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Simon ChristView profile

Researcher

• Computational Biology
• Biophysics
• Reproducible Science
• +4 more

Dr. Simon Christ is a Researcher at the Department of Computational Biology within the Institute of Cell Biology and Biophysics at Leibniz University Hannover's Faculty of Natural Sciences. His office is located in Building 4160, Room 110 at Herrenhäuser Str. 2, 30419 Hannover. He can be contacted via phone (+49 511 762 16174) or email (christ@cell.uni-hannover.de). Christ holds a Doctor rerum naturalium (PhD) from the University of Potsdam (2020) and a Master of Science from Humboldt University of Berlin (2014). His doctoral research focused on morphological transitions of vesicles under nonuniform conditions, while his master's thesis examined non-Markovian systems in statistical physics. His research focuses on computational approaches to biological systems, including: Development of reproducible scientific workflows and open-source tools Agent-based simulations of biological processes Numerical solutions of differential equations in biological contexts Computational modeling of membrane dynamics and vesicle behavior Research software engineering practices and tools He is proficient in multiple programming languages including Julia, C/C++, MATLAB, Mathematica, Python, and R, and is a certified instructor for The Carpentries. Christ's recent publications demonstrate a strong focus on research software engineering, computational biology methods, and biophysical modeling of cellular systems. His work frequently appears in computational biology, biophysics, and software engineering journals, with consistent contributions to open science practices and reproducible research methodologies. No specific awards or student advising relationships are mentioned in the available documentation. His current work appears to be conducted within the computational biology research group at Leibniz University Hannover, with collaborations extending to other institutions including the University of Potsdam and Max Planck Institute.

David M. SidhuView profile

Assistant Professor

• Language Processing
• Sound Symbolism
• Iconicity
• +5 more

Dr. David M. Sidhu is an Assistant Professor in the Department of Psychology at Carleton University, affiliated with the Faculty of Arts and Social Sciences. He holds a Ph.D. from the University of Calgary and completed a postdoctoral fellowship at University College London, funded by SSHRC. His research focuses on multimodal language processing, sound symbolism, and embodied cognition, exploring how language sounds connect to sensory and conceptual meanings. Education: Ph.D. in Psychology, University of Calgary Postdoctoral Research, University College London (SSHRC-funded) Research Interests: Sound symbolism and iconicity in language Cross-modal associations (e.g., shape-sound mappings) Embodied cognition and conceptual metaphor theory Name symbolism and social perception Language evolution and lexical development Funded by NSERC and SSHRC, his CLaSSI Lab investigates topics like the bouba/kiki effect, vowel symbolism in size perception, and the cognitive mechanisms behind name stereotypes. Recent work includes studies on temporal semantics and affective sound congruence. Lab & Collaborations: Director of the CLaSSI Lab, collaborating with institutions internationally. Research emphasizes experimental methods and large-scale language analyses.

• Group dynamics
• Inclusion and diversity management
• Industrial and organizational psychology
• +11 more

Filip Agneessens serves as a Teaching Fellow in the Department of Sociology and Social Research at the University of Trento, Italy, with contact details including office location at Via Verdi, 26 - 38122 Trento and email filip.agneessens@unitn.it. His academic role focuses on advancing social network methodologies within sociological and organizational contexts. His research expertise encompasses Group dynamics, Social network analysis, Organizational behavior, and Diversity management, with particular emphasis on negative ties in networks, ethnic diversity effects, and methodological innovations for workplace network studies. This interdisciplinary focus bridges sociology, psychology, and organizational science to examine how network structures shape group processes and individual outcomes. Agneessens' recent publications reveal consistent scholarly contributions in social network analysis across domains including education (e.g., bullying dynamics in schools), public administration (governance networks), and organizational behavior (workplace conflict). His work demonstrates methodological leadership through the 2022 book Analyzing Social Networks Using R and empirical studies applying longitudinal and multilevel network approaches to real-world group phenomena.

Natalya Pya ArnqvistView profile

Associate Professor

• Statistical Regression Modelling
• Functional Data Analysis
• Shape Preserving Smoothing
• +7 more

Natalya Pya Arnqvist is an Associate Professor in Mathematical Statistics at Umeå University, Sweden. She is affiliated with the Department of Mathematics and Mathematical Statistics and has active roles in research groups focused on Functional Data Analysis, Semiparametric Regression, and Statistical Learning for Spatio-Temporal Data. Her career includes prior positions at Nazarbayev University (2015-2020), University of Bath (2011-2015), and KIMEP University (2000-2005). Education : PhD in Statistics (University of Bath, UK, 2007-2010), CSc in Physical and Mathematical Sciences (Institute for Mathematics, Kazakhstan, 2000-2005). Her research centers on statistical regression modelling and functional data analysis , with significant contributions to shape constrained additive models (SCAMs) and model-based functional clustering . She has developed R packages such as scam , fdaMocca , and nilde for these methodologies. Recent publications highlight applications in applied demography , defect detection , and nonlinear state space modeling , reflecting her focus on bridging statistical theory with industrial and ecological challenges. She teaches undergraduate and graduate courses in probability, regression, and statistical learning.

