Find Academic Supervisors

Georg JäggleView profile

Professor

• Educational Robotics
• STEM Education
• Sustainability Education
• +3 more

Prof. Georg Jäggle is a Professor of Vocational Education at the University College of Teacher Education Vienna (PH Wien), specializing in educational robotics, STEM pedagogy, and sustainability education. He leads the Department of Vocational Education within the Institute for Secondary Level Vocational Education (I:SBB). His work integrates technology-enhanced learning environments, focusing on project-based methodologies to bridge gaps between education and industry needs. Key projects include the Recycling Heroes initiative (2022–2024), which employs citizen science to promote circular economy awareness, and the EU-funded ER4STEM project (2017–2018), fostering STEM engagement through robotics. Prof. Jäggle holds a Dipl.-Ing. (FH) and a Dr. in engineering education, with prior industry experience as an engineer and vocational school teacher. His research emphasizes digital competence development, intergenerational learning, and inclusive education strategies. He has coordinated interdisciplinary teams across 10+ EU and national projects, including RoboCoop (2018–2022) and Makers@school (2017–2019), which implemented robotics and maker culture in schools. His teaching spans applied mathematics, engineering, and automation technology at both academic and vocational levels. Research interests include technology integration in vocational training, lifelong learning frameworks, and the impact of educational robotics on student motivation. Recent publications (2022–2024) explore STEM career self-efficacy, sustainability curriculum innovation, and cross-generational robotics collaboration. He currently serves in the Center for Research Management (Zentrum Forschungsmanagement) at PH Wien, advancing applied research in MINT (STEM) education and educational technology.

Olaf KöllerView profile

Professor

• Educational Psychology
• Empirical Educational Research
• Educational Science
• +7 more

Prof. Dr. Olaf Köller is the Executive Scientific Director of the Leibniz Institute for Science and Mathematics Education (IPN) in Kiel and Professor of Empirical Educational Research at the Christian-Albrechts-University of Kiel. He leads the Department of Educational Science and Educational Psychology at IPN, where he has been a central figure since 2009. His work bridges institutional research leadership with academic scholarship in education and psychology. His research interests focus on individual development processes in school contexts , diagnosis of school skills , methodological challenges in large-scale assessments , and the evaluation of teaching and school development programs . He is deeply engaged in education monitoring and empirical research design. His current projects include the Leibniz Lab Pandemic Preparedness, KISS-Pro (AI in speech and writing for language learning), and LePAS (visitor research in museums), reflecting a strong commitment to applying educational research to real-world challenges. The recent publications highlight a trend toward understanding long-term educational impacts, student psychological functioning, and the societal role of schools during crises. His work spans cognitive, emotional, and systemic dimensions of learning, with increasing attention to AI and pandemic resilience in education. Scientific Awards: No awards explicitly mentioned in the text. Prof. Köller has supervised various research groups and projects, though specific advisees are not listed. He has been involved in major national and institutional grants through the IPN and Leibniz Association. His leadership roles indicate extensive experience in securing and managing research funding, particularly in large-scale educational assessment and policy-oriented research. He is a key member of research teams at the IPN, particularly within the Department of Educational Science and Educational Psychology. His work is integrated into broader research lines including subject-specific learning , teacher professional competence , science communication , and methodological research with machine learning . These teams work on interdisciplinary projects that combine psychology, education, and data science.

Jeanette HoffmannView profile

Professor

• Children's Literature Didactics
• Multilingual Education
• Graphic Storytelling
• +3 more

Prof. Jeanette Hoffmann holds a full professorship in the Didactics of German Literature at the Faculty of Educational Sciences , Free University of Bozen-Bolzano. Her academic journey includes studies in German, mathematics, and educational science at the University of Münster, followed by research roles at the Free University of Berlin and Technical University Dresden. She earned her doctorate with an award-winning study on intercultural literary dialogues and completed teaching qualifications in Montessori education. Previously a professor in Dresden, she now leads educational initiatives focusing on multilingualism, graphic storytelling, and intercultural learning. Education & Professional Path: Studied at University of Münster (German, Mathematics, Protestant Theology, Educational Science) Doctoral research at Free University Berlin (awarded for study on literary conversations) Second State Examination at Montessori elementary school Berlin Professor positions at Technical University Dresden and University of Teacher Education Upper Austria Research Interests: Focuses on children's literature didactics , graphic storytelling , multilingual education , and intercultural learning . Her work bridges empirical research with practical pedagogy, emphasizing dialogue-based learning and innovative classroom methodologies. Awards & Recognition: Double award for her doctoral thesis (2011) 2024 Prize for Excellent Doctoral Supervision by the Symposium on German Didactics Lab & Teaching Activities: Directs the KinderLiteraturWerkstatt (ChiLiLab), a research lab exploring literary aesthetics through children's books. Engages in projects like the IMAGO initiative promoting multilingual storytelling in South Tyrol. Teaches courses on literary learning, media socialization, and early literacy pedagogy.

