Find Academic Supervisors

• Photonics
• Biophotonics
• Optical Microscopy
• +3 more

Dr. Wolfgang Hübner is a Researcher at the Faculty of Physics at University of Bielefeld, Germany, affiliated with the Biomolecular Photonics Group. His work focuses on advanced optical imaging techniques applied to cellular and molecular structures. He maintains an active research program as evidenced by numerous publications from 2023-2025. His research interests center on photonics, biophotonics, optical microscopy, super-resolution imaging techniques, cellular biophysics, and molecular imaging. Dr. Hübner's work bridges physics and biology, developing and applying cutting-edge microscopy methods to address biological questions at the nanoscale level. His recent publications demonstrate a strong focus on super-resolution microscopy techniques, particularly structured illumination microscopy, fluorescence lifetime imaging, and correlative imaging approaches. His research investigates cellular structures like liver sinusoidal endothelial cells, dystroglycan mutants, and mitochondrial dynamics, revealing how advanced optical methods can visualize biological processes at unprecedented resolution. Dr. Hübner's research shows consistent development in both methodological advances in optical imaging and biological applications. His work spans from fundamental optical engineering to biomedical applications, demonstrating interdisciplinary expertise across physics, engineering, and cell biology.

Guoqiang YuView profile

Professor

• Machine Learning
• Pattern Recognition
• Optimization
• +9 more

Guoqiang Yu is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. He holds a joint appointment at the Virginia Tech Research Center - Arlington. His research focuses on integrating machine learning, signal processing, and statistical methods to develop computational tools for analyzing multiplatform biomedical data. Key areas include neuroinformatics, bioinformatics, and systems biology, with applications in understanding human diseases through genomic, proteomic, and imaging data integration. Education: Ph.D. in Electrical Engineering, Virginia Tech (2011) Postdoctoral Fellowship at Stanford University (2012) M.S. Tsinghua University (2004) B.S. Shandong University (2001) Research Interests: Machine learning methodologies for biomedical data analysis, pattern recognition in complex datasets, optimization algorithms for high-dimensional data, stochastic signal processing, and their applications in neurodegenerative diseases (e.g., ALS, Alzheimer's), glial cell biology, and precision medicine. His work emphasizes developing open-source tools like ABDS, CAM3.0, and SynQuant for data normalization, deconvolution, and quantitative imaging analysis. Awards & Service: NSF Career Award (2018) Dean's Award for Excellence in Research (2022) Member of NIH BRAIN Initiative Consortium (2021–present) Associate Editor for BMC Bioinformatics (2017–present) Labs & Teams: Leads the Yu Lab at Virginia Tech, collaborating with multidisciplinary teams in neuroscience, bioengineering, and computational biology. Active in NIH-funded consortia focused on brain data science and large-scale neuroimaging initiatives.

• Computational Neuroscience
• Neural Networks
• Deep Learning
• +4 more

Prof. Sander M. Bohte holds a part-time appointment as a Professor of Computational Neuroscience at the Swammerdam Institute for Life Sciences (SILS), University of Amsterdam, and is a researcher at the CWI Machine Learning group. His research focuses on computational models of neural information processing, emphasizing spiking neural networks, predictive coding, and reinforcement learning. He bridges computational neuroscience and machine learning, exploring how biological insights can improve neural network designs and vice versa. Key collaborations include work with Cyriel Pennartz (UvA), Pieter Roelfsema (NIN), and Steven Scholte (B&C). His applied research spans scientific machine learning applications in finance and genomics. He actively supervises MSc thesis students, prioritizing those from UvA, with projects ranging from biologically inspired neural architectures to efficient spiking network simulations. Research highlights include developing biologically plausible learning rules for deep networks, predictive coding models for sensory data, and spiking network models for working memory tasks. His work also addresses challenges in temporal dynamics and scalable neural computation, leveraging both theoretical and applied perspectives.

