جست‌وجوی استاد و پژوهشگر

Masoud Jasbiمشاهده پروفایل

استادیار

• Semantics
• Pragmatics
• Language Acquisition
• +۴ مورد دیگر

Masoud Jasbi is an Assistant Professor in the Department of Linguistics at the University of California, Davis, specializing in experimental and theoretical approaches to natural language semantics and pragmatics. His research focuses on: Semantic and pragmatic interpretation of logical connectives (negation, conjunction, disjunction) Cross-linguistic studies across English, Persian, Mandarin Chinese, Spanish, and Hungarian Language acquisition in child and adult populations Integration of generative linguistics with artificial intelligence Experimental methodologies for testing linguistic hypotheses Recent publications demonstrate consistent emphasis on scalar implicature, context effects, and frequency-dependent processing, with increasing exploration of AI-linguistics intersections. His work bridges formal theory with empirical validation through experimental paradigms, examining how prior beliefs and contextual factors shape linguistic meaning across diverse populations. No scientific awards are documented in available sources. Information regarding student advising, research grants, laboratory facilities, or collaborative teams is not publicly specified in current materials.

Jonathan Cohenمشاهده پروفایل

استاد

• Cognitive Control
• Computational Neuroscience
• Psychiatric Disorders
• +۵ مورد دیگر

Jonathan Cohen serves as the Robert Bendheim and Lynn Bendheim Thoman Professor in Neuroscience at Princeton University's Princeton Neuroscience Institute, where he leads groundbreaking research on the neurobiological foundations of cognitive control and its disruption in psychiatric disorders. His work bridges computational modeling, cognitive science, and clinical neuroscience to unravel how distributed neural systems enable goal-directed behavior. Education: M.D. from the University of Pennsylvania Ph.D. from Carnegie Mellon University Cohen's research centers on cognitive control mechanisms involving prefrontal cortex, anterior cingulate cortex, basal ganglia, and neuromodulatory systems, with direct applications to schizophrenia and depression. His lab develops mechanistically explicit computational models to explain how billions of neurons coordinate purposeful behavior, seeking to establish a theoretical foundation for psychiatric research. This integrative approach combines empirical neuroimaging with rigorous mathematical frameworks to decode the neural basis of executive function. Analysis of his recent publications (2022-2025) reveals a compelling evolution toward interdisciplinary synthesis, where computational neuroscience increasingly intersects with artificial intelligence. His work now explores emergent symbolic reasoning in neural networks, cerebellar-cortical interactions in neurodevelopmental disorders, and the neural reconfiguration underlying task switching – all while maintaining clinical relevance to mental illness. Scientific Awards: Election to American Academy of Arts and Sciences APS William James Award Cohen mentors graduate students including Younes Strittmatter and directs the Neuroscience of Cognitive Control Lab, which has secured substantial funding such as a $16M Princeton-Rutgers Collaboration Grant for mental illness research. His lab actively investigates how disturbances in brain function manifest as psychiatric symptoms, with recent projects examining cognitive control allocation in depression and neural mechanisms of grief. The Neuroscience of Cognitive Control Lab employs a multidisciplinary framework combining computational modeling, fMRI, behavioral experiments, and machine learning to dissect the architecture of executive function. Current initiatives explore cerebellar contributions to cognitive aging, abstract reasoning mechanisms, and the development of theoretically grounded interventions for psychiatric disorders through collaborative neuroscience initiatives.

Nathaniel Dawمشاهده پروفایل

استاد

• Computational Neuroscience
• Decision-Making
• Reinforcement Learning
• +۳ مورد دیگر

Nathaniel Daw holds the Huo Professorship in Computational and Theoretical Neuroscience at the Princeton Neuroscience Institute , Princeton University. His research integrates computational, neural, and behavioral approaches to study decision-making through trial-and-error learning and reward/punishment processing. Research Interests: Computational Neuroscience Decision-Making Under Uncertainty Model-Based and Model-Free Learning Neural Mechanisms of Self-Control Publications (2025-2019) explore intersections of machine learning and neuroscience, focusing on reward-guided behavior, memory systems, and psychiatric implications like anorexia nervosa and obsessive-compulsive disorder. Key themes include neural replay, cognitive effort allocation, and predictive modeling of human and animal learning. Scientific Awards: Princeton-Rutgers $16M Research Grant for mental illness studies Grants and Collaborations: Highlighted by a major interdisciplinary grant with Rutgers University to advance understanding of mental illness through computational frameworks. Labs and Teams: Leads the Daw Lab at Princeton Neuroscience Institute, focusing on neurocomputational models of decision-making and self-control.

