جست‌وجوی استاد و پژوهشگر

Chris Köcherمشاهده پروفایل

پژوهشگر

• Algorithms
• Theory & Logic
• Programming Languages
• +۹ مورد دیگر

Chris Köcher is a Researcher at the Max Planck Institute for Software Systems (MPI-SWS) in Kaiserslautern, Germany. His work focuses on theoretical computer science, with a strong emphasis on formal methods, automata theory, and verification of complex systems. He leads research in areas such as algorithms, programming languages, cyber-physical systems, and security. His research bridges foundational theory and practical applications, addressing challenges in distributed systems, mobile systems, and privacy-preserving technologies. His research interests span formal language theory, verification of concurrent systems, and the analysis of storage mechanisms in automata. Notably, he has contributed to the study of queue automata, multi-pushdown systems, and the decidability of reachability problems in lossy systems. His work often intersects with theoretical computer science and algebraic structures, exploring topics like monoids and trace languages. Chris’s publications reflect a deep engagement with foundational questions in computer science, including the verification of cooperating multi-pushdown systems, the complexity of semilinear set separability, and the formal analysis of hard attention mechanisms in transformers. His research has implications for improving the reliability and security of software systems through rigorous mathematical foundations. He is affiliated with MPI-SWS, a leading research institution in software systems, and contributes to the institute’s mission to advance theoretical and applied research in computer science. His work is supported by collaborations within the institute and the broader academic community.

Chelsea Cookمشاهده پروفایل

استادیار

• Social Behavior
• Neurobiology
• Behavioral Genetics
• +۳ مورد دیگر

Dr. Chelsea Cook is an Assistant Professor in the Department of Biological Sciences at Marquette University. Her research focuses on understanding social behavior through a holistic lens, integrating neurobiology, behavioral genetics, and ecological context. She uses honey bees as a model system to study collective behaviors like thermoregulation and foraging, as well as the effects of social isolation. Dr. Cook holds a B.S. from SUNY Cortland (2009), a Ph.D. from the University of Colorado Boulder (2016), and completed postdoctoral training at Arizona State University (2016–2020). Key research areas include honey bee thermoregulation—specifically how group dynamics influence fanning behavior—and the physiological impacts of social isolation in bees. She employs advanced techniques like electrophysiology and microbiome analysis to explore these topics. Dr. Cook’s work is supported by grants from the National Science Foundation, USDA, and NIH. Current Funding: NSF Integrative Organismal Systems Grant (2023–2026) Previous Funding: USDA SBIR Grants (2017–2020), NIH Postdoctoral Fellowship (2018–2019) Her lab, the Cook Research Team, emphasizes inclusivity and supports graduate students including Ph.D. candidates Casey Lambert, Rachael Halby, and Justine Nguyen. The lab also develops innovative tools like mobile indoor apiary systems to extend honey bee research seasons. Professional affiliations include the Animal Behavior Society and Entomological Society of America. Her recent publications span topics from microbiome roles in social behavior to neurotransmitter modulation of attention in honey bees.

Marieke Gilmartinمشاهده پروفایل

دانشیار

• Neuroscience
• Fear Learning
• Memory Formation
• +۴ مورد دیگر

Marieke Gilmartin is an Associate Professor and Associate Chair for Research in the Department of Biomedical Sciences at Marquette University. She leads a research team investigating fear memory formation and its neurobiological underpinnings, with a focus on understanding mechanisms contributing to disorders like PTSD and sex differences in psychiatric vulnerability. Education: Ph.D. in Neuroscience, Penn State College of Medicine (2007) Postdoctoral Training, University of Wisconsin-Milwaukee (with Dr. Fred Helmstetter) Research Interests: Neural circuits supporting fear memory encoding and retrieval Sex differences in fear-related neural circuits Role of stress and sex hormones in memory persistence Optogenetic and electrophysiological approaches in rodent models Current Projects: Hippocampal contributions to aversive learning Sex differences in fear circuitry across aging and Alzheimer's Neural encoding modulation by circulating sex hormones Advising & Team: Doctoral Students: Grace Schamber, Lila Metko Master's Student: Charlotte Siu Undergraduate Researchers: Emma Eshoo, Julie Schultz, Adam Warda, Collin Schlut, Raya Broeren, Isabella Vasser, Alessio Valsecchi Labs & Resources: Laboratory focuses on preclinical rodent models Utilizes optogenetics, electrophysiology, and molecular techniques Lab website: [link not provided]