Lisa MertenView profile

Researcher

• Digital Media Studies
• Political Communication
• Algorithmic Personalization
• +6 more

Dr. Lisa Merten is a Senior Researcher at the Leibniz Institute for Media Research | Hans Bredow Institute (HBI), where she leads computational social science projects like POLTRACK, examining the relationship between information repertoires and political polarization. She also coordinates the DFG-funded 'Public Connection' project, exploring how users engage with diverse publics through media practices. Her research focuses on digital media environments, algorithm-driven personalization, and the impact of social media on news consumption and public opinion. Education : - Studied Communication Science at Universität Leipzig, TU Dresden, Universiteit van Amsterdam, and Boston University - German Academic Scholarship Foundation recipient Research Interests : - Algorithmic publics and personalization - Political information seeking and polarization - Digital methods for media analysis - Journalism and online commentary dynamics Key Projects : - Co-led the POLTRACK project with GESIS and universities of Bremen/Konstanz - Investigated Instagram influencers' reach in 'Media Use and Social Cohesion' with Hannah Immler - Pioneered browser data donation methods in journalism studies Awards : - 2021 Digital Journalism 'Article of the Year' for work on social media news curation Teaching : - Held part-time junior professorship in Computational Social Science at University of Konstanz (2021) - Taught at University of Hamburg, Augsburg, and Kiel University of Applied Sciences Labs/Teams : - Core member of HBI's Computational Social Science team - Collaborates with GESIS, Bremen/Konstanz universities, and international partners

Rense CortenView profile

Associate Professor

• Computational Social Sciences
• Experimental Sociology
• Social Media
• +7 more

Rense Corten is an Associate Professor of Sociology at Utrecht University's Faculty of Social and Behavioural Sciences. His work focuses on trust, cooperation, and social networks in contexts like the sharing economy, online criminal networks, and digital society. He holds a PhD in Social Sciences (2009) and MSc in Sociology (2004), both from Utrecht University. Key research areas include computational social sciences, experimental sociology, and the governance of digital systems. He has held academic positions since 2009, including a visiting scholar at Stanford University (2010–2011) and leadership roles in projects like the NWO Vidi grant-funded study on trust in the sharing economy. His research explores how social networks influence behavior in contexts such as peer-to-peer markets, social media, and citizen collectives. Notable contributions include studies on reputation systems in cryptomarkets and the role of social cohesion in care initiatives. Awards include the NWO Vidi grant (2016). He is affiliated with research groups like the Dynamics of Youth (DoY) and the Utrecht Platform for Applied Data Science.

• Neighborhood Stigma
• Criminal Justice System
• Educational Policy
• +8 more

Gijs Custers is an Assistant Professor at the Department of Law, Society and Crime within the Erasmus School of Law at Erasmus University Rotterdam. His research focuses on neighborhood stigma, criminal justice system dynamics, educational mobility, and urban policy impacts, particularly in Rotterdam. He employs quantitative methods (e.g., multilevel modeling, GIS) and computational text analysis to study social processes like gentrification and racialization. His work includes examining the effects of policies such as the National Program Rotterdam South (NPRZ) on educational outcomes and analyzing media representations of neighborhood stigma over time. Recent studies address implementation barriers in educational policies, the role of neighborhood organizations in resource brokering, and Rotterdam's urban transformation from a 'sick man' to a 'capital of cool.' Key research interests span criminology, urban sociology, and social inequality, with contributions to understanding how social policies influence educational and spatial inequalities. His interdisciplinary approach bridges criminology, urban studies, and quantitative methods.

