جست‌وجوی استاد و پژوهشگر

Felix Voigtlaenderمشاهده پروفایل

استاد

• Mathematics of Machine Learning
• Neural Networks
• Approximation Theory
• +۱۱ مورد دیگر

Felix Voigtlaender is a Professor for Mathematics with a focus on Reliable Machine Learning at the Mathematical Institute for Machine Learning and Data Science, KU Eichstätt-Ingolstadt. He has held previous academic positions at TU Munich as an Emmy Noether Group Leader, at the University of Vienna as a Senior Scientist, and at KU Eichstätt and TU Berlin in research and postdoctoral roles. PhD: RWTH Aachen University, 2015 Master: RWTH Aachen University, 2013 Bachelor: RWTH Aachen University His research lies at the intersection of mathematics and machine learning, with a strong emphasis on the theoretical underpinnings of neural networks. He investigates approximation properties, expressiveness, and the existence of adversarial examples. His work also extends into harmonic analysis, functional analysis, and multiscale systems such as wavelets and shearlets. He is deeply interested in the mathematical foundations of data science, including sampling theory and information-based complexity. The recent publications highlight a consistent focus on the mathematical analysis of neural networks, particularly approximation capabilities and universal approximation theorems in both real and complex domains. His work often provides sharp theoretical bounds and deep insights into the behavior of deep learning models in high-dimensional settings. Friedrich-Wilhelm-Award 2016 for his PhD thesis Felix Voigtlaender has supervised students and is actively involved in teaching and academic development, notably contributing to the launch of a new BSc program in Data Science at KU Eichstätt-Ingolstadt. He has collaborated with prominent researchers such as Götz Pfander, Gitta Kutyniok, and Hartmut Führ, and has been funded through competitive grants like the Emmy Noether program. He leads a research group focused on rigorous mathematical approaches to machine learning challenges. He is affiliated with the Mathematical Institute for Machine Learning and Data Science, a newly founded institute at KU Eichstätt-Ingolstadt, which will be based in Ingolstadt. His team is involved in both theoretical exploration and practical applications of data science methods.

Elisa Thomasمشاهده پروفایل

دانشیار

• Innovation Ecosystems
• Entrepreneurial Ecosystems
• Regional Development
• +۱۲ مورد دیگر

Elisa Thomas is an Associate Professor at the Department of Innovation, Management and Marketing, UiS School of Business, University of Stavanger, Norway. Her research focuses on innovation ecosystems, responsible research and innovation, and the evolving role of universities in regional development. She actively collaborates with international scholars and contributes to leading journals and book publications. Her research interests include: Entrepreneurial and innovation ecosystems Responsible research and innovation (RRI) Living labs and user involvement University engagement with regions Science and technology parks Green and social innovation The recent publications of Elisa Thomas reveal a strong thematic focus on the transformation of universities into engaged institutions that actively shape regional innovation ecosystems. Her work examines dynamic capabilities in university alliances, the inclusion of marginalized users such as the elderly in innovation processes through virtual spaces, and the role of intermediaries in innovation search. There is a consistent emphasis on open, responsible, and socially inclusive innovation models, particularly in emerging economies and the Global South. Scientific contributions include: Key chapters in influential books published by Routledge and De Gruyter Articles in top journals such as Journal of Technology Transfer, R&D Management, and Growth and Change Active participation in international conferences including ISPIM and Academy of Management Elisa Thomas advises on innovation management and responsible innovation practices. She has led and contributed to research projects involving stakeholder knowledge management, digital health, and smart agriculture. Her collaborative network spans Brazil, the UK, Norway, and beyond, reflecting a strong international research profile. She has also organized and presented at research seminars and workshops on engaged universities and innovation tools. She is involved in research groups such as EcoHubI, NEECI, and GESMAC, which focus on innovation, sustainability, and responsible technology development. These groups serve as platforms for interdisciplinary collaboration and knowledge dissemination in innovation studies.

