing algorithmic media consumption patterns, aiming to create industry-ready resources for journalists navigating technological transformation.

• Digital Accessibility
• Human-Computer Interaction
• User Experience (UX)
• +5 more

Rubén Alcaraz Martínez is an Associate Professor at the Department of Library Science, Documentation and Audiovisual Communication at the University of Barcelona's Faculty of Information and Audiovisual Media. He teaches in the bachelor's degree in Information Management and Digital Documentation, the master's degree in Digital Content Management, and the master's degree in Digital Humanities at UB, while also collaborating with UPF on master's programs in Search Engines, Digital Documentation, and UX, as well as with EINA (UAB) on Graphic Design and Art and Design Research programs. Doctor in Engineering and Information Technologies (UdL) Master in Digital Content Management (UB/UPF) Diploma in Library Science and Documentation (UB) Senior Technician in Web Application Development Alcaraz Martínez's research focuses on digital accessibility, human-computer interaction, usability and user experience (UX), digital repositories and libraries, and search engine optimization (SEO). His work demonstrates a strong commitment to inclusive design, particularly in making data visualizations accessible for people with low vision. He has pioneered methodologies for evaluating the accessibility of statistical charts and has explored the integration of search experience optimization (SXO) with information architecture and web accessibility. His recent publications (2023-2025) reveal a research trajectory centered on practical applications of digital humanities, with particular emphasis on accessibility challenges in data visualization, SEO ethics, and the intersection of AI with design practices. His work bridges theoretical frameworks with practical implementation, often involving user testing and real-world applications. The publications span multiple disciplines including information science, human-computer interaction, digital humanities, and media studies, demonstrating his interdisciplinary approach to digital information systems. Rubén Alcaraz Martínez is actively involved in the Adaptabit and Culture and Digital Content research groups at the University of Barcelona. He has previously served as a member and coordinator of the COBDC's Free Software Working Group for Information Professionals and has provided consulting services related to web accessibility for public administration and SEO for both public and private companies. His work extends to the development of teaching materials on accessible document composition, color contrast in web accessibility, and accessible data visualizations through the Open University of Catalonia (FUOC).