Find Academic Supervisors

• High-performance computing
• Parallel computing
• Scientific computing
• +7 more

David E. Bernholdt is a Researcher at Oak Ridge National Laboratory in Tennessee, USA. With a prolific publication record spanning from 1995 to 2025, he has established himself as a leading expert in high-performance computing and scientific software development. His work focuses on advancing computational methods and software infrastructure for scientific applications across multiple domains. Dr. Bernholdt's research interests center around high-performance computing, parallel computing, and scientific software engineering. He has made significant contributions to component-based software architecture, fault tolerance in extreme-scale applications, and sustainable software ecosystems for scientific computing. His work bridges the gap between theoretical computer science and practical scientific applications, with particular emphasis on making scientific software more reliable, efficient, and maintainable. His recent publications demonstrate a strong focus on research software engineering practices, sustainability, and productivity. He has been actively involved with the IDEAS Productivity Project and Better Scientific Software (BSSw.io) initiative, working to establish community best practices and improve the professional recognition of research software engineers in scientific computing. Dr. Bernholdt has collaborated extensively with researchers across multiple institutions and has contributed to significant projects in exascale computing, climate modeling, and quantum chemistry computations. His work on the Common Component Architecture and related frameworks has influenced software development practices in scientific computing.

Ratna SahaView profile

Senior Lecturer

• Machine Learning
• Artificial Intelligence
• Image Processing
• +6 more

Dr. Ratna Saha is a Senior Lecturer at Torrens University Australia, affiliated with the Business and Hospitality Centre for Artificial Intelligence Research and Optimisation (AIRO). She holds a PhD in Computer Science from Flinders University and a BSc in Computer Science and Engineering from Khulna University, Bangladesh. With over 17 years of experience in academia and industry, she focuses on machine learning, medical image analysis, AI-driven healthcare solutions, and generative AI in education. Her research bridges technical innovation with societal impact, particularly in underdeveloped regions. Education: PhD in Computer Science (2020), Flinders University, Australia (Focus: Image Segmentation with Prior Guidance in Cervical Cytology) BSc in Computer Science and Engineering (2007), Khulna University, Bangladesh (Focus: Component-Based Face Detection via SVM) Research Interests: Dr. Saha’s work spans machine learning applications in healthcare diagnostics (e.g., cervical cell segmentation), AI-driven education innovation, and industry 4.0 transformations. She emphasizes interdisciplinary approaches, integrating image processing techniques with medical pathology and leveraging generative AI to enhance learning experiences. Her recent studies explore assembly line optimization in manufacturing and social impacts of business incubation centers in developing economies. Collaborations and Impact: Her research networks span Australia, Bangladesh, and Pakistan, with projects addressing global challenges such as cervical cancer detection and skill development in IT sectors. She actively mentors HDR students in machine learning-based disease prediction and data-driven decision-making frameworks. Current projects include optimizing labor costs in assembly lines using time-study models and fostering entrepreneurial ecosystems through smart educational tools. Professional Experience: Senior Lecturer (Torrens University, 2020–present) Casual Academic (Flinders University, 2016–2020) Lecturer (Khulna University, 2012–present) Senior Software Engineer (Dohatec New Media, 2010–2012) Research Themes: Her publications reflect expertise in medical image segmentation (e.g., cervical nucleus detection), optimization algorithms (e.g., evolutionary harmony search), and socio-economic transformations via technology. Recent trends highlight her shift toward applied AI in education and industry 4.0, complementing her foundational work in computer vision and medical diagnostics. Advising and Grants: Dr. Saha supervises PhD students in machine learning and data-driven healthcare. She has secured research grants focusing on AI applications in education and healthcare, and her work is cited across interdisciplinary fields (h-index: 6, 102 citations).