Zhong-Lin LuView profile

Academic

• Visual Perception
• Cognitive Neuroscience
• Computational Modeling
• +6 more

Zhong-Lin Lu is a Distinguished Professor of Psychology and Social and Behavioral Science at The Ohio State University, holding concurrent appointments in Optometry and the Translational Data Analytics Institute. He directs the Center for Cognitive and Brain Sciences and the Center for Cognitive and Behavioral Brain Imaging. Previously, he held the William M. Keck Chair in Cognitive Neuroscience at the University of Southern California. He earned his Ph.D. in Physics from New York University (1992), following an M.S. (1991) and B.S. in Theoretical Physics from the University of Science and Technology of China (1989). His research bridges computational neuroscience, vision science, and cognitive psychology, focusing on visual perception, attention, perceptual learning, and functional brain imaging. Key methods include fMRI, EEG, and hierarchical Bayesian modeling. His work addresses clinical applications in amblyopia, myopia, and glaucoma, alongside foundational studies on decision-making and neural plasticity. He has developed novel techniques like the quantitative Contrast Sensitivity Function (qCSF) and quasiconformal mapping for retinotopic brain mapping. His labs emphasize translational research linking computational models to real-world applications. Awards: APS Fellow (2007), Society of Experimental Psychologists Early Investigator Award (2003) Leadership: Directed USC's Dornsife Cognitive Neuroscience Imaging Center (2004–2011) Interdisciplinary roles: Co-Director of OSU's Humanities/Cognitive Sciences Summer Institute Current research explores visual processing across lifespan, neural mechanisms of perceptual learning, and optimizing fMRI data through advanced computational methods. His work integrates basic science with clinical and applied domains, influencing driver safety, vision correction, and neurotechnology development.

Jo KennedyView profile

Associate Professor

• Financial Engineering
• Quantitative Finance
• Mathematical Finance
• +3 more

Dr. Jo Kennedy is an Associate Professor in the Department of Statistics at the University of Warwick, where she has been since 1998. Previously, she held positions at the University of Oxford and the University of Bristol. Her research focuses on financial derivatives, particularly interest rate derivatives and modeling requirements for market practitioners. She co-authored the influential book Financial Derivatives in Theory and Practice (2nd ed., 2004) with Phil Hunt. Kennedy holds a PhD in probability theory from the University of Cambridge, following her undergraduate and MSc degrees from the University of Sydney. Her work emphasizes advanced stochastic models, including Markov-functional models, stochastic volatility, and the SABR framework. Recent publications explore cross-currency models, non-Gaussian drivers, and the implications of model choice for hedging strategies. Her research bridges theoretical finance with practical applications in derivatives pricing and risk management. Despite her prolific output, no scientific awards are explicitly mentioned in the provided text. She collaborates actively with colleagues like J. Gogala, D. Pham, and L. Kaisajuntti, contributing to computational finance and applied mathematical finance journals. Her office is located in Room 3.06 of the MSB building at the University of Warwick.

• Education Policy
• Equity in Education
• Research Methods
• +3 more

Professor Stephen Gorard is a leading academic in education and public policy at Durham University, serving as Director of the Durham University Evidence Centre for Education (DECE) and holding a Professorship in the School of Education. He is also a Fellow of the Wolfson Research Institute for Health and Wellbeing and has held senior roles at Cardiff, York, and Birmingham universities. His work focuses on equitable and effective education evaluation, with over £20 million in external funding and extensive advisory roles in government and international bodies. Education: Bachelor's in Psychology with Statistics (University College London) PGCE in Mathematics and Computing (University of London Institute of Education) Masters in Education Management (Cardiff University) PhD in Social Sciences (Cardiff University) Research Interests: Equity and effectiveness in lifelong education, robust research methods, evidence-based policy, and addressing educational disadvantage through interventions like contextual admissions and Pupil Premium analysis. His work bridges theory and practice, emphasizing rigorous methodologies and policy impact. Publications & Awards: Over 1,000 books and papers, including landmark studies like Creating a Learning Society . Recognized with awards such as the Emerald Literati Prize and Society of Educational Studies Book Prize. His 2018 article on grammar schools remains the most downloaded in its journal. Grants & Advisory Roles: Advisor to UK government departments, the EU Horizon 2020 program, and international agencies. Led projects on teacher shortages, educational equity, and systemic reforms. His grants include funding from ESRC, British Academy, and international bodies like Kazakhstan's National Center for Science. Labs/Teams: Director of DECE, a hub for evidence-based education policy research. Collaborates globally on projects like the International Approach to Developing Early Career Researchers .