Minesh Patelمشاهده پروفایل

استادیار

• Computer Architecture
• Memory Systems
• Hardware Security
• +۳ مورد دیگر

Minesh Patel is an Assistant Professor at Rutgers, The State University of New Jersey. His research focuses on computer architecture, memory systems, and hardware security, with a particular emphasis on DRAM reliability, RowHammer vulnerabilities, and in-memory computing. He has contributed to frameworks like DRAM Bender and NEON , which explore novel hardware-software co-design approaches. His work bridges theoretical analysis with experimental validation, addressing challenges in modern memory systems such as error mitigation, latency reduction, and security exploits. Recent projects include studying RowHammer under temperature variations and exploring resistive RAM-based neural networks. He has also published in medical informatics, analyzing surgical outcomes during the pandemic, suggesting interdisciplinary collaboration interests. Patel's research has implications for both academic and industry applications, including cloud computing security and embedded systems optimization. His publications span journals and conferences in computer engineering, cybersecurity, and healthcare analytics.

• Computational Neuroscience
• Machine Learning
• Cognitive Science
• +۳ مورد دیگر

Qiong Zhang is an Assistant Professor jointly appointed in the Psychology Department and Computer Science Department at Rutgers University. Her research bridges human learning and machine learning, focusing on comparing human memory systems with AI solutions and informing intelligent machine design through cognitive insights. She holds a Ph.D. from Carnegie Mellon University's Machine Learning Department under the School of Computer Science and completed postdoctoral training at Princeton University. Education: Ph.D., Machine Learning, Carnegie Mellon University (School of Computer Science) Postdoctoral Training, Princeton University Her research investigates cognitive constraints in memory systems and their neural bases. She employs computational modeling, behavioral experiments, and neural imaging to explore how memory optimizes performance under constraints. Recent work includes an NSF-funded project on external cue benefits/detriments in memory search. Her lab's findings aim to enhance memory interventions and inform AI design. Grants and funding include support from the National Science Foundation and Rutgers Brain Health Institute. She explores interdisciplinary topics like episodic memory in storytelling (comparing humans and LLMs), and cognitive resource modeling during memory encoding.

Pietro Pietriniمشاهده پروفایل

استاد

• Clinical Neuroscience
• Cognitive Neuroscience
• Molecular Biology
• +۶ مورد دیگر

Pietro Pietrini is a Full Professor and Director of the IMT School for Advanced Studies Lucca since 2015. Previously, he held roles including Chair of Clinical Biochemistry and Molecular Biology at the University of Pisa Medical School. He earned an MD from the University of Pisa and a Ph.D. in Neuroscience from Scuola Superiore Sant'Anna, with board certification in Psychiatry. His research integrates neuroimaging (MRI, PET, TMS) and neuropsychological tools to study brain function in sensory deprivation, mental disorders, and social behavior. He pioneered studies on cortical reorganization in the blind and developed a multidisciplinary approach spanning neuroscience, ethics, and linguistics. Education: MD (1986, University of Pisa), Ph.D. Neuroscience (Scuola Superiore Sant'Anna), Board Certification in Psychiatry (University of Pisa). Research focuses on: neuroplasticity in sensory-deprived brains, affective neuroscience, and the neural basis of antisocial behavior. Key projects include studying empathy in incarcerated populations and the ethics of neuroscience in legal contexts. Scientific Awards: Commander of the Order of Merit of the Italian Republic (2015), International Recognition in Biological Psychiatry and Neuroscience. Labs/Teams: Leads the MOMILAB, focusing on translational research at the intersection of neuroscience and social sciences.

