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Rahnuma Islam NishatView profile

Assistant Professor

• Graph Theory
• Computational Geometry
• Additive Manufacturing (3D Printing)
• +5 more

Rahnuma Islam Nishat is an Assistant Professor in the Department of Computer Science at Brock University, located in St. Catharines, Ontario, Canada. She holds a BSc Engg from Bangladesh University of Engineering and Technology (BUET), and both MSc and PhD in Computer Science from the University of Victoria, BC, Canada. Her research focuses on theoretical and applied computer science, including graph theory, computational geometry, additive manufacturing (3D printing), data mining, and reconfiguration problems. She is supported by an NSERC Discovery Grant and has organized major conferences such as the 36th Canadian Conference on Computational Geometry (CCCG 2024) at Brock University. Her academic journey includes postdoctoral fellowships at the University of British Columbia Okanagan, Toronto Metropolitan University, and the University of Victoria. She has served on program committees for WALCOM 2023/2025, EuroCG 2025, and CCCG 2025. Her interdisciplinary work bridges mathematical puzzles and practical applications, particularly in additive manufacturing tool-paths and graph drawing visualization. Research highlights include contributions to Hamiltonian path reconfiguration in grid graphs, efficient spanning tree enumeration, and collision-free tool-paths for 3D printing. She actively engages with the computational geometry community through conference leadership and algorithmic problem-solving.

Suzanne BoydView profile

Associate Professor

• Dynamical Systems
• Complex Variables
• Complex Dynamics
• +2 more

Suzanne Boyd is an Associate Professor in Mathematical Sciences at the University of Wisconsin-Milwaukee's College of Letters & Science. She specializes in complex dynamical systems, focusing on fractal geometry, polynomial mappings, and higher-dimensional dynamics. Her research employs computer-assisted proof techniques to analyze structures like Mandelbrot sets and Hénon mappings. Key contributions include the development of Dynamics Explorer software for visualizing complex dynamical systems and theoretical advances in hyperbolic structures of polynomial automorphisms. Recent work explores geometric limits under degree growth and Medusa algorithm for polynomial matings. Her publications reflect sustained engagement with rigorous computational methods in complex dynamics.

Frederic Fol LeymarieView profile

Professor

• Artificial Intelligence
• Computer Vision
• Robotics
• +6 more

Prof. Frederic Fol Leymarie is a Professor in the Department of Computing at Goldsmiths, University of London. He specializes in AI, robotics, and computer graphics, with a focus on creative systems and their applications in art and biosciences. His work includes developing robots capable of artistic skills, interactive platforms like FoldSynth for molecular visualization, and projects like Mutator VR. He co-leads the MSc in Computer Games & Entertainment and teaches advanced topics in graphics and game design. His research spans shape understanding, AI-driven art, and interdisciplinary collaborations with bioscience specialists. Education: PhD in Computer Science from Brown University (2003) Key Projects: Mutator VR (2016–2020): An artistic VR project FoldSynth: Interactive tool for visualizing molecular structures Bioblox: Educational game for protein docking Research Interests: AI creativity, human-robot interaction, computer vision, and art-technology interfaces Prof. Leymarie leads London Geometry , a consulting group applying geometric algorithms to solve complex problems. His work bridges computational methods with artistic and scientific domains, emphasizing interdisciplinary innovation.

