جست‌وجوی استاد و پژوهشگر

• Psychobiology
• Psychology of Perception
• Neuroscience
• +۳ مورد دیگر

Marco TURI is a Researcher at the University of Salento, affiliated with the Department of Human and Social Sciences in Lecce, Italy. His expertise spans Psychobiology and Psychology of Perception, focusing on topics such as perceptual learning, autism spectrum disorder (ASD), and numerical cognition. He holds a PhD and has contributed extensively to interdisciplinary research in cognitive neuroscience and developmental psychology. Education: PhD in Psychology or Neuroscience (inferred from publications; specific details not explicitly listed). Research Interests: TURI's work explores perceptual adaptation mechanisms, implicit learning, and neurodevelopmental disorders. His studies often utilize eye-tracking and pupilometry to investigate sensory and cognitive processes in clinical populations (e.g., ASD, dyslexia) and neurotypical individuals. Recent projects address how contextual cues modulate visual perception and how sleep disorders affect behavioral outcomes in children with ASD. Publications Trends: TURI's articles predominantly focus on experimental psychology, cognitive neuroscience, and clinical applications. Key themes include ASD diagnosis, numerical skill development, and the neural basis of perception. His work bridges clinical and experimental methods, often involving collaborations with neuroscientists, clinicians, and robotics researchers. Labs/Teams: Collaborates with multidisciplinary teams at the University of Salento and international partners, including studies involving robotics for ASD therapy and neuroimaging for developmental disorders.

• Cognitive Psychology
• Bilingualism
• Language Acquisition
• +۵ مورد دیگر

Julia Hofweber is an Assistant Professor in Cognitive Psychology at Northeastern University London, specializing in multilingual cognitive processes, particularly language acquisition and code-switching effects in bilingualism. She holds affiliations with the Department of Psychology within the College of Social Sciences. Her research explores how bilingualism influences executive functions, sign language learning, and creativity through funded projects across institutions like UCL and the Indian Statistical Institute. Her research interests include bilingualism, visual statistical learning in sign languages, and the cognitive underpinnings of language switching. Current studies investigate code-switching patterns in Turkish-English bilinguals, aphasia-related language switching, and human vs. machine-generated code-switching comparisons. Articles highlight themes such as implicit sign learning, code-switching impacts on executive control, and socio-linguistic dynamics in business communication. Collaborations include projects funded by the Leverhulme Trust, the Centre of Literacy and Multilingualism, and Northeastern University grants. Hofweber co-supervises PhD candidate Lizzy Aumônier on theory of mind and literacy development in deaf and ASD populations. Her work bridges experimental psychology with applied linguistics, emphasizing translanguaging and interdisciplinary approaches to multilingualism.

• Stochastic Analysis
• Numerical Methods for Partial Differential Equations
• Applied Probability
• +۳ مورد دیگر

Prof. Dr. Felix Lindner is a Professor in the Department of Analysis and Applied Mathematics at the University of Kassel. His research focuses on stochastic partial differential equations (SPDEs), numerical analysis of stochastic processes, and their applications in computational mathematics and mechanics. He holds a PhD in Mathematics from Dresden and has contributed extensively to the study of SPDE regularity, numerical schemes for stochastic dynamics, and convergence analysis of approximation methods. Research Interests: Stochastic Analysis and Numerics Stochastic Partial Differential Equations (SPDEs) Numerical Methods for PDEs/SDEs Convergence and Stability of Numerical Schemes Applications in Material Science and Mechanical Engineering Recent Publications Trends: Advances in weak and strong convergence rates for SPDE approximations Stochastic modeling of fiber dynamics and composite materials Development of adaptive numerical methods for SPDEs Analysis of singular behavior in stochastic heat equations Students: Current doctoral advisees include Quinten Kürpick, Manuel Lorenz, Felipe Trolldenier, and P. Tobias Werner. Former student Saeed Hadjizadeh completed his research under Lindner's supervision. Labs/Teams: Lindner leads a research group focused on stochastic computational methods, collaborating with industry partners on fiber dynamics modeling and numerical analysis of mechanical systems.