Jürgen GieslView profile

Professor

• Program Verification
• Automated Deduction
• Termination Analysis
• +12 more

Jürgen Giesl is a Professor at the Teaching and Research Area Computer Science 2 within the Department of Computer Science at RWTH Aachen University , Germany. He leads research in programming languages, formal verification, automated deduction, and term rewriting systems. Research Interests: Automated Termination and Complexity Analysis of Programs Dependency Pairs and Term Rewriting Systems Verification of Probabilistic and Integer Programs Static Analysis and Symbolic Execution Model Checking and Constrained Horn Clauses Development of Automated Tools (AProVE, LoAT) His recent research, reflected in the latest publications, focuses on termination and complexity analysis for probabilistic programs, polynomial loops, and integer programs, using advanced techniques such as dependency pairs, loop acceleration, and semiring semantics. He also contributes to SMT solving and transitive relation learning for infinite-state model checking. Scientific Awards: Best Tool Paper Award at iFM 2017 Silver Medal (Second Best Paper) at SEFM '16 Best Paper Honourable Mention at IJCAR 2024 Best Student Paper Honourable Mention at IJCAR 2024 Advising and Grants: Giesl has supervised numerous PhD and Master’s students, including prominent researchers such as Fabian Frohn, Jens Hensel, Nils Lommen, and Marcel Hark. He leads a large research group focused on automated verification and has contributed extensively to international verification competitions. His work is supported by ongoing research grants and collaborations with leading institutions in formal methods. Labs and Teams: He leads the Programming Languages and Verification research group at RWTH Aachen, which develops and maintains the AProVE and LoAT tools. These tools are central to automated termination and complexity analysis and are regularly submitted to international competitions such as TERMCOMP and VBS.

G. CaltaisView profile

Assistant Professor

• Concurrency Theory
• Causal Models
• Knowledge Models
• +4 more

G. Caltais is an Assistant Professor in the Formal Methods and Tools (FMT) group at the University of Twente's Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS). Previously, they worked as an independent junior scientist at the University of Konstanz (Chair for Software and Systems Engineering) and as a post-doctoral researcher at ETH Zürich (Chair of Software Engineering). They hold a PhD from Reykjavík University and Radboud University. Research Focus: Formal modeling of computer systems, emphasizing automata theory, concurrency, causal/knowledge models, and software-defined networks (SDNs). Current projects include DyNetKAT (formal SDN analysis), Zorro (zero downtime knowledge models), and exploring cyclic structures in software correctness. Committees: Served on program committees for CALCO 2023, ESOP 2023, FORTE 2022-2023, FSEN 2023-2025, IEEE NFV-SDN 2020-2022, SPIN 2022-2024, and others. Organized events like CREST@ETAPS 2019/2023 and EXPRESS/SOS 2023. Student Projects: Offers B.Sc./thesis proposals on DyNetKAT visualization, causal analysis of SDN safety violations, and learning formal network models from real datasets. Contact for custom project ideas.

Vladimir FilkovView profile

Professor

• Open Source Software Sustainability
• Artificial Intelligence in Health
• Empirical Software Engineering
• +7 more

