جست‌وجوی استاد و پژوهشگر

• Computational Neuroscience
• Neuroimaging
• Brain Connectivity
• +۷ مورد دیگر

Dr. Vahab Youssofzadeh is an Assistant Professor in the Department of Neurology at the Medical College of Wisconsin. He is a member of both the Neuroscience Research Center and the Wisconsin Institute of NeuroScience (WINS), reflecting his interdisciplinary approach to neuroscience research. Dr. Youssofzadeh's research focuses on computational neuroscience and neuroimaging, with particular emphasis on brain connectivity, language processing, and neural oscillations. His work utilizes advanced techniques including magnetoencephalography (MEG), functional MRI, and computational modeling to investigate both healthy brain function and neurological disorders. His research spans multiple domains including language networks, epilepsy, Alzheimer's disease, and motor adaptation. His publication record demonstrates significant contributions to the field, with numerous high-impact papers on brain connectivity analysis, language processing, and computational approaches to neurological disorders. His most recent work includes studies on transcranial stimulation for language disorders, intracortical stimulation for sensory restoration, and advanced methods for mapping brain networks. Dr. Youssofzadeh is actively involved in collaborative research, as evidenced by his extensive publication record with multiple co-authors from various institutions. His work bridges computational methods with clinical neuroscience applications, contributing to both theoretical understanding and potential clinical interventions. As a member of the Neuroscience Research Center and WINS, Dr. Youssofzadeh participates in the broader neuroscience community at the Medical College of Wisconsin, contributing to interdisciplinary research initiatives and training the next generation of neuroscientists.

Ralph Ernstorferمشاهده پروفایل

استاد

• Ultrafast Phenomena
• Condensed Matter Physics
• Materials Science
• +۴ مورد دیگر

Prof. Dr. Ralph Ernstorfer is a leading physicist holding dual appointments at the Technical University Berlin (Professor, Institute for Optics and Atomic Physics) and the Fritz Haber Institute of the Max Planck Society (Group Leader, Department of Physical Chemistry). His career includes tenure as a Max Planck Research Group leader (2010–2017) and W2 scientist (2017–2021) at FHI. He specializes in ultrafast dynamics of quantum materials, with affiliations spanning TU Berlin, Max Planck Institutes, and international collaborations. His research explores electron-phonon interactions, topological materials, and surface dynamics using advanced spectroscopic methods. Key themes include: Ultrafast electron and lattice dynamics in 2D systems (e.g., bismuthene, black phosphorus) Singlet fission mechanisms in organic semiconductors Non-equilibrium phenomena in ferromagnets and ferroelectrics Development of time-resolved photoemission techniques Recent publications (2019–2025) demonstrate prolific output in high-impact journals, focusing on momentum-resolved spectroscopy, phonon scattering, and computational materials science. His lab pioneers methodologies for studying energy flow in solids at femtosecond timescales. Prof. Ernstorfer advises researchers at the Fritz Haber Institute and TU Berlin, though specific student names are not listed. His group maintains active instrumentation development for ultrafast experiments, including momentum microscopes and spectral analysis tools.

Celise Haldinمشاهده پروفایل

پژوهشگر ارشد

• Cognitive Neuroscience
• Speech and Language Rehabilitation
• Aphasia
• +۳ مورد دیگر

Celise Haldin is a Post-Doctoral Fellow at the Laboratoire de Psychologie et NeuroCognition (LPNC) at Grenoble Alpes University, France. Her research centers on neurocognitive mechanisms of language and speech, with emphasis on rehabilitation strategies for post-stroke aphasia and speech disorders. Her academic background includes a PhD in Cognitive Sciences, Psychology and Neurocognition (2019-2023) from Grenoble Alpes University, preceded by a Master 2 in Neuropsychology and Clinical Neuroscience (2016-2017), Master 1 in Molecular and Cellular Biology (2015-2016), and Bachelor in Biology (2012-2015), all from Grenoble institutions. She also served as a Research Engineer Assistant at LPNC from 2017-2019. Haldin's research explores neurocognitive functioning of language/speech, disorders thereof, and innovative sensorimotor rehabilitation approaches. She investigates how visual feedback of articulators and multisensory integration can enhance speech therapy outcomes in chronic non-fluent aphasia, utilizing multimodal assessment techniques including behavioral, acoustic, and neuroimaging methods. Her publication record (2016-2025) demonstrates consistent focus on aphasia rehabilitation, particularly multifactorial studies of language recovery after left-hemisphere stroke, sensorimotor therapy case reports, and neuroimaging investigations of speech production. This work bridges cognitive neuroscience, clinical linguistics, and rehabilitation science with strong translational potential. Scientific Awards: None documented in source material. Advising and Grants: No information provided regarding student supervision or research funding. She operates within the interdisciplinary framework of LPNC, contributing to cognitive neuroscience research groups focused on language processing and neurorehabilitation. Her work intersects with clinical teams specializing in stroke recovery and speech pathology at Grenoble Alpes University's medical research ecosystem.

