جست‌وجوی استاد و پژوهشگر

Miguel Machullaمشاهده پروفایل

پژوهشگر

• Music Psychology
• Gender Studies
• Queer Studies
• +۳ مورد دیگر

Miguel Machulla is a Researcher at the Department of Journalism and Communication Research, Hanover University of Music, Drama and Media. His academic background includes a Master's in Biology, Music, and Education from Ruhr University Bochum and Technical University Dortmund. He has held research positions at TU Dortmund, University of Cologne, and currently contributes to the BMBF-funded project Degree 5.0 focused on digital teacher education. Research Interests: Machulla's work bridges music psychology, gender/queer studies, and media interactions. He investigates how music shapes intergroup dynamics, supports LGBTQ+ identity formation, and enhances educational methodologies. Key themes include queer music analysis, media's role in environmental advocacy, and video-based reflective practices in pedagogy. Publications: His recent articles and conference papers (2023-2025) demonstrate empirical rigor in studying music's sociocultural impact, particularly through quantitative analysis of queer music characteristics, music functions in identity development, and innovative task designs for music education. Methodologically, he employs structural equation modeling, discourse analysis, and cross-cultural comparisons. Affiliations: Active member of the German Society for Music Psychology (DGM), Society for Music Research (GfM), and Research Hub at Hanover University. Associated with the Cologne Systematic Musicology Lab and BMBF project Degree 5.0 .

• Software Engineering
• Program Comprehension
• Eye Tracking in Software Development
• +۴ مورد دیگر

Professor Jonathan I. Maletic is affiliated with the University of Kent, UK, and is a leading researcher in software engineering with a particular focus on program comprehension and eye tracking in software development. He has published extensively in top venues such as Empirical Software Engineering, IEEE Transactions on Software Engineering, and the International Conference on Program Comprehension.

David M. Geiserمشاهده پروفایل

استاد

• Molecular Evolutionary Genetics
• Fungal Systematics
• Phylogenetics
• +۷ مورد دیگر

David M. Geiser is a Professor in the Department of Plant Pathology and Environmental Microbiology at Pennsylvania State University, where he conducts research in molecular evolutionary genetics and systematics of fungi. His work focuses on the phylogenetics and taxonomy of Fusarium and related plant pathogens, with implications for agriculture and food safety. His research interests include molecular phylogenetics, species complex delineation, DNA sequencing, fungal genomics, and mycotoxin production. He is a leading expert in the taxonomy and identification of Fusarium , contributing to major resources such as FUSARIUM-ID. His work integrates genomic, phylogenetic, and morphological data to resolve fungal species boundaries and improve diagnostic accuracy. The recent publications show a strong trend in phylogenomics, fungal taxonomy, and molecular diagnostics, particularly within the Fusarium genus. His research spans species discovery, misidentification issues in culture collections, trichothecene potential, and the development of genomic tools for pathogen identification. These works reflect a deep engagement with both fundamental fungal systematics and applied plant pathology. Elected to leadership in the Mycological Society of America Dr. Geiser has supervised and collaborated with numerous researchers, including PhD students and postdoctoral scholars, though formal advisees are not explicitly listed. He has been involved in multiple collaborative grants focused on fungal genomics, taxonomy, and plant disease diagnostics. His laboratory is part of a broad network of fungal researchers and contributes to international efforts in fungal nomenclature and classification. His lab is involved in high-impact collaborative research, particularly in the areas of fungal phylogenomics and diagnostic tool development. He is part of a global consortium working on fungal taxonomy and sequence-based nomenclature, contributing to standards that shape modern mycology.

• Urban Morphology
• Parametric Design
• Urban Design
• +۴ مورد دیگر

Michela Barosio is an Associate Professor at the Department of Architecture and Design (DAD) at Politecnico di Torino. She serves as Degree Course Leader in Architecture and contributes to teaching through various modules including Architectural and Urban Composition, Computational Design, and Design Studios. Her research focuses on urban morphology, parametric design, and sustainable urban regeneration while actively participating in European educational networks. Scientific Director for two non-commercial collaboration agreements with Chintana srl Steering Committee member for EAAE and ProArch associations Research Focus Michela explores the intersection of urban morphology and computational design, aiming to develop perception-centered design methodologies. Her work addresses sustainable urban transitions and the educational impact of digital tools in architecture. Current projects examine how architectural qualification influences multidisciplinary urban development and educational innovation. Teaching Activities She teaches across multiple degree levels, including: Master of Science in Architecture and Computational Design (2022-2025) Architectural Design in the Age of AI (2025) Atelier Fundamentals of Design series (2019-2025) Scientific Contributions 2025 EDULEARN conference publication on transversal skills and architectural representation 2024 EUR AU conference paper on perception-centered design algorithms 2022 EAAE conference proceedings on urban transitions Collaborative Projects Principal investigator for EDUPED project (2024-2027) on educational campuses as energy districts Scientific Director for multiple Chintana srl collaborations

