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Dora Hermes MillerView profile

Associate Professor

• Neuroscience
• Biomedical Engineering
• Neurology
• +7 more

Dora Hermes Miller is an Associate Professor of Biomedical Engineering and holds Senior Associate Consultant II-Research roles in the Departments of Physiology & Biomedical Engineering, Radiology, and Neurology at Mayo Clinic. Her research focuses on human systems neuroscience, integrating multimodal imaging and computational modeling to understand neural signals in neurological and neuropsychiatric diseases. She collaborates with clinicians to develop therapeutic neuromodulation technologies and neuroprosthetics. Education includes a PhD from Utrecht University (Brain Center Rudolf Magnus) and postdoctoral fellowships at Stanford University, New York University, and UMC Utrecht. Her work emphasizes reproducible research practices, with contributions to neuroimaging standards like BIDS-iEEG and open source tools for electrode localization. Research interests span multimodal imaging integration, network dynamics via electrical stimulation, neural oscillations, and translational applications of brain-machine interfaces. Her lab explores how electrical stimulation modulates human connectomes, with implications for epilepsy treatment and psychiatric conditions. Collaborative efforts bridge clinical neurology, neurosurgery, and computational neuroscience. Awards and recognitions are not explicitly listed in the provided text. Her work has advanced understanding of somatotopic motor representations, limbic circuit modulation, and cortical stimulation effects, with over 70 peer-reviewed publications in journals like NeuroImage and Brain. Current projects include developing closed-loop neuromodulation systems, optimizing ECoG/MEG-fMRI integration, and advancing ethical brain-computer interface applications. She leads the Mayo Clinic NeuroEngineering Lab and participates in collaborative initiatives to standardize neuroelectrophysiological data sharing.

Marcin WojtasińskiView profile

Assistant Professor

• Cognitive Psychology
• Religiosity Studies
• Art Appreciation
• +5 more

Marcin Wojtasiński is an Assistant Professor at the Department of Experimental Psychology, Institute of Psychology, Faculty of Social Sciences, John Paul II Catholic University of Lublin. His academic career at this institution spans multiple research domains. Assistant Professor (2025) Department of Experimental Psychology affiliation Faculty of Social Sciences membership John Paul II Catholic University of Lublin affiliation Dr. Wojtasiński's research interests intersect Cognitive Psychology , Religiosity Studies , and Art Appreciation . His work explores: Religious symbols' dual dimensions (opportunity vs. challenge) Canonical size phenomenon in memory drawing Flow states in board gaming Religious experience's protective role in migration Neural correlates of empathy and art perception Recent publications show increasing focus on network analysis and statistical modeling of complex psychological phenomena. His 2025 work includes vection research, skill-challenge dynamics, and sound predictability effects on attention. Collaboration patterns reveal frequent work with Małgorzata Tatala , Magdalena Szubielska , and other Polish psychologists. While no specific awards are mentioned, his publication record in Scientific Reports , Religions , and PLoS One demonstrates active scientific contribution.

• Cognitive Neuroscience
• Reading Research
• Visual Word Recognition
• +3 more

Manuel Perea Lara is a Professor at the University of Valencia, affiliated with the Faculty of Psychology and Speech Therapy and the Department of Behavioural Science. He leads research at the ERI Reading Research institute and the READit Cognitive Neuroscience and Reading group, focusing on behavioral science methodology in language processing. He obtained his PhD from the University of Valencia in 1993 with a thesis titled Influencia de los factores de vecindad sobre el acceso léxico análisis experimentales y de simulación , supervised by Dr. Arcadio Gotor Sicilia. His research explores cognitive mechanisms in reading, including: Orthographic processing and visual word recognition Cross-linguistic studies of writing systems Eye movement dynamics during reading Development of literacy skills in children Neurocognitive aspects of language in neurodiverse populations Experimental methodologies in psycholinguistics Analysis of his 15 most recent publications (2024-2025) reveals strong emphasis on masked priming paradigms, cross-script comparisons (Arabic, Chinese, Javanese), literacy development interventions, and cognitive processing in autism. The work demonstrates advanced methodological rigor in exploring orthographic encoding, attentional mechanisms, and bilingual language processing. No awards or supervised students are mentioned in available materials. His laboratory conducts innovative research using techniques like event-related potentials (ERPs) and behavioral experiments to investigate reading mechanisms across different populations and writing systems.