Vladimir Filkov is a Professor in the Department of Computer Science at the University of California, Davis, College of Engineering. He leads two research labs: the DECAL Lab and the AI for Health Lab. He is actively engaged in research, teaching, and service, with a focus on open-source software sustainability, AI in healthcare, and data science. He has held leadership roles such as General Chair of ASE 2024 and inaugural Director of Translational Data Science at UCD DataLab. Professor, Department of Computer Science, UC Davis Director, DECAL Lab Director, AI for Health Lab General Chair, ASE 2024 Director of Translational Data Science, UCD DataLab (2020–2024) His research centers on the sustainability of open-source software, using socio-technical and governance data to forecast project success and evolution. He also investigates AI applications in health, particularly multimodal models for atrial fibrillation and NLP in medicine. His work bridges empirical software engineering, data science, and healthcare informatics, with strong community engagement through forums and podcasts. He has led major NSF, Google, and Sloan Foundation-funded projects on OSS sustainability and UC-wide OSPO initiatives. The recent publications highlight a strong trend in empirical software engineering, particularly around open-source governance, lifecycle analysis, and sustainability forecasting. There is also a growing emphasis on health-related AI, including multimodal models for cardiac conditions and natural language processing in clinical settings. The work combines data-driven modeling with real-world impact in both software ecosystems and healthcare. ACM Distinguished Member ACM SIGSOFT Distinguished Paper Award Vladimir Filkov has successfully secured competitive grants from the NSF (GCR, Phase I and II), Google, and the Sloan Foundation. He advises PhD students including Likang Yin, Raiyan Jahangir, and postdoc Stefan Stanciulescu. His mentoring spans topics in software engineering, AI, and computational biology. He has organized major research forums and collaborative initiatives across the UC system. He leads the DECAL Lab and the AI for Health Lab at UC Davis, fostering interdisciplinary research in software sustainability and healthcare AI. These labs support graduate students, postdocs, and collaborative projects with national and international partners.

• Parallel Computing
• Compiler Optimization
• Performance Tuning
• +3 more

Dr. Mary Hall is a Professor in the School of Computing at the University of Utah, specializing in compiler optimization, parallel computing, and high-performance computing (HPC). Her work focuses on autotuning techniques, compiler-driven performance optimization, and minimizing data movement in computations to enhance efficiency. She has contributed significantly to frameworks like Bricks and Peak , advancing code generation for GPUs and block-structured grids. Her research also addresses educational initiatives, such as improving student retention in introductory computing courses and fostering diversity in the computing workforce through NSF-funded programs. Her research interests span compiler technology, stencil computations, and energy-efficient HPC applications. Key projects include optimizing geometric multigrid methods, developing communication-avoiding algorithms, and integrating machine learning into autotuning. She has led efforts to streamline performance portability across heterogeneous architectures and has published extensively on scheduling languages and compiler-driven optimizations. Mary Hall’s contributions include advancing data layout strategies for sparse tensors and DNNs, as well as fostering reproducibility in computational research through collaborative NSF REU programs. Her work emphasizes practical tools like ytopt and Rigel , which automate performance tuning for scientific applications. She remains active in both academic and industrial HPC communities, addressing challenges in extreme heterogeneity and scalable computing.

Thilini JayasingheView profile

Assistant Professor

• Statistics
• Regression Analysis
• Data Analysis
• +5 more

Thilini Jayasinghe is an Assistant Professor in the Department of Mathematics at the University of Dayton, part of the College of Arts and Sciences. She joined the faculty in Fall 2023 as a full-time academic member. Her research focuses on statistical modeling, data analysis, and machine learning applications. Research Interests: Regression Analysis Data Analysis Shape Analysis Statistical Machine Learning Her recent publications show a strong trend in applying statistical and machine learning techniques to real-world problems, including cost forecasting in the newspaper industry and fraudulent online review detection using deep neural networks. This reflects a growing interdisciplinary focus combining traditional statistics with modern AI methods. Scientific Awards: No awards listed in the provided text. Dr. Jayasinghe advises students and is involved in research and teaching activities. She previously served as an adjunct instructor at Wittenberg University before joining the University of Dayton. Her work contributes to both theoretical and applied statistics, with a practical orientation toward industrial and technological applications. Labs and Research Teams: No specific labs or research teams are mentioned in the provided information.

Saša AdamovićView profile

Professor

• Cryptography
• Biometric Security
• Internet of Things (IoT) Security
• +3 more

Prof. Dr. Saša Adamović is a faculty member at Singidunum University in Belgrade, Serbia, affiliated with the Faculty of Informatics and Computer Science . His academic career spans over 15 years with a focus on Cryptography, Biometrics, and IoT Security . Born October 15, 1985, in Drvar, Bosnia and Herzegovina PhD in Advanced Security Systems (2013), Master’s in Contemporary Information Technologies (2008), and Bachelor’s in Business Informatics (all from Singidunum University) His research interests include: Biometric Cryptography Blockchain for IoT Security Catalan Number Applications in Cryptography Machine Learning in Security Systems Virtualization and Energy Efficiency Steganography and Forensics Recent publications highlight trends in: IoT Healthcare Security (2022) Biometric Authentication (2021) Combinatorial Cryptography (2021) Machine Learning for Diabetes Prediction (2021) Distance Learning Security (2023) He served as one of five Serbian representatives in the Open World program (USA) for expert exchange in technology development.

