Find Academic Supervisors

• Natural Language Processing
• Health Informatics
• Machine Learning
• +3 more

Zeljko Kraljevic is a researcher in Biostatistics & Health Informatics with a focus on applying natural language processing (NLP) and machine learning to healthcare data. His work addresses challenges in electronic health record (EHR) analysis, clinical decision support systems, and predictive modeling for hospitalization outcomes. He contributes to UN Sustainable Development Goals through innovations in healthcare data services and pandemic response analysis. Research interests include NLP for medical text processing, transformer models in healthcare, and AI-driven solutions for clinical workflows. Notable projects involve developing tools like MiADE (Medical AI Data Extractor) and enhancing the National Early Warning Score (NEWS2) for critical care monitoring. Collaborations span academic institutions and healthcare providers, emphasizing real-world applicability of AI techniques. Recent articles highlight advancements in redaction techniques for EHR privacy, multi-task learning frameworks for medical NLP, and generative models for patient timeline analysis. His work bridges computational methods with clinical needs, aiming to improve healthcare efficiency and patient outcomes through data-driven approaches.

Wen YeView profile

Assistant Professor

• User Experience Design
• Empathy-Driven Design
• Wearable Technology
• +3 more

Wen (Vivian) Ye is an Assistant Professor of Practice in the Department of Design at the University of Texas at Austin. She holds dual Master of Fine Arts degrees from the University of California–Davis and the Marangoni Institute in Italy. Her academic role combines teaching design thinking and advanced design studio courses with industry experience from roles as a fashion designer at VERO MODA and a global fashion buyer for Armani. Her research focuses on user experience, empathy-driven design, and wearable technology, emphasizing cross-disciplinary collaboration. Ye actively participates in National Science Foundation (NSF) projects aimed at improving transportation and power systems through electric vehicles and clean energy solutions. She designs educational materials for these initiatives, bridging academic and industrial applications. Her work highlights innovation in sustainable technology and human-centered design principles. Her recent publications span data science code generation, neural network adaptation, and uncertainty quantification in machine learning. These contributions address foundational challenges in AI and computational methodologies, reflecting her dual expertise in design and technical systems.

Jon-Fredrik NielsenView profile

Research Professor

• Magnetic Resonance Imaging (MRI)
• Medical Imaging Technology
• Functional MRI
• +4 more

Jon-Fredrik Nielsen is a Research Professor in the Departments of Radiology and Biomedical Engineering at the University of Michigan. His research focuses on advanced MRI technologies, including pulse sequence design, functional MRI, and quantitative imaging. He leads projects funded by NIH grants such as R21AG061839, R01EB023618, and R21EB019653, emphasizing innovations in MRI hardware and software. Research Interests: Steady-state MRI and RF pulse design Functional MRI and biomarker development Blood flow imaging and computational modeling Publications reflect contributions to open-source MRI frameworks (Pulseq/TOPPE), artifact correction, and novel imaging protocols. He holds patents related to MRI imaging techniques (e.g., US 9,791,530). Grants: NIH R21AG061839 (PI), NIH R01EB023618, NIH R21EB019653, and University of Michigan MCubed grants. Projects include improving fMRI reliability and developing vendor-agnostic MRI sequences. Labs/Teams: Active in the fMRI engineering group, contributing to software tools like TOPPE and Pulseq-Graphical Programming Interface.

Manuel MaarekView profile

Associate Professor

• Cybersecurity
• Computer Security
• Secure Programming
• +6 more

Manuel Maarek is an Associate Professor at the School of Mathematical & Computer Sciences, Heriot-Watt University, specializing in Computer Science. His research focuses on cybersecurity, secure programming, and educational technologies, particularly leveraging serious games and collaborative methodologies for enhancing security awareness. His work integrates formal methods with practical applications, addressing topics like Bitcoin transaction analysis, developer-centric security training, and blockchain privacy. He has co-authored over 29 peer-reviewed publications and received awards including the 2018 Teaching Excellence Award and the 2022 EICC Best Paper Award. Key Research Themes: Cybersecurity Education, Secure Programming Practices, Cryptocurrency Analysis, Game-Based Learning Collaborations: Active in international collaborations, particularly in cybersecurity co-design and blockchain forensics Maarek’s recent projects include the 'Serious Slow Game Jam' methodology for cybersecurity education and investigations into large language models for student feedback. He actively contributes to improving software engineering education through integrated learning platforms like Canvas and GitLab.

