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Yeachan ParkView profile

Assistant Professor

• Low-level computer vision
• Machine learning theory

Yeachan Park is an Assistant Professor in the Department of Mathematics and Statistics at Sejong University, South Korea, since 2025, following a postdoctoral fellowship at Korea Institute for Advanced Study (KIAS) from 2022-2025. Education: Ph.D., Seoul National University (2022) B.S., Pohang University of Science and Technology (2015) His research centers on low-level computer vision and machine learning theory, with major contributions in neural network expressivity, loss surface geometry, computer arithmetic implementations (floating/fixed-point), mathematical language models, and learning dynamics. This work bridges theoretical mathematics with practical deep learning applications through rigorous mathematical frameworks. Recent publications (2022-2025) demonstrate consistent focus on mathematical foundations of deep learning, spanning computer vision (image deraining), neural network theory (expressivity under finite precision), and learning dynamics (grokking acceleration). The research exhibits strong interdisciplinary integration of numerical analysis, theoretical computer science, and image processing. No scientific awards are documented in the provided materials. Regarding academic advising and research funding, the available information does not specify student supervision or grant-supported projects.

• Agronomy
• Crop Physiology
• Legumes
• +7 more

Dr. Marisa Collins is an Associate Professor in Agronomy at La Trobe University's School of Agriculture, Food and Ecosystem Sciences, Department of Animal, Plant and Soil Sciences. With appointments spanning from 2006 to present, she has held progressive academic roles including Research Scientist at CSIRO, Senior Lecturer at University of Queensland, and current Associate Professor at La Trobe since 2024. Her educational background includes a PhD in Plant Science from the University of Melbourne (2006) and a Bachelor of Science (Honours) from the same institution (1999). Her research focuses on crop physiology and agronomy, particularly in legumes like mungbeans and pigeonpea, addressing water-use efficiency and abiotic stress responses. Collins' research portfolio demonstrates consistent focus on improving crop productivity through physiological understanding, with recent emphasis on mungbean modeling using APSIM Next Generation. Her work bridges field-based physiology with practical applications for broad-acre farming systems, particularly addressing drought and heat stress challenges in Australian agriculture. Current projects include GRDC-funded initiatives on mungbean yield gaps and NEXTGEN modeling. She has secured substantial research funding from GRDC, Agrifutures, ACIAR, and CRDC, with active projects including 'Genetic initiative to transform symbiotic nitrogen fixation' (2024-2028) and 'Development of RD&E Plan for the Lavender Industry' (2021-2022). Her collaborative network spans Australian research organizations and international partners. As an educator, Collins has coordinated the Bachelor of Agriculture program at La Trobe since 2022 and developed multiple agronomy courses. She has supervised numerous research students and serves on the Australian Council of Deans of Agriculture Learning and Teaching Academy. Her teaching philosophy emphasizes field-based practicals and industry engagement.

Bentley OakesView profile

Assistant Professor

• Digital Twins
• Model Transformations
• Software Engineering
• +6 more

Bentley Oakes is an Assistant Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. He holds a Ph.D. (2018) and M.Sc. (2015) from McGill University, and a B.Sc. (2013) from the University of Manitoba. His research focuses on Digital Twins , Model Transformations , and Knowledge Representation for cyber-physical systems. Ph.D. in Computer Science, McGill University, Canada M.Sc. in Science, McGill University, Canada B.Sc. in Computer Science, University of Manitoba, Canada His work bridges Model-Driven Engineering and Artificial Intelligence to advance the rigorous development of digital twins. Key research areas include: Digital Twin Engineering : Frameworks for systematic development, reporting, and validation. Knowledge Representation : Ontologies and contracts for modeling domain expertise. Model Transformations : Symbolic execution and debugging of ATL/DSLTrans transformations. Verification : Formal methods for cyber-physical systems and co-simulations. Oakes has published extensively in MODELS , MSR , TOSEM , and SoSyM . His recent studies on rationale extraction in open-source software and DevOps approaches for built assets highlight interdisciplinary innovations. He has been recognized for Outstanding Reviewing Contributions , receiving multiple Best Reviewer Awards at leading software engineering venues. At Polytechnique Montréal, he teaches courses such as: INF6900AE : Scientific and Technical Communication I INF7900AE : Scientific and Technical Communication II LOG6953FE / LOG6310E : Digital Twin Engineering LOG8371E : Software Quality Engineering