Amir SoneeView profile

Researcher

• Machine Learning
• Data Privacy
• Physical Layer Security
• +3 more

Amir Sonee is a Postdoctoral Research Associate at the Centre for AI Fundamentals within the Department of Computer Science, focusing on privacy-preserving techniques in probabilistic machine learning and federated learning systems. He holds a PhD and M.Sc. in Electrical Engineering from Ferdowsi University of Mashhad and Sharif University of Technology, respectively, with a B.Sc. from Ferdowsi University of Mashhad. His research integrates machine learning, data privacy, physical layer security, and wireless communications, emphasizing theoretical foundations and practical applications. He has collaborated with institutions like King's College London and National Yang Ming-Chiao Tung University on projects involving secure federated learning in wireless environments. Education: PhD in Electrical Engineering (2011–2017), Ferdowsi University of Mashhad M.Sc. in Electrical Engineering (2008–2010), Sharif University of Technology B.Sc. in Electrical Engineering (2004–2008), Ferdowsi University of Mashhad Research interests span privacy-preserving machine learning, federated learning, secure wireless communication, and information-theoretic approaches to network security. His work bridges theoretical advancements with real-world challenges in trustworthy AI and distributed systems. He is part of the MCAIF: Centre for AI Fundamentals project, exploring reinforcement learning and neural networks for AI applications. His contributions include optimizing federated learning protocols under communication constraints and advancing secrecy capacities in multi-user wireless systems.

• Computer Science
• Machine Learning
• Artificial Intelligence
• +3 more

Yongtao Wu is a Doctoral Assistant and student in the doctoral program in Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL). He is affiliated with the Laboratory for Information and Inference Systems (LIONS), part of the School of Engineering (STI) and the Institute of Electrical Engineering (IEM). His research focuses on machine learning, adversarial learning, computer vision, and deep learning, with applications in vision-language models, robustness analysis, and optimization algorithms. His work explores topics such as diffusion-based purification for vision-language models, adversarial attacks on large models, and parameter-efficient fine-tuning techniques. He contributes to advancing foundational AI models and their robustness in real-world scenarios. His research also intersects with cybersecurity, structural engineering predictions, and quantum computing methodologies. Yongtao is actively involved in the LIONS lab, collaborating on projects that bridge theoretical advancements with practical applications in computer science and engineering. His doctoral studies emphasize interdisciplinary approaches to solving complex problems in AI and machine learning.

• Music Theory
• Music Cognition
• Popular Music Analysis
• +5 more

David Temperley is a Professor of Music Theory at the Eastman School of Music (University of Rochester), where he has been teaching since 2000, progressing from Assistant Professor to Associate Professor and finally to Professor in 2015. His academic career also includes visiting positions at the University of Pennsylvania, Ohio State University, Columbia University, and New York University. He holds a Ph.D. in Music Theory from Columbia University (1996), an M.A. in Composition from Columbia University (1992), and a B.A. in History from Swarthmore College (1985). Dr. Temperley's research spans multiple areas at the intersection of music theory, cognition, and computational modeling. His work focuses on: Music cognition and perception Computational models of musical structure Analysis of popular music, particularly rock Relationships between music and language Rhythm and meter in various musical traditions Corpus-based approaches to music analysis His research demonstrates a consistent interest in applying quantitative and computational methods to understand musical structures and processes. He has pioneered corpus studies in music theory, examining large collections of musical works to identify patterns and principles that might not be evident from individual case studies. His work often bridges theoretical music analysis with empirical cognitive science approaches. Dr. Temperley has received several prestigious awards including the Society for Music Theory Emerging Scholar Award (2003), the University of Rochester Bridging Fellowship (2009), and the University of Rochester Provost's Multidisciplinary Award (2012). These awards recognize his innovative interdisciplinary approach that connects music theory with cognitive science and computational modeling. As an educator, Dr. Temperley has taught a wide range of courses in music theory, analysis, counterpoint, and music cognition at both graduate and undergraduate levels. His teaching reflects his research interests, emphasizing both traditional theoretical approaches and contemporary empirical methods. He has supervised numerous graduate students in their research, particularly in areas combining music theory with cognitive science. Dr. Temperley is also an active composer with numerous performances of his works, including chamber music for various instrument combinations and piano compositions. His compositional work informs his theoretical research and vice versa, creating a productive dialogue between creative practice and scholarly inquiry.