Julie Bertelsمشاهده پروفایل

پژوهشگر

• Developmental Cognitive Neuroscience
• Infant Development
• Attentional Processes
• +۵ مورد دیگر

Julie Bertels is a developmental cognitive neuroscientist at Université libre de Bruxelles , affiliated with the ULBabyLab (CO3, CRCN) in the Faculty of Psychology and the Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC) in the Faculty of Medicine. Her research focuses on attentional mechanisms, statistical learning, and their modulation by emotion in early human development, utilizing EEG and MEG neuroimaging techniques. Her work bridges behavioral and neurophysiological approaches, investigating how infants process sensory stimuli and extract environmental regularities. Key themes include the neural basis of visual statistical learning, the role of emotional content in attentional capture, and the neurodevelopmental impacts of prematurity. Recent publications highlight her contributions to advancing optically pumped magnetometer (OPM-MEG) technology for prenatal and infant brain studies, as well as multisensory integration in speech perception. Collaborations span neurodevelopmental disorders, cognitive fatigue, and speech-in-noise perception. Julie supervises doctoral research, including Benocci (2025) on auditory-visual scene analysis and musical skills. Her lab memberships include CO3 , CRCN , and LCFC , integrating psychology, medicine, and engineering perspectives. Current studies examine cortical tracking of speech, statistical learning in premature infants, and the interplay between brain maturation and sensory experience. Her work advances understanding of early cognitive development and neurophysiological methodologies.

Loucas Pillaud-Vivienمشاهده پروفایل

پژوهشگر

• Machine Learning
• Stochastic Processes
• High-Dimensional Statistics
• +۳ مورد دیگر

Loucas Pillaud-Vivien is a researcher at the Applied Probability team within CERMICS (École des Ponts ParisTech), specializing in the high-dimensional stochastic dynamics underlying machine learning optimization algorithms. His work bridges statistical theory and computational methods, focusing on understanding algorithmic behavior in modern ML systems. Education : PhD from ENS/Inria Paris (2020) under Francis Bach; postdoctoral research at EPFL, New York University (Courant Institute), and the Simons Foundation (Flatiron Institute). Research interests include stochastic processes, optimization theory, and high-dimensional statistical learning. Key themes involve analyzing gradient-based methods, implicit regularization effects, and the interplay between computational efficiency and statistical accuracy. Recent publications emphasize topics such as gradient flow dynamics, variational inference, and spectral bias in neural networks. His work often addresses fundamental questions about algorithmic convergence and generalization in overparameterized models. Loucas is affiliated with the CERMICS laboratory, contributing to interdisciplinary projects at the intersection of applied mathematics and machine learning.

Damek Shea Davisمشاهده پروفایل

استادیار

• Optimization
• Stochastic Algorithms
• Nonconvex Optimization
• +۴ مورد دیگر

Damek Shea Davis is an Assistant Professor in Cornell University's School of Operations Research and Information Engineering, College of Engineering. He received his Ph.D. in Mathematics from UCLA in 2015 and joined Cornell in 2016. Davis focuses on mathematical foundations of data science, with particular expertise in optimization, statistical learning, and numerical algorithms. His research develops theoretical frameworks and efficient algorithms for nonconvex, nonsmooth, and stochastic optimization problems. Key contributions include convergence guarantees for stochastic subgradient methods on nonconvex functions, analysis of optimization landscapes for phase retrieval, and development of methods for high-dimensional statistical estimation. His work bridges mathematical optimization with machine learning and signal processing applications. Davis has received numerous honors including the Sloan Research Fellowship (2020) and INFORMS Optimization Society Young Researchers Prize (2019). His publications advance understanding of optimization algorithm behavior in statistically challenging regimes, with recent work exploring geometric convergence properties and optimality conditions.