Zachary K. CollierView profile

Assistant Professor

• Causal Machine Learning
• Structural Equation Modeling
• Educational Measurement
• +3 more

Zachary K. Collier is an Assistant Professor in the Department of Educational Psychology at the University of Connecticut. He holds a Ph.D. in Measurement and Statistics from the University of Florida (2018) and a B.S. in Special Education from Winthrop University (2013). His research integrates Causal Machine Learning and Structural Equation Modeling with critical perspectives to advance equitable data analysis methods. He developed the CritSEM framework to examine how social and structural inequalities shape measurement constructs. Founded the MUDD Lab (Methods for Unstructured and Difficult to use Data) in 2020 Secured over $9 million in external funding His work emphasizes fairness, data ethics, and methodological transparency across education, public health, and social sciences. Selected publications demonstrate innovative applications of machine learning to educational measurement challenges. Scientific recognition includes: Emerging Scholar Featured Mathematician

William UspalView profile

Associate Professor

• Microhydrodynamics
• Statistical Mechanics
• Active Matter
• +3 more

William Uspal is an Associate Professor of Mechanical Engineering at the University of Hawaii's College of Engineering. He holds a PhD in Physics from MIT (2013) and a B.Phil in Engineering Physics, Mathematics & Philosophy from the University of Pittsburgh (2007). His postdoctoral research at the Max Planck Institute for Intelligent Systems (2013–2018) focused on microhydrodynamics and active matter systems. Research interests include microhydrodynamics, statistical mechanics, active matter, self-organization, and soft matter simulation. His work explores colloidal systems, catalytically active particles, and microfluidic engineering with applications in nanotechnology and biomimetic robotics. Key contributions include studies on chemotaxis of nanoparticles, bacterial biohybrid microswimmers, and topographical guidance of active matter. His research bridges theoretical modeling (e.g., diffusiophoretic forces) with experimental systems (e.g., Janus particles and Hele-Shaw flow dynamics). Teaching includes ME311 Thermodynamics (Fall 2018). He collaborates internationally, with publications in journals like Nature Communications, Science Advances, and Physical Review Letters.

Victor ChurchillView profile

Assistant Professor

• Computational Mathematics
• Scientific Machine Learning
• Image Reconstruction
• +5 more

Victor Churchill is an Assistant Professor of Mathematics at Trinity College since 2023. He holds a Ph.D. and A.M. from Dartmouth College, an M.S. from New York University's Courant Institute, and a B.A. from Boston College. His research focuses on computational mathematics, scientific machine learning, and image reconstruction, particularly in Bayesian uncertainty quantification for synthetic aperture radar imaging and learning unknown dynamical systems using neural networks. He has held a postdoctoral position at The Ohio State University under Dr. Dongbin Xiu and previously worked at Dartmouth under Dr. Anne Gelb. Research Highlights: His work includes deep learning of PDEs, ensemble prediction for robust neural network training, and chaotic system learning from partial observations. Recent contributions address coarse time-scale observations and uncertainty quantification in SAR imaging. He was awarded the SIAM Science Policy Fellowship (2023-2024) to engage with federal science policy advocacy. Teaching: He teaches computational science courses at both undergraduate and graduate levels, integrating his research into lectures through case studies and data-driven examples. His pedagogical approach emphasizes applied computational mathematics and real-world problem-solving. Affiliations: Previously affiliated with The Ohio State University as a Visiting Assistant Professor of Scientific Computation. Active in computational math communities, including SIAM policy engagement. Personal Interests: An avid runner with marathon personal bests, he also enjoys bonsai cultivation, architectural design, and animal care. His unconventional hobbies include experimenting with hair color transformations.