Xiusi ChenView profile

Research Fellow

• Large Language Models
• Natural Language Processing
• AI for Science
• +5 more

Xiusi Chen is a Postdoctoral Research Fellow in the Blender Lab at the University of Illinois Urbana-Champaign (UIUC), working under Prof. Heng Ji. His research focuses on improving reasoning, alignment, and decision-making capabilities of Large Language Models (LLMs). Previously, he completed his Ph.D. in Computer Science at UCLA under Prof. Wei Wang, and earned M.S. and B.S. degrees in Computer Science from Peking University under Prof. Jun Gao. His educational background includes: Ph.D. in Computer Science, University of California, Los Angeles (UCLA), advised by Prof. Wei Wang M.S. in Computer Science, Peking University, advised by Prof. Jun Gao B.S. in Computer Science, Peking University, advised by Prof. Jun Gao Dr. Chen's research program targets three interconnected areas: advancing Large Language Models (particularly in low-resource reasoning and alignment), developing NLP applications for AI in Science and recommendation systems, and modeling complex decision-making processes in sports domains. His work bridges theoretical foundations with practical implementations, resulting in numerous publications in top-tier conferences including ACL, ICML, ICLR, and KDD. Analysis of his recent publications reveals a strong focus on making LLMs more efficient, reliable, and capable of complex reasoning tasks across diverse domains. His significant academic contributions include: 2023 Best Poster Award (Honorable Mention) at SDM 2023 2023 SIAM Student Travel Award 2021 and 2022 SIGIR Student Travel Grants from ACM SIGIR Multiple academic scholarships from Peking University Co-creation of the widely adopted Amazon Reviews'23 dataset (500k+ HuggingFace downloads) Dr. Chen actively serves the research community as Workshop Organizer for KDD 2025, Program Committee member for major conferences (KDD, WSDM, ICML, NeurIPS, ICLR, AAAI), and journal reviewer. He maintains a strong commitment to mentoring, offering dedicated time for students (especially from underrepresented groups) to discuss research and career development. Starting Fall 2025, he will be seeking academic positions to continue his research on language agents and decision-making systems. Currently based in the Siebel Center for Computer Science, Dr. Chen collaborates with the Blender Lab team on advancing NLP and LLM capabilities. His work has practical impact through widely adopted resources like the Amazon Reviews'23 dataset and theoretical contributions through his publications on reasoning frameworks and alignment techniques.

Corey BradyView profile

Associate Professor

• Mathematics Education
• Computational Modeling
• STEAM Education
• +5 more

Corey Brady is an Associate Professor at the Department of Teaching & Learning and Associate Dean for Research and Outreach at Southern Methodist University's Simmons School of Education and Human Development. He holds a Ph.D. in Mathematics Education from the University of Massachusetts, Dartmouth, alongside degrees in English Literature (MA) and Pure Mathematics (MS). Dr. Brady's research focuses on mathematical and computational modeling from a constructionist perspective, emphasizing collective learning and STEAM activity . His work includes design-based research on block-based programming environments and embodied participatory simulations. Recent publications (2023-2021) span journals like Frontiers in Education , Science Education , and Educational Studies in Mathematics , with sub-fields ranging from geometric transformations to disaster preparedness systems. He has received recognition including the Outstanding Paper Award at ICLS 2023 and Best Paper at ICLS 2020 . Previously, he held faculty roles at Vanderbilt and Northwestern Universities, led educational technology development at Texas Instruments, and taught at middle school to community college levels. His interdisciplinary approach bridges mathematics, computer science, and affective learning.

• Software Systems
• Programming Languages
• Compiler Construction
• +5 more

Johannes Waldmann is a Professor and Dean of Computer Science at the Faculty of Computer Science and Media, Leipzig University of Applied Sciences (HTWK Leipzig). He serves as a Member of the Faculty Council and maintains active engagement with students through regular office hours on Wednesdays from 10:00-10:30, requiring advance registration via email or the OPAL learning platform. Professor Waldmann's research spans theoretical computer science with practical applications, focusing on programming paradigms and languages, compiler construction, and computational complexity across different calculation models. His expertise includes declarative programming approaches (functional and constraint programming), automata theory, and formal languages. More recently, he has applied his theoretical knowledge to develop auto-grading systems for e-learning environments, bridging foundational computer science with educational technology. His teaching philosophy emphasizes mathematical foundations in computer science education, as reflected in his reference to G.H. Hardy's "A Mathematician's Apology," where he notes that "pure mathematics is distinctly more useful than applied" because "mathematical technique is taught mainly through pure mathematics." As Dean of Computer Science, he plays a pivotal leadership role in shaping academic programs and ensuring their alignment with both theoretical rigor and industry relevance at HTWK Leipzig.