Aras Bachoمشاهده پروفایل

پژوهشگر ارشد

• Partial Differential Equations
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Dr. Aras Bacho is a Postdoctoral Research Associate in the Computing and Mathematical Sciences Department within the Division of Engineering and Applied Science at the California Institute of Technology (Caltech), where he works with Professor Houman Owhadi. His research spans mathematical analysis, partial differential equations, and machine learning applications. Dr. Bacho completed his PhD in Partial Differential Equations at the Technical University of Berlin with a thesis titled "On the Nonsmooth Analysis of Doubly Evolution Inclusions of First and Second Order with Applications" under the supervision of Prof. Dr. Etienne Emmrich. He also holds M.Sc. degrees in Mathematics from both the Technical University Berlin and Université Pierre et Marie Curie in Paris (through an ERASMUS+ scholarship), as well as a B.Sc. in Technomathematics from the Technical University Berlin. Dr. Bacho's research interests focus on the intersection of mathematical analysis and machine learning. His work explores partial differential equations, nonsmooth analysis, physics-informed neural networks, and applications in robotics and computational physics. His research bridges theoretical mathematics with practical applications in artificial intelligence and engineering systems. He has developed novel approaches to physics-informed machine learning and has made contributions to the mathematical foundations of scientific machine learning. Dr. Bacho's publications demonstrate a strong trajectory from theoretical mathematical analysis toward increasingly applied work in scientific machine learning. His recent work shows particular interest in physics-informed neural networks, equation discovery, and the mathematical foundations of AI reliability. He has published on topics ranging from classical PDE theory to cutting-edge applications of deep learning in scientific computing, with a focus on mathematical rigor and practical applicability. 2008: First prize in the Nationwide Mathematics Competition in Germany (Bundeswettbewerb Mathematik) 2009: DPG Prize for special achievements in physics issued by the German Physical Society 2012: Perfect score in the Math Advent Calendar organized by the DFG Research Center MATHEON 2014-2015: Erasmus+ Scholarship for studies at Université Pierre et Marie Curie 2017: PhD-Scholarship issued by TU Berlin (4 Months) 2021-present: Mentoring Program for young researchers Dr. Bacho has presented his work at numerous international conferences including the Workshop on Control of Self-Organizing Nonlinear Systems, the Hausdorffschool on Modeling and analysis of evolutionary problems, and the International Council for Industrial and Applied Mathematics (ICIAM) 2023. His research has been supported by various grants including his PhD scholarship from TU Berlin and participation in mentoring programs for young researchers. He collaborates with researchers across mathematics, computer science, and engineering disciplines. Dr. Bacho is actively involved in the scientific machine learning community, collaborating with researchers at Caltech and beyond. His work at the intersection of mathematical analysis and AI places him at the forefront of scientific machine learning research, with particular emphasis on the mathematical foundations of physics-informed neural networks and the reliability of AI systems for scientific applications.

John C. Bowmanمشاهده پروفایل

استاد

• Statistical closures in turbulence
• Spectral Reduction
• 3D vector graphics
• +۱۰ مورد دیگر

John C. Bowman is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. He has maintained an active teaching career with courses spanning undergraduate to graduate levels, including Honours Calculus (Math 117/118), Real Variables (Math 417), Coding Theory (Math 422), Linear Algebra II (Math 225), and Statistical Theories of Turbulence (Math 655). His office is located in CAB 521, and he has consistently provided regular office hours for student consultation across multiple courses. Dr. Bowman earned his BS in Engineering from the University of Alberta, followed by an MA and PhD from Princeton University. His educational background has informed his diverse research trajectory spanning mathematical physics, computational mathematics, and scientific visualization. His teaching philosophy emphasizes deep conceptual understanding, as evidenced by his development of comprehensive hyperlinked 3D lecture notes for Honours Calculus. His primary research focuses on statistical theories of turbulence, particularly his development of Spectral Reduction as a reduced statistical description of turbulence. His work shows remarkable agreement with full numerical simulations, even in flows containing long-lived coherent structures. Additional research interests include 3D vector graphics (notably the Asymptote language he developed), implicit dealiasing of convolutions, exponential integrators, and exactly conservative integrators for numerical simulations. He has been involved with the University of Alberta's Geophysical Fluid Dynamics Research Group, where his work bridges theoretical mathematics with practical computational applications. Dr. Bowman's publication record demonstrates consistent contributions to computational mathematics and fluid dynamics over several decades. His recent work (2015-2024) shows continued innovation in computational methods for turbulence simulation, with particular attention to dealiasing techniques, efficient convolution algorithms, and novel approaches to spectral methods. His research has practical applications in assessing dissipation mechanisms in large-eddy simulations and modeling high-Reynolds number turbulence. As an educator, Dr. Bowman has developed comprehensive teaching materials and maintains an active engagement with students through office hours and detailed course websites. His commitment to mathematical education extends to high school outreach through contributions to the "Pi in the Sky" mathematics magazine. His work on the Asymptote vector graphics language has created a valuable tool for mathematical visualization and scientific publishing that integrates seamlessly with TeX.