Darcy Burgundمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Visual Cognition
• Memory
• +۴ مورد دیگر

Darcy Burgund serves as Professor of Psychology and Director of the Neuroscience Program at Macalester College. Based in Olin-Rice Science Center Room 330, she leads cognitive neuroscience research and teaches courses in neuroimaging techniques, cognitive neuroscience, and research methods. Her contact email is dburgund@macalester.edu. Her academic background includes: BA from Skidmore College PhD from University of Minnesota Research Interests: Dr. Burgund specializes in high-level visual cognition and memory with emphasis on object recognition. Her work investigates hemisphere asymmetries through divided-visual-field paradigms and identifies neural correlates using fMRI. Key focus areas include: Face perception and emotional processing Racial bias in visual cognition Neural adaptation mechanisms Task-dependent cognitive processing Publication Trends: Her 2020-2025 publications demonstrate consistent focus on visual cognition using multimodal approaches. Recent work examines race-based perceptual differences through eye-tracking, hemisphere asymmetries in emotional processing, and gender identity effects on visual tasks. Methodologically, she bridges behavioral paradigms with neuroimaging to explore how visual information is processed across diverse contexts. Advising: Professor Burgund actively mentors students, with numerous co-authored publications featuring undergraduate researchers. Her advisees contribute significantly to projects on facial expression processing, race bias investigations, and neural adaptation studies. Labs & Teams: She directs the Burgund Lab (https://sites.google.com/macalester.edu/burgundlab), which employs fMRI, eye-tracking, and behavioral methods to study visual cognition. The lab maintains an active research program investigating how the brain processes complex visual stimuli across emotional, racial, and gender dimensions.

Eric Ruggieriمشاهده پروفایل

استاد

• Statistics
• Bayesian Statistics
• Change Point Models
• +۶ مورد دیگر

Eric Ruggieri is a Professor and Chair of the Mathematics & Computer Science Department at College of the Holy Cross. His academic career spans statistical research with applications in climate science, computational biology, and medical research. Dr. Ruggieri's educational background includes: Ph.D. in Applied Mathematics from Brown University (2010) Sc.M. in Applied Mathematics from Brown University (2006) B.A. in Mathematics and Computer Science from Providence College (2005) His primary research focuses on Bayesian statistics , particularly change point models and variable selection techniques. Dr. Ruggieri has made significant contributions to the identification of change points in climatic time series, where a change point acts as a regime boundary indicating shifts in the climate system. His work has examined major transitions in Earth's climate history, including the formation of permanent Northern Hemisphere glaciers around 2.7 million years ago and the shift in glacial melting cycles from 40,000 to 100,000 years approximately 1 million years ago. He has developed computationally efficient algorithms for change point detection that address the exponential growth in potential solutions as dataset size increases. Beyond climate applications, his research extends to medical statistics, including studies on hospital-onset bloodstream infections in ICU settings, and he maintains a strong interest in sports statistics as both a research area and teaching tool. Dr. Ruggieri's publication record demonstrates a consistent focus on developing and applying statistical methodologies to real-world problems. His work shows a progression from theoretical developments in Bayesian change point detection and variable selection to diverse applications across climate science, medical research, and sports analytics. The majority of his publications appear in high-impact statistics and climate journals, reflecting the interdisciplinary nature of his research. Dr. Ruggieri has been actively involved in mentoring undergraduate students, particularly in sports statistics projects. As Department Chair, he demonstrates leadership within the academic community while maintaining an active research program. His work with NOAA climate data shows engagement with important contemporary issues like global temperature anomaly analysis.