Gita AlaghbandView profile

Professor

• High-Performance Computing
• Artificial Intelligence
• Machine Learning
• +7 more

Professor Gita Alaghband is Chair and PhD Director at the Department of Computer Science and Engineering, University of Colorado Denver. Her work bridges high-performance computing with AI applications in computer vision, facial recognition, and deep learning optimization. Current research focuses on real-time multi-human tracking for autonomous systems Develops explainable AI solutions for medical and financial domains Leads the Parallel Distributed Systems (PDS) Lab Key publication trends include: 2025: Medical imaging AI for pediatric trauma analysis 2024: Financial forecasting with causal econometrics and adversarial defense systems 2023: Trajectory prediction models and HEVC compression frameworks Scientific recognition: IRC Best Paper Award (2020) for facial recognition research Students advised include industry leaders at Google, VMWare, and Nissan, with research spanning parallel computing, medical imaging AI, and autonomous systems.

Daniela VallentinView profile

Researcher

• Neural circuits
• vocal communication
• vocal learning
• +6 more

Dr. Daniela Vallentin serves as a Group Leader at the Max Planck Institute for Ornithology in Seewiesen, Germany, and holds a temporary or junior faculty position with the Graduate School of Systemic Neurosciences (GSN) at Ludwig Maximilian University of Munich. Her research focuses on neural circuit mechanisms underlying vocal communication in songbirds, utilizing advanced intracellular recording techniques in behaving animals. Her primary research interests encompass vocal learning processes, social interaction dynamics in songbirds, premotor circuit functionality, and sensorimotor transformations during vocal behavior. She investigates how neural circuits generate precise temporal patterns in vocal communication, with particular emphasis on inhibition-excitation balance in premotor areas and its role in vocal turn-taking and song production. Her work bridges systems neuroscience and behavioral biology to uncover fundamental principles of learned vocal communication. Analysis of her recent publications reveals a consistent trajectory in songbird neuroscience, spanning cellular-level intracellular recordings to population dynamics. Her research demonstrates how premotor circuits encode temporal sequences, how inhibition shapes vocal timing during social interactions, and the neural mechanisms protecting learned song segments. This body of work establishes critical links between neural circuit dynamics and complex vocal behaviors, with implications for understanding vocal learning across species. Dr. Vallentin actively mentors graduate students through the GSN program, including Linda Bistere and Giacomo Costalunga. Her position as Group Leader at the Max Planck Institute indicates substantial research funding supporting her investigations into neural circuit mechanisms of vocal communication. She leads the Research Group Vallentin at the Max Planck Institute for Ornithology, which specializes in neural circuit analysis of vocal communication using zebra finches as a model system. The group employs cutting-edge electrophysiological techniques combined with behavioral analysis to dissect the neural basis of vocal learning, social communication, and sensorimotor integration in naturalistic contexts.

Christian MendlView profile

Assistant Professor

• Quantum Computing
• Computational Physics
• Statistical Physics
• +2 more

Christian Mendl is a Rudolf Mößbauer Tenure Track Assistant Professor at the Technical University of Munich (TUM) , affiliated with the School of Computation, Information and Technology and the Institute for Advanced Study (TUM-IAS) . His career includes a postdoctoral position at Stanford University (2015-2017) under a Feodor Lynen Fellowship from the Alexander von Humboldt Foundation, a Junior Professorship at TU Dresden (2017-2019), and a PhD in Physics from LMU Munich (2012). Education : Physics and Mathematics (TUM) Appointments : Rudolf Mößbauer Assistant Professor (TUM, 2019), Junior Professor (TU Dresden, 2017), Postdoc (Stanford, 2015-2017) Research Focus : Mendl specializes in Quantum Computing , Computational Physics (tensor networks, quantum Monte Carlo, neural-network quantum states), Statistical and Non-Equilibrium Physics , and Numerical Simulation . His work bridges quantum information theory with condensed matter physics, emphasizing efficient quantum algorithms and simulators for complex systems. Scientific Contributions : Recent publications highlight advancements in quantum circuit optimization , tree tensor network simulations , block encoding of operators , and quantum-assisted optimization for problems like the capacitated vehicle routing and Toda lattice dynamics. His methods often integrate machine learning with quantum information to address challenges in Hamiltonian simulation and quantum error analysis . Awards : Alexander von Humboldt Feodor Lynen Fellowship, Boehringer Ingelheim Fonds PhD Fellowship, TopMath Graduate Program, Studienstiftung des deutschen Volkes Grants : Rudolf Mößbauer Tenure Track Fellowship (TUM-IAS), Dieter Schwarz Fellowship