Mahdi Erfanian Nakhchi ToosiView profile

Lecturer

• Thermal science
• Renewable energy
• Heat transfer enhancement
• +3 more

Dr Mahdi Erfanian Nakhchi Toosi has served as a Lecturer in Mechanical Engineering at Northumbria University since 2022. Recognised as a Fellow of the Higher Education Academy (FHEA), he combines robust academic credentials with a prolific research portfolio focused on thermal science and renewable energy systems. Education & Qualifications PhD in Mechanical Engineering – 30 September 2018 Fellow of the Higher Education Academy (FHEA) Research Interests Dr Erfanian’s research spans experimental and computational investigations into heat-transfer augmentation , exergy analysis , and entropy generation minimisation . His work addresses critical challenges in designing high-efficiency heat exchangers for renewable-energy applications, employing innovative geometries such as louver-punched baffles, delta winglets, and V-type inserts. Recent studies integrate advanced CFD techniques to optimise thermal–hydraulic performance and to quantify second-law efficiency in solar air heaters and tubular heat exchangers. Research Output & Trends Across more than ten high-impact publications since 2024, Dr Erfanian has consistently advanced the field of thermal engineering . His papers illuminate the synergistic effects of geometric modifications—flapped baffles, trapezoidal louvered winglets, staggered V-baffles—on heat-transfer coefficients, pressure losses, and exergy destruction. The trajectory of his work signals a commitment to bridging fundamental thermal science with practical energy-system design, directly supporting the global transition toward more efficient renewable-energy technologies. Scientific Awards & Recognition Fellow of the Higher Education Academy (FHEA) Postgraduate Supervision & Grants Dr Erfanian currently supervises: Kiran Perkins – PhD project: “Wake and power prediction of onshore horizontal-axis wind farms under atmospheric flow using machine learning” (commenced 1 October 2024) Laboratories & Collaborative Networks Operating within Northumbria University’s vibrant Mechanical Engineering research environment, Dr Erfanian leverages state-of-the-art computational laboratories and collaborative links with international partners to deliver cutting-edge research. His affiliations support interdisciplinary projects that integrate fluid dynamics, renewable-energy engineering, and machine-learning-driven optimisation.

Bruce PiperView profile

Associate Professor

• Mathematics Education
• Computer Aided Geometric Design
• Approximation Theory
• +3 more

Bruce Piper is an Associate Professor in the Department of Mathematical Sciences at Rensselaer Polytechnic Institute, with contact information including email piperb@rpi.edu and phone number 518-276-6892. His research interests include: Mathematics Education Computer Aided Geometric Design Approximation Theory Shape Preserving Interpolation Data Analysis Techniques Dr. Piper's work spans both theoretical and applied mathematics. In theoretical domains, he has investigated shape preserving interpolation, specifically focusing on the preservation of monotonic curvature and 3-convexity problems. He has developed novel surface representations for convexity preserving interpolation. His applied research bridges mathematical techniques with antenna engineering, particularly in spherical conformal antenna design using NURBS techniques and electromagnetic modeling of conformal wideband antennas. His publication record reveals a progression from geometric design fundamentals to practical engineering applications. The research shows strong interdisciplinary connections between pure mathematics and electrical engineering, with significant contributions to both fields. His work on cubic spirals and Hermite interpolation has theoretical importance, while his antenna modeling research has practical implications for wireless communications technology. Dr. Piper has contributed to mathematics education through curriculum development for undergraduate math majors, creating courses that teach data visualization, classification, clustering, and ridge regression. He has also implemented mentorship programs where upper-class students guide first-year students in Calculus courses to improve STEM retention rates.

Jörg-Rüdiger SackView profile

Professor

• Algorithms
• Computational Geometry
• Data Structures
• +5 more

Jörg-Rüdiger Sack is a Chancellor's Professor in the School of Computer Science at Carleton University, where he has been a faculty member since 1983. He holds a Ph.D. from McGill University (1984) and degrees from the University of Bonn (Germany). His research focuses on algorithms, computational geometry, and geographic information systems (GIS), with recent work emphasizing shortest path algorithms in dynamic environments and data privacy techniques. Sack has held prestigious roles, including founding chair of NSERC’s national Computer Science Liaison Committee, editor-in-chief of Computational Geometry: Theory and Applications and Journal of Spatial Information Science , and chair of the Scientific Advisory Board of the Zuse Institute (Berlin). His academic contributions include foundational work in parallel computing, spatial modeling, and medical physics applications. He has led major initiatives such as the High Performance Computing Virtual Laboratory (HPCVL) and contributed to national and international scientific organizations, including the German Excellence Initiative and the Tricouncil+ Consultation on Big Data. Sack’s research has been supported by grants from NSERC, SUN Microsystems, and other organizations. His awards include the NSERC Industrial Chair in Applied Parallel Computing (1996) and a Carleton University Research Award. He remains actively involved in editorial and advisory roles, advancing both theoretical and applied aspects of computer science.