Yoram Alhassidمشاهده پروفایل

استاد

• Theoretical Nuclear Physics
• Many-Body Quantum Systems
• Nuclear Many-Body Problem
• +۱۵ مورد دیگر

Yoram Alhassid is the Frederick Phineas Rose Professor of Physics in the Department of Physics at Yale University, where he leads a research group focused on theoretical nuclear and many-body physics. His work bridges nuclear physics, mesoscopic systems, and ultracold atomic gases, using advanced computational methods such as quantum Monte Carlo and the configuration-interaction shell model. His research interests include the nuclear many-body problem, femtoscience and nanoscience (nuclei, quantum dots, nanoparticles), and cold atomic Fermi gases. He has developed and applied the shell model Monte Carlo (SMMC) method to study statistical and collective properties of nuclei, such as level densities, deformation, and pairing correlations. He has extended these methods to study cold Fermi gases, particularly in the unitary regime, where he has investigated pseudogap phenomena, heat capacity, and pairing gaps. The recent publications highlight a strong focus on nuclear level densities, γ-ray strength functions, deformation effects, and quantum Monte Carlo methodologies. There is a clear trend toward microscopic, ab initio calculations of nuclear and many-body properties, with increasing attention to odd-mass and deformed nuclei, as well as the interplay between pairing and collective phenomena. Scientific Awards: Frederick Phineas Rose Professor of Physics Alhassid advises students and postdoctoral researchers, as evidenced by numerous co-authored publications. His group has developed advanced computational tools and codes (e.g., HF-SHELL) for finite-temperature mean-field and shell model calculations. The research is supported by high-performance computing and has implications for nuclear astrophysics, radioactive beam facilities, and quantum simulation with cold atoms. His lab focuses on theoretical and computational modeling of finite-size quantum many-body systems, with close collaborations across nuclear theory, condensed matter, and atomic physics. The group emphasizes method development, benchmarking against mean-field theories, and extracting model-independent signatures of physical phenomena such as deformation and pairing.

• Biochemistry
• RNA Structure
• Structural Biology
• +۶ مورد دیگر

Jessica Brown is an Associate Professor in the Department of Chemistry and Biochemistry at the University of Notre Dame, where she leads a research group focused on RNA structure and function. Her lab investigates the structural, biochemical, and cellular roles of RNA triple helices, with a particular emphasis on the MALAT1 triple helix and its implications in cancer biology. Education: Ph.D. in Biochemistry, The Ohio State University (2010) B.S. in Chemistry and Biological Sciences, Wright State University (2005) Her research lies at the intersection of biochemistry, structural biology, and molecular oncology. She explores fundamental questions about RNA triple helix formation, stability, protein interactions, and degradation pathways. Using techniques such as X-ray crystallography, cryo-EM, and high-throughput assays, her work aims to uncover the biophysical principles governing RNA triplexes and their regulatory roles in human disease. The recent publications from her lab highlight a strong trend in RNA modification, non-coding RNA structure, and therapeutic targeting of oncogenic RNAs. Her studies span from mechanistic enzymology (e.g., METTL16 kinetics) to disease-associated structural rearrangements in MALAT1, reflecting a multidisciplinary approach that bridges basic science and translational applications. Scientific Awards: NIH Pathway to Independence Award (K99/R00), 2014–2019 American Cancer Society Postdoctoral Fellowship, 2012–2014 OSU Presidential Fellowship, 2010 American Heart Association Predoctoral Fellowship, 2008–2010 Dr. Brown advises graduate students and postdoctoral researchers in her lab, fostering training in cutting-edge biochemical and structural methods. While specific grant details are not listed, her sustained research output and prestigious fellowships indicate active external funding. Her lab is positioned at the forefront of RNA structural biology, with future work likely to expand the understanding of the 'triplexome' and develop tools for targeting stable RNA structures in disease. Labs and Research Teams: The Brown Laboratory at the University of Notre Dame employs a collaborative, interdisciplinary team of researchers using structural, biochemical, and cellular approaches to study RNA triple helices. The lab actively recruits graduate and postdoctoral trainees and contributes to major discussions in RNA biology and epitranscriptomics.

