Ali Mahdavi-Amiri is an Assistant Professor of Professional Practice and serves as Program Chair and Director of the Professional Computer Science program at the School of Computing Science, Simon Fraser University. He holds a PhD in Computer Science from the University of Calgary and an MSc from Sharif University of Technology. His research focuses on deep geometry processing, computational fabrication, geometric modeling, and computer graphics. Teaching interests include visual computing, artificial intelligence, and computer graphics. He currently teaches courses such as CMPT 742 (Visual Computing Lab I) and CMPT 894 (Directed Reading) in the 2025 academic sessions. No scientific awards or grants are explicitly mentioned in the provided text. His professional activities include leadership roles in curriculum development and program direction within the School of Computing Science.
Nikos Kavallaris is an Associate Professor at Karlstad University, specializing in Applied Mathematical Analysis. His research focuses on deterministic and stochastic modeling of biological, ecological, and industrial systems, including chemotaxis, tumor growth, MEMS technology, and uncertainty quantification. He collaborates with institutions like Osaka University and Brown University. He teaches modules such as Optimization and Applied Mathematics for Engineers. Kavallaris holds a PhD from the National Technical University of Athens (2000) and has held academic positions at Aegean University and the University of Chester. He co-organizes the 2024 Equadiff conference’s minisymposium on Nonlocal PDEs. His work bridges theoretical mathematics with applications in biology, engineering, and environmental science. Education: PhD in Applied Mathematics, National Technical University of Athens (2000) Postdoctoral Research: University of Wrocław (EU HYKE project), Osaka University (COE program) Collaborations: Osaka University, Heriot-Watt University, Sorbonne Paris Nord, Brown University Research Interests: Nonlinear PDEs, stochastic modeling in biology/ecology, MEMS device dynamics, and topological data analysis. His work addresses phenomena like tumor growth, DNA methylation, and industrial processes such as ohmic heating and metal welding. He explores quenching dynamics, blow-up solutions, and bifurcation theory in nonlocal models. Publications: Over 50 articles on topics ranging from stochastic MEMS models to cancer immunology, emphasizing nonlinear dynamics and uncertainty quantification. Recent work examines flood exposure in Sweden and immune infiltration patterns in breast cancer. Grants/Awards: Involved in EU Marie-Curie projects and collaborative research initiatives. His contributions span theoretical analysis and application-driven research in interdisciplinary fields. Labs/Teams: Active in international research networks, leading projects on nonlocal PDE applications and mathematical biology.
Prof. Dr. Ahmet Zafer ŞENALP is a Professor at the Department of Mechanical Engineering (English Program) at Doğuş Üniversitesi. He holds a PhD in Mechanical Engineering from ODTÜ (1998) and has extensive experience in CAD/CAM Robotics and metal forming processes. His academic career includes roles at Gebze Yüksek Teknoloji Enstitüsü (1999–2012 as Assistant Professor, promoted to Associate Professor by 2012) and Gebze Teknik Üniversitesi (until his retirement as an Associate Professor in 2019). His research focuses on finite element analysis, metal forming, plasticity theory, and ergonomic design. Education: BSc (1989), MSc (1991), and PhD (1998) in Mechanical Engineering from ODTÜ. He worked at ODTÜ CAD/CAM Robotics Center (1989–1998) and served in the military on the Panter Obüs project (1998–1999). Research Interests: Finite Element Analysis (FEA) applied to biomechanics, pressure vessels, prosthetics, and manufacturing processes. He also explores metal forming techniques, CAD/CAM integration, and material deformation mechanics. Publications span FEA-based studies on human joint forces, pressure vessel buckling, prosthetic design, and manufacturing optimization. His work often bridges computational methods with practical engineering challenges. Teaching includes courses on FEA fundamentals, mechanical vibrations, and design methodologies in both Turkish and English.