Roberto Rodriguez-Echeverriaمشاهده پروفایل

دانشیار

• Software Engineering
• Model-Driven Engineering
• Machine Learning
• +۵ مورد دیگر

Roberto Rodriguez-Echeverria is an Associate Professor at the Computer Languages and Systems Department of the Universidad de Extremadura (Spain). He leads the Applied Informatic Technology Institute and co-founded MetrikaMedia, a SaaS company for multimedia content measurement. His research focuses on software engineering, model-driven engineering, machine learning, and legacy system modernization. Education: Bachelor and Master in Computer Science (2000), Universidad de Extremadura Master on Education and IT (2012), Universitat Oberta de Catalunya PhD in Computer Science (2014), Universidad de Extremadura Research interests include data-driven software development, medical image segmentation using SAM models, and educational technology applications like chatbot performance analysis in exams. He explores reproducible data science workflows and hybrid recommendation systems for media. Recent work highlights include advancing loss function analysis for COVID-19 lung scans and developing cost-efficient medical image segmentation techniques leveraging SAM's zero-shot capabilities. He also studies the impact of AI tools like ChatGPT on academic assessment methods. His contributions span over 50 publications since 2004, with recent focus on smart city applications, cloud cost estimation, and cooperative social process modeling. Rodriguez-Echeverria's work bridges theoretical computer science with practical industry solutions through his lab's applied research initiatives. Labs/Teams: Applied Informatic Technology Institute (Director) i3 Lab (Affiliated)

• Neuroscience
• Visual Cortex
• Neural Circuits
• +۴ مورد دیگر

Kristina Nielsen is an Associate Professor of Neuroscience at Johns Hopkins University School of Medicine and a researcher at the Zanvyl Krieger Mind/Brain Institute. She investigates the function, development, and plasticity of higher-level visual cortex circuits. PhD, Max Planck Institute for Biological Cybernetics (Germany) Postdoctoral work at Salk Institute (2006-2012) with Ed Callaway and Rich Krauzlis Her research focuses on: Structure-function relationships in higher visual cortex Neural mechanisms of object recognition Developmental plasticity of visual circuits Two-photon microscopy applications in primates Viral vector-based circuit analysis Key article trends include primate visual cortex organization, motion and shape integration, and cortical development mechanisms. She has contributed to journals like Nature , Neuron , and Journal of Neuroscience . Scientific awards: CNRS 2025 bronze and silver medals Current lab members include graduate researchers Dallas Khamiss, Emmanuel Osikpa, and Brandon Nanfito. Her team has received ANR funding for projects like 'Earlier stages of development of the motion pathway' and 'Encoding of 2D and 3D shape in primate V4.'

Benjamin Brossمشاهده پروفایل

مدرس پاره‌وقت

• Video Coding
• Signal Processing
• Machine Learning
• +۳ مورد دیگر

Benjamin Bross is a part-time lecturer at HTW University of Applied Sciences Berlin and heads the Video Coding Systems group at Fraunhofer Heinrich Hertz Institute. He specializes in video coding standards, including HEVC (H.265) and VVC (H.266), contributing to their development and standardization. His work emphasizes open-source implementations like VVenC and VVdeC, deployed in broadcast and streaming systems. Education: Dipl.-Ing. in Electrical Engineering (RWTH Aachen University, 2008). Active in ITU-T VCEG and ISO/IEC MPEG since 2010, leading core experiments and editing key standards. Recognized with IEEE Best Paper (2013), SMPTE Merit (2014), and an Emmy (2017) for HEVC contributions. Research focuses on advanced compression techniques, machine learning integration, and real-time encoding. His team develops VVC tools for 4K/UHD, low-latency streaming, and adaptive bitrate systems. Recent work includes optimizing partitioning strategies and reducing encoding complexity in VVC implementations. Awards highlight his impact on video technology: IEEE Consumer Electronics Best Paper (2013), SMPTE Journal Certificate (2014), and an Emmy for HEVC (2017). Teaching emphasizes practical coding standards and their applications in multimedia systems.