Nicolas Flammarionمشاهده پروفایل

استادیار

• Machine Learning
• Statistics
• Optimization
• +۳ مورد دیگر

Nicolas Flammarion is a tenure-track Assistant Professor in Computer Science at the École Polytechnique Fédérale de Lausanne (EPFL). He holds positions in multiple departments including the Theory in Machine Learning (TML) lab under the School of Computer and Communication Sciences (IC). His roles include teaching and doctoral program leadership across disciplines like Communication Systems and Computer Science. Education: PhD in 2017 from École Normale Supérieure (Paris), advised by Alexandre d’Aspremont and Francis Bach. Postdoctoral fellowship at UC Berkeley under Michael I. Jordan. Research focuses on machine learning theory, optimization, and statistical methods. Key areas include adversarial robustness, algorithmic generalization, and optimization dynamics. Notable contributions include work on SGD/GD comparisons, adversarial benchmarking (RobustBench), and safety in AI systems. Awards include the 2018 Fondation Mathématique Jacques Hadamard PhD Prize, 2021 NeurIPS Outstanding Paper Award, and 2024 Trust & Safety Google Research Award. Supervises PhD students in topics like algorithm design and machine learning theory. Labs/Teams: Leads TML lab and contributes to EPFL's doctoral programs in Informatics and Communication Sciences. Active in curriculum development for advanced machine learning topics.

Karinne Sauvalمشاهده پروفایل

پژوهشگر

• Psycholinguistics
• Cognitive Psychology
• Reading Acquisition
• +۴ مورد دیگر

Karinne Sauval is a Researcher at Université Libre de Bruxelles (ULB) affiliated with the Faculty of Psychological Sciences and Education. She is actively involved in the Language, Learning, and Cognitive Development (LCLD) lab under the CRCN research center. Her research focuses on the mechanisms of visual word recognition during reading acquisition and in skilled reading. She investigates the implicit learning of orthographic regularities, examining how readers capture these patterns and how they impact visual word recognition. Combines behavioral (response time, accuracy) and neurophysiological (ERPs) measures Applies paradigms like priming, masked priming, and learning Studies bilingualism and interplay between orthographic and phonological representations Her work intersects with broader themes of literacy, phonology, statistical learning, and cognitive development. Publications reflect a trajectory analyzing: 2022: Orthographic regularities in diverse contexts 2018: Phonemic features in lexical access 2017-2014: Phonological coding, priming effects, and bilingual reading

• Parkinson's Disease
• Cognitive Neuroscience
• Neuropsychology
• +۳ مورد دیگر

Maria Pennacchio serves as a Research Fellow at the Interdepartmental Center for Mind/Brain Sciences (CIMEC) within the University of Trento, Italy. Her position is based at the Rovereto campus (Corso Bettini, 31), where she conducts interdisciplinary research bridging neuroscience, psychology, and clinical medicine. Her research program centers on cognitive and emotional deficits in Parkinson's disease, with particular emphasis on emotion recognition (facial and bodily expressions), theory of mind , moral cognition , and diagnostic criteria for mild cognitive impairment . She employs neuropsychological assessments, behavioral experiments, and machine learning techniques to analyze clinical data, frequently exploring the relationship between motor symptoms and cognitive-emotional deficits. Her work extends to related conditions like Takotsubo Cardiomyopathy, investigating neural mechanisms of stress-induced cardiac events. Analysis of her 2022-2025 publications reveals a cohesive research trajectory focused on Parkinson's disease, with six publications demonstrating methodological evolution from traditional neuropsychological testing to machine learning applications. Her work consistently examines how non-motor symptoms impact patient quality of life, with recent studies addressing diagnostic gaps in PD-MCI and moral decision-making deficits. She maintains a core collaboration network led by Costanza Papagno, with regular contributions from Chiara Longo, Alessandra Dodich, and Luca Turella. No scientific awards or honors were documented in the provided source material. While the provided text does not list formal advisees, her role as Research Fellow suggests involvement in mentoring within CIMEC's training programs. Her publication record indicates active participation in research projects funded through University of Trento channels, though specific grants are not detailed. Dr. Pennacchio's work directly contributes to clinical applications through improved cognitive assessment protocols for Parkinson's patients. She operates within CIMEC's interdisciplinary ecosystem, which integrates psychology, neuroscience, and medicine to investigate mind-brain relationships. Her current projects appear focused on refining diagnostic criteria for cognitive impairment in movement disorders and expanding investigations into socio-cognitive deficits across neurological conditions.

Ritske Jongمشاهده پروفایل

استاد

• Cognitive Psychology
• Attention Mechanisms
• Electroencephalography
• +۲ مورد دیگر

Ritske Jong is a Professor in Experimental Psychology at the Faculty of Behavioural and Social Sciences. His research utilizes electroencephalography (EEG) to explore cognitive mechanisms underlying attention, task-switching, and time perception. He investigates neural correlates of performance variability and decision-making under cognitive constraints. Research Focus: Jong's work spans attentional bias, temporal processing, and neurocognitive modeling. Key themes include EEG signatures of time perception, resit exam effects on learning investment, and implicit temporal regularities. His lab employs behavioral experiments and neuroimaging to dissect cognitive control mechanisms. Publication Trends: Recent articles (2017–2022) cluster in three domains: 1) EEG-informed temporal cognition studies, 2) attentional processing in learning environments, and 3) computational modeling of cognitive tasks. Cross-disciplinary collaborations extend to healthcare technology and educational psychology.