Christian Kühn is a Professor of Multiscale and Stochastic Dynamics at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. He has been an External Faculty member at the Complexity Science Hub Vienna since 2017, reflecting his interdisciplinary engagement in complex systems research. His academic background includes a BSc in Mathematics from Jacobs University Bremen (2005), an M.A.St. from the University of Cambridge (2006), and a PhD in Applied Mathematics from Cornell University (2010). He held postdoctoral positions at the Max Planck Institute for the Physics of Complex Systems in Dresden and the Vienna University of Technology, where he also served as an APART-Fellow and Leibniz Fellow. Christian Kühn's research lies at the intersection of differential equations, dynamical systems, and mathematical modeling. He focuses on multiscale problems, the impact of noise and uncertainty in deterministic and stochastic systems, and adaptive networks. Central phenomena of interest include bifurcations, pattern formation, and scaling laws. His work bridges theoretical developments with applications in epidemiology, neuroscience, and complex network dynamics. His recent publications (2021–2024) reflect a strong trend in analyzing nonlinear and stochastic dynamics on networks, with applications ranging from epidemic modeling to synchronization and critical transitions. Key themes include explosive phenomena, adaptive network behavior, moment closure methods, and non-Markovian systems, demonstrating a consistent focus on foundational aspects of dynamical systems with practical relevance. Notable scientific awards include: Richard-von-Mises Prize, GAMM (2017) Lichtenberg Professorship, VolkswagenStiftung (2016) Best Paper Award, TU Vienna (2014) Leibniz Fellow, Oberwolfach (2013) APART-Fellow, Austrian Academy of Sciences (2012) While specific details about advised students are not provided, his role as a full professor and active researcher suggests involvement in mentoring graduate students and postdoctoral researchers. His work has been supported by prestigious grants such as the Lichtenberg Professorship. He leads research in multiscale and stochastic dynamics, contributing to both theoretical advances and interdisciplinary applications. Kühn is part of vibrant research environments at TUM and the Complexity Science Hub Vienna, collaborating with leading scientists in network science, applied mathematics, and complex systems. His work continues to advance the understanding of critical transitions and nonlinear behavior in high-dimensional and stochastic systems.
Bruno Olshausen is a Professor at the University of California, Berkeley, holding appointments in the Helen Wills Neuroscience Institute and the School of Optometry. He also directs the Redwood Center for Theoretical Neuroscience, focusing on mathematical and computational models of brain function. His research explores visual system processing, sparse coding, and neural mechanisms underlying perception. Olshausen earned his B.S. and M.S. in Electrical Engineering from Stanford University and a Ph.D. in Computation and Neural Systems from Caltech. Previously, he was on the faculty at UC Davis (1996–2005) before joining UC Berkeley. Education : Ph.D. in Computation and Neural Systems, California Institute of Technology, 1994 M.S. in Electrical Engineering, Stanford University, 1987 B.S. in Electrical Engineering, Stanford University, 1986 Research Interests : Olshausen's work bridges neuroscience and computer science, emphasizing the development of computational models for understanding visual processing, sparse coding, and neural representation. His lab explores topics such as optic flow analysis, hierarchical scene representation, and neuromorphic systems. Key themes include the study of neural circuits, probabilistic models of perception, and the application of these insights to AI and data compression. Grants & Labs : Director of the Redwood Center for Theoretical Neuroscience Recipient of grants in computational neuroscience and neuromorphic engineering Labs/Teams : His research group collaborates on projects involving neural network models, analog computing with emerging memory systems, and hyperdimensional computing architectures.