• Artificial Intelligence
• Blockchain
• Digital Humanities
• +۵ مورد دیگر

Anthony Masure is an Associate Professor at Geneva University of Art and Design (HEAD – Genève) , part of the University of Applied Sciences and Arts Western Switzerland (HES-SO) . His work bridges Artificial Intelligence , Blockchain , and Digital Humanities through critical design lenses. He explores tensions between creative automation and human agency , questioning how machine learning and smart technologies reshape authorship, value systems, and ecological practices in design. Key research areas: AI ethics , NFT implications , post-digital design , and technological accountability Projects like "Design et machine learning : l'automatisation au pouvoir?" (2021-2023) and "Post digital Graphic Design" (2019-2022) demonstrate his focus on critical technological engagements Advocates for open access and non-traditional research formats through initiatives like HEAD-Publishing His recent articles analyze generative AI in design, NFT governance , and circular design frameworks. Collaborations with researchers like Guillaume Helleu and Yves Citton emphasize socio-technical implications. Current projects include "L'organisation autonome automatisée (OAA)" (2024), exploring future enterprise models under climate constraints.

Julian Oertelمشاهده پروفایل

پژوهشگر

• Software Engineering
• Code Summarization
• Human Factors in Software Development
• +۴ مورد دیگر

Julian Oertel, a Ph.D. candidate at the University of Rostock's Institute of Computer Science, specializes in software engineering, code summarization, and human factors in software development. His work explores AI techniques like large-language models for accelerating software development. His research spans: Software comprehension and code summarization Human-computer interaction in AI-assisted programming Model-driven engineering (MDE) and modeling experience (MX) Time-sensitive networking (TSN) and network calculus His recent publications focus on GitHub Copilot's impact on coding workflows and empirical studies of model-driven engineering. Oertel's work bridges theoretical analysis with practical tool evaluation in modern software development contexts.

Paul Baysمشاهده پروفایل

استاد

• Visual Working Memory
• Computational Neuroscience
• Cognitive Psychology
• +۵ مورد دیگر

Professor Paul Bays is a leading cognitive neuroscientist at the University of Cambridge, holding a professorship in the Department of Psychology within the School of Biological Sciences. He directs the Computational Cognition Group (Bays Lab), where his research focuses on understanding the neural and computational mechanisms underlying visual working memory and perception. His laboratory employs a diverse range of methodologies including visual psychophysics, extended reality (XR) technology, eye and body movement recordings, mathematical models, and artificial neural networks. Bays Lab collaborates extensively with researchers using brain imaging, recording and stimulation techniques, as well as neuropsychologists studying cognitive aging, mental illness, and neurological disorders. Professor Bays' research has revolutionized our understanding of visual working memory, demonstrating that memory capacity operates as a flexible resource rather than a fixed number of "slots." His work has shown how this resource is dynamically allocated across visual features and how it influences perception, decision-making, and action. A major focus examines how working memory bridges discrete transitions in visual input during eye movements, allowing for continuous visual experience despite frequent shifts in gaze. His laboratory has made significant contributions to understanding sensory prediction and motor learning, particularly regarding tactile attenuation (the phenomenon where self-generated touch feels weaker than externally generated touch). Recent work has applied efficient coding principles to explain various perceptual phenomena including weight illusions. Research Theme: Adaptive Brain Computations Research Theme: Lifelong Brain Development and Brain Ageing Research Theme: Brains and Machines Professor Bays actively supervises PhD students and postdoctoral researchers in the Bays Lab, fostering the next generation of cognitive neuroscientists. His laboratory has developed valuable research tools including the Analogue Report Toolbox for analyzing visual working memory data.

Sandra Diaz Pierمشاهده پروفایل

پژوهشگر

• Computational Neuroscience
• High Performance Computing
• Machine Learning
• +۳ مورد دیگر