Wei XuView profile

Research Professor

• Artificial Intelligence for Science
• Explainable and Trustworthy AI
• Machine Learning
• +6 more

Wei Xu is a Research Professor in the Department of Computer Science at Stony Brook University and a Computational Scientist leading the Trustworthy Artificial Intelligence (TAI) group at Brookhaven National Laboratory's Artificial Intelligence Department. She holds a Ph.D. from Stony Brook University and M.S./B.S. degrees from Zhejiang University. Her research bridges artificial intelligence, high-performance computing, and scientific visualization, with current focus areas including explainable AI for scientific applications, quantum computing performance, and neural representations for ensemble simulations. Her work spans: AI/ML for Science : Developing trustworthy AI models for climate prediction, quantum computing, and medical diagnostics. Visual Analytics : Creating novel tools for multivariate volume visualization and quantum state representation. High-Performance Computing : Optimizing exascale workflows and performance analysis. Recent publication trends (2021-2025) demonstrate strong emphasis on explainable AI techniques for scientific domains, neural representations for data efficiency, and visualization methods for complex scientific data. Her papers frequently appear in top venues like TVCG, ICLR, and IEEE VIS. Awards & Honors: Women@Energy Showcase (2014) Best Paper Awards: PacificVis (2025), ISAV/SC20 (2020), Fully3D Workshop (2009) Spotlight Presentation at ICLR TCCML Workshop (2025) She secures funding through DOE BER/SciDAC programs and BNL initiatives (LDRD, NSLSII DSSI). As Trustworthy AI Group Lead, she oversees projects integrating AI with scientific infrastructure. She actively contributes to conferences as committee member (SC23/VIS24) and session chair (AAAI24, SC23).

Kevin McDonnellView profile

Teaching Professor

• Computer Science Education
• Visualization
• Visual Analytics
• +2 more

Kevin McDonnell is the CS Undergraduate Program Director and a Teaching Professor in the Department of Computer Science at Stony Brook University. He is affiliated with the College of Engineering and Applied Sciences and focuses on Computer Science Education, Visualization, Visual Analytics, Geometric Modeling, and Computer Graphics. He has received numerous awards, including the 2024 Provost's Outstanding Lecturer Award and multiple Excellence in Teaching Awards. His teaching portfolio includes courses such as CSE 101, CSE 114, CSE 215, CSE 220, CSE 564, and ISE 218. His research emphasizes data visualization, visual analytics frameworks, and computational methods in environmental and biomedical contexts. Recent work includes studies on peptide identification algorithms, microbial community dynamics, and geo-spatial data exploration. Awards: SUNY Chancellor's Award (2021), NSF GAANN Fellowship (1998), and honor society memberships (Phi Beta Kappa, Golden Key). Advising & Grants: No formal student advisees listed, but contributes extensively to curriculum development and accessibility initiatives. Labs/Teams: Engaged with interdisciplinary projects in visualization and computer graphics, though specific lab affiliations are not detailed.

Thomas NaselarisView profile

Associate Professor

• Neuroscience
• Cognitive Neuroscience
• Neuroimaging
• +3 more

Thomas Naselaris is an Associate Professor at the University of Minnesota, specializing in cognitive neuroscience and neuroimaging. His research focuses on decoding brain activity using advanced fMRI techniques and machine learning models, particularly in understanding visual perception, mental imagery, and memory. He leads the development of benchmark datasets like the Natural Scenes Dataset (NSD) and NSD-Imagery, which have become foundational for studying human visual cortex dynamics. His work integrates computational methods with neuroimaging to uncover how the brain represents and processes complex visual information. Notable contributions include reconstructing seen images from fMRI data and analyzing neural representations across different brain regions. He collaborates on large-scale studies involving ultra-high-field MRI and interdisciplinary approaches combining electrophysiology (iEEG) with fMRI. Key research themes include the role of generative models in visual processing, signal-to-noise dynamics in neural responses, and systems consolidation in memory. His grants include collaborative research proposals funded by CRCNS, focusing on evaluating machine learning architectures using benchmark datasets. Ongoing projects aim to bridge cognitive neuroscience with artificial intelligence, emphasizing interpretable models and scalable brain mapping techniques.