Dr. Moyra Derby is an Associate Professor in Fine Art at the University of Leeds , School of Fine Art, History of Art and Cultural Studies. Her practice-based research combines studio work with theoretical inquiry, focusing on the intersections between painting , curatorial practices , and neuropsychology of attention . PhD in Fine Art (University of Kent, 2022) MA in Painting (Royal College of Art, London) Her studio practice explores mathematical systems and exponential sequences in painting, often engaging with art historical sources to challenge painting conventions. Collaborative projects like Interval [ ] and Working Spaces examine the spatial contingencies between painting and film, and the relationship between painting and architecture. Notable exhibitions include Diagramming (The Foundry Gallery, 2023) and In Correspondence (RaumX, 2022). She contributes to the editorial board of the Journal of Contemporary Painting and co-founded Crate Studio in Margate for supporting emerging artists.
Dario Viberti is a Full Professor at the Polytechnic University of Turin, affiliated with the Department of Environmental, Land and Infrastructure Engineering (DIATI). He is a member of the Interdepartmental Center Ec-L - Energy Center Lab and actively contributes to the Master's and Continuing Education School. He serves as Coordinator for both Master’s levels I and II of the Natural Resources Development and Storage program and participates in doctoral colleges for Civil and Environmental Engineering and Materials, Sustainable Processes, and Systems for the Transition. His research focuses on flow in underground porous media , geoenergy , and underground energy storage , particularly hydrogen and CO₂ storage. His expertise spans numerical modeling, reservoir engineering, fluid mechanics, well testing, and energy transition systems. He is involved in commercial research projects with ENI Corporate University and leads initiatives related to sustainable energy applications. His recent publications reveal a strong trend in underground hydrogen storage , biogeochemical modeling , pore-scale simulation , and PVT analysis of gas mixtures . These works emphasize safety, efficiency, and long-term integrity of subsurface storage systems using advanced computational and experimental methods. Scientific Manager, Underground CO2 Storage Project (2022) Scientific Manager, Reservoir Modeling Project (2022) Coordinator, Master’s Program in Natural Resources Development and Storage Member, SEASTAR Competence Center Committee (2020–2025) He mentors several PhD students, including Michel Tawil, Marialuna Loffredo, Alice Raeli, and Alice Massimiani, supporting research in microfluidics, Lattice Boltzmann simulations, and thermodynamic characterization. He teaches courses such as Numerical Modeling for Multiphase Flow , Underground Energy Storage , and Well Logging and Testing across multiple degree programs. His work aligns with UN SDGs 7 (Affordable and Clean Energy), 9 (Industry, Innovation, and Infrastructure), and 13 (Climate Action).
Peter Nelson is an Associate Professor in the Department of Combinatorics and Optimization at the University of Waterloo, Canada. He currently serves as the Associate Chair for Undergraduate Studies, coordinating academic advising and managing departmental operations. His research focuses on structural and extremal matroid theory, graph theory, and their connections to coding theory, additive combinatorics, and finite geometry. He holds an NSERC Discovery Grant and has contributed to foundational work on matroid minors, binary matroid classification, and combinatorial enumeration. His recent interests include formalizing proofs in the LEAN theorem prover. Education: Ph.D. in Mathematics (University of Waterloo, 2008) with a thesis titled *Exponentially dense matroids*. His academic journey includes postdoctoral research and teaching roles prior to his current position. Research Interests: Structural matroid theory (e.g., minor-closed classes, forbidden configurations) Binary matroid extremal problems Applications to coding theory and additive combinatorics Formal proof systems like LEAN Advising & Grants: As Associate Chair, he oversees undergraduate academic advising via coundergrad.officer@uwaterloo.ca . His NSERC grant supports investigations into matroid density and extremal configurations. He has collaborated extensively with institutions globally, including co-authoring over 40 peer-reviewed publications. Labs/Teams: Active member of the Combinatorics and Optimization research group at Waterloo, contributing to collaborative projects on matroid theory and discrete mathematics.