Nyamsuren EnkhboldView profile

Lecturer

• Artificial Intelligence
• Cognitive Science
• Software Engineering
• +5 more

Dr. Nyamsuren Enkhbold is a Lecturer in Computer Science (Software Engineering) at the School of Computer Science and Information Technology, University College Cork. He holds a PhD from the University of Groningen (Cognitive Modeling), an MSc from KAIST (Computer Science), and a BSc from Huree University (Computer Science). His research focuses on intelligent software development at the intersection of AI, Cognitive Science, and GIS, with notable contributions to geo-analytical question-answering systems and EU-funded projects like RAGE (serious games) and QuAnGIS (GIS workflow automation). Current interests include GenAI applications via fine-tuning and prompt engineering. He teaches CS3062 - Computing in Society CS6403 - Case Studies in Computing Entrepreneurship and previously contributed to courses at Utrecht University and KAIST. His work has been supported by a School of Computer Science Starter Fund (€10k). Research highlights include developing reusable AI components for serious games and advancing GIS systems that automatically generate workflows from user queries. His publications span cognitive modeling, spatial semantics, and NLP-driven geo-analytical tools.

Qiang GuoView profile

Senior Lecturer

• Formal Methods
• Software Engineering
• Distributed Systems
• +5 more

Dr Qiang Guo is a Senior Lecturer in Computer Science at Teesside University, affiliated with the Department of Computing & Games within the School of Computing, Engineering and Digital Technologies. He holds a PhD in Computer Science from Brunel University London and has previously worked as a Research Associate/Fellow at the University of Sheffield, Robert Gordon University, and the University of Cambridge, as well as a Senior Software Engineer at MasterCard. PhD, Computer Science, Brunel University London (2023) Master, Computational Intelligence, University of Plymouth Bachelor, Industrial and Electrical Automation, Xi'an University of Architecture and Technology PGCert in Higher Education, Teesside University His research focuses on Formal Methods in Software Engineering , particularly model-checking and testing of Distributed and Concurrent Systems . He integrates Artificial Intelligence techniques into Software Conformance Testing and explores Algorithmic Skeleton-based Parallel Computing for software engineering applications. His work addresses verification challenges in Erlang/OTP systems, extended finite state machines, and timed components. Dr Guo’s publications highlight a trajectory in Formal Verification , Automated Testing , and Optimization Techniques . Key themes include the application of Genetic Algorithms to finite state machine testing, Process Algebra (μCRL/mCRL) for telecommunications systems, and Testability Transformations to improve structural coverage. Trends reflect interdisciplinary bridges between Software Engineering and AI . As a Principal Investigator (PI) for the Innovate UK-funded project MC-IoT: Model Checking IoT Systems (2024-25, £31,907), he leads research in IoT system verification. He also contributed as Co-Investigator (Co-I) to the Parrot AI Enterprise Project (2023, £40k). Dr Guo is affiliated with the Centre for Digital Innovation , where he contributes to advancing software engineering methodologies. His work involves collaboration with academic and industrial partners to address real-world challenges in system verification and distributed computing.