• Earth System Modeling
• Scientific Programming
• Climate Science
• +3 more

Nadine Wieters is a Researcher at the Alfred Wegener Institute (AWI) in Bremerhaven, Germany, specializing in Climate Dynamics. Her primary affiliation is within the Climate Sciences division, focusing on Earth System Modeling and Scientific Programming. She contributes to the development of modular infrastructure for coupled Earth system models through projects like ESM-Tools. Her research emphasizes advancing computational frameworks to enhance model interoperability and efficiency. Key collaborations include work on the AWI-CM model prototype and the esm-interface library. Wieters actively participates in international conferences such as EGU General Assemblies and deRSE events, presenting advancements in software tools for climate research. With a focus on software engineering for climate science, her work bridges computational methods and environmental modeling, supporting both standalone and coupled simulations. She is based at the AWI's Bremerhaven site, contributing to the institute's broader mission in polar and marine research through innovative technical solutions.

• Multimodal Interaction
• Artificial Intelligence
• Human-Computer Interaction
• +3 more

Hung-Hsuan Huang is an academic researcher focused on multimodal interaction, embodied conversational agents, and educational technology. His work spans topics such as virtual agent behavior, gamified learning systems, and nonverbal communication analysis. Collaborating frequently with institutions like Osaka University, he has published extensively in venues including HCI, ICMI, and IVA conferences. His research often integrates machine learning techniques with human-computer interaction challenges, particularly in educational and collaborative environments. Key research areas include: (1) Designing virtual agents for tutoring and teacher training, (2) Multimodal assessment systems using speech and body language analysis, (3) Gamification mechanisms for knowledge refinement and skill evaluation, and (4) Cross-cultural interaction frameworks for virtual guides. Recent work emphasizes applications in language proficiency assessment and teacher training simulations. Notable contributions include developing frameworks for integrating embodied conversational agents, simulating group discussion dynamics, and creating virtual classroom environments for skill evaluation. His methodologies often combine deep learning with behavioral analysis to address challenges in human-agent collaboration.

BUKET TOPTAŞView profile

Lecturer

• Machine Learning
• Medical Imaging
• Deep Learning
• +6 more

Buket Toptaş is a Lecturer at the Department of Software Engineering, Faculty of Engineering and Natural Sciences at Bandırma Onyedi Eylül University. She previously served as a Research Assistant (2019–2022) before transitioning to her current role. Her research focuses on machine learning, medical imaging, deep learning, and image processing, with applications in healthcare diagnostics and biomedical engineering. She has contributed to projects such as the Early Diagnosis of Diabetic Retinal Aneurysms with Retinal Image Analysis , completed in 2022. Her academic duties include teaching courses like Machine Learning, Data Mining, and Image Processing at both undergraduate and graduate levels. She is currently supervising a Master’s thesis on Colonoscopic Image Polyp Segmentation with Yaren Akgöl. Her work spans over 30 publications in journals like Soft Computing , Biomedical Signal Processing and Control , and IEEE Access , with a focus on retinal imaging, polyp detection, and algorithm optimization. Her conference contributions include papers on optimization algorithms (e.g., Biogeography-Based Optimization) and flame detection systems. She actively participates in university research projects and maintains an extensive portfolio of peer-reviewed articles addressing challenges in medical image analysis and deep learning.

Kjosev JosifView profile

Researcher

• Power Electronics
• Embedded Systems
• Circuit Design
• +3 more

Kjosev Josif is an academic affiliated with the Faculty of Electrical Engineering and Information Technologies at Ss. Cyril and Methodius University in Skopje. He holds a position in the Department of Electronics within the Institute of Electronics. His work focuses on practical applications of electronics in education and industrial systems, with a particular emphasis on power electronics, embedded systems, and circuit design. His research interests include developing educational tools using embedded systems (e.g., toys as teaching aids), optimizing power conversion techniques, and designing instrumentation for network infrastructure. He has contributed to advancements in switched-capacitor converters, stepper motor synchronization, and low-component-count power solutions. His publications reflect a blend of theoretical and applied electronics engineering, spanning from foundational circuit design (1996) to modern applications like 3D printer motor control (2018). While no formal awards are listed, his work demonstrates sustained engagement in both academic research and pedagogical innovation. He is currently active in the academic community, contributing to the Department of Electronics’ research and teaching activities without any indication of emeritus or retired status.