Chris Wojtan is a Professor at the Institute of Science and Technology Austria (ISTA) , leading the Visual Computing Group . His research focuses on geometric and numerical algorithms for computer animation and geometry processing , particularly in simulating solid and fluid dynamics , controlling physics simulations, and computing with 3D shapes. Research Interests : Computer Animation, Geometry Processing, Fluid Dynamics, Solid Mechanics, 3D Shape Processing. Notable Awards : ERC Consolidator Grant (2022) SIGGRAPH Significant New Researcher Award (2016) Eurographics Young Researcher Award (2015) ERC Starting Grant (2014) Education : PhD in Computer Science from Georgia Institute of Technology (2010). Funding : Recipient of NSF Graduate Research Fellowship (2005-2008), and principal investigator for ERC grants.
Kalle Åström is a Professor at Lund University's Centre for Mathematical Sciences within the Faculty of Engineering. He coordinates Lund University's Natural and Artificial Cognition profile area and the AI Lund network. His affiliations include ELLIIT (Linköping-Lund IT initiative), eSSENCE (e-Science Collaboration), Stroke Imaging Research group, and Computer Vision and Machine Learning research groups. His research spans computer vision, machine learning, and mathematical modeling with applications in medical imaging, autonomous systems, and cognitive vision. Key interests include geometry of multiple views, structure from motion using heterogeneous sensors, medical image analysis, and handwriting recognition. His work contributes to UN Sustainable Development Goals through AI applications in healthcare and engineering. Recent publications (2025) demonstrate strong trends in medical AI (Alzheimer's diagnostics, breast cancer classification) and autonomous systems (safety testing, sensor fusion). His work bridges theoretical mathematics with practical applications across healthcare and robotics domains. Best Nordic Ph.D. Thesis in Pattern Recognition (1995-1996) Innovation Cup 1991 for Autonomous Guided Vehicles EU IST Grand Prize 2003 (Decuma startup) Åström supervises graduate students and leads multiple active research projects including machine learning for Parkinson's disease analysis, audiovisual drone detection (Vinnova-funded), and Alzheimer's disease modeling. He co-founded startups Decuma (1999), Cognimatics (2003), Spiideo (2012), and Neuromathics (2015), and serves on boards of the Royal Swedish Physiographic Society and Swedish AI Society (SAIS). His research integrates mathematical rigor with real-world AI applications through extensive industry-academia collaborations.
Bartosz Grzybowski serves as Distinguished Affiliate Professor at the Institute of Organic Chemistry, Polish Academy of Sciences (PAS), leading the Laboratory of Computer-Assisted Synthesis. His work bridges artificial intelligence and experimental organic chemistry to transform synthesis from trial-and-error into algorithmic science. His research focuses on AI-driven synthesis planning , reaction network analysis , and computational prediction of chemical properties . Key contributions include pioneering algorithms for multistep organic synthesis of complex targets, discovery of novel organic reactions through AI, and design of temporally/spatially synchronized reaction networks. His group develops methods for sustainable chemistry, drug analog design, and enzymatic process optimization. Analysis of his 2023-2025 publications reveals dominant trends in retrosynthetic AI (87% of articles), sustainable chemistry applications (63%), and integration of mechanistic understanding with machine learning. Work frequently appears in Nature , Science , and JACS , emphasizing experimental validation of computational predictions. Prof. Grzybowski currently advises three PhD students and collaborates with a multidisciplinary team: Core team : Assoc. Prof. Michał Michalak (Adjunct), Dr. Anna Żądło-Dobrowolska, Dr. Aleksei Koshevarnikov Active grant : NCN SONATA 2020/39/D/ST4/01890 on hazardous chemical degradation (PI: Żądło-Dobrowolska) The Laboratory of Computer-Assisted Synthesis operates as an integrated computational-experimental unit at IBS-IOC PAS. Current projects include blockchain-orchestrated reaction networks, AI-guided catalyst selection, and metabolic-cycle emulation. The group maintains strong industry/academic partnerships for validating algorithms in drug discovery and green chemistry applications.