• Computer Vision
• Machine Learning
• Stereo Matching
• +۴ مورد دیگر

Wei Qin Chuah is a Lecturer at the School of Engineering at RMIT University, Australia, specializing in computer vision and machine learning. His research focuses on developing robust and generalizable models for various computer vision applications, particularly in the domains of stereo matching, depth estimation, and autonomous vehicle systems. Dr. Chuah's research interests span several key areas with particular emphasis on domain generalization and domain adaptation techniques that enable models trained on synthetic data to perform well on real-world applications. He has made significant contributions to stereo matching and depth estimation systems, developing methods to improve long-range perception for autonomous vehicles. His work on Neural Radiance Fields (NeRF) addresses challenges in learning from corrupted or uncurated image collections. Additionally, he has explored applications of computer vision in diverse domains including industrial inspection , veterinary assistance systems , and traffic management around construction sites. Analysis of Dr. Chuah's publication trends reveals a consistent focus on addressing the challenge of synthetic-to-real domain shift , developing innovative methods like Information-Theoretic Shortcut Avoidance (ITSA) to prevent models from learning spurious correlations in synthetic training data. His recent work extends to keypoint estimation for industrial applications using micro-CT imaging and transfer learning, as well as developing artifact-free Neural Radiance Fields from uncurated image collections. Dr. Chuah is currently supervising research projects including Learning Robust and Generalisable Models for Computer Vision Using Animation , indicating his ongoing commitment to training the next generation of computer vision researchers. His collaborative work spans multiple institutions, with frequent co-authorship patterns suggesting strong research partnerships with colleagues including Tennakoon R, Hoseinnezhad R, and Bab-Hadiashar A. His research bridges theoretical advances in machine learning with practical applications in autonomous systems, industrial automation, and healthcare.

Nurhak Polatمشاهده پروفایل

پژوهشگر

• Digital Anthropology
• Data Politics
• Authoritarianism
• +۷ مورد دیگر

Nurhak Polat is a Researcher at the Institute of Social and Cultural Anthropology, University of Bremen, since 2025. His work critically examines the interplay between data, politics, surveillance, and government in digital societies, with a regional focus on Turkey. He is affiliated with the Research Group 'Soft-Authoritarianism' and collaborates with interdisciplinary teams like 'Worlds of Contradiction' at Central European University. PhD in European Ethnology, Humboldt University of Berlin (2017) Magisterstudium in Cultural and Social Anthropology, University of Vienna (2008) Bachelor in Social Anthropology, Istanbul University (2003) His research explores how data infrastructures and digital technologies shape authoritarian networks, democratic possibilities, and social contradictions. Key themes include data-based governmentality, pandemic surveillance, and counter-data practices. He employs multi-local ethnographic methods to analyze power dynamics in digital governance and reproductive technologies. Polat's scientific contributions include a monograph on reproductive technologies in Turkey and co-edited volumes on soft authoritarianism. He has received stipends from the Österreichische Orient-Gesellschaft Hammer-Purgstall and actively organizes academic events on digital authoritarianism and solidarity in academia. He contributes to collaborative projects like 'YISARES' and 'Politics of Repression – Strategies and Resistances in Scholarship,' emphasizing interdisciplinary dialogue and critical engagement with authoritarian shifts in digital contexts.

Michael Morganمشاهده پروفایل

استاد

• Behavioural Neuroscience
• Visual Neuroscience
• Psychophysics
• +۷ مورد دیگر

Professor Michael Morgan is a renowned neuroscientist at City, University of London , specializing in the visual system. He joined the institution in 2000 and has held a Professor of Neuroscience appointment. His career spans prestigious institutions including the University of Cambridge , McGill University , Durham University , University College London (UCL) , and University of Edinburgh . Education: PhD, MA, and ScD in Natural Sciences (Experimental Psychology) from the University of Cambridge His research focuses on behavioural neuroscience with applications to vision science , particularly in motion processing , spatial vision , perceptual decision-making , and visual adaptation mechanisms . Current projects funded by the EPSRC and Wellcome Trust explore selective attention , signal detection , and visual space recalibration . Recent publications highlight his work on change detection , direction-selective adaptation , density/contrast modulation , and stereopsis . Collaborations with colleagues like J.A. Solomon and S. Grant span both Applied Vision Research Centre and external institutions such as UCI and Institute of Ophthalmology . Scientific Honors: Elected Fellow, The Royal Society (2005–present) His work bridges psychophysics , visual perception , and neural mechanisms , with implications for understanding both normal and clinical visual processing.