Monica Kristiansen HoloneView profile

Associate Professor

• Software Reliability
• Blockchain in Software Traceability
• Agile Software Development
• +3 more

Monica Kristiansen Holone is the Head of Department at the Department of Computer Science and Communication at Oslo Metropolitan University (HIOF). She holds an Associate Professor rank and oversees the Master Programme in Applied Computer Science. Her academic background includes a PhD in Mathematics from the University of Oslo, focusing on quantitative methods for software reliability assessment in compound software systems. Her research interests span software reliability, blockchain applications in traceability, agile methodologies, and education in software project management. She has published extensively on topics such as component dependencies, failure probability modeling, and interdisciplinary teaching strategies. Dr. Holone’s work emphasizes bridging theory and practice, particularly in collaborative frameworks and educational innovation. Her recent publications reflect a focus on blockchain’s role in traceability and the integration of low-code/no-code tools in DevOps. Despite no explicitly listed awards, her contributions to software dependability and pedagogy are evident through her numerous peer-reviewed articles and editorial roles.

Neserin AliView profile

Researcher

• Proteomics
• Osteoarthritis Research
• Protein Biochemistry
• +3 more

Neserin Ali is an Associate Researcher at Lund University, affiliated with the Lund OsteoArthritis Division - Clinical Epidemiology Unit and the LU Profile Area: Proactive Ageing. She holds roles as a Research Project Participant and Profile Area Member. Her primary academic focus lies in proteomics, osteoarthritis research, and molecular pathogenesis. Ali is an Assistant Supervisor in the active project 'Proteomics in Osteoarthritis' (2022–present), contributing to doctoral research in this field. Her research interests revolve around understanding early molecular mechanisms of osteoarthritis through proteomic analysis of synovial fluid and meniscal tissues. Key projects include identifying proteolytic activity biomarkers and studying mineral crystal thickness in cartilage and bone. Collaborations span global institutions, enhancing interdisciplinary insights into joint degeneration and aging-related conditions. Ali has published 14 peer-reviewed articles (2020–2024), including work on synovial fluid protein networks and meniscus proteomics. Notable contributions include demonstrating increased proteolytic activity in osteoarthritis patients and uncovering mineral composition differences in healthy versus degenerative knee tissues. Her work aligns with UN Sustainable Development Goals addressing health and well-being. While no formal awards are listed, her active research role and project supervision highlight her academic engagement. She can be reached at neserin.ali@med.lu.se and via ORCID 0000-0002-8483-471X .

Jaco van de PolView profile

Professor

• Model Checking
• Quantum Computing
• Symbolic Computation
• +5 more

Jaco van de Pol is a Professor at the Department of Computer Science, Aarhus University. His research focuses on formal methods, quantum computing, and symbolic computation. He specializes in model checking, timed automata, decision diagram algorithms, and quantum circuit synthesis. Education background includes expertise in computer science with a strong foundation in theoretical computer science and formal verification techniques. Key research interests include: Development of scalable symbolic algorithms for large systems Quantum circuit optimization using SAT-based approaches External memory implementations of decision diagrams Formal verification of real-time and parametric systems Recent work emphasizes quantum computing applications, such as layout-aware CNOT circuit synthesis for NISQ processors, and novel algorithms for parametric timed games. His contributions include the Adiar framework for external memory BDD manipulation and the Sylvan decision diagram library. No academic awards or grants are explicitly mentioned in the provided materials. He has advised multiple collaborations but no specific student names are listed. His research group focuses on advancing formal methods through practical tools and theoretical advancements.

