Rocio Lilen Segura is an Assistant Professor in the Department of Civil, Environmental and Sustainable Engineering at Santa Clara University's School of Engineering. She holds a Ph.D. in Civil Engineering from Sherbrooke University (2019) and a B.Sc. in Civil Engineering from Del Comahue National University (2013). Dr. Segura specializes in infrastructure resilience against extreme events and climate change, focusing on probabilistic risk assessment of critical infrastructure systems like dams and levees. Her work bridges civil engineering with machine learning and climate justice, integrating built, natural, and social systems to address environmental challenges. 2023 Severo Ochoa Mobility Programme grant 2019-2022 MITACS Accelerate industrial postdoc scholarship 2019 Léonard de Vinci medal and Scholarship Her research spans seismic risk reduction, surrogate modeling, and uncertainty quantification in dam engineering, with over 10 publications in journals like Advances in Civil Engineering and Water Journal. She previously collaborated with Hydro-Quebec during her postdoctoral research.
Professor Ole-Christoffer Granmo is a distinguished academic at the University of Agder, Norway, where he serves as Professor in the Department of Information and Communication Technology. He is the Founding Director of the Centre for Artificial Intelligence Research (CAIR) at the University of Agder, leading cutting-edge research in artificial intelligence and machine learning. Dr. Granmo obtained his master's degree in 1999 and his PhD in 2004, both from the University of Oslo. His academic journey has been marked by significant contributions to the field of AI, most notably the creation of the Tsetlin machine in 2018, for which he received the AI research paper of the decade award from the Norwegian Artificial Intelligence Consortium (NORA) in 2022. Professor Granmo's research primarily focuses on logical and causal world modeling across multiple modalities including images, sound, and natural language. His work spans logical auto-encoding, convolution, regression, transformer architectures, and reinforcement learning, all with the overarching goal of creating ultra-low-power artificial general intelligence through transparent logical learning and reasoning. His publications reveal a strong emphasis on interpretable AI systems, hardware implementations, and applications across diverse domains including cybersecurity, healthcare, social media analysis, and bioinformatics. AI Research Paper of the Decade (2022) - Norwegian Artificial Intelligence Consortium (NORA) Eight paper awards in machine learning Professor Granmo has coordinated over seven research projects and mentored 55+ master's students and nine PhD students. His leadership extends to co-founding the Norwegian Artificial Intelligence Consortium (NORA) and establishing two companies: Anzyz Technologies AS and Tsense Intelligent Healthcare AS. As an advisor at Literal Labs, he actively bridges academic research with practical industry applications, demonstrating his commitment to translating theoretical innovations into real-world solutions that address complex challenges across multiple sectors.
Ross J. Kang is a Canadian mathematician currently serving as an Associate Professor at the Korteweg–de Vries Institute for Mathematics within the Faculty of Science at the University of Amsterdam since 2022. He is an active member of the Discrete Mathematics and Quantum Information group and the NETWORKS consortium. Previously, he held positions as Assistant/Associate Professor at Radboud University Nijmegen (2014-2022), Assistant Professor at Utrecht University (2013), and Researcher at Centrum Wiskunde & Informatica (2012-2013). His academic journey includes postdoctoral positions at Durham University (2010-2012) and McGill University (2008-2010), where he was advised by Bruce Reed and Louigi Addario-Berry. DPhil in Mathematics, University of Oxford (2008) - Thesis: 'Improper colourings of graphs', advised by Colin McDiarmid BSc (Hons) in Mathematics and Computer Science, University of Victoria (2003) - Governor General's Silver Academic Medal recipient Ross J. Kang's research focuses on probabilistic and extremal combinatorics, random discrete structures, graph coloring, geometric graphs, and algorithms. His work bridges theoretical mathematics with practical applications, exploring fundamental questions in discrete mathematics. He has made significant contributions to understanding graph coloring problems, particularly in the contexts of list coloring, distance coloring, and strong coloring. His research often employs probabilistic methods to establish bounds and structural properties in graph theory. Kang's work on the hard-core model, local occupancy method, and triangle-free graphs has advanced our understanding of the interplay between local constraints and global structure in discrete systems. Analysis of his recent publications reveals a strong emphasis on graph coloring problems, particularly list coloring variants and their extensions. His work frequently explores the relationship between graph structure (such as