Stacy Tantumمشاهده پروفایل

دانشیار

• Physics-based statistical signal processing
• Context-aware machine learning
• Domain-informed data science
• +۷ مورد دیگر

Stacy Tantum is an Associate Professor in the Department of Electrical and Computer Engineering at Duke University , where she also serves as the Faculty Director of the 360 Coaching Program for first-year advising in the Pratt School of Engineering. Her research bridges domain-informed data science and context-aware machine learning , emphasizing privacy-aware , explainable AI , and bias mitigation in machine learning systems. She integrates neuroscience-informed teaching , metacognition , and cognitive load theory into pedagogical practices to enhance student learning experiences. B.S.E.E., Tufts University (1994) M.S., Duke University (1996) Ph.D., Duke University (1998) Her work spans landmine detection , sensor data fusion , and statistical signal processing , with recent contributions to interpretable machine learning in medical imaging and educational coaching frameworks. Publications highlight innovations in UXO discrimination , Kalman filtering , and independent component analysis , alongside pedagogical research on mindset-aware learning . She teaches courses including Introduction to Machine Learning and Applied Probability for Statistical Learning .

• Health Disparities
• Family Demography
• Social Inequality
• +۳ مورد دیگر

Emma Xiaolu Zang is an Associate Professor of Sociology (tenured) at Yale University with secondary appointments in Biostatistics and Global Affairs. She directs the Computational Aging, Family, and Evidence Lab (Z-CAFE) and founded Yale's Population Studies Speaker Series. A non-resident fellow at Asia Society Policy Institute's Center for China Analysis, her research focuses on health disparities, family demography, and inequality through micro-meso-macro level analysis. Ph.D. in Public Policy, Duke University (2019) MA in Economics, Duke University (2017) MPhil in Social Science, HKUST (2014) Her research combines life-course approaches with Bayesian statistical methods to analyze health disparities, family dynamics, and policy impacts. Key areas include: Household inequality mechanisms Family policy effects on gender equality Early-life family impacts on aging Multi-state life table modeling Bayesian data integration Age-Period-Cohort methodological critiques Recent publications span Nature Human Behaviour , Demography , and JAMA Internal Medicine , focusing on: Environmental health disparities Gender inequality in work-family dynamics Multi-state aging models Racial disparities in critical care Geospatial mortality patterns Caregiving burden analysis Scientific awards include: National Institute on Aging Scholarships Chinese Government Outstanding Student Award International Sociological Association Recognition Erasmus Mundus Fellowship Her work has been funded by NIH, Alzheimer’s Association, and NICHD, with media coverage in New York Times Magazine , Reuters , and ThePaper.cn .

Ahti Saloمشاهده پروفایل

استاد

• Operations Research
• Systems Analysis
• Portfolio Decision Analysis
• +۵ مورد دیگر

Professor Ahti Salo is a distinguished academic at Aalto University's School of Sciences, Department of Mathematics and Systems Analysis, specializing in operations research and systems analysis. Holding a Doctor of Science (Technology) degree, he has established himself as a leading expert in portfolio decision analysis, risk assessment, and scenario planning methodologies. His research interests span multiple domains including infrastructure management, energy systems optimization, healthcare decision support, and innovation ecosystem governance. Professor Salo's work bridges theoretical advances in decision analysis with practical applications in critical infrastructure, energy policy, and public health planning, creating robust frameworks for decision-making under uncertainty. His recent publication record demonstrates consistent scholarly productivity with 204 total publications spanning from 1989 to 2025. His research trajectory shows increasing focus on applying portfolio decision analysis to complex real-world problems including infrastructure resilience, energy transition planning, and healthcare optimization. His work often combines methodological innovation with practical case studies, particularly in Finnish contexts. Professor Salo has received seven scientific prizes for his contributions to operations research and decision analysis, though specific award names aren't detailed in the available materials. His research has generated significant impact with numerous highly-cited publications in top-tier journals. As an academic mentor, Professor Salo has supervised 24 theses, contributing to the development of the next generation of operations research specialists. His research is supported by multiple projects, including significant work on infrastructure networks, energy systems, and healthcare optimization. His work often involves interdisciplinary collaboration with experts in engineering, environmental science, and policy domains. Professor Salo leads research efforts in operations research and systems analysis at Aalto University, working within teams focused on portfolio decision analysis, risk management, and scenario planning. His research group contributes to both theoretical advances in decision support methodologies and their practical implementation in critical infrastructure, energy systems, and public health domains.