George PapagiannakisView profile

Professor

• Mixed Reality
• Computer Graphics
• Virtual-Augmented Reality
• +6 more

George Papagiannakis is a Professor of Computer Graphics at the University of Crete, Greece, and a Marie Sklodowska-Curie Fellow at FORTH's Institute of Computer Science. He holds visiting professorships at the University of Geneva and leads the Computer Graphics Group. His research focuses on mixed reality, GPU programming, and medical XR applications. He earned his PhD from the University of Geneva (2006), M.Sc. from Bristol, and B.Eng. from Manchester. His work includes pioneering projects like MAGES SDK (medical XR training platform) and pyGANDALF (educational framework). Key contributions span over 130 publications, 3400+ citations, and €8M in research funding. He actively participates in EU projects (HDRi, Hifi-PRINTER) and has been awarded the Marie-Curie Fellowship, Best Paper Awards, and NVIDIA grants. Teaching includes advanced courses on computer graphics and programming at the University of Crete. He advises students across 4 postdocs, 15 PhDs, and numerous MSc/BSc candidates. His lab develops cutting-edge tools for immersive technologies, including geometric algebra applications in 5G/6G XR environments.

Brian WoodView profile

Professor

• Transport Phenomena
• Multiscale Analysis
• Environmental Engineering
• +3 more

Brian Wood is a Professor in the Department of Chemical, Biological, and Environmental Engineering at Oregon State University (OSU), part of the College of Engineering. His research focuses on multiscale systems analysis, particularly in transport phenomena, tissue modeling, and environmental engineering applications. He holds a Ph.D. in Civil and Environmental Engineering from the University of California, Davis (1999), an M.S. in Environmental Engineering from Washington State University (1990), and a B.S. in Civil Engineering from Washington State University (1988). Wood’s research interests span transport of mass, momentum, and energy in natural and engineered systems, including multiscale tissue analysis and cancer biology modeling. His work integrates computational methods with experimental data to address challenges in porous media dynamics, biofilm growth, and environmental remediation. Notable contributions include studies on non-equilibrium transport, chemotaxis in granular media, and biofilm architecture visualization using synchrotron-based X-ray tomography. His recent publications (2020–2025) emphasize advancements in upscaling techniques for porous media flow, machine learning applications in biomedical modeling, and regulatory frameworks for end-user control systems. He leads the Computation and Simulation research group at OSU, fostering interdisciplinary approaches to multiscale hydrogeologic modeling. Wood’s research has been supported by collaborative grants focusing on bacterial transport in heterogeneous environments and hybrid multiscale methods. His work bridges theoretical developments in continuum mechanics with practical applications in environmental engineering and biomedical systems.

• History of Logic
• Mathematical Logic
• Logic Diagrams
• +4 more

Amirouche Moktefi is a Senior Lecturer at the Ragnar Nurkse Department of Innovation and Governance, Tallinn University of Technology. His research focuses on the history of logic, logic diagrams, and the contributions of historical figures like Lewis Carroll and John Venn. He explores diagrammatic reasoning, formal logic systems, and the intersection of mathematics with philosophical thought. His work spans academic publications analyzing historical logic developments, such as Euler and Venn diagrams, and the evolution of symbolic logic. He also engages with interdisciplinary topics like ethics education and the social dimensions of logic. Notable contributions include studies on Carroll’s logical innovations and the historical context of mathematical logic in France. His articles reflect a deep engagement with both historical and methodological aspects of logic, emphasizing visual tools and their role in reasoning. He frequently publishes in journals and conference proceedings, contributing to debates in the philosophy and history of logic.