Yuning DingView profile

Researcher

• Natural Language Processing
• Educational Technology
• Automatic Essay Scoring
• +3 more

Yuning Ding is a PhD Student and Research Assistant in the junior research group 'EduNLP' at the Research Center CATALPA (Center of Advanced Technology for Assisted Learning and Predictive Analytics), FernUniversität in Hagen, since January 2022. Her work focuses on Natural Language Processing applications for educational technology, specifically developing systems for automatic essay scoring and generating formative feedback for learners and summative feedback for teachers. Her educational background includes: M.Sc. in Applied Cognitive and Media Science with Specialization in Cognition & Artificial Intelligence at University of Duisburg-Essen (2017-2019) B.Sc. in Applied Cognitive and Media Science at University of Duisburg-Essen (2014-2017) B.A. in Communications at University of International Relations, Beijing (2009-2013) Ding's research centers on leveraging NLP to enhance writing education through AI-driven assessment and feedback systems. Her work bridges computational linguistics and pedagogy, with particular emphasis on argument mining, cohesion analysis, and cross-lingual content scoring. She investigates how transformer models and multi-task learning can improve the reliability and educational value of automated writing evaluation, while addressing critical issues like fairness and adversarial vulnerability in scoring systems. Her research demonstrates how NLP can provide actionable insights for both students and educators in writing development. Analysis of her publication trends reveals a strategic progression from foundational work on content scoring and error analysis toward sophisticated integrated systems. Recent work emphasizes multimodal feedback generation, argument-cohesion integration, and cross-lingual transfer, with increasing focus on real-world implementation challenges including fairness, robustness, and user experience. Her research spans multiple languages and educational contexts, reflecting a commitment to globally applicable educational technology. Within CATALPA, Ding actively collaborates across disciplines through the center's vibrant knowledge-sharing culture. She participates in project presentations and colloquia that facilitate cross-pollination of ideas between computer science, linguistics, and educational theory. Her work on the DARIUS corpus and FEAT-writing system demonstrates tangible contributions to educational resource development and interactive learning environments.

K Ram PrabhakarView profile

Researcher

• Computer Vision
• Image Processing
• Machine Learning
• +5 more

K Ram Prabhakar is an Assistant Research Scientist at Johns Hopkins University, advised by Prof. Rama Chellappa. Previously, he earned his Ph.D. from the Video Analytics Lab at the Computational and Data Sciences department of the Indian Institute of Science under Prof. R. Venkatesh Babu. He also worked as a Research Scientist at TCS Research in Bangalore. His research focuses on Computer Vision and Computational Photography , particularly in High Dynamic Range (HDR) Imaging , 3D vision, and image processing. His work spans applications in biometrics, remote sensing, and medical imaging. Recent publications highlight advancements in distillation-guided representation learning for video authentication diffusion models for fairness in face generation gait recognition thermal modality integration for low-light imaging and SAR (Synthetic Aperture Radar) applications . Scientific awards include IAPR Best Biometrics Student Paper Award (2024) Best Student Paper - Runner Up at BMVC 2021 . He has advised multiple students, including Yuxiang, Vishal, and Susmit, and contributed to projects on HDR deghosting, image fusion, and deep learning for computer vision tasks.

• Parallel Programming
• GPU Programming
• Image Processing
• +5 more

Sándor Szénási is a Professor at the Department of Informatics within the Faculty of Economics and Informatics at J. Selye University, where he serves as the person responsible for the Applied Informatics study program. With over two decades of academic experience, he has established himself as a leading researcher in parallel programming, GPU programming, and image processing, with recent expansion into machine learning applications. Eötvös Loránd University, Faculty of Science and Informatics (2001-2004): Information technology teacher Budapest Polytechnic, John von Neumann Faculty of Information Technology (1997-2001): B. Engineer in Information Technology Óbuda University (2010-2013): PhD in Applied Informatics Habilitation at Óbuda University (2019): Information Science and Technology Professor inauguration at Óbuda University (2022) Szénási's research spans computational methods with practical applications across multiple domains. His early work focused on parallel and GPU programming for image segmentation and heat transfer simulation. More recently, he has integrated machine learning techniques with traditional computational approaches, particularly in metaheuristic optimization, speech processing, and inverse problem solving. His interdisciplinary research bridges computer science with transportation safety, manufacturing, and medical applications. His recent publications reveal a clear evolution toward hybrid computational approaches that combine machine learning with traditional algorithms. There is a strong emphasis on optimization techniques, particularly metaheuristics enhanced with machine learning components. His work spans diverse application areas including speech emotion recognition, autonomous vehicle control, additive manufacturing, and heat transfer simulation, while maintaining a core focus on computational efficiency and parallel processing. Szénási has been actively involved in multiple EFOP-funded research projects including 'Improvement of higher education institutes for better teaching quality and accessibility,' 'Dynamics and control of autonomous vehicles,' and 'Solving the Inverse Heat Conduction Problem with Machine Learning.' His collaborative work with researchers like Gábor Kertész, Zoltán Vámossy, and Imre Felde demonstrates his commitment to interdisciplinary research.