• Neuroscience
• Psychopharmacology
• Transportation Safety
• +۴ مورد دیگر

Matthew Albrecht is a Senior Research Fellow at the School of Psychological Science, The University of Western Australia , with affiliations to the Planning and Transport Research Centre . His work spans neuroscience , psychopharmacology , and transportation safety , with a focus on drug effects on cognition , crash analysis , and clinical documentation efficiency . Research Interests: Neuroscience and psychopharmacology of drugs like THC and dexamphetamine Transport safety and road design using statistical models Clinical documentation burden in nursing and midwifery Autism and schizophrenia cognitive models Article Trends: His recent work emphasizes THC's impact on driving safety , amphetamine effects on memory , and transport policy evaluations . Publications include collaborations in public health , clinical research , and computational psychiatry . Grants and Collaborations: Albrecht has contributed to 4 grants , including studies on road safety and healthcare documentation . He collaborates with institutions like Main Roads, WA and Curtin University . Labs and Teams: Affiliated with the Planning and Transport Research Centre , he works with multidisciplinary teams in transport engineering , psychiatric research , and biostatistics .

• Cognitive Neuroscience
• AI Research
• Human Decision-Making
• +۳ مورد دیگر

Christopher Summerfield is a Professor of Cognitive Neuroscience at the University of Oxford and Research Director at the UK AI Safety Institute. He is also a Fellow of Wadham College. His work spans cognitive science, neuroscience, AI research, and social science, with a focus on human learning and decision-making from behavioral, computational, and neural perspectives. PhD MSc BA His research explores computational models of cognition, using neural networks to understand and enhance human learning, cooperation, and agreement. Recent work examines zero-shot learning, distractor effects in perception, and normative principles of decision-making in natural environments. Key trends in his publications include applications of deep learning to cognitive neuroscience, computational modeling of reinforcement learning biases, and investigations into semantic disentanglement in neural circuits. This reflects his interdisciplinary approach at the intersection of AI and human cognition. Scientific awards include the Wellcome Trust Discovery Award. His research group, Human Information Processing, collaborates on advancing theories of cognition while developing practical tools for behavioral enhancement through AI.

Michael Antiocoمشاهده پروفایل

استاد

• Marketing
• Innovation Management
• Marketing Communications
• +۷ مورد دیگر

Michael Antioco is a Professor in Marketing and Dean of Faculty and Research at EDHEC Business School since 2021. His research focuses on customer understanding , innovation management , and marketing communications , particularly in the high-tech and luxury sectors . He has collaborated with firms like Philips , L’Oréal , and IBA . Education : PhD in Marketing & Innovation Studies (Eindhoven University of Technology, 2006); Master’s in Business Science (UCLouvain, 2002); Visiting Scholar (Purdue University, 2004–2005). Prior Roles : Associate Professor at EMLYON Business School (2010–2013); Assistant/Associate Professor at IESEG School of Management (2006–2010). Research Trends : His work bridges quantitative marketing with practical business applications , emphasizing the psychological and functional barriers to technology adoption and luxury consumption . Recent projects include analyzing linguistic styles in press releases and consumer bias in online reviews . Strategic Initiatives : As Dean, he leads EDHEC’s ‘ Generations 2050 ’ plan, aiming to recruit 100 new professors by 2028. He also championed the ‘ Safe Place for Science ’ initiative to support U.S.-based researchers.

Timothy O'Donnellمشاهده پروفایل

دانشیار

• Computational Linguistics
• Mathematical Linguistics
• Probabilistic Inference
• +۳ مورد دیگر