Florian HeckerView profile

Lecturer

• Sound Art
• Electro-Acoustic Music
• Post-War Modernity
• +3 more

Florian Hecker is an Affiliate and Lecturer at the Art, Culture, and Technology Program within MIT's School of Architecture and Planning. His work bridges post-war modernist composition, electro-acoustic music, and interdisciplinary art, exploring how sound shapes perception and space. Born in 1975, Hecker holds a diploma in Fine Arts from the Akademie der Bildenden Künste, Vienna, and studied Computational Linguistics and Psycholinguistics at Ludwig Maximilian University, Munich. His research focuses on sonic installations and performances that isolate auditory events to challenge material boundaries, creating immersive works that evoke memory and sensation. He has exhibited globally, including solo shows at MMK Frankfurt, IKON Birmingham, and Chisenhale Gallery London, and participated in dOCUMENTA (13) and Venice Biennale collaborations. Notable projects include No night No day (2009) with Cerith Wyn Evans and Chimerizations (2013), a book co-authored with MIT's Stefan Helmreich. Hecker’s discography spans over 25 releases, collaborating with artists like Aphex Twin and Yasunao Tone. His MIT-linked project Chimerization (MIT Project) (2011) exemplifies his integration of academic and artistic methodologies. Performances include venues such as Tate Modern, MIT Media Lab, and ZKM Karlsruhe.

Malik BoykinView profile

Assistant Professor

• Social Psychology
• Racial Inequality
• Data Science
• +5 more

Dr. Malik Boykin is an Assistant Professor in the Department of Cognitive, Linguistic, and Psychological Sciences at Brown University. He holds affiliate appointments with the Center for the Study of Race and Ethnicity in America (CSREA) and the Data Science Initiative (DSI). His research focuses on intergroup relations, racial identity, prejudice, mentorship, and the role of technology in societal structures, particularly addressing racial inequality. Boykin received a PhD in Social and Personality Psychology from UC Berkeley, an M.A. from Columbia University, and a B.S. from the University of Maryland. His academic background includes a Presidential Postdoctoral Fellowship at Brown. Notable awards include the Ford Fellowship Foundation grant and funding from the Society for Multivariate Experimental Psychology. Boykin teaches courses such as CLPS 1770 (Psychology of Stigma and Prejudice) and CLPS 2002 (Core Topics in Cognitive Sciences). His work intersects disciplines like social psychology, data science, and ethics, emphasizing emancipatory approaches to technology and systemic justice. Boykin’s research explores topics such as attitudes toward HBCUs, AI ethics in workplaces, and bias in algorithmic decision-making. He advocates for interdisciplinary solutions to fairness in machine learning and equitable educational policies. His contributions span academic publications, policy recommendations, and collaborations with the RISE Lab to advance anti-racist practices in academia and technology.

Douglas NollView profile

Professor

• Biomedical Engineering
• Radiology
• Magnetic Resonance Imaging
• +9 more

Douglas Noll is the Ann and Robert H. Lurie Professor in the Department of Biomedical Engineering at the University of Michigan , with additional appointments as Professor of Radiology and affiliate of multiple research institutes including the Michigan Alzheimer’s Disease Research Center, Michigan Neuroscience Institute, and Michigan Institute for Imaging Technology and Translation (MIITT). He co-directs the Functional MRI Laboratory and leads the NeuroImaging Core at the Michigan Alzheimer’s Disease Research Center. Prof. Noll’s research focuses on MRI technology development , particularly for functional MRI (fMRI) . His group specializes in high-speed MRI acquisition , signal processing , and image reconstruction to map brain function. Key areas include artifact elimination , physiological modeling , and quantitative imaging for neurological and psychiatric disorders. Recent collaborations apply MRI to histotripsy therapy monitoring. The 15 most recent articles reflect his work in dynamic MRI reconstruction , artifact correction , and cross-vendor protocol standardization . Topics span B0 shimming , histotripsy targeting , and non-Cartesian sampling , emphasizing spatiotemporal modeling and machine learning integration for accelerated imaging. Scientific Awards : Ann and Robert H. Lurie Professor. Research Collaborations : Functional MRI Laboratory, Michigan Alzheimer’s Disease Research Center, Michigan Institute for Imaging Technology and Translation (MIITT).