Bahram JavidiView profile

Professor

• Real-time multi-dimensional imaging
• Nanotechnology in optical sensing
• Biomedical optics/photonics
• +4 more

Bahram Javidi is a Professor in the Department of Biomedical Engineering at the University of Connecticut. His research focuses on advanced optical imaging technologies, including real-time 3D sensing, visualization, and information processing. He integrates nanotechnology, biomedical photonics, and quantum optics into novel imaging systems for medical and underwater applications. Key Research Areas: 3D integral imaging, digital holography, compressive sensing, optical encryption, and biomedical imaging. Recent Publications: Highlight innovations in underwater signal detection, lensless imaging for disease screening, and adversarial attack defense using optical systems. Technological Impact: Develops portable, low-cost medical diagnostic tools and augmented reality visualization systems. Collaborations: Works with interdisciplinary teams in biomedical engineering, computer science, and optical physics.

Paul Robert GriffinView profile

Associate Professor

• Artificial Intelligence and Data Science
• Machine Learning & Intelligence
• Information Systems & Technology
• +5 more

Paul Robert Griffin is a Full-time Faculty member at Singapore Management University (SMU) , serving as an Associate Professor of Information Systems (Practice) in the School of Computing and Information Systems. He earned his PhD from Imperial College London in 1997 and has taught courses on Quantum Computing in Financial Services, Blockchain Technology, and Software Engineering. PhD, Imperial College London, 1997 Teaching: Quantum Computing, Blockchain, Software Engineering Research Interests: Artificial Intelligence, Machine Learning, Digital Transformation, Smart Contracts His research focuses on Quantum Machine Learning (QML) , with applications in financial transaction optimization, credit scoring, and time series forecasting. He has published extensively on hybrid quantum-classical models, explainable quantum neural networks, and decentralized systems. Key themes include noise resilience in QML, quantum finance, and blockchain ethics. The 15 most recent articles highlight trends in Quantum Computing for Finance , Smart Contracts , Decentralized Systems , and Explainable AI . His work bridges theoretical advancements with practical implementations in banking technology, including corporate banking operations and financial risk prediction. Advisees include PhD students ONG Weng Hwee (Lawrence) , Ramaprasad RAMAKRISHNAN , and YAU Hon Tak (Sean) . His work integrates digital transformation with cutting-edge quantum and blockchain technologies to address challenges in financial services and distributed systems.

• Natural Language Processing
• Vision-Language Models
• Medical AI
• +3 more

Parminder Bhatia is a prominent research scientist at Amazon with over 49 publications and 1,400+ citations spanning natural language processing, vision-language models, and medical AI. As a key contributor to Amazon's AI research initiatives, Bhatia has developed influential frameworks including A³Tune for medical vision-language alignment, SIMA for visual-language modality improvement, and ReCode for evaluating code generation robustness. Their work bridges theoretical advances with practical applications across healthcare, software engineering, and multimodal systems. Bhatia's research primarily focuses on enhancing large language models through innovative alignment techniques, efficient fine-tuning strategies, and robustness evaluation frameworks. Key contributions include solving attention distribution challenges in medical VLMs, improving cross-file context understanding for code completion, and developing self-improvement mechanisms for visual-language alignment without external dependencies. Their work demonstrates consistent innovation in addressing fundamental limitations of current AI systems while maintaining practical applicability across diverse domains. Analysis of Bhatia's 15 most recent publications reveals a strong emphasis on medical AI applications (40%), code generation/analysis (30%), and foundational LLM improvements (30%). The research shows an evolving trajectory from basic NLP tasks toward complex multimodal integration, with increasing focus on practical constraints like computational efficiency, robustness to perturbations, and adaptation to specialized domains. Notably, over 60% of recent work involves medical applications, establishing Bhatia as a leader in healthcare AI.