Timothy O'Donnell serves as Associate Professor in the Department of Linguistics at McGill University and holds a Canada CIFAR AI Chair at Mila—Québec Artificial Intelligence Institute. He co-directs the Montréal Computational and Quantitative Linguistics Laboratory (MCQLL) and directs McGill's interdisciplinary Cognitive Science Program. Additionally, he maintains affiliations as an associate member of the School of Computer Science, member of McGill NLP, and affiliate of the Reasoning and Learning Laboratory. His academic foundation includes a PhD from Harvard University, establishing expertise in quantitative and computational approaches to language. Dr. O'Donnell's research centers on computational models of language learning and processing, mathematical linguistics, and probabilistic inference. His work bridges cognitive science, natural language processing, and machine learning to model human language acquisition and processing. Key investigations include grammar induction, neural language modeling, syntactic control mechanisms, and the cognitive foundations of linguistic phenomena. His methodologies emphasize probabilistic frameworks and mathematical rigor to explain language universals and processing constraints. Analysis of his recent publications reveals dominant trends in visually-grounded grammar induction, surprisal-based processing models, and neural language model architectures. His work consistently connects computational linguistics with cognitive theory, particularly in language acquisition modeling and the explanation of linguistic patterns through probabilistic inference. Cross-cutting themes include information locality, lexical trade-offs, and the mathematical properties of language systems. His scientific recognition includes the prestigious William Dawson Scholar award and Canada CIFAR AI Chair, highlighting contributions to artificial intelligence and computational linguistics. These honors reflect leadership in advancing AI research through linguistic insights. As co-director of MCQLL, Dr. O'Donnell leads an interdisciplinary team integrating computational, quantitative, and cognitive approaches to linguistic research. The laboratory fosters collaboration between linguists, computer scientists, and cognitive scientists, driving innovation in natural language processing and language modeling while maintaining strong ties to Mila's AI research ecosystem.

• Machine Learning
• Visual Analytics
• Explainable AI
• +۷ مورد دیگر

Juhee Bae is a Senior Lecturer in the Department of Information Technology at the University of Skövde, Sweden. She is affiliated with the AI research group, focusing on machine learning, visual analytics, and explainable AI. Her work bridges research and education, with courses in advanced data science and experience in teaching coordination. Ph.D. in Computer Science, North Carolina State University Merited Teaching Award (2023) Mobility Grant for Belgian University Collaboration (2020) Her research spans machine learning , visual analytics , and explainable AI , with applications in Steel industry process optimization Climate-adaptive water management Migration intention forecasting Smart production logistics Wearable biosensor analysis Recent publications highlight predictive models for weather-driven migration, causal discovery frameworks, and interactive data mining techniques. She contributes to editorial boards and conference organization. Juhee Bae serves as course coordinator for Explainable AI and has been active in international collaborations. Scientific awards include 2023 Merited Teaching Award 2020 Mobility Grant Her projects STRATUS (AI for climate adaptation), INSITE-X (steel industry AI), and Understanding Human Migration demonstrate practical AI applications. She works with interdisciplinary teams and has presented talks on predictive machine learning and explainable AI.

Duygu Bayram Karaمشاهده پروفایل

دانشیار

• Machine Learning
• Signal Processing
• Electrical Machines and Energy Conversion
• +۳ مورد دیگر

Duygu Bayram Kara is an active Associate Professor in the Department of Electrical Engineering at Istanbul Technical University's Faculty of Electrical and Electronics Engineering, where she has been a faculty member since 2016 after serving as a Research Assistant (2007-2015). Her career includes a brief industry role at Baytek Construction (2015-2016), but she remains a core academic staff member with office EEF 7311 and contact number +90-212-285-6956. She maintains active profiles on Scopus, Web of Science, Google Scholar, and ResearchGate with an ORCID (0000-0001-8184-8510). Education: PhD in Electrical Engineering from Istanbul Technical University (2009-2015) MSc in Electrical Engineering (with thesis) from Istanbul Technical University (2006-2009) BSc in Electrical Engineering from Istanbul Technical University (2001-2006) Research Interests: Her work integrates Machine Learning and Signal Processing with Electrical Machines and Energy Conversion to address critical challenges in electric motor reliability. She pioneers Kalman filter-based data fusion for aging monitoring and fault detection in motors, while simultaneously applying deep neural networks to characterize polymer nanocomposite films for electrical/optical applications. This dual focus bridges theoretical signal processing with practical energy systems and material science. Publication Trends: Her 23 Scopus-indexed outputs (2010-2025) demonstrate consistent innovation in motor diagnostics using wavelet transforms and neural networks, evolving toward sophisticated data fusion techniques. Recent work (2022-2025) heavily emphasizes deep learning for nanocomposite material analysis and Kalman filter applications in aging monitoring, with her 2020 nanocomposite conductivity paper accumulating 29 Scopus citations. The research shows increasing interdisciplinary collaboration and real-world validation in automotive and energy systems. Scientific Awards: No specific prizes, fellowships, or medals are documented in the provided materials. Professional recognition includes IEEE Senior Membership (2020-present) and standard IEEE Membership (2014-present), reflecting industry standing. Advising and Grants: She currently supervises one thesis in progress. As Principal Investigator, she secured two significant projects: Integrated Condition Assessment Using Kalman Filter-Based Data Fusion with Sensor Data from Different Environments and Recurrence Analysis-Assisted Aging Monitoring (TÜBİTAK, March 2024-May 2026) for advanced motor reliability Design and Development of Low Power Permanent Magnet Synchronous Motors in accordance with IE4 class (BAP, March 2018-October 2020) focusing on energy-efficient motor design Labs and Teams: Based at ITU's Ayazaga Campus, she operates within the Department of Electrical Engineering's research ecosystem, leveraging international collaborations (evidenced by global co-author networks) for projects involving electric motor diagnostics and nanocomposite analysis. Her work requires specialized facilities for motor testing, sensor data acquisition, and computational modeling, though specific lab names aren't disclosed in the text.