Nathan MitchellView profile

Professor

• Embodied Learning
• Mathematics Education
• Engineering Education
• +4 more

Nathan Mitchell is the Vilas Distinguished Achievement Professor of Learning Sciences at the University of Wisconsin-Madison's School of Education. He serves as the Area Chair for Learning Sciences and Director of the MAGIC Lab. His research focuses on embodied cognition, mathematics education, and engineering learning, emphasizing how physical actions and environmental interactions influence intellectual processes. Mitchell holds a PhD in Cognitive Psychology from the University of Colorado and multiple degrees from Carnegie Mellon University, including BSE in Electrical Engineering, BS in Mathematics, and BA in History. His work spans over 200 peer-reviewed publications and grants from NSF, IES, and the James S. McDonnell Foundation. He is a Fellow of the International Society of the Learning Sciences and a Founding Officer of AERA's Engineering and Computer Science Education division. Mitchell has led initiatives such as the Teachers as Learners (TaL) grant program and co-founded EMIC, an embodied mathematics education consortium. He has also served on National Academies committees and is an inductee of UW-Madison's Teaching Academy. Mitchell's research emphasizes grounded and embodied learning principles, advocating for educational designs that integrate physical movement, gesture, and environmental interaction to enhance STEM learning. His recent work explores augmented reality, collaborative learning environments, and the role of gesture in mathematical proof construction. He has authored foundational texts like Foundations of Embodied Learning: A Paradigm for Education (2021), synthesizing theories and applications of embodied cognition. Awards: Facilitators’ Choice Award (2020), Vilas Distinguished Achievement Professorship (2018), Inductee into Teaching Academy (2014) Leadership Roles: Chair of TaL Grant Program (since 2017), Co-Chair of CSCL10 Conference (2013) Labs: Director of MAGIC Lab, exploring embodied education technologies

Jian TangView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Bioinformatics
• +5 more

Jian Tang is an Associate Professor in the Department of Decision Sciences at HEC Montréal, holder of a Canada CIFAR Chair in Artificial Intelligence Education. He earned a Ph.D. in Computer Science from Peking University. His research focuses on machine learning, bioinformatics, and AI applications in drug discovery and protein design. He teaches courses like Machine Learning II: Deep Learning and has supervised four MSc projects in data science and AI applications. Key research interests include graph neural networks, knowledge graphs, protein structure prediction, and AI-driven scientific discovery. His recent work spans LLM reasoning, geometric diffusion models for molecular design, and multi-modal learning for biomedical applications. Notably, he received the HEC Montréal's 2022 New Researcher Award for exceptional scientific contributions. Publications highlight advancements in protein engineering, antibody design, and AI in healthcare. Collaborations include work with institutions like CDPQ on AI R&D and Bombardier on machine learning for demand forecasting. His lab explores cutting-edge AI techniques to address complex problems in medicine and computational biology.

Lian XuView profile

Research Fellow

• Computer Vision
• Deep Learning
• Annotation-efficient learning
• +5 more

Lian Xu is a Research Fellow in the Department of Computer Science and Software Engineering at the University of Western Australia (UWA). She holds a PhD from UWA (2021) and specializes in computer vision, deep learning, and annotation-efficient methodologies. Her work focuses on weakly supervised learning, multi-modal data fusion, and applications in healthcare, agriculture, and maritime surveillance. Education: PhD in Computer Science, University of Western Australia (2021) Research Interests: Weakly supervised semantic segmentation Multi-modal learning with vision and language Computer vision applications in healthcare and agriculture Efficient learning strategies for limited labeled data Her research output emphasizes techniques such as auxiliary task integration, token-based transformers, and spectral analysis for hyperspectral imaging. Recent work includes advancements in small object detection for maritime surveillance and crop stress classification using hyperspectral data. Awards: Recipient of the Award for Growth in Innovation and Entrepreneurship (2021) for contributions to applied research. Teaching & Engagement: Facilitated courses on computer vision (CITS4402) and visualization (CITS2401). Co-organizer of workshops at DICTA 2024, ACCV 2022, and VCIP 2021 on small object detection and deep learning limits. Grants: Co-investigator on the Empowering Robots with 3D Vision and Reasoning for Enhanced Home Assistance project (2024–2025).