• topological data analysis
• machine learning
• mathematical statistics
• +۶ مورد دیگر

Maxim Evgenievich Beketov is a Research Fellow at the Faculty of Computer Science of the National Research University Higher School of Economics (HSE), where he has been working since 2020. He is affiliated with the Institute of Artificial Intelligence and Digital Sciences and the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis, contributing to cutting-edge research in computational methods and artificial intelligence. His educational background includes: Master's degree (2017) in Applied Mathematics and Physics from Moscow Institute of Physics and Technology Bachelor's degree (2015) in Applied Mathematics and Physics from Moscow Institute of Physics and Technology Beketov's research spans multiple interdisciplinary fields with a strong mathematical foundation. His primary interests include topological data analysis, machine learning, mathematical and Bayesian statistics, differential geometry, and computational neuroscience. He applies these methods to problems in dimensionality reduction, variety assessment, and graph neural networks. His work bridges theoretical mathematics with practical applications in artificial intelligence and neuroscience, particularly in understanding cognitive processes through topological approaches. An analysis of his recent publications reveals a strong focus on topological methods in machine learning, with increasing emphasis on applications to neuroscience and cognitive mapping. His work demonstrates a progression from theoretical mathematical foundations toward practical implementations in spiking neural networks, traffic control systems, and music information retrieval. The interdisciplinary nature of his research connects computer science, mathematics, and neuroscience through topological approaches. His scientific achievements include: High Professional Potential Group (HSE Personnel Reserve), Category: New Researchers (2025) Beketov has been actively involved in academic teaching, offering courses including Introduction to Discrete Differential Geometry and Mathematical Analysis. His research is supported through the HSE University Basic Research Program, as acknowledged in his publications. He collaborates with researchers across multiple institutions, as evidenced by his co-authorship on papers with numerous collaborators. He is a core member of the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis, where he contributes to projects involving topological data analysis, machine learning, and computational neuroscience. His work in the laboratory focuses on developing advanced mathematical methods for analyzing complex data structures, with applications ranging from cognitive neuroscience to transportation systems.

• Anti-corruption
• Sustainable Development
• Governance Integrity
• +۳ مورد دیگر

Patty Zakaria serves as Associate Professor in the Master of Data Analytics program at University of Niagara Falls Canada, leveraging over two decades of expertise in international relations, research design, and policy analysis. Her work focuses on optimizing data-driven decision-making processes and driving strategic initiatives in anti-corruption and sustainable development governance. Her academic foundation includes a PhD in International Relations and Comparative Politics and Master of Arts in Labour and Industrial Relations from Wayne State University. This training underpins her methodological rigor in statistical analysis and field research across global contexts. Professor Zakaria's research centers on anti-corruption strategies, sustainable development governance, and AI applications for environmental sustainability. She employs advanced statistical software (SPSS, R, STATA) to analyze corruption dynamics and generational technology adoption patterns, with fieldwork conducted in Macedonia, Lebanon, and Croatia. Her current flagship project examines how millennials and Gen Z adopt AI for environmental solutions across global regions. Her significant contributions to the field are recognized through: Nomination for Minister’s Award of Excellence (2024) Nomination for Rising Star at Global University Systems (2022) OECD Research Edge winner for anti-corruption research (2018) Daimler Chrysler MAIR Grant (2006) As lead of the Patty Zakaria Research Group Limited, she directs projects including U.S. Department of State-commissioned anti-corruption indices and Transparency International consultancy. Her funding portfolio spans OECD initiatives, government contracts, and corporate partnerships like the GOTOX Inc. customer insights project. Students benefit from her practical approach that bridges academic research with real-world policy applications in the Master of Data Analytics program. The research group maintains active collaborations with international bodies including Transparency International, OECD, and John Jay College, producing policy reports that shape anti-corruption frameworks across the Western Hemisphere. Professor Zakaria's dual expertise in statistical methodology and field research creates unique opportunities for data-driven social impact.