Tobias GlasmachersView profile

Professor

• Machine Learning
• Artificial Intelligence
• Optimization Algorithms
• +6 more

Prof. Dr. Tobias Glasmachers is a Full Professor at the Institut für Neuroinformatik , Ruhr-Universität Bochum, Germany, specializing in the Theory of Machine Learning . He leads the Optimization of Adaptive Systems group and holds appointments in both Computer Science and Interdisciplinary AI research. Key Research Areas : Optimization algorithms, evolutionary computation, reinforcement learning, supervised learning, and neural networks Technical Focus : Gradient-based methods, support vector machines, and adaptive coordinate descent Applications : Robotics, waste sorting facilities, 3D game environments (e.g., Doom/Minecraft), and human-centered AI design Notable Contributions : Development of LM-MA-ES evolution strategy, Hessian Estimation Evolution Strategy, and tachAId tool for ethical AI design. His work bridges theoretical analysis with practical implementations across diverse domains. Teaching : Offers courses in Informatik 1 - Programmieren, Machine Learning: Supervised Methods, and Evolutionary Algorithms. Supervises numerous Bachelor's and Master's theses on AI/ML applications.

Mohammad F. ObeidView profile

Associate Professor

• Virtual Reality
• Medical Simulation
• Surgical Training
• +2 more

Mohammad F. Obeid is Associate Professor of Advanced Technology at Shenandoah University, specializing in medical VR simulations. He holds a PhD in Modeling and Simulation Engineering from Old Dominion University. His research develops VR platforms for surgical training and healthcare education. Research focuses on: Extended reality (XR) for medical interventions Predictive surgical simulations Computer-assisted clinical training systems Publication analysis reveals strong emphasis on VR applications in nursing education (2024-2025) and pectus excavatum surgical training (2015-2020). Recent works demonstrate progression toward immersive healthcare education platforms. Awards include: Irish Research Council Scholarship Dumbarton Oaks Residency Terry Barry Prize

• Behavioral/Mental Health
• Adolescent Health
• Biostatistics
• +2 more

Dr. Yuh-Pey Anne Buu is a Professor with dual appointments in the Departments of Health Promotion and Behavioral Sciences, and Biostatistics and Data Science at UTHealth Houston School of Public Health. As a biostatistician and developmental psychologist, her research examines substance use patterns through advanced statistical modeling. Her NIH-funded research program focuses on: Methodological approaches for analyzing e-cigarette and polysubstance use data Longitudinal patterns in adolescent substance use Biostatistical applications in behavioral health Dr. Buu leads multiple R01 projects from NIDA including methodological work on substance use measurement. She co-chairs NIH working groups on medical cannabis research and serves as standing member of the Addiction Risks and Mechanisms Study Section. As Associate Editor for Nicotine & Tobacco Research, she shapes methodological standards in substance use research. Her teaching portfolio includes longitudinal data analysis, multivariate statistics, and addiction studies courses taught at multiple universities. She mentors students and junior faculty across statistical and behavioral health disciplines.

Gregory RutledgeView profile

Professor

• Molecular Engineering of Soft Condensed Matter
• Polymer Science and Engineering
• Statistical Mechanics and Molecular Simulation
• +1 more

Gregory Rutledge is a Professor of Chemical Engineering at the Massachusetts Institute of Technology (MIT), where he leads research in molecular engineering of soft condensed matter and polymer science. His work bridges statistical mechanics, molecular simulation, and electrospinning technologies. Education: Ph.D. from MIT (1990), B.S. from University of Virginia (1983) His research focuses on understanding and manipulating molecular behavior in polymers and soft materials, with applications in membrane technology, fiber production, and phase transitions. Recent work involves 3D visualization of membrane fouling and flow-enhanced nucleation modeling. Key publication trends span Materials Science , Chemical Engineering , and Microscopy , emphasizing electrospinning , polymer membranes , and molecular simulation across 2010-2019. Scientific Awards: AIChE Braskem Award (2022) AIChE Fellow (2017) ACS PMSE Fellow (2015) Fiber Society Founder's Award (2014) NSF National Young Investigator (1994) As a DuPont Professorship recipient and multiple award-winning researcher, he has mentored students and shaped advancements in polymer engineering. His work has been supported by grants from NSF, 3M, and DuPont.

Thad DunningView profile

Professor

• Comparative Politics
• Political Economy
• Methodology & Formal Theory
• +9 more