degree constraints, girth, or forbidden subgraphs) and coloring properties. A notable trend is his development and application of the local occupancy method to establish improved bounds for chromatic numbers in various graph classes. His research also demonstrates a consistent interest in extremal problems, seeking optimal configurations under specific constraints, particularly in the context of triangle-free graphs and geometric representations. NWO Open Competition M-1 grant entitled 'Asymptotic triangle-free structure (3Free)', 2022-2026 NWO Vidi grant entitled 'On the edge: theory and techniques at the frontiers of edge-colouring', 2017-2023 NWO Veni grant entitled 'Generalised colouring for random graph models', 2012-2015 Van Gogh travel grants (2020-2021 with Marthe Bonamy; 2016-2017 with Louis Esperet) Governor General's Silver Academic Medal (2003) Ross J. Kang has successfully supervised multiple PhD students including Eoin Hurley (defending May 2025), Stijn Cambie (defended April 2022), and François Pirot (winner of 2020 prix Charles Delorme). His research is supported by significant grants from the Netherlands Organisation for Scientific Research (NWO), including the prestigious Open Competition M-1 grant. Kang is actively involved in the academic community through his editorial role at Combinatorial Theory, co-organization of conferences like the Dutch Days of Combinatorics, and leadership in initiatives such as Innovations in Graph Theory, a diamond open access journal he helped launch in August 2023. As a member of the Discrete Mathematics and Quantum Information group at the University of Amsterdam and the NETWORKS consortium, Kang collaborates with researchers across various institutions. He has established strong international connections through his Van Gogh travel grants and participation in collaborative projects like the Sparse (Graphs) Coalition sessions. His research group focuses on theoretical aspects of discrete mathematics with connections to quantum information science, and he maintains active collaborations with researchers across Europe and North America.
Markus Lange-Hegermann serves as Professor of Mathematics and Data Science at Ostwestfalen-Lippe University of Applied Sciences (TH OWL) since 2018 and holds a board position at the Institute for Industrial Information Technology (inIT). His career bridges academic research and industrial applications, with expertise in translating machine learning theory into practical engineering solutions for automation and manufacturing sectors. His educational foundation includes a Diplom (Master equivalent) in Computer Mathematics from RWTH Aachen University (2004-2008) followed by a Dr. rer. nat. (PhD equivalent) in algorithmic differential algebra (2008-2014). Prior to academia, he gained industry experience at FEV GmbH as an R&D engineer (2014-2017) and P3 automotive GmbH as a Data Science Consultant (2017-2018). Lange-Hegermann's research centers on probabilistic machine learning with distinctive emphasis on physics-informed approaches. He develops Gaussian process methodologies that incorporate differential equations to model time dependencies, uncertainties, and physical constraints in industrial systems. His work enables robust data-based modeling and optimization for cyber-physical systems, with applications spanning predictive maintenance, process control, and quality assurance in manufacturing. Analysis of his 15 most recent publications (2024-2025) reveals consistent innovation in physics-integrated machine learning, particularly using Gaussian processes to solve partial differential equations and optimal control problems. The research demonstrates strong industrial applicability across domains including medical imaging, material science, automotive engineering, and brewing processes, with recurring themes of anomaly detection in time-series data and uncertainty-aware decision making. His scientific contributions have earned significant recognition: Forschungspreis TH OWL (2024) Top reviewer award at NeurIPS (2023) Outstanding reviewer award at NeurIPS (2021) Best poster award at Bosch AI CON (2019) Borchers Plakette for outstanding dissertation (2014) Springorum Denkmünze for outstanding diploma (2009) As chairman of the Data Science study program and vice chairman of undergraduate examination boards, Lange-Hegermann actively shapes academic curricula while supervising graduate theses. His governance roles include serving on professorship search committees at multiple institutions and contributing to examination regulations. He maintains active research funding through collaborations with industrial partners and reviews proposals for initiatives like It's OWL and 3IA Côte d’Azur. Lange-Hegermann leads the Mathematics and Data Sciences research group within inIT, fostering collaboration between theoretical machine learning and industrial automation. He co-founded AICOmmunityOWL and the Informatics Europe working group on Data Analysis and Reporting, while organizing machine learning reading groups and data science hackathons to bridge academic research with industrial problem-solving.