Alia Khanمشاهده پروفایل

دانشیار

• Optical remote sensing
• Uncrewed aerial systems
• Satellite-based sensors
• +۹ مورد دیگر

Alia Khan serves as an Associate Professor in the Aerospace Department at the University of Colorado Boulder, where she conducts groundbreaking research in optical remote sensing of Earth's surface using uncrewed aerial systems and satellite-based sensors. Her work bridges aerospace engineering with environmental science, focusing on critical climate-related phenomena in polar and mountainous regions. Dr. Khan earned her PhD in Civil and Environmental Engineering from the University of Colorado Boulder in 2016, following an MS in Environmental Studies from the same institution in 2012, and a BSPH with Honors in Environmental Science and Engineering from the University of North Carolina Chapel Hill in 2009. Her research interests center on autonomous system development for environmental monitoring, with particular expertise in aquatic biogeochemistry, aerosols, and cryosphere studies including glacial and snow hydrology. She has pioneered methods using UAV technology to study snow algae, glacier ablation, and black carbon impacts on ice sheets. Her work spans from Antarctic ecosystems to Andean snowpacks and Arctic oceans, addressing critical climate change indicators through innovative remote sensing approaches. Analysis of Dr. Khan's recent publications reveals a strong focus on polar and cryospheric research, with particular attention to black carbon impacts on ice sheets, snow algae monitoring, and the application of UAV technology for environmental observation. Her interdisciplinary work connects atmospheric science, glaciology, and remote sensing to address pressing climate change questions, with increasing emphasis on human impacts in polar regions and the development of cost-effective monitoring technologies. National Science Foundation - CAREER Grant Awardee (2022) US Fulbright Scholar to the Chilean Antarctic Program (2019) Antarctic Service Medal, National Science Foundation (2012) National Science Foundation Graduate Research Fellowship (2010) Dr. Khan has successfully mentored students including Colby F. Rand and S. Healy, who appear as first authors on multiple publications. Her research has been supported by significant grants including the NSF CAREER award and Fulbright Scholarship, enabling extensive field work in Antarctica, the Arctic, and mountainous regions worldwide. She teaches GEEN 1400 First Year Engineering Projects and Drone Remote Sensing, integrating her research expertise into undergraduate education. Her work involves extensive collaboration with international research teams across polar regions, including participation in Antarctic research programs and Arctic monitoring initiatives. Dr. Khan's laboratory focuses on developing and deploying UAV-based remote sensing systems for environmental monitoring, with particular emphasis on cryospheric applications and the development of cost-effective monitoring solutions for challenging environments.

• Atmospheric remote sensing
• Aerosol-cloud interactions
• Ice formation processes
• +۳ مورد دیگر

Dr. Patric Seifert is a Scientist at the Leibniz Institute for Tropospheric Research (TROPOS) in Leipzig, Germany, where he has been working since 2010. He is affiliated with the Remote Sensing of Atmospheric Processes department and leads the Ground-Based Remote Sensing group. His work focuses on atmospheric remote sensing, particularly using the LACROS instrument suite for aerosol and cloud observations. He also maintains a strong connection with the University of Leipzig, where he earned his PhD and currently teaches courses on cloud radar and remote sensing to master's students. PhD in Meteorology from University of Leipzig (2010) Diploma in Meteorology from University of Leipzig (2006) Student of Meteorology at University of Leipzig (2000-2006) Dr. Seifert's research centers on understanding aerosol-cloud interactions, particularly how aerosols influence ice formation processes in the atmosphere. His work involves extensive use of ground-based remote sensing instruments, including the LACROS suite, to observe cloud and aerosol properties. He investigates how aerosol particles affect numerical weather forecast models and develops methods to derive continuous datasets of aerosol and cloud microphysical properties through the Cloudnet data processing scheme. His current research spans multiple international projects across diverse geographical locations including the Arctic, Southern Ocean, Cyprus, and Antarctica. Analysis of Dr. Seifert's recent publications reveals a strong focus on cloud microphysics, particularly mixed-phase and ice clouds. His work frequently employs advanced remote sensing techniques including polarimetric radar and lidar to study ice nucleation processes, particle shape distribution, and aerosol-cloud interactions. A significant portion of his recent research relates to the MOSAiC expedition in the Arctic, examining cloud life cycles and the impact of wildfire smoke on cirrus formation. His publications also highlight innovative approaches to cloud seeding experiments using UAVs and the development of new methods for analyzing cloud radar data. Member of Deutsche Meteorologische Gesellschaft (DMG) since 2006 Ordinary Member of the DFG Transregio programme Arctic Amplification: Climate Relevant Atmospheric and Surface Processes and Feedback Mechanisms (AC3) since 2021 Dr. Seifert actively collaborates on numerous international research projects including ACTRIS/Cloudnet, DACAPO-PESO (focusing on the Southern Ocean), PICNICC (examining hemispheric cloud contrasts), SPOMC (polarimetric observations of clouds), COALA (Antarctic observations), ATMO-ACCESS, ACTRIS-D, and EXCELSIOR. His work often involves mentoring students and conducting outreach activities to demonstrate TROPOS's contributions to understanding aerosol-cloud interactions and career opportunities in atmospheric sciences. He has been instrumental in operating and extending the LACROS instrument suite for various field campaigns around the world. Dr. Seifert leads the Ground-Based Remote Sensing group at TROPOS, which operates the LACROS (Leipzig Aerosol and Cloud Remote Observations System) infrastructure. This mobile observation platform has been deployed in diverse environments including Punta Arenas, Chile for the DACAPO-PESO campaign, Cyprus for multiple projects, and Antarctica for the COALA campaign. His team collaborates with international partners across Europe, New Zealand, and the Americas to study atmospheric processes in different climatic regions, with particular emphasis on aerosol-cloud interactions in pristine environments.