Seung-Goo Kimمشاهده پروفایل

پژوهشگر

• Neuroscience
• Cognitive Science
• Music Psychology
• +۴ مورد دیگر

Dr. Seung-Goo Kim is a Postdoctoral Researcher at the Max Planck Institute for Empirical Aesthetics (since 2021), working under Prof. Daniela Sammler. His career spans postdoctoral roles at Duke University, University of Cambridge, and MPI for Human Cognitive and Brain Sciences, with a PhD in Psychology from University of Leipzig (2017, summa cum laude). He holds graduate degrees in Cognitive Science and dual BA in Economics/Psychology, with early training in Musical Composition. PhD: University of Leipzig & MPI for Human Cognitive and Brain Sciences Postdoc: Duke University (USA), University of Cambridge (UK) Current affiliation: Max Planck Institute for Empirical Aesthetics, Frankfurt/Main His research focuses on the neural encoding of music and speech , particularly: shared mechanisms of musical emotion and prosody; intersubject variability in musical reward sensitivity; temporal structure processing; pitch perception correlates; and applications in clinical neuroscience. Key methodologies include MEG, fMRI, and computational modeling of auditory systems. Major scientific awards include: Johanna Quandt Young Academy Fellowship (2023) Postdoc Research Grant Award (Duke University, 2021) Summa cum laude PhD (2017) Conference Scholarship (Mariani Foundation, Italy, 2014) Best Poster Presentation Award (Korean Society of Human Brain Mapping, 2010) His work includes collaborations with leading labs in neuroaesthetics , music cognition , and clinical neuroscience , with recent publications analyzing tonal hierarchy, dissonance encoding, and myeloarchitecture of impulsivity. Current projects emphasize computational models of auditory processing and empirical aesthetics.

• Computer Vision
• 3D Reconstruction
• Neural Networks
• +۳ مورد دیگر

Fangjinhua Wang is a Researcher affiliated with the Department of Computer Science at ETH Zurich, working within the Professorship for Computer Science. Their role involves contributing to cutting-edge research in fields such as 3D reconstruction, computer vision, and neural networks. The research focuses on advancing methodologies like scene graph manipulation, multi-view stereo techniques, and holistic human-scene reconstruction. Collaborations and projects emphasize practical applications in robotics, computer graphics, and AI-driven systems. While no explicit education details are provided, the research trajectory reflects a deep engagement with computational methods for 3D modeling and vision-based systems. The work bridges theoretical advancements with real-world applications, addressing challenges in scene understanding, object interaction, and human-robot collaboration. Research interests are centered on interdisciplinary topics including neural representation learning, robust visual localization, and the integration of geometric priors for high-fidelity reconstructions. The output demonstrates a commitment to both foundational research and applied solutions in computer science and robotics.

Michele Morningstarمشاهده پروفایل

استادیار

• Neurocognitive patterns and neural networks
• Neurodevelopment
• Psychology and cognitive sciences
• +۶ مورد دیگر

Michele Morningstar is an Assistant Professor in the Department of Psychology at Queen's University, Faculty of Arts and Science. She serves as Director of the Development of Social and Emotional Communication (DSEC) Lab and holds cross-appointments in the Centre for Neuroscience Studies. Her research examines the biological, cognitive, and social mechanisms underlying emotional communication development from childhood through adolescence. Education: Post-doctoral fellowship in Pediatrics & Neuroscience, Nationwide Children's Hospital & Ohio State University (2020) PhD in Clinical Psychology, McGill University (2017) BA in Psychology, McGill University (2011) Research focuses on neurodevelopment of emotional communication using multimodal methods including functional neuroimaging and vocal analysis. Key interests include: how nonverbal cues (tone of voice, facial expressions) facilitate emotional understanding; neural correlates of social cognition maturation; and relationships between emotional skills, social functioning, and psychological well-being across development. Current projects investigate adolescent neural responses to peer emotions and vocal emotion recognition in clinical populations. Publications show consistent focus on developmental social neuroscience, with recent trends examining: (1) Neural mechanisms of vocal emotion processing in typical and atypical development (2) Social-cognitive changes during adolescence (3) Clinical applications in epilepsy and psychopathology. Work emphasizes multimodal approaches integrating neural, behavioral, and acoustic data. Teaching includes PSYC251: Developmental Psychology. Actively mentors graduate students through the DSEC Lab which employs diverse methodologies to study emotional communication development. Lab research has been funded by Canadian Institutes of Health Research and other national/international agencies.