David Christiniمشاهده پروفایل

استاد

• Cardiac Electrophysiology
• Computational Biology
• Biomedical Engineering
• +۳ مورد دیگر

David Christini, PhD is a Professor in the Department of Physiology and Pharmacology at SUNY Downstate Health Sciences University, where he also serves as Senior Vice President for Research. His work bridges computational and experimental cardiac electrophysiology, focusing on dynamics from cellular to organ levels. Research Interests: Dr. Christini’s lab investigates cardiac electrical activity using advanced modeling and stem cell-derived cardiomyocyte experiments. Key areas include arrhythmia mechanisms, fibroblast-myocyte interactions, and nonlinear dynamics in heart rhythm disorders. Publications Overview: His recent work spans computational modeling calibration, stem cell cardiomyocyte heterogeneity, drug cardiotoxicity screening, and optical dynamic clamping techniques, reflecting interdisciplinary approaches to cardiac safety and disease mechanisms. Administrative Role: As Senior Vice President for Research, he oversees institutional research strategy and innovation initiatives at SUNY Downstate.

Bertil Bjerkanمشاهده پروفایل

دانشیار

• Social Development in Infancy
• Early Communication (Normal and Abnormal Processes)
• Deafblindness
• +۴ مورد دیگر

Bertil Bjerkan is an Associate Professor at the Department of Psychology , UiT The Arctic University of Norway. His work focuses on developmental psychology, clinical psychology (rehabilitation), and communication disorders, with a particular emphasis on deafblindness and early communication processes. Role: Associate Professor University: UiT The Arctic University of Norway Department: Psychology Email: bertil.bjerkan@uit.no Research Interests: Social development in infancy Normal and abnormal early communication processes Deafblindness Stuttering Language disorders Clinical rehabilitation Teaching Responsibilities: PSY-1503: Språk og språkvansker PSY-2541: Barns utvikling PSY-2543: Praksis i barnehage PSY-1001: Innføring i psykologi PSY-1007: Utviklingspsykologi Collaborations: Affiliated with the Regional Centre for the Deaf-Blind (UNN) and member of the Clinical Psychology research group at UiT.

Gary F. Templetonمشاهده پروفایل

استاد

• Organizational Learning
• Construct Development
• Messy Data Problems
• +۴ مورد دیگر

Gary F. Templeton is a Professor of Management Information Systems (MIS) at West Virginia University's John Chambers College of Business and Economics. He holds a PhD in MIS from Auburn University and has previously held faculty positions at the University of Alabama in Huntsville and Mississippi State University. Education: PhD in MIS, Master of MIS, MBA, and Bachelor of Finance from Auburn University Teaching Experience: 10,000+ students at undergraduate, master's, and doctoral levels in business analytics, decision support systems, programming, and healthcare-related topics His research spans three decades, focusing on organizational learning, methodological challenges in MIS research, and the economics of information technology firms. Current work emphasizes messy data problems like non-normality and missing data, with applications in healthcare analytics, cybersecurity, and financial technology. Recent publications (2021-2025) examine topics including regression imputation methods, cluster analysis applications, AI frameworks for financial portfolio optimization, and vulnerability diffusion models in software ecosystems. His research demonstrates interdisciplinary applications of MIS principles across business, healthcare, and public policy domains. Key Contributions: Developed the Two-Step Normality Transformation method Created frameworks for organizational learning constructs Advanced methodologies for handling messy data in MIS research Explored digital resource clusters in software ecosystems Investigated economic structures of emerging technology providers Professor Templeton has taught across multiple MIS domains including systems analysis and design, information resource management, and advanced research topics. His recent work increasingly integrates artificial intelligence and machine learning techniques to address complex business and technical challenges.

Diana Zidarovمشاهده پروفایل

استاد مدعو

• Knowledge Translation
• Health Program Evaluation
• Implementation Science
• +۵ مورد دیگر

Diana Zidarov is an Adjunct Professor at the School of Rehabilitation, University of Montreal, with affiliations at the CRIR (Interdisciplinary Research Centre in Rehabilitation of Metropolitan Montreal) and IURDPM (University Institute for Rehabilitation in Physical Disabilities of Montreal). Her research focuses on knowledge translation, health program evaluation, implementation science, and rehabilitation quality improvement. B.Sc. Physiotherapy, University of Montreal (1995) M.Sc. Rehabilitation Sciences, University of Montreal (2008) Ph.D. Public Health, University of Montreal (2015) Postdoctoral Fellowship, McGill University (2019) Key research projects include developing clinical information systems for spinal cord injury rehabilitation, creating mobility circuits for stroke patients, implementing early mobilization programs for elderly patients, and establishing registries for osseointegration procedures. Her methodology emphasizes participatory, qualitative, and mixed approaches. Recent publications analyze chronic pain management metrics, telerehabilitation implementation, and outcome measure utilization in rehabilitation settings. While no specific scientific awards are listed in the provided text, her work consistently addresses healthcare quality and patient-centered care.