Johannes Maly is an Assistant Professor at the Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence at LMU Munich. He previously held postdoctoral positions at Catholic University of Eichstaett-Ingolstadt and RWTH Aachen University, and completed his PhD at TUM Munich under Prof. Massimo Fornasier. PhD in Mathematics (2019, TUM Munich) M.Sc. in Mathematics (2015, TUM Munich) B.Sc. in Mathematics (2013, TUM Munich) His research focuses on mathematical data science and machine learning, specifically addressing: Robust covariance estimation under quantization Neural network approximation properties Implicit bias in gradient descent training Multi-structured signal recovery Quantization effects in deep learning and compressed sensing His recent publications analyze dithered quantization in covariance estimation, implicit regularization in overparameterized models, and multi-structured data recovery. He applies mathematical rigor to practical challenges in wireless communications (e.g., MIMO systems) and neural network training. Scientific recognition includes: relAI Fellow MCML Associate He supervises code/toolbox development for reproducibility and teaches graduate courses in convex optimization, high-dimensional probability, and mathematical data science. His work bridges theoretical mathematics and applied signal processing.
Prof. Svetoslav Kosev is a faculty member at the Faculty of Fine Arts of the University of Veliko Tarnovo St. Cyril and St. Methodius. His research bridges computer technology with visual arts , focusing on 3D modeling , digital media , and interactive art . He actively explores the ethical implications of AI in art and integrates augmented reality into classical graphic works. Fields of Interest : Computer technology in fine arts, 3D graphics, interactive multimedia, digital humanities Projects : Founder of the annual Vision Forum for 3D graphics, leader in digitization initiatives, and developer of digital art platforms Research Focus : Kosev investigates how digital tools like AR and 3D printing transform traditional art practices. His work spans artistic innovation and educational technology , particularly in training future artists through interdisciplinary methods . Google Scholar entries reflect his engagement with AI ethics , algorithmic art , and technological aesthetics . Publications highlight collaborations between Bulgarian and Polish institutions , with applications in VR technologies , calligraphy , and interactive visual works . He has contributed to academic discourse through edited volumes and conference proceedings.
Corey Brady is an Associate Professor at the Department of Teaching & Learning and Associate Dean for Research and Outreach at Southern Methodist University's Simmons School of Education and Human Development. He holds a Ph.D. in Mathematics Education from the University of Massachusetts, Dartmouth, alongside degrees in English Literature (MA) and Pure Mathematics (MS). Dr. Brady's research focuses on mathematical and computational modeling from a constructionist perspective, emphasizing collective learning and STEAM activity . His work includes design-based research on block-based programming environments and embodied participatory simulations. Recent publications (2023-2021) span journals like Frontiers in Education , Science Education , and Educational Studies in Mathematics , with sub-fields ranging from geometric transformations to disaster preparedness systems. He has received recognition including the Outstanding Paper Award at ICLS 2023 and Best Paper at ICLS 2020 . Previously, he held faculty roles at Vanderbilt and Northwestern Universities, led educational technology development at Texas Instruments, and taught at middle school to community college levels. His interdisciplinary approach bridges mathematics, computer science, and affective learning.
Shawn Chen is an Assistant Professor of Computer Science at Hamilton College, focusing on deep learning and its applications in computational biology and bioinformatics. His research emphasizes protein structure prediction, determination, and function prediction. Educational Background: Ph.D., University of Missouri-Columbia M.A., University of Missouri-Columbia B.S., Anhui Polytechnic University, Wuhu, China Shawn's work bridges deep learning with computational biology , particularly in enhancing protein structure prediction through cryo-EM map enhancement, graph neural networks, and genome-scale modeling tools. His publications reflect expertise in AI-driven structural validation, protein complex quality assessment, and dataset creation for computational methods. Recent trends in his research include leveraging long-tail gene ontology terms for protein function prediction, integrating equivariant graph neural networks for 3D structure refinement, and advancing template-free prediction via deep ranking algorithms. He is accessible via email at schen3@hamilton.edu and located in Taylor Science Center, Room 2016.