• Autonomous Underwater Vehicles
• Machine Learning
• Marine Robotics
• +2 more

Vibhav Bharti serves as a Research Fellow in Computer Science at Heriot-Watt University's School of Mathematical & Computer Sciences. His work focuses on bridging theoretical machine learning with practical marine robotics applications, contributing to UN Sustainable Development Goals through technological innovation. Research interests span Autonomous Underwater Vehicles , Machine Learning reliability , and Marine Robotics systems . Key contributions include developing deep reinforcement learning frameworks for AUV docking, creating the Stonefish platform for marine ML research, and advancing multi-beam echo-sounder object detection. His fingerprint reveals expertise in underwater vehicle pipelines, sensor data processing, and learning-based assessment systems. Recent publications demonstrate a clear trajectory toward real-world implementation of simulation-trained AI systems, with strong emphasis on safety-critical applications. Work spans 2017-2025 with consistent output in top robotics conferences including OCEANS and TAROS. Collaborations show extensive international network in marine technology, particularly with researchers in underwater perception and machine learning validation. Current projects focus on enhancing reliability of ML components in autonomous systems and improving image quality assessment through edge detection techniques.

Mark GiesbrechtView profile

Professor

• Symbolic Mathematical Computation
• Computer Algebra
• Algebraic Algorithms
• +5 more

Mark Giesbrecht is a Professor in the David R. Cheriton School of Computer Science and currently serves as the Dean of the Faculty of Mathematics at the University of Waterloo, Canada. His research focuses on Symbolic Mathematical Computation, including computational methods in algebra, number theory, and the development of computer algebra systems like Maple and LinBox. He has held significant administrative roles, including Director of the Cheriton School of Computer Science (2014–2020) and Director of Undergraduate Studies (2002–2005). His awards include ACM Distinguished Scientist (2013) and an NSERC Synergy Award (2003). Research Interests: Symbolic Computation, Sparse Polynomial Algorithms, Algebraic Complexity, and Computer Algebra Systems. He has supervised numerous graduate students, many of whom have pursued academic and industrial careers. His work bridges theoretical foundations and practical implementations, contributing to both algorithm design and software development. Professional Activities: He has served as Program Chair of ISSAC (2013), editor of the Journal of Symbolic Computation (2003–2020), and organizer of workshops on symbolic-numeric computing. His contributions span editorial roles, conference organization, and community leadership in computer algebra. Grants & Funding: While specific grants are not detailed here, his research has been supported by NSERC and industry collaborations, reflected in awards like the NSERC Synergy Award. Labs & Teams: He is affiliated with the Symbolic Computation Group at Waterloo and the Ontario Research Centre for Computer Algebra (ORCCA).

Augustine WigleView profile

Research Fellow

• Network Meta-Analysis
• Bayesian Statistics
• Survey Sampling
• +2 more

Augustine Wigle is currently an NSERC Postdoctoral Fellow at McGill University in the Department of Epidemiology, Biostatistics and Occupational Health, supervised by Prof. Erica Moodie. His research focuses on developing statistical methodologies for network meta-analysis and methane emissions quantification to address healthcare and climate challenges. His educational background includes: PhD in Statistics (2025) from the University of Waterloo, supervised by Dr. Audrey Béliveau MMath in Statistics (2020) from the University of Waterloo HBSc in Mathematical Sciences (2018) from the University of Guelph Wigle's research spans Network Meta-Analysis and Bayesian statistics for evidence synthesis in clinical trials, alongside environmental statistics for methane emissions quantification. He develops methods for uncertainty quantification , treatment hierarchy ranking , and multi-site data integration , driven by real-world applications in oil/gas emissions and climate change mitigation. His work bridges theoretical innovation with practical impact in public health and environmental science. His 2022-2025 publications reveal a dual trajectory: advancing network meta-analysis techniques (treatment hierarchies, component models) while creating Bayesian frameworks for methane quantification (sensor data, multi-stage frameworks). This demonstrates consistent methodology development across biostatistics and environmental science, emphasizing robust uncertainty estimation and real-world applicability. His scientific recognition includes: NSERC Postdoctoral Fellowship Wigle actively contributes to open-source statistical tools, maintaining the BUGSnet R package for Bayesian network meta-analysis while developing poth (treatment hierarchy uncertainty) and methaneInventory (emission inventories). His GitHub-hosted projects foster collaborative methodological advancement in evidence synthesis and environmental monitoring.