• Educational Technology
• Artificial Intelligence in Education
• Learning Analytics
• +5 more

Niels Pinkwart is a Professor at Humboldt University of Berlin within the Faculty of Mathematics and Natural Sciences and Institute of Computer Science, serving as Member of the Faculty Council. His research group focuses on Computer Science and Society and Computer Science Didactics, developing AI-driven educational solutions for diverse learning contexts including vocational training, language acquisition, and autism support. His research centers on human-centered AI in education, with emphasis on adaptive learning systems, creativity assessment in programming environments, and inclusive technologies. Recent work examines physical embodiment in educational robots, bias mitigation in assessment algorithms, and neural engagement patterns in VR learning for neurodiverse populations. He investigates how multimodal feedback systems and LLM integration can enhance language learning while maintaining pedagogical alignment. Current publications reveal a strong trend toward practical AI implementation in real-world educational settings, particularly in vocational contexts and global health education. His team develops deployable tools like the AI Campus 2.0 platform and Zirkus Empathico 2.0 game, with increasing focus on ethical AI deployment and cross-cultural applicability as evidenced by Nigeria-based healthcare studies. The research consistently bridges theoretical frameworks with classroom-ready applications. Professor Pinkwart supervises doctoral candidates including Heinrich Mellmann, Kerstin Wagner, Gregor Wrobel, and Michael Piechotta, with disputation dates scheduled through late 2025. His work receives funding through collaborative projects like the Verbundprojekt KI-Campus 2.0, where HU Berlin leads the platform development component. The research group maintains strong industry and international healthcare partnerships, particularly in digital transformation initiatives for resource-constrained environments. He directs development of specialized educational technologies including sound-driven robot quiz systems with fair responder detection and multimodal feedback, alongside serious games for social-emotional development in autism. Current lab activities focus on scaling bias-mitigated exam assembly tools for vocational education while expanding VR-based cognitive engagement metrics across neurodiverse populations.

• Animal Science
• Reproductive Biology
• Meat Science
• +4 more

Sian MacKintosh is a Post-Doctoral Research Associate at the Institute of Biological, Environmental, and Rural Sciences (IBERS) at Aberystwyth University. Her research primarily focuses on animal production systems and their impact on livestock product quality, with particular emphasis on beef quality. She has published numerous research papers and participated in significant research projects related to agricultural science. Her educational background includes: Bachelor's degree in Equine Science from Aberystwyth University Master's degree in Equine Science from Aberystwyth University PhD from the Royal Veterinary College, London, on "Development of 3D Culture Models of Epithelial and Stromal Cells to Study the Pathophysiology of the Bovine Endometrium" Dr. MacKintosh's research interests span multiple areas of animal science and agricultural production. Her work primarily focuses on understanding how different animal production systems affect the quality of livestock products, particularly beef. She has conducted extensive research on the relationship between cattle diets and meat quality characteristics such as fatty acid profiles, texture, and eating quality. Her early work involved developing 3D culture models to study bovine reproductive biology, specifically the endometrium, which has important implications for understanding reproductive health in cattle. Her publication record shows a clear trajectory from fundamental reproductive biology research to applied agricultural science. Early in her career, she focused on developing 3D culture models of bovine endometrial cells to understand reproductive pathologies. More recently, her work has shifted toward practical applications in livestock production, examining how feed components like lucerne extract and field peas affect meat quality and poultry production. This progression demonstrates her ability to translate basic scientific knowledge into practical agricultural applications that address real-world challenges in food production. Dr. MacKintosh has been actively involved in research projects, including the "Enhancing agriculture productivity and ecosystem resilience in multifunctional landscapes" project from 2015-2018, where she served as a Co-Investigator. She has also participated in numerous conferences related to animal science and reproduction, demonstrating her engagement with the broader scientific community. Her work contributes to several UN Sustainable Development Goals, particularly those related to sustainable agriculture and food systems. Her research has been recognized through citations and mentions in policy documents, news outlets, and social media platforms, indicating the relevance and impact of her work beyond academic circles. She has collaborated with researchers across multiple institutions, suggesting strong networking capabilities and interdisciplinary approach to her research.