• Artificial Intelligence
• Neuroscience
• Robotics
• +۳ مورد دیگر

Madeleine Bartlett is a Research Fellow at the University of Waterloo, affiliated with the Post Doctoral Scholars and Visitors groups. Her work bridges neuroscience and artificial intelligence, focusing on biologically plausible algorithms and their applications in robotics and human-robot interaction. Her research explores how neural mechanisms inspire computational models for reinforcement learning, action representation, and social signal processing. Key areas include integrating biological constraints into machine learning systems and developing robots capable of interpreting human social cues, particularly for therapeutic applications in autism spectrum disorder (ASD). Her publications highlight contributions to continuous action representations in basal ganglia models, biologically-based memory systems for reinforcement learning, and statistical rigor in human-robot interaction studies. She has also pioneered methods for estimating engagement in social HRI using novel neural architectures like Legendre memory units. While no formal awards or grants are listed, her work demonstrates significant innovation at the intersection of neuroscience-inspired AI and socially assistive robotics. She contributes to advancing robots as therapeutic tools through validated autonomous systems that mimic human guidance patterns observed in ASD therapy contexts.

James Jeanneمشاهده پروفایل

استادیار

• Neuroscience
• Neural Circuits
• Sensory Processing
• +۴ مورد دیگر

James Jeanne is an Assistant Professor in the Department of Neuroscience at Yale School of Medicine. He holds affiliations with the Interdepartmental Neuroscience Program, Molecular Medicine, Pharmacology, and Physiology, as well as the Wu Tsai Institute. His research focuses on understanding how neural circuits implement computations supporting behavior, particularly in Drosophila melanogaster. He earned a B.S.E. in Electrical Engineering from Princeton University (2005), a Ph.D. in Computational Neuroscience from UC San Diego (2012), and completed postdoctoral work at Harvard Medical School (2017). Primary Research Themes: Sensory processing, working memory, decision-making, and neural circuit mechanisms in Drosophila Techniques: 2-photon optogenetics, whole-cell electrophysiology, connectomics, and calcium imaging Recent awards include the 2020 Klingenstein-Simons Fellowship and 2019 Smith Family Foundation Award. His lab investigates how circuit architecture enables flexible behavioral responses to sensory stimuli, with recent work exploring synaptic plasticity's role in odor tracking and decision-making algorithms in mushroom body circuits. Key collaborations include work with Rachel Wilson (Harvard), Timothy Gentner, and Tatyana Sharpee. His team has published in high-impact journals like Nature Communications , Current Biology , and Neuron .

Eduardo Blancoمشاهده پروفایل

دانشیار

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +۷ مورد دیگر

Eduardo Blanco is an Associate Professor in the Department of Computer Science at the University of Arizona. His research focuses on advancing Natural Language Processing (NLP) with an emphasis on understanding negation, hate speech dynamics, and computational linguistics applications. He explores how language models can better interpret ambiguous statements, predict conversation incivility, and counteract harmful online content through structured responses. Blanco’s work spans theoretical and applied NLP domains, including semantic role labeling, sentence embedding evaluation, and biomedical text analysis using protein language models. His research often integrates large language models with task-specific frameworks to address challenges in zero-shot learning and negation robustness. Key areas of investigation include: Negation processing and affirmative interpretation Social media discourse dynamics and hate speech detection NLP for biomedical applications (glycosylation site prediction) Semantic similarity alignment in sentence embeddings His recent articles highlight advancements in counterhate speech strategies, temporal QA systems, and LLM-based peer review tools. Notably, he secured the NSF CAREER Award for studying negation through positive interpretations (2022). Collaborative efforts focus on interdisciplinary teams addressing challenges in computational linguistics, with grants supporting projects like the ALIGN-SIM benchmarking framework and BEMEAE event argument extraction system.