• Modern and Contemporary Chinese Literature
• Chinese Literary Criticism
• Cultural Studies
• +۱ مورد دیگر

Chen Jinsong serves as a Lecturer at the Center for Humanities, Southern University of Science and Technology (SUSTech) since 2019, following his tenure as Director of SUSTech's Party and Government Office (2017-2018). His academic work centers on modern and contemporary Chinese literature, literary criticism, and cultural analysis within China's evolving socio-literary landscape. His educational foundation includes: Bachelor of Literature in Chinese Linguistic Literature from Hubei Normal University Master of Literature in Modern and Contemporary Chinese Literature from Shenzhen University PhD in Modern and Contemporary Chinese Literature from Sun Yat-sen University Chen's research explores humanistic themes in contemporary Chinese fiction, urban literary movements, and author-specific studies (e.g., Mo Yan, Su Tong). His interdisciplinary approach bridges literary analysis with cultural commentary, emphasizing ethical dimensions and social realities. With over 100 publications, he contributes significantly to scholarly discourse while maintaining active creative practice in prose and poetry. Analysis of his 36 journal articles (2005-2018) reveals consistent focus on contemporary Chinese authors, new urban literature, and critical evaluation frameworks. His work demonstrates evolving engagement with literary modernity, human nature in everyday contexts, and spiritual dimensions of existence, often contextualized within China's rapid societal transformations. Scientific recognition includes: 2011 Literature Review Award from 'Frontier Literature and Art Review' Chen teaches SUSTech's mandatory 'Writing and Communication' course and contributes to the Writing and Communication Courses research office. While no graduate student advising or research grants are documented, his publications have been selected for China's National College Entrance Examination and other major educational assessments, underscoring his pedagogical influence. His dual role as scholar and creative writer enriches SUSTech's humanities curriculum through practical literary engagement. As part of SUSTech's Center for Humanities—a school-level unit structured into six research offices—Chen operates within the Writing and Communication Courses office, which develops the university's core writing pedagogy and supports interdisciplinary academic communication across STEM and humanities disciplines.

Alexander Tsodikovمشاهده پروفایل

استاد

• Biostatistics
• Biomathematics
• Survival Analysis
• +۶ مورد دیگر

Alexander Tsodikov is a Professor in the Department of Biostatistics at the University of Michigan School of Public Health. His career spans multiple institutions including the Curie Institute, University of Leipzig, University of Utah, and University of California, Davis. Education: PhD in Applied Mathematics (1991), M.Sc. in Applied Mathematics (1988), both from St. Petersburg State Technical University. Research focuses on biostatistical methodology for cancer studies, including: Survival analysis and cure models Semiparametric inference Frailty/mixture models Age-period-cohort modeling Statistical consulting in clinical research Methodological contributions include generalized self-consistency approaches and computational methods like EM/MM algorithms. Research supported by National Cancer Institute grants. Scientific Awards: Elected Member, International Statistical Institute (2011-present) Key affiliations: Member, American Statistical Association Member, International Biometric Society Co-Investigator, Cancer Interventions and Surveillance Modeling Network (CISNET) Students advised include Quoc Tran , Kelley Kidwell , Jonathan Rice , and Chin-Shang Hu .

Milad Toutounchianمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Data Science
• +۳ مورد دیگر

Milad Toutounchian serves as an Assistant Teaching Professor within the Department of Information Science at Drexel University's College of Computing & Informatics (CCI). Prior to joining Drexel, he gained industry experience as a data scientist, lead data science instructor at Make School, and mentor at Springboard's Data Science Career Track. His professional background bridges academic theory with practical industry applications. Dr. Toutounchian's educational background includes: PhD in Electrical Engineering from Simon Fraser University BSc in Telecommunication from Sharif University of Technology His primary research interests lie in applied machine learning and deep learning, with a focus on real-world problem solving. Recent work explores AI-driven medical diagnostics, particularly in breast cancer detection using multi-scale and multi-view transformer frameworks. Earlier research addressed signal processing challenges in wireless communications, including beamforming for MIMO systems and cognitive radio networks. This evolution highlights a transition from communications engineering to data science applications in healthcare. Analysis of his publication history reveals a clear shift in research focus. From 2010 to 2016, his work centered on wireless communications and signal processing, contributing to MIMO systems and beamforming techniques. Since 2023, he has pivoted toward healthcare applications of AI, developing deep learning frameworks for human-centric breast cancer diagnostics. This trajectory demonstrates adaptability across technical domains while maintaining core expertise in machine learning methodologies. No scientific awards or major honors were documented in the available information. While his biography notes teaching responsibilities and industry mentoring experience, specific details about graduate student advising or research grants were not provided in the source material. No information regarding laboratory affiliations, research teams, or collaborative groups was mentioned.