Philippa JacksonView profile

Associate Professor

• Biological Psychology
• Nutrition and Brain Function
• Cognitive Neuroscience
• +4 more

Philippa Jackson is an Associate Professor of Biological Psychology and Director of the Brain, Performance and Nutrition Research Centre at Northumbria University. She serves as Impact Lead on the Research and Knowledge Exchange Committee in the Department of Psychology. Her research focuses on the relationship between dietary components (nutrients, whole foods, supplements) and brain function/behavior, particularly investigating omega-3 fatty acids' effects on cognition, sleep, stress, and well-being across the lifespan. She has expertise in conducting large-scale randomized controlled trials, collaborating closely with industry partners. Education: PhD in Psychology (2010), BSc (Hons) Psychology (2003). Research Interests: Bioactive lipids' neurocognitive impacts, individual differences in supplementation responses, and essential nutrients' role in sleep regulation. Current projects include investigating differential effects of omega-3 isomers (DHA vs. EPA) and developing nutritional interventions for cognitive enhancement. Student Supervision: Currently guiding PhD candidates Ben Hughes (gaming performance and Nepalese Pepper) and Jessica Greener (dietary phospholipids and brain health). Awards: None explicitly listed in provided text. Key Contributions: Over 10 peer-reviewed articles since 2021, with emphasis on plant extracts (mango leaf, coffeeberry), nutraceuticals, and marine oils' effects on human cognition and physiology. Active in translational research bridging nutritional science and clinical applications.

• Video Coding
• Image Compression
• Machine Learning for Video Processing
• +5 more

Seyoon Jeong is a Professor in the Department of Computer Science and Engineering at Korea University's College of Engineering, with an extensive research career spanning over two decades in video coding, image compression, and computer vision. His scholarly contributions demonstrate consistent engagement with cutting-edge research topics in multimedia processing. Jeong's research primarily focuses on video and image compression technologies, with recent emphasis on Video Coding for Machines (VCM), High Efficiency Video Coding (HEVC), and machine learning applications for visual data processing. His work bridges traditional signal processing with modern AI techniques, particularly in optimizing compression algorithms for both human perception and machine vision tasks. Over the past five years, his research has increasingly concentrated on feature map compression, multi-scale processing, and the development of specialized coding techniques for machine-oriented applications. Analysis of his recent publications (2020-2024) reveals a clear research trajectory toward optimizing video coding specifically for machine vision systems rather than traditional human viewing. This emerging field addresses the challenge of efficiently transmitting visual data to AI systems that process video for tasks like object detection and scene understanding, where conventional perceptual quality metrics are less relevant. His work often involves collaboration with researchers from Korean institutions and international partners, reflecting the global nature of multimedia research. While specific awards aren't documented in the provided materials, his consistent publication record in top-tier journals and conferences (IEEE Transactions, CVPR, NeurIPS) indicates significant recognition within the multimedia research community. His research has practical applications in areas including 5G video transmission, machine vision systems, and efficient visual data processing for AI applications.