Dr. Sandra Diaz Pier is a Scientific Lead at the Jülich Supercomputing Centre (JSC) within the Jülich Research Centre , Germany. Specializing in computational neuroscience , high performance computing (HPC) , and machine learning , she bridges neuroscience and advanced computational methods through her research. Education: B.Sc. in Electronic Systems Engineering, Mexico M.Sc. in Computer Science (focus: machine learning, quantum computing), Mexico Second M.Sc. in Electrical Engineering, Ontario, Canada Ph.D. in Computer Science, Germany (2021) Her research focuses on modeling and simulating brain dynamics and plasticity at multiple scales, leveraging HPC to accelerate large-scale neural network simulations. She actively contributes to EU projects like the Human Brain Project (HBP) , Virtual Brain Cloud , and EBRAINS 2.0 , emphasizing infrastructure development and educational training. Her work includes open-source tools such as the NEST simulator , The Virtual Brain , and L2L , enabling efficient parameter exploration and multiscale co-simulation frameworks. The 15 most recent publications highlight her interdisciplinary approach, spanning topics from quantum computing in biomolecular simulations to neural plasticity algorithms and cloud-based brain modeling . These articles reflect her expertise in integrating machine learning , multi-scale simulation , and HPC infrastructure for neuroscience challenges, including seizure propagation, Parkinson’s disease progression, and swarm intelligence in spiking networks. She leads technical coordination in projects like EBRAINS and serves as a task leader in the HBP infrastructure work package , while also organizing workshops and hackathons for open-source tools. Her role involves supporting domain scientists through methodological research and workflow optimization for brain simulations.

Alastair Kayمشاهده پروفایل

مدرس

• Quantum Computation
• Quantum Information Theory
• Quantum Cryptography
• +۱۳ مورد دیگر

Dr. Alastair Kay is a Lecturer in the Department of Mathematics at Royal Holloway, University of London. His research focuses on theoretical quantum computation, quantum information theory, and quantum cryptography, particularly addressing challenges in quantum state transfer, error correction, and networked quantum systems. He holds a PhD from the University of Cambridge under Prof. Artur Ekert and a physics degree from Keble College, University of Oxford. His research spans topics such as Quantum state transfer protocols using engineered Hamiltonians Quantum error correction mechanisms for experimental systems Entanglement properties in graph states and spin networks Applications of quantum computing in cryptography and information theory The articles listed reflect his work in quantum information science, computational physics, and theoretical cryptography. Key trends include advancements in fault-tolerant quantum communication, optimization of spin chain dynamics, and foundational studies in quantum correlations and nonlocality. Alastair actively develops software tools like quantikz for quantum circuit diagrams and ConTeXi for LaTeX equation integration in Microsoft Office. He also emphasizes open science principles and reproducibility in quantum research through personal commentary and collaboration with his fiancée, a Panton Fellow in open research practices.

Julian Tachellaمشاهده پروفایل

پژوهشگر

• Signal Processing
• Machine Learning
• Computational Imaging
• +۵ مورد دیگر

Dr. Julian Tachella is a CNRS Research Scientist at the Sisyph Laboratory of École Normale Supérieure de Lyon, with co-founder/CSO roles at Blur Labs. His career spans signal processing, machine learning, and computational imaging, focusing on inverse problems and self-supervised learning. Affiliation: CNRS (French National Centre for Scientific Research), Sisyph Laboratory, École Normale Supérieure de Lyon Co-founder & CSO: Blur Labs (AI/Imaging startup) Research Interests: At the intersection of signal processing and deep learning , his work addresses imaging inverse problems through self-supervised methodologies (e.g., UNSURE, Generalized R2R) that eliminate ground-truth requirements. Key contributions include equivariant imaging frameworks for stability, spline sketches for photon-counting lidar compression, and uncertainty quantification techniques with equivariant bootstrapping. Recent Trends: 2025 publications emphasize lightweight architectures for multi-domain reconstruction (CT, super-resolution) and noise-agnostic SURE methods. 2024 works focus on audio declipping , compressed lidar , and nonlinear algorithm unrolling with applications in autonomous vehicles and medical imaging. Scientific Awards: Best Student Paper Award at ICASSP’22 Collaborations & Leadership: He leads the DeepInverse open-source project and develops algorithms for real-time 3D lidar reconstruction. His team includes researchers from University of Edinburgh and Grenoble INP, with applications in automotive lidar and underwater imaging.

Jeffrey P. Bakerمشاهده پروفایل

استاد

• Medical History
• Pediatrics
• Public Health
• +۶ مورد دیگر

Jeffrey P. Baker is a Professor in the Department of History at Duke University, with a joint appointment in Pediatrics. His work bridges medical history, bioethics, and public health, focusing on the intersection of medicine, technology, and societal values. Research Interests: History of neonatal medicine, vaccine development, autism studies, health disparities, and medical technology. Education: Ph.D. in History (Duke University, 1993); M.D. (Duke University, 1984). Teaching: Courses include Research in Medical Humanities and Practical & Patients: Historical Clinical Medicine . Dr. Baker’s research has chronologically examined premature infant care (notably in The Machine in the Nursery ), vaccine controversies (e.g., thimerosal-autism debates), and structural health disparities. Recent projects use computational methods to detect early autism markers via computer vision analysis, collaborating with teams at Duke and beyond. His publications span 20th-century medical history, with a focus on pediatrics, immunization, and technology. While no explicit awards are listed, his interdisciplinary work impacts medical education models and public health policy. Dr. Baker’s Durham, NC-based studies highlight how local history shapes community trust in academic health centers. He actively integrates historical perspectives into medical clerkships, emphasizing longitudinal understanding of clinical practices. Current projects explore digital phenotyping tools and the social history of neonatology.