• Cognitive Psychology
• Language Processing
• Meaning Representation
• +۱ مورد دیگر

Kay Livesay is an Associate Professor in the Department of Psychology at Linfield University, teaching core courses including PSYC 283 (Introduction to Cognition), PSYC 288 (Psychology of Language), PSYC 252 (Research Methods), and PSYC 389 (Cognitive Neuroscience). Her educational credentials include a B.S. from the University of California, Los Angeles, and M.A. and Ph.D. degrees from the University of California, Riverside. Research focuses on cognitive psychology with specialized expertise in lexical/sentential language processing and meaning representation. Her experimental work combines behavioral methods with computational modeling to investigate semantic networks, priming effects, and context-space dynamics in language comprehension. Publications from 1998-2003 reveal consistent emphasis on mediated priming mechanisms, corpus-based word recognition, and multi-level discourse analysis, demonstrating interdisciplinary integration of neuroscience, linguistics, and cognitive modeling. Scientific awards: No awards mentioned in source material. She actively mentors undergraduate researchers through hands-on projects in her lab, recently presenting collaborative findings at the WPA conference in Irvine. Students join via direct email inquiry to pursue thesis work in experimental cognition. Leads a dedicated undergraduate research team in Pioneer Hall investigating real-time language processing using reaction-time paradigms and computational simulations.

• Astroparticle Physics
• Cosmic Neutrinos
• Multi-messenger Astronomy
• +۴ مورد دیگر

Olga Botner is a Professor at the Department of Physics and Astronomy, specifically in the High Energy Physics division, at Uppsala University. Her research focuses on astroparticle physics, with particular emphasis on the exploration of the Universe through cosmic neutrinos, which are considered ideal astrophysical messengers due to their ability to travel unaffected by magnetic fields and unhindered by matter and radiation. She is an active member of the IceCube Collaboration, which operates the world's largest neutrino telescope, the IceCube Observatory located at the South Pole. Professor Botner's research interests center around astroparticle physics, particularly the exploration of the Universe with cosmic neutrinos that may have been created in the environments of black holes. Her work is crucial for multi-messenger astronomy, which combines observations of different cosmic messengers like photons, cosmic rays, gravitational waves, and neutrinos to gain a comprehensive understanding of astrophysical phenomena. Her research spans neutrino astronomy, dark matter detection, cosmic ray physics, and the development of advanced analysis techniques including machine learning applications in neutrino data analysis. The recent publications by Professor Botner demonstrate a strong focus on neutrino physics and astroparticle research. Her work spans multiple areas including neutrino oscillation studies, dark matter searches, cosmic ray anisotropy, multi-messenger astronomy, and the development of advanced instrumentation like the Radio Neutrino Observatory in Greenland (RNO-G). A significant portion of her recent work involves the application of machine learning techniques to neutrino data analysis, particularly convolutional neural networks for atmospheric neutrino oscillation parameter measurements. Her research also explores fundamental physics questions related to sterile neutrinos and potential beyond Standard Model physics. As a key member of the IceCube Collaboration, Professor Botner contributes to one of the most significant neutrino observatories in the world, located at the South Pole. This international collaboration involves researchers from numerous institutions worldwide working together to detect and study high-energy neutrinos from cosmic sources. Her work connects fundamental particle physics with astrophysical observations, bridging the gap between these two fields to address some of the most profound questions about our Universe.