Andrea PoliniView profile

Professor

• Blockchain Technology
• Business Process Modeling
• IoT Systems
• +5 more

Andrea Polini is a Full Professor at the University of Camerino, focusing on interdisciplinary research at the intersection of blockchain technology, business process modeling, IoT systems, and software engineering. His work emphasizes formal methods for ensuring correctness in distributed systems, smart contract security, and model-driven approaches for IoT integration. Research interests include blockchain-based choreography execution, mutation testing strategies for smart contracts (e.g., ReSuMo and SUMO tools), and frameworks for IoT application portability (X-IoT). He also investigates process mining in public administration and humanitarian contexts, such as analyzing collaboration in crisis mapping platforms like the HOT Tasking Manager. Polini’s contributions span over 60 publications since 2014, with a strong focus on practical tools and methodologies (e.g., BProVe for business process verification, FloBP for IoT-enhanced processes). His work bridges theoretical computer science with real-world applications in healthcare, urban mobility (Tangramob framework), and disaster response systems.

• Biotechnology innovation management
• Pharmaceutical drug development
• Drug pricing and affordability
• +2 more

Professor Jan Rosier , Élan Full Professor of the Business of Biotechnology at University College Dublin's School of Biomolecular and Biomedical Science, combines pharmaceutical science expertise with business strategy. Holding doctorates from Ghent University and Cranfield School of Management, he previously served as VP at Johnson & Johnson and CEO of FORMAC Pharmaceuticals NV. Current affiliations: University College Dublin, Trinity College Dublin (Adjunct), University of Turin (Visiting) PhD in Pharmaceutical Sciences & PhD in Management Research Interests: Focus on biotechnology innovation management, drug pricing economics, and formulation technologies for HIV treatments. His work explores absorptive capacity in R&D and alternative mechanisms for drug affordability. Teaching: Coordinates modules in biotech economics, innovation strategy, and pharmaceutical management at UCD. Advocates student interaction and class discussion pedagogy. Scientific Awards: Johnson & Johnson Leadership Award for PREZISTA development Johnson & Leadership Award for INTELENCE development Fulbright Scholarship (Harvard University) NATO Research Fellowship (MIT) Professional Memberships: Advisory Board member of Incentives for Global Health's Health Impact Fund, American Association of Pharmaceutical Scientists.

Raed Al KontarView profile

Associate Professor

• Federated Learning
• Personalized Data Science
• Uncertainty Quantification
• +5 more

Raed Al Kontar serves as an Associate Professor with tenure in the Industrial & Operations Engineering (IOE) department at the University of Michigan's College of Engineering. He leads the Data Science Lab and holds affiliate appointments with the Michigan Institute for Data Science and Computational Discovery and Engineering. His research bridges probabilistic modeling with engineering applications, focusing on personalized, collaborative, and distributed data analytics where knowledge from diverse sources is integrated while preserving privacy. His research program addresses three core questions across descriptive, predictive, and prescriptive analytics: extracting shared/unique patterns across datasets, enabling collaborative model improvement while maintaining data privacy, and optimizing distributed trial-and-error processes. Key research areas include Federated Learning, Uncertainty Quantification, Bayesian Optimization, and Digital Twins, with applications spanning healthcare, manufacturing, and materials science. Current funding includes NSF (including a 2022 CAREER award), NIH, NLM, and industry partnerships. Research Trends: His recent publications demonstrate a strong focus on heterogeneous data integration through novel matrix/tensor factorization techniques, personalized PCA frameworks, and consensus-based distributed optimization. The work consistently bridges theoretical guarantees (e.g., identifiability conditions) with practical applications in hydrogel development, 3D printing, and pharmaceutical safety. Award Highlights: His group has won 12 best paper awards since 2022 across INFORMS, ASA, and IISE, including the 2024 Wilson Prize and INFORMS Data Mining Section's Best General Track Paper. Dr. Al Kontar has successfully placed multiple PhD students in tenure-track positions including Naichen Shi (Northwestern), Xubo Yue (Northeastern), and Seokhyun Chung (University of Virginia). His lab maintains active collaborations with medical researchers (NIH/NLM projects), materials scientists (autonomous experimentation), and pharmaceutical safety experts. The lab combines theoretical innovation with real-world impact, exemplified by their NSF CAREER-funded work on the Internet of Federated Things.