• Protein Engineering
• Biomaterials
• Biological Macromolecules
• +۵ مورد دیگر

Sesilja Aranko is an Assistant Professor at Aalto University , affiliated with the Department of Bioproducts and Biosystems. Her research focuses on protein engineering, biomolecular condensates, and sustainable materials derived from biological macromolecules. Research Groups : Cellular Engineering Email : sesilja.aranko@aalto.fi Her work explores protein self-assembly mechanisms, particularly in spider silk and collagen systems, leveraging liquid-liquid phase separation for advanced biomaterial design. Recent studies investigate how polymer length and modular protein architecture control condensate properties, alongside developing bio-inspired adhesives from recombinant proteins and nanocellulose. She has pioneered the use of Catcher/Tag click-reaction tools for protein engineering and developed sustainable methods for spider silk production with inherent functionalization potential. Collaborative projects span marine biology (sea cucumbers) and keratin waste upcycling for textile applications. Key methodologies include intein-mediated protein splicing, segmental isotopic labeling, and structural characterization of protein ligation systems. Her research bridges fundamental biophysics with industrial applications in sustainable chemistry and biomimetic composites.

• Self-regulated Learning
• Learning Analytics
• Online Education
• +۴ مورد دیگر

Dr. Jacqueline Wong is an Assistant Professor in the Department of Education and Pedagogy at the Faculty of Social and Behavioural Sciences, Utrecht University. Her academic work focuses on enhancing educational practices through evidence-based research in self-regulated learning and learning analytics, with particular applications in higher education contexts. Dr. Wong earned her Ph.D. (cum laude) from Erasmus University Rotterdam with a thesis entitled "Enhancing Self-Regulated Learning Through Instructional Supports and Learning Analytics in Online Higher Education," which received the Best Thesis Award 2021 from the Erasmus Graduate School of Social Sciences and the Humanities. She completed her Master's degree (cum laude) in Educational Psychology, where she examined the use of movements to support text comprehension. Prior to joining Utrecht University, she was a postdoctoral researcher at Delft University of Technology (2020-2022) working with the Program of Innovation in Mathematics Education (PRIME). Dr. Wong's research program spans several interconnected areas: the application of learning analytics to support self-regulated learning, the design of instructional interventions that promote metacognitive awareness, and the investigation of how emerging technologies like generative AI can enhance educational experiences. Her work frequently examines how technology can be leveraged to support students' metacognitive processes and goal-setting behaviors in both traditional and online educational settings. She has made significant contributions to understanding how conversational agents and learning analytics can provide personalized support for students' self-regulated learning processes. Analysis of her recent publications reveals a clear trajectory toward integrating emerging technologies with established educational theories. Her 2024-2025 work increasingly focuses on the intersection of artificial intelligence and educational psychology, particularly examining how generative AI tools can enhance academic text accessibility and provide adaptive support for learning. She also continues to investigate cognitive aspects of learning in educational games and augmented reality environments, with implications for both educational practice and learning theory development. Among her notable achievements, Dr. Wong received the Best Thesis Award 2021 from the Erasmus Graduate School of Social Sciences and the Humanities. This recognition highlights the significant contribution her doctoral work has made to the field of educational psychology and learning sciences. Her systematic reviews, including examinations of student engagement in mathematics education and gamification in MOOCs, have provided valuable syntheses of research in growing areas of educational technology. Dr. Wong has been an active research member of the Leiden-Delft-Erasmus Centre for Learning and Education (LDE-CEL) and the network for Self-Regulated Learning in Digitalised Schools (SeReLeDiS). Her collaborative approach to research is evident in her extensive co-authorship network across multiple institutions. Before joining Utrecht University, she worked with lecturers to promote student motivation and self-regulated learning in blended service mathematics courses and examined the effectiveness of technology tools for learning mathematics. Dr. Wong's work bridges theoretical frameworks with practical implementations, often involving close partnerships with educators to ensure research relevance to real-world classroom challenges. Her current research appears to be moving toward deeper integration of AI technologies with educational theory, particularly examining how generative AI can support metacognitive processes and self-regulated learning across diverse educational contexts.