Thad Dunning is Robson Professor of Political Science at the University of California, Berkeley, where he directs the Center on the Politics of Development. He teaches graduate and undergraduate courses on comparative politics, political economy, and methodology. His academic career includes previous positions as Professor of Political Science at Yale University from 2006 to 2013. Dunning received his Ph.D. in political science and an M.A. in economics from the University of California, Berkeley in 2006. He also holds an M.A. in Political Science from Berkeley (2002), an M.A. in Latin American Studies from Stanford University (2000), and a B.A. with highest honors from Brown University (1996). Professor Dunning's research spans multiple domains of political science with particular emphasis on comparative politics, political economy of development, and methodological innovation. His substantive work examines ethnic voting patterns, the consequences of political representation for minority groups, clientelist distribution systems, and the relationship between natural resource wealth and democratic institutions. Methodologically, he has made significant contributions to causal inference, natural experiments, and the integration of quantitative and qualitative approaches. His research draws on fieldwork and natural experiments across Latin America, Africa, and India, with a focus on how political institutions shape distribution in developing countries and how distributive patterns influence democratic processes. Dunning's publications reveal a consistent focus on methodological rigor and innovation alongside substantive contributions to comparative politics. His work increasingly emphasizes cumulative learning and knowledge accumulation through coordinated research efforts, as evidenced by his leadership in the Metaketa Initiative. The interdisciplinary nature of his research bridges political science, economics, and methodology, with particular attention to how cross-cutting social cleavages affect political behavior and outcomes in ethnically diverse societies. Luebbert Book Award (2014) for Brokers, Voters, and Clientelism Best Book Award (2020) from Experimental Research Section of APSA for Information, Accountability, and Cumulative Learning Best Book Award (2013) from Experimental Research Section of APSA for Natural Experiments in the Social Sciences Best Book Award (2009) from Comparative Democratization Section of APSA for Crude Democracy Gaddis Smith International Book Prize (2009) for Crude Democracy Mancur Olson Award (2008) for best dissertation Professor Dunning has advised numerous doctoral students who have gone on to prestigious academic positions at institutions including Harvard, Princeton, Columbia, and the University of Chicago. His current advisees include scholars working on political economy, ethnic politics, and methodological innovation, many affiliated with Berkeley's Center on the Politics of Development. He has received significant funding for research projects, including a principal investigator role for the EGAP Regranting Initiative (2013-2017) and funding from J-PAL for research on tax compliance in Uruguay. As director of the Center on the Politics of Development, Dunning leads a collaborative research team focused on understanding political institutions in developing countries. The center supports interdisciplinary research connecting scholars across Berkeley and globally, with particular emphasis on methodological innovation and policy-relevant scholarship. Current projects examine topics including political accountability, ethnic politics, clientelism, and the political economy of natural resources.

Barbara SolenthalerView profile

Lecturer

• Computer Graphics
• Physics-Based Simulations
• Facial Animation
• +3 more

Barbara Solenthaler is a Lecturer at the Department of Computer Science, ETH Zurich. Her research focuses on physics-based simulations, facial animation, and machine learning applications in computer graphics.

• Numerical Optimization
• Derivative-Free Optimization
• Stochastic Optimization
• +3 more

Lindon Roberts is a Lecturer in the School of Mathematics and Statistics at the University of Sydney . He holds a DPhil (Doctor of Philosophy) from the University of Oxford and previously served as an MSI Fellow at the Australian National University. His research focuses on numerical optimization, including nonconvex, derivative-free, and stochastic optimization, with applications in machine learning. He currently teaches courses such as MATH2070 (Optimization and Financial Mathematics) and FMAT3888 (Projects in Financial Mathematics). Education & Professional Background: PhD (DPhil) in Mathematics, University of Oxford MSI Fellow, Australian National University Research Interests: Numerical optimization techniques Derivative-free and stochastic optimization methods Algorithm design for machine learning applications Nonconvex optimization challenges His work bridges theoretical optimization and practical implementation, particularly in imaging and data-driven fields. Funding & Grants: 2024: 'Robust Derivative-Free Algorithms for Complex Optimisation Problems' (Australian Research Council DECRA) 2022: 'Theory and Algorithms for Numerical Optimisation' (University of Sydney Faculty Startup) Key Research Trends in Publications: Recent articles emphasize optimization algorithms for imaging (e.g., X-ray and neutron beam techniques) and bilevel learning frameworks. His work on inexact hypergradients and scalable subspace methods highlights advancements in handling complex optimization landscapes.

Steven A. FrankView profile

Professor

• Evolutionary Biology
• Cancer Research
• Genetics
• +5 more

Steven A. Frank is the Donald Bren Professor and UCI Distinguished Professor at the University of California, Irvine. His research integrates evolutionary theory with cancer biology, genetics, and systems biology. Frank's work focuses on understanding how evolutionary principles shape biological systems, particularly in cancer development, regulatory networks, and microbial ecology. He has authored influential books including Dynamics of Cancer and Foundations of Social Evolution . Research Interests: Evolutionary mechanisms in cancer and aging Regulatory networks and robustness Genomic conflict and cooperation Evolutionary dynamics of microbial communities Publications highlight innovative approaches such as applying control theory to biological systems and bridging evolutionary theory with machine learning. Frank's work emphasizes interdisciplinary methods to address questions in oncology, genetics, and ecology.