• Cyber-Physical Systems
• Runtime Verification
• Formal Methods
• +5 more

Dejan Nickovic is a Professor affiliated with the Department of Cyber-Physical Systems at TU Wien. His research focuses on runtime verification, formal methods for cyber-physical systems (CPS), and software testing. He leads projects such as TAIGER (2023–2027) and ARTIST (2021–2026), advancing CPS safety and AI robustness. His work bridges theoretical formalisms with practical applications in robotics, IoT, and embedded systems. Key research themes include specification mining, fault localization, and automated verification. He has developed tools like DeepRIoT for robotic-IoT integration and FIM for Simulink fault analysis. His professorial dissertation (2020) formalized runtime verification techniques for CPS. Recent publications emphasize adaptive architectures, backdoor detection in AI, and hyperproperties for security. He collaborates extensively with academia and industry, addressing challenges in CPS testing and verification through grants like ZK 35-G (2019–2024). His lab supports platforms like CPS/IoT Ecosystem for research and education. Projects: TAIGER, ARTIST, EdgeAI, ProbInG Supervised students: N. Manjunath (PhD, 2021), Florian Exenberger (2019), Markus Heindl (2017)

• Compiler Optimization
• Embedded Systems
• Robotics Software Engineering
• +4 more

Dietmar Schreiner is a Senior Lecturer at TU Wien's Faculty of Informatics within the Department of Compilers and Languages. His research focuses on compiler optimization, real-time systems, robotics software engineering, and component-based automotive software. He has contributed to major projects like C3Pro (2010–2015) and ALL-TIMES (2007–2010), addressing challenges in embedded systems and automotive software. He advises students on robotics and embedded systems projects, including thesis works like 'Robotersteuerung mittels Genetic Programming für die RoboCup Standard Platform League' (2011) and 'Reasoning capabilities for a cognitive-assistive assembly system' (2017). He is actively involved in the Austrian Kangaroos robotics team and has received the Best Poster Award at SAC 2007 for work on UML-based component modeling. His teaching responsibilities include courses such as 'Introduction to Programming 1', 'Programming Principles of Mobile Robotics', and 'Fundamentals of Digital Systems'. He has also contributed to EU and national grants, advancing research in timing analysis and automotive system software.

Carlos Javier Pérez GonzálezView profile

Associate Professor

• Statistics
• Operations Research
• Reliability Engineering
• +3 more

Carlos Javier Pérez González serves as an Associate Professor in the Department of Mathematics, Statistics and Operations Research at the University of La Laguna, Spain. He is affiliated with The Institute of Mathematics and Applications and contributes to two PhD programs: Mathematics and Statistics, and Industrial Engineering, Informatics and Environmental Engineering. Education PhD in Mathematics and Statistics from University of La Laguna (2009), thesis: "Avances en el diseño óptimo de planes de muestreo en fiabilidad" (Advances in Optimal Design of Sampling Plans in Reliability), supervised by Dr. Arturo Javier Fernández Rodríguez Research Focus His primary expertise spans Statistics and Operations Research with emphasis on reliability engineering, acceptance sampling methodologies, and censoring techniques. He develops advanced statistical models for multi-component reliability systems, stress-strength analysis, and risk-based quality control frameworks. His work bridges theoretical statistics with industrial applications through Bayesian approaches and optimization algorithms. Publication Trends Recent publications (2020-2025) demonstrate consistent innovation in reliability test planning under censoring constraints, multi-component system analysis, and adaptive sampling schemes. His research increasingly incorporates real-world applications including marine conservation studies in the Canary Islands and emergency management data analysis using R programming. Research Integration As an active member of the Statistics research group at the Institute of Mathematics and Applications, he bridges methodological development with interdisciplinary collaborations across industrial engineering, environmental science, and public safety domains through data-driven approaches.