Brian Andersonمشاهده پروفایل

استاد

• Affective Science
• Cognitive Neuroscience
• Attentional Control
• +۵ مورد دیگر

Brian Anderson is the Charles Puryear Professor of Liberal Arts at Texas A&M University, affiliated with the Department of Psychology and the Institute for Neuroscience. His research focuses on how learning shapes attentional control mechanisms, employing methodologies like human psychophysics and fMRI. Key areas include threat habituation in workplace accidents, implicit bias, and the neurocognitive basis of attentional prioritization. Research interests span affective science, value-driven attention, and threat-related biases. Notable work explores how reward/punishment histories modulate attentional capture and how these processes contribute to addiction and psychopathology. Collaborative projects address real-world applications like reducing construction worker accident risks via virtual reality interventions. Recent publications emphasize attentional theories (e.g., 'Trichotomy revisited'), workplace safety ('Attention failures cause accidents'), and neural correlates of value-driven attention. His work bridges basic cognitive science with applied contexts, including transportation safety and mental health. Scientific Awards: APS Fellow, Charles Puryear Professorship Affiliations: Institute for Neuroscience, Learning & Attention Lab Director Lab Focus: Virtual reality experiments, oculomotor tracking, fMRI studies

Ziyu Chenمشاهده پروفایل

استادیار مهمان

• Mathematical Statistics
• Machine Learning
• Time-Frequency Analysis
• +۳ مورد دیگر

Ziyu Chen is a Marshall H. Stone Visiting Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. His research focuses on intersections of machine learning, mathematical statistics, and signal processing, with emphasis on equivariant models, time-frequency analysis, and generative frameworks. He has contributed to theoretical guarantees for group-invariant algorithms and applications in biomedical signal analysis. Education background not explicitly listed, but his research trajectory suggests advanced training in applied mathematics and computational statistics. Research interests include: Generative Models with Symmetry Exploitation Statistical Guarantees for Machine Learning Algorithms Synchrosqueezed Transforms for Signal Analysis Graph Neural Networks and Spectral Embeddings Recent publications emphasize methodologies for handling symmetric data structures, improving time-frequency localization, and clinical applications of machine learning. Notable work includes equivariant score-based models, group-invariant GANs, and disentangling frequency modes in complex time series. Recipient of teaching awards at UMass Amherst, though specific prizes not listed. No documented grants or advising roles found in provided text. Active in interdisciplinary research spanning mathematics, computer science, and biomedical engineering.

Enrico Tassiمشاهده پروفایل

پژوهشگر

• Interactive Theorem Proving
• Formal Verification
• Type Theory
• +۵ مورد دیگر

Enrico Tassi is a researcher at Inria, France, affiliated with the STAMP team. His work focuses on the technology of formal proofs, particularly type theory, interactive theorem proving, and its application to formalizing mathematics. He is a key contributor to the development of tools like Coq-Elpi , Hierarchy-Builder , and the small scale reflection Coq extension. His research spans proof assistant architecture, software design, and formal verification, with a strong emphasis on free software principles. Enrico completed his Ph.D. at the University of Bologna in 2008, where he designed the Matita interactive theorem prover. He contributed to the formalization of the Odd Order Theorem and the Paral-ITP project, aiming to scale Coq for large mathematical libraries. His software projects include Elpi , a meta-language for proof assistants, and past contributions to Debian as a developer (2006–2016), focusing on integrating the Lua language into Debian systems. His publications highlight innovations in unification algorithms, tactical design, and proof structuring techniques. Articles address challenges in bi-directional type inference, pattern-based proof command focusing, and nonuniform coercion implementations. His work on small-scale reflection and Hierarchy-Builder demonstrates expertise in algebraic hierarchies and formal library management. Current efforts target enhancing OCaml-based systems through Elpi's high-level programming capabilities. Key Collaborations : Mathematical Components team (INRIA), D.A.M.A. Project Major Tools : Coq-Elpi, Hierarchy-Builder, Matita Formalization Efforts : Ordered Uniformities, Odd Order Theorem, Finite Group Theory