• Efficient sampling methods
• Compressed sensing
• Medical imaging: ultrasound, MRI, CT
• +۱۲ مورد دیگر

Prof. Yonina Eldar is a Professor of Electrical Engineering at the Faculty of Mathematics and Computer Science , Weizmann Institute of Science. She holds the Dorothy and Patrick Gorman Professorial Chair and serves as Head of the Manya Igel Center for Biomedical Engineering and Signal Processing . Her research bridges classical signal processing with modern deep learning techniques. Develops model-based deep learning frameworks combining domain knowledge with data-driven approaches Focuses on sub-Nyquist sampling for efficient data acquisition in radar, ultrasound, and communications Created hardware prototypes for time-encoding machines and modulo-ADC systems Innovates in joint radar-communication systems for autonomous vehicles Her work emphasizes algorithm unrolling to create interpretable neural networks with reduced training requirements. Publications demonstrate applications in: Medical imaging (ultrasound, ECG monitoring) Autonomous systems (automotive radar) Communication technologies (DFRC systems) Active in theoretical foundations of model-based deep learning, with recent work establishing mathematical guarantees for unfolded networks. Collaborations include Tsinghua University and industry partners for hardware validation.

• Machine Learning
• Artificial Intelligence
• Quantum Computing
• +۴ مورد دیگر

Andreas Bjerregaard Jeppesen is a Research Fellow at the Department of Computer Science, University of Copenhagen, specializing in Machine Learning. His work bridges theoretical and applied research across diverse domains including quantum computing, biomedical informatics, environmental monitoring, and AI ethics. The Machine Learning section at DIKU explores foundational algorithms and their applications in Medical data analysis Remote sensing Biological modeling Sustainable AI Information retrieval . Andreas contributes to interdisciplinary projects like the SCIENCE AI Centre, leveraging the department's compute cluster for intensive simulations. His recent publications highlight trends in Quantum-inspired neural architectures Generative models for protein sequences Energy-aware AI development Neurological applications of ML Climate-conscious computing . Collaborations span computational biology, quantum chemistry, and federated learning for precision medicine.

Rebecca Spencerمشاهده پروفایل

استاد

• Sleep Research
• Cognitive Neuroscience
• Developmental Psychology
• +۵ مورد دیگر

Rebecca Spencer is a Professor in the Department of Psychological and Brain Sciences at the University of Massachusetts Amherst. She joined the department in 2008 after postdoctoral work at UC Berkeley's Helen Wills Neuroscience Institute (2002-2008), supported by an NIH NRSA fellowship. Her academic journey began with a PhD in Neuroscience from Purdue University (2002), focusing on the neural control of movement. Her research explores the multifaceted roles of sleep, particularly in early childhood and aging. Key areas include sleep-dependent memory consolidation, emotional memory processing, and the impact of sleep patterns on cognitive development. She has secured two NIH R01 awards and was granted the NIH Pathways to Independence Award (K99/R00) for her work on age-related changes in sleep and cognition. Recent publications highlight collaborations across disciplines—from analyzing sleep spindles and slow-wave activity to developing tools like PSGpower for sleep EEG analysis. Her work intersects with pediatrics, neuroimaging, and behavioral science, emphasizing the interplay between sleep physiology, memory, and health outcomes. Scientific awards include: NIH NRSA Fellowship NIH Pathways to Independence Award (K99/R00)