Hrushikesh MhaskarView profile

Research Professor

• Approximation Theory
• Computational Harmonic Analysis
• Machine Learning
• +3 more

Hrushikesh Mhaskar is a Research Professor of Mathematics at Claremont Graduate University (CGU) since 2012, with a prior 32-year tenure at California State University, Los Angeles. He holds a PhD in Mathematics from Ohio State University, alongside an MS in Computer Science and an MSc from the Indian Institute of Technology, Mumbai. His research focuses on approximation theory, computational harmonic analysis, machine learning, and signal processing, with significant contributions to neural network theory and kernel-based methods. Mhaskar has authored over 150 papers, two books, and five edited volumes. His work includes pioneering studies on weighted polynomial approximation, Fourier domain conversions, and manifold learning. He currently serves on editorial boards for journals like Applied and Computational Harmonic Analysis and Journal of Approximation Theory , and collaborates with institutions like the University of California, Santa Barbara. Awards include five Alexander von Humboldt Fellowships and a John von Neumann Distinguished Professorship. His research is supported by the NSF and previously by the U.S. Air Force and intelligence agencies. Notable contributions include developing eignets for function approximation on manifolds and analyzing deep vs. shallow networks' approximation capabilities. His work bridges theoretical mathematics with practical applications in biomedical data analysis (e.g., blood glucose prediction) and signal processing.

Kate ArringtonView profile

Professor

• Cognitive Psychology
• Cognitive Control
• Human Multitasking
• +4 more

Kate Arrington is a Professor in the Department of Psychology at Lehigh University, internationally recognized for her research on cognitive control and multitasking. She earned her Ph.D. in Cognitive Psychology from Michigan State University in 2002 and completed an NRSA-funded postdoctoral fellowship at Vanderbilt University before joining Lehigh in 2005. Role: Chair of the Department of Psychology Contact: 610.758.4230 | cma205@lehigh.edu | Chandler-Ullmann room 130 Research Interests: Cognitive psychology, cognitive control mechanisms, human multitasking, and applied cognition. She investigates interactions between stimulus-driven and goal-directed processes in task selection and performance, exploring individual and situational variability in cognitive control. Developed the voluntary task switching paradigm for lab-based studies Applied research in intelligent tutoring systems and multimodal learning interfaces Scientific Awards & Leadership: NIH and NSF grant funding for multitasking and cognitive control research Rotating Program Officer, NSF Perception, Action, and Cognition program (2015-17) Chair, Women in Cognitive Science (from 2021) Director, Lehigh’s Institute for Data, Intelligent Systems, and Computation Teaching: Cognitive Psychology, Cognition in Practice and Policy, Research Methods and Statistics.

Jeffrey S KatzView profile

Professor

• Comparative Cognition
• Learning and Memory
• Cognitive Neuroscience
• +7 more

Jeffrey S. Katz is a Professor in the Department of Psychological Sciences at Auburn University's College of Liberal Arts, with a joint appointment in Electrical and Computer Engineering. He holds the Mike and Leann Rowe Endowed Professorship and serves as Director of the Cognitive and Behavioral Sciences (CaBS) Program. His research focuses on comparative cognition, integrating behavioral and neuroimaging methods to study learning, memory, and cognition across species, particularly humans and working dogs. Ph.D. in Experimental Psychology, Tufts University B.A. in Psychology, Ithaca College Dr. Katz's research interests include comparative cognition , concept learning , change detection , and canine cognition , with a strong emphasis on using fMRI and operant methods. His lab explores the neural and behavioral bases of cognition in both humans and animals, particularly in the context of working dogs and detection tasks. His recent publications reveal a strong trend toward canine neuroimaging , cross-species concept learning , and sensory memory , particularly gustation. The research bridges psychology, neuroscience, and engineering, often involving interdisciplinary collaborations and student involvement. Scientific awards and honors include: APA Fellow (Divisions 3 and 6) Gerald and Emily Leischuck Endowed Presidential Award for Excellence in Teaching (2015) Honors College Professor of the Year (2016) Comparative Cognition Society Recognition of Service Award (2014) Psi Chi Excellence in Teaching Awards Auburn University Alumni Professorship Dr. Katz has secured research grants from the NSF and NIMH, advising numerous graduate and undergraduate students in the Comparative Cognition Lab. He is deeply involved in teaching, offering courses such as Cognitive Neuroscience, Animal Cognition, and Advanced Experimental Psychology. His lab collaborates with Auburn’s Canine Performance Sciences and the MRI Research Center, providing rich training opportunities for students. The Comparative Cognition Lab conducts research at the Biological Research Facility, the MRI Research Center, and through field studies with working dogs. The lab fosters a collaborative environment focused on understanding the mechanisms of learning and memory across species.