• Computational Intelligence
• Agent-Based Simulation Systems
• Neuroscience
• +4 more

Darius Plikynas is a Senior Researcher at the Smart Technologies Research Group within the Institute of Data Science and Digital Technologies at Vilnius University . His research integrates computational intelligence methods with agent-based simulation to model cognitive and social processes. Position: Senior Researcher, Chief Researcher in the Project Address: Akademijos St. 4, room 224, Vilnius Contact: +370 5 210 9333, +370 620 95101 Email: darius.plikynas@mif.vu.lt Personal page: http://www.dariusplikynas.eu Dr. Plikynas' research spans interdisciplinary domains including neuroscience, physics methods, complexity theory, and distributed cognitive systems. He led the 2017–2019 project "Development of a metric, conceptual and simulation model of the social impact of cultural processes" under the LMT Research Group Funding Program. His recent publications (2016–2025) reflect trends in combining machine learning with social science questions (fake news analysis, propaganda detection) and agent-based modeling of cultural/social capital dynamics. Key collaborations include Leonidas Sakalauskas, Rimvydas Laužikas, and Arunas Miliauskas. Scientific supervision includes doctoral students: Andrius Budrionis (University of Tromsø, Norway) Ieva Rizgelienė (PhD topic: "Propaganda detection and classification in social media using hybrid deep learning") He also serves as an expert at Vilnius University and has contributed to projects involving: 2D financial market visualization Neural oscillation-based cognitive modeling Indoor navigation for blind individuals Cultural participation impact on social capital

Fadi AL-TURJMANView profile

Professor

• Artificial Intelligence of Things (AIoT)
• Wireless Sensor Networks
• Blockchain Applications
• +7 more

Prof. Dr. Fadi AL-TURJMAN serves as the founding Dean of the Faculty of AI and Informatics at Near East University (NEU), Cyprus. He holds multiple leadership roles including Head of the Software Engineering Department and Director of the AI and Robotics Institute and the International Research Center for AI and IoT. With a PhD in Computer Science from Queen’s University (2011), he specializes in AIoT systems, wireless networks, and blockchain integration. Affiliation: Near East University Leadership: Founding Dean for AI and Informatics, Director of AI & Robotics Institute Research Focus: His work bridges Artificial Intelligence of Things (AIoT) , Blockchain Applications , and Smart Networking . He explores cybersecurity frameworks for smart cities, quantum state optimization techniques, and novel AI-driven solutions for healthcare, agriculture, and energy systems. Key Article Trends: Recent publications emphasize Transformer models for environmental monitoring, blockchain-enabled security protocols , and evolutionary algorithms for resource optimization. His research spans interdisciplinary domains including medical diagnostics, vehicular networks, and sustainable infrastructure. Scientific Awards: Lifetime Golden Award of Dr. Suat Gunsel (2022) Multiple Best Research Awards at International Venues Labs & Teams: Directs the International Research Center for AI and IoT at NEU, leading multidisciplinary teams in developing advanced networking technologies and AI-driven solutions for global value chain applications.

Jinming ZhangView profile

Professor

• Educational Measurement
• Psychometric Theory
• Multidimensional Item Response Theory (MIRT)
• +5 more

Jinming Zhang is a Professor at the University of Illinois at Urbana-Champaign , affiliated with the College of Education and the Educational Psychology department. He also holds appointments in Statistics and the Center for East Asian and Pacific Studies . His research focuses on advanced statistical methodologies for educational and psychological measurement. Research Interests: Dr. Zhang specializes in multidimensional item response theory (MIRT) , dimensionality assessment , large-scale assessments , generalizability theory , and test security . His work addresses critical challenges in psychometric modeling, including bias correction, item compromise detection, and standards alignment for English Language Learners (ELL). Notable Contributions: He developed the DETECT procedure for dimensionality analysis and pioneered real-time item monitoring systems for computerized adaptive testing (CAT) security. His empirical studies span applications to the National Assessment of Educational Progress (NAEP) and Law School Admission Test (LSAT) analysis.