• System-on-Chip (SoC) Design
• FPGA-Based High-Level Synthesis
• Real-Time Embedded Systems
• +5 more

Timo Hämäläinen is a Professor of Computer Engineering at Tampere University, leading the Unit of Computing Sciences within the Faculty of Information Technology and Communication Sciences. He is a core member of the System-on-Chip research group and the Computer Engineering Team, actively contributing to the System-on-Chip Hub initiative. His research focuses on System-on-Chip (SoC) design methodologies, FPGA-based high-level synthesis, real-time embedded systems, and open-source hardware-software co-design frameworks like RISC-V and HEVC encoding. He has pioneered work on agile SoC development processes and validation techniques for large-scale hardware systems. Key research areas include: High-level synthesis (HLS) optimization for FPGAs Real-time processor architectures and context-switching latency reduction Formal verification of IP-XACT-compliant SoC designs Resilient RISC-V MPSoC implementations Hardware-accelerated algorithms for signal processing and networking His recent publications (2023-2025) emphasize agile SoC development frameworks, hardware-software co-design for real-time systems, and validation methodologies for large-scale embedded systems. He has contributed to open-source projects like Kvazaar HEVC encoder and the Kactus2 IP-XACT toolchain. Academic advising includes students such as Santéri Mäki-Äijö (formal verification), Arto Oinonen (RISC-V tooling), and Sakari Lahti (processor modeling). His work integrates academic research with industrial collaboration through frameworks like the Fault-slip-Through quality assessment methodology.

• Plate Tectonics
• Geodynamics
• Seismic Tomography
• +3 more

Edward Clennett is an Associate Lecturer at the School of Natural Sciences, Birkbeck, University of London. He specializes in reconstructing plate tectonic history through deep time using geodynamic models, seismic tomography, and plate kinematic analyses. His teaching includes the Global Tectonics undergraduate module. Education: PhD in Geosciences from The University of Texas at Austin (2024), MSc in Earth Sciences from the University of Oxford (2019). Research focuses on integrating geodynamic, seismic, and kinematic data to improve plate reconstruction accuracy. Key interests include tectonic controls of critical minerals, crustal thickness changes during deformation, and the interplay between Earth’s interior and surface processes. Publications emphasize novel methods in plate tectonic modeling, including sediment lubrication effects on plate velocities and four-dimensional reconstructions combining multiple datasets. Awards: UTIG Outstanding Graduate Student Award (2024), Fulbright Scholarship (2020), BP Prize for best Master's project (2019). Advising and grants: No specific grants listed, but active in supervision of research projects related to plate tectonics and geodynamic modeling. Labs/Teams: Collaborates with institutions like the University of Texas Institute for Geophysics and the Jackson School of Geosciences on geodynamic projects.

• Artificial Intelligence
• Ethical Guidelines
• Quality Requirements
• +3 more

Nagadivya Balasubramaniam is a Doctoral Researcher at Aalto University's Department of Computer Science within the School of Science, affiliated with Professor Casper Lassenius' research group. Their work focuses on ethical guidelines and quality requirements for AI systems, emphasizing transparency, explainability, and practical implementation. They also contributed to an e-learning application enhancing healthcare professionals' cross-cultural communication skills. Research Interests: Ethical frameworks for AI systems Requirement engineering for transparent AI Practical application of ethical guidelines Cross-cultural e-learning solutions Collaborations: Active in international research networks, including contributions to REFSQ conferences and interdisciplinary healthcare projects. Grants & Advising: No formal advisees listed, but collaborates with research teams on funded projects related to AI ethics and software engineering. Labs/Teams: Part of the Department of Computer Science's AI and Software Systems research group at Aalto University.

• Artificial Intelligence
• Machine Learning
• Programming Education
• +3 more

Nicola Dainese is a Doctoral Researcher in the Department of Computer Science at the School of Science. His work focuses on the intersection of artificial intelligence and programming education, leveraging large language models (LLMs) to improve code generation, feedback mechanisms, and symbolic regression. Current projects include STN LITERACY (2023-2026) and FCAI Flagship (2020-2022, extended to 2026). Collaborations span institutions like the Finnish Center for Artificial Intelligence (FCAI) and international research teams. Research interests include: Large Language Models (LLMs) for code generation and feedback Model-based reinforcement learning in programming education Symbolic regression and function discovery via AI Educational technology for student assessment Open-source language models and ethical AI applications Recent publications highlight trends in leveraging LLMs for programming feedback, code repair, and symbolic regression. Key subfields include Monte Carlo Tree Search, prompt engineering, and vision-language models for AI planning. Active project member in DATALIT , addressing data literacy for responsible decision-making. Contributed to benchmarking studies on educational program repair and code world modeling.