Paul RuvoloView profile

Professor

• Machine Learning
• Robotics
• Computer Vision
• +3 more

Dr. Paul Ruvolo is a Professor of Computer Science at Olin College in Needham, MA. His research focuses on developing assistive technologies for people with sensory and motor impairments, leveraging machine learning, robotics, and computer vision. He holds a Ph.D. and M.S. in Computer Science and Engineering from the University of California San Diego, and a B.S. in Computer Science from Harvey Mudd College. Key research areas include creating systems that learn through imitation and experience, such as navigation aids for the visually impaired and educational tools for orientation and mobility. He leads projects like Co-Designing Assistive Apps with Students Who Are Blind, emphasizing participatory design. His work integrates Bayesian statistics, numerical optimization, and linear algebra to solve complex sensorimotor tasks. Education: Ph.D., Computer Science and Engineering, UC San Diego M.S., Computer Science and Engineering, UC San Diego B.S., Computer Science, Harvey Mudd College Awards: NSF IGERT Fellowship for 'Learning and Vision in Humans and Machines' Recent publications highlight innovations in AR navigation systems, smartphone-based SLAM for indoor environments, and educational tools for blind users. His work bridges computational methods with real-world accessibility challenges, emphasizing interdisciplinary collaboration and user-centric design. Dr. Ruvolo’s lab, linked at occam.olin.edu , focuses on assistive technologies. He actively contributes to Teach Access and other initiatives promoting inclusive technology education. His research has applications in robotics, healthcare, and educational technology.

• Digital Humanities
• East Asian Studies
• Computational Linguistics
• +3 more

Paul Vierthaler is affiliated faculty in the Department of Data Science at the College of William & Mary's College of Arts & Sciences. His work bridges computational methods with humanities research, particularly in East Asian studies. Specializing in digital humanities, he explores cross-linguistic textual analysis, historical narratives, and bibliometric trends in premodern China. His research employs advanced algorithms for text reuse detection and examines intersections between fiction, history, and public knowledge in Late Imperial China. Key research themes include: Application of computational linguistics to Buddhist Chinese and Classical Tibetan texts Analyzing architectural networks in Baroque Rome through historical data Quantitative analysis of printing practices in Ming-Qing China Study of quasi-historical narratives and their role in public discourse His publications span 2014-2022, demonstrating a trajectory from social history of East Asia to increasingly computational methodologies. This work intersects disciplines like art history, library science, and cultural studies. No academic awards or grants are listed in the provided texts.

Trent GauglerView profile

Associate Professor

• Applied Statistics
• Computational Statistics
• Interdisciplinary Research
• +3 more

Trent Gaugler is an Associate Professor of Mathematics at Lafayette College, specializing in applied and computational statistics. He emphasizes collaborative research across disciplines and integrates simulation-based methods into his teaching. His work focuses on leveraging computational tools to address complex data analysis challenges. Gaugler holds a Ph.D. from Pennsylvania State University and a B.S. from Bucknell University. Education: Ph.D., Mathematics, Pennsylvania State University B.S., Mathematics, Bucknell University Research Interests: Gaugler’s work bridges statistical theory and real-world applications, particularly in interdisciplinary collaborations. He develops computational methods to analyze complex data sets, emphasizing practical solutions for fields like health sciences, environmental studies, and social sciences. His teaching philosophy emphasizes hands-on learning with real-world data, fostering student confidence and analytical skills. Recent Research Trends: His articles reflect a focus on interdisciplinary applications of statistics, including health outcomes, community response to environmental factors (e.g., noise pollution), and generational workforce dynamics. He frequently employs randomized controlled trials and longitudinal studies to assess interventions and social phenomena. Community Engagement: Gaugler collaborates with local nonprofits like the United Way and Easton Area Neighborhood Center, applying statistical methods to address community challenges such as education equity and health disparities. He mentors students in applied projects, enhancing their practical research skills. Labs/Teams: His collaborations span Lafayette College’s departments and external organizations, highlighting his commitment to bridging academic expertise with real-world impact.