Eero Simoncelliمشاهده پروفایل

استاد

• Vision
• Image Processing
• Computational Neuroscience
• +۷ مورد دیگر

Eero Simoncelli is a Silver Professor at New York University , with affiliations in the Center for Neural Science , Department of Mathematics , Center for Data Science , and Department of Psychology . He serves as Scientific Director at the Center for Computational Neuroscience (FlatIron Institute, Simons Foundation). Co-developed graduate courses: Mathematical Tools for Neural and Cognitive Science (since 1999), Representation and Analysis of Visual Images (since 1998) Led summer courses: Computational Neuroscience: Vision (1992, 1994, 1996, 1998, 2000, 2002, 2004, 2006, 2008, 2010, 2012, 2014, 2016, 2018) Research focuses on computational modeling of vision, neural coding, and statistical analysis of sensory systems. His lab explores computational vision , including image processing techniques (wavelet transforms, steerable pyramids), neural population coding, and perceptual modeling. He has contributed to understanding divisive normalization in visual cortex and statistical models for image texture. Key academic contributions include: Developing mathematical frameworks for neural data analysis Advancing signal processing techniques for visual neuroscience Creating educational resources on linear algebra, Fourier analysis, and Bayesian inference Awards: IEEE Fellow for contributions to signal processing and computational neuroscience Teaching resources include handouts on Geometric Linear Algebra , Linear Systems Theory , and Signal Detection Theory .

• Contemporary Philosophy
• Political Ontology
• Comparative Literature
• +۷ مورد دیگر

Abraham Rubín Álvarez is an Assistant Professor at the Department of Sociology, Political Science and Administration and Philosophy, affiliated with the Faculty of Education and Social Work at the University of Vigo. He holds a PhD in Philosophy from UNED (2011) with the thesis 'Acontecimiento y diferentes desplazamientos ontológicos y sus repercusiones políticas', supervised by Dr. Francisco José Martínez Martínez. As a faculty member, he contributes to research in contemporary philosophy, political ontology, and critical theory through his teaching and publications. PhD in Philosophy, UNED (2011) Current Assistant Professor, University of Vigo Member of BiFeGa Research Group Rubín Álvarez's research focuses on event philosophy, subjectivity formation, and anti-capitalist thought. He explores intersections between ontology and political praxis, with particular attention to Spinoza, Deleuze, Negri, and Žižek. His work examines bioeconomics, body colonization, and utopian alternatives to capitalist structures. While his academic publications dominate the record, Rubín Álvarez also engages in musical projects like Lamatumbá and O Sonoro Maxín, demonstrating interdisciplinary interests that bridge philosophical inquiry with cultural production.

Paul Sheridanمشاهده پروفایل

استادیار

• Text Analysis
• Ontologies
• Computational Literary Studies
• +۶ مورد دیگر

Paul Sheridan is an Assistant Professor at the School of Mathematical and Computational Sciences, University of Prince Edward Island, specializing in text analysis and ontologies for computational literary studies. He develops novel term weighting schemes through statistical significance testing to improve document retrieval, classification, and summarization methods. His Literary Theme Ontology (LTO) provides the first controlled vocabulary of literary themes for media annotation and information retrieval. Research Grants: AI/machine learning for engine maintenance (2024), keyword extraction efficacy analysis (2023–2024), GPT-2 unnatural language generation (2022–2025) Academic Leadership: Statistics and Analytics Program Lead (2024–present), ACENET Research Directorate member His recent publications focus on lexical diversity analysis, term dispersion quantification, and statistical foundations of TF-IDF. He supervises students in projects spanning energy-efficient NLP, causal inference in finance, and low-resource language embeddings. Paul actively contributes to open-source projects like stoRy and PAFit packages.