Rose MarraView profile

Professor

• Technology-Enhanced Learning Environments
• STEM Education
• Online Learning Strategies
• +3 more

Rose Marra is a Professor and Director at the School of Information Science & Learning Technologies, University of Missouri. She holds a PhD from the University of Colorado (1996) following an MS in Computer Science from the University of Kansas (1984) and a BS from Rockhurst University (1984). Her research focuses on technology-enhanced learning environments, STEM education, and online learning design. Prior to academia, she worked as a software engineer at AT&T Bell Labs. Education: PhD in Educational Leadership & Innovation, University of Colorado (1996) MS in Computer Science, University of Kansas (1984) BS in Computer Science, Rockhurst University (1984) Professional Roles: Professor at University of Missouri since 2000 Former Assistant Professor at Pennsylvania State University (1996–2001) Director of Instructional Services, Penn State (1996–2001) Dr. Marra’s work emphasizes problem-based learning in engineering education, leveraging technology to foster metacognition and self-directed learning. She co-developed the SeeBeefGenetics cattle breeding simulation and pioneered the SeQoL online learning efficacy questionnaire. Her research addresses gender equity in STEM and the impact of remote learning transitions on engineering students. Her publications analyze student self-efficacy, remote learning challenges, and curriculum design. She collaborates on initiatives like the Iron Range Engineering PBL Curriculum and the National Girls Collaborative Project to enhance diversity and inclusion in engineering.

Yukika AWAZUView profile

Associate Professor

• Knowledge Management
• Strategic Management
• Global Digital Strategies
• +5 more

Yukika AWAZU is an Associate Professor at IÉSEG School of Management, specializing in Strategy and Sustainability. She holds a Ph.D. in Business (Bentley University, 2013) and dual Master's degrees in Business Administration and Economics (University of Illinois at Chicago, 2002). Her research focuses on Knowledge Management, Global Digital Strategies, Social Robotics, and Wellness. She previously served as Associate Professor and Head of Research at Bangkok University's Institute for Knowledge & Innovation South-East Asia (2015–2016). Notable awards include the Henry E. Rauch Doctoral Fellowship (2006) and H.B. Earhart Student Fellowship (1997). Her teaching spans courses like Sustainability Strategies, Management of Information Systems, and Digital Strategies. Research interests emphasize innovation processes, organizational knowledge practices, and technology implementation challenges. Recent work explores project memory in large-scale initiatives, artifact-mediated knowledge, and public data democratization. She has authored over 50 peer-reviewed articles, books on engaged knowledge management, and contributed to conferences globally.

• Tectonics
• Structural Geology
• 3D Geological Modelling
• +3 more

Laurent Ailleres is a Senior Lecturer at Monash University's School of Earth Atmosphere and Environment. He holds a role as a Senior Research Fellow in the School of Geosciences. His research focuses on tectonics, structural geology, 3D geological modeling, and integration of geophysical datasets. Key projects include three-dimensional Bayesian modeling of geological/geophysical data (ARC-funded), ore body modeling (AMIRA P1249), and improving usability of Loop3D software (Auscope). Research Interests: Laurent's work spans tectonic boundaries, structural analysis using aeromagnetic data, 3D multi-scale modeling, and geophysical inversion techniques. He emphasizes combining geological and geophysical datasets to advance mineral exploration and crustal architecture understanding. Recent Articles: His 2022 work on Paleoproterozoic gold events in West Africa and 2021 studies on automated geological mapping and LoopStructural software showcase his contributions to 3D modeling and geophysical integration. Articles often address fault modeling, structural overprinting, and igneous intrusion analysis. Grants & Collaborations: Leads multiple ARC, CRC, and industry-funded projects. Collaborations span institutions globally, focusing on mineral systems, crustal dynamics, and open-source geoscience tools. Labs/Teams: Central to the Loop Project team developing LoopStructural software. Engaged with international research networks for 3D geological modeling applications.

• Machine Learning
• Bandit Algorithms
• Reinforcement Learning
• +2 more

Hamish Flynn is a Researcher in the Department of Engineering Artificial Intelligence and Machine Learning. His work focuses on developing advanced algorithms for sequential decision-making systems, with a strong emphasis on theoretical guarantees and practical implementations. His research interests span several core areas of machine learning: Bandit algorithms (contextual, linear, and multi-armed variants) PAC-Bayesian theory and applications Reinforcement learning systems Online optimization under uncertainty Statistical learning theory Flynn's recent publications (2022-2025) demonstrate consistent focus on improving sequential learning methods. Key research themes include: non-iid noise modeling in bandits, confidence bound optimization for sequential regression, sparse nonparametric methods, and hardware-efficient machine learning implementations. His work frequently combines theoretical frameworks (PAC-Bayes, martingale theory) with practical applications in reinforcement learning and adaptive systems. No scientific awards, students advised, grant activities, or lab affiliations are mentioned in the available information.

Zheying ZhangView profile

Lecturer

• Requirements Analysis
• Domain Modelling
• Quality Assurance
• +10 more

Zheying Zhang is a Lecturer at Tampere University within the Faculty of Information Technology and Communication Sciences , Department of Computing Sciences . She holds a Ph.D. in Computer Science and Information Systems from the University of Jyväskylä (2004) and was awarded a Docent (Adjunct Professor) title in Software Development from the University of Tampere (2013). Education: Ph.D. (2004), Docent (2013) Her research focuses on Requirements Engineering , with specific interests in Requirements Analysis Domain Modelling AI-Assisted Requirements Management Software Quality Assurance Process Improvement Data-Driven Software Development Medical Device Software Current Master's thesis topics include AI for Software Engineering, DevOps pipelines, and cross-disciplinary software ethics. Recent publications highlight trends in AI applications for requirements management, software evolution analysis , and accessibility testing . She actively supervises PhD candidates like Xiaozhou Li and Wenhui Lu, with works spanning open-source projects and medical software quality. Scientific Contributions: 54 research outputs (2003-2025), including 3 2025 conference papers

• Clone Detection
• Data Protection
• Natural Language Processing
• +3 more

Muslim Chochlov serves as a Researcher within the Department of Computer Science & Information Systems and is an active member of Lero – the Irish Research Centre for Software. His work bridges theoretical computer science with practical industrial software solutions through rigorous empirical studies. Research Focus: Specializes in code clone detection systems using BERT-based neural networks and ensemble inference methods, with significant contributions to data protection frameworks in digital health applications during the COVID-19 pandemic. Publication Trends: Demonstrates a clear evolution from foundational software architecture studies (2015-2020) toward AI-driven code analysis (2021-2025), with 73% of recent output focused on scalable industrial clone detection techniques and citizen-centered health informatics tools. Collaboration Network: Maintains extensive cross-institutional partnerships through Lero's national research infrastructure, evidenced by multi-author publications spanning computer science, public health, and data protection domains. His 2022 contact tracing app analysis achieved notable impact with 20 citations and 100+ reader captures. Technical Leadership: Develops practical methodologies for industrial codebase analysis including nearest-neighbor BERT implementations and ensemble inference systems that improve detection recall while addressing data protection requirements in sensitive health applications.

Mikko AroView profile

Professor

• Special Education
• Educational Psychology
• Learning Disabilities
• +5 more

Mikko Aro is a Professor of Special Education at the University of Jyväskylä, affiliated with the Department of Education within the Faculty of Education and Psychology. He co-directs the Center of Excellence in Learning Dynamics and Intervention Research (InterLearn), funded by the Academy of Finland (2022–2029). His research focuses on learning disabilities, particularly in literacy and mathematics, and the development of effective interventions. Aro holds a PhD in Psychology from the University of Jyväskylä and has extensive international training in child neuropsychology and neuropsychology. His research interests span typical and atypical learning processes, including the role of cognitive, motivational, and emotional factors in skill acquisition. He has led projects on learning difficulties, intervention efficacy, and assessment practices. Key contributions include studies on reading fluency, math anxiety, and the integration of self-efficacy interventions into educational strategies. Aro’s extensive publications reflect his commitment to advancing understanding of learning disabilities and improving educational support systems. He collaborates with institutions like the Finnish National Agency for Education and the European Commission on projects addressing teacher training, assessment methodologies, and multisite cohort studies. His work emphasizes evidence-based interventions, such as computer-assisted training for struggling readers and strategies to enhance self-efficacy. He advocates for clearer frameworks in educational support, particularly through the Response-to-Intervention (RTI) model. Aro’s research bridges cognitive science, educational policy, and practical classroom applications, aiming to create inclusive and effective learning environments.