Bineet Ghosh is an Assistant Professor in the Department of Computer Science at The University of Alabama's College of Engineering . He leads the Trustworthy Autonomy Lab , focusing on safety verification for autonomous systems with timing uncertainties. His educational background includes a PhD from UNC Chapel Hill (2023), an M.Sc. from Chennai Mathematical Institute, and a B.Sc. (First Class First with Gold Medal) from Ramakrishna Mission Vidyamandira. PhD in Computer Science (UNC Chapel Hill, 2023) M.Sc. in Computer Science (Chennai Mathematical Institute, 2016) B.Sc. (First Class First with Gold Medal) in Computer Science (Ramakrishna Mission Vidyamandira, 2014) His research spans Artificial Intelligence, Autonomous Vehicles, Deep Learning, Embedded Systems, Robotics, and Cyber-Physical Systems , with a focus on statistical verification methods and safety-aware scheduling. Recent publications analyze neural architecture sizing, GPU partitioning, and uncertainty-aware monitoring in autonomous systems. Scientific awards include the Chateaubriand Fellowship (2021), Best Presentation Award at ACM SIGBED (2022), and Best Paper Candidate at RTCSA (2022). He previously worked as a research intern at Tata Research Labs and software developer at Oracle.
Asif Ali Zaman is an Associate Professor in the Department of Mathematics at the University of Toronto's Faculty of Arts and Science. He specializes in analytic and probabilistic number theory, with applications to algebraic structures and arithmetic statistics. His work intersects prime distribution, zeros of L-functions, Chebotarev density theorem, random multiplicative functions, and binary quadratic forms, extending to elliptic curves, modular forms, and mass equidistribution. PhD in Mathematics (2017), University of Toronto NSERC Postdoctoral Scholar (2017–2019), Stanford University MSc in Mathematics (2012), University of British Columbia BSc in Mathematics (2010), Simon Fraser University His research leverages log-free zero density estimates, Deuring-Heilbronn phenomenon, and Artin's holomorphy conjecture to derive bounds for primes, ℓ-torsion class groups, and equidistribution on modular surfaces. Recent publications (2025–2022) focus on Tauberian theorems, multiplicative chaos, and large sieve inequalities. Grants include sponsored research on L-functions (2022–2027) and computational projects (2025). He supervises Masters and PhD students in number theory and teaches multivariable calculus and cryptology courses.
Federica Sandrone is a Lecturer at the School of Architecture, Civil and Environmental Engineering (ENAC) at École Polytechnique Fédérale de Lausanne (EPFL), where she also serves as a Scientist at the Laboratory of Experimental Rock Mechanics (LEMR) within the Institute of Civil Engineering. Her academic career spans over 15 years with continuous contributions to tunnel engineering and rock mechanics research. Her research focuses on the intersection of rock mechanics and tunnel engineering, with particular expertise in tunnel pathology analysis, TBM performance in challenging geological conditions, and long-term tunnel behavior. Sandrone's work bridges theoretical analysis with practical engineering applications, addressing real-world problems in tunnel infrastructure management and maintenance. Her research methodology combines field investigations, laboratory testing, and numerical modeling to understand complex geomechanical behaviors. Analysis of her recent publications reveals a consistent focus on tunnel inspection methodologies, TBM performance prediction in difficult ground conditions, and the long-term behavior of tunnel structures. Her work has evolved from fundamental tunnel pathology studies to more advanced applications involving GIS integration, probabilistic modeling, and modern inspection techniques including laser scanning and image analysis. Engineer at SBB-Infrastructure (2008-present) responsible for Tunnels Management and Maintenance Assistant for Tunnel Engineering courses (2007-present) PhD supervision including Erika Paltrinieri's 2015 thesis on TBM performance Development of tunnel inspection methodologies and condition assessment procedures Her teaching activities include courses in Rock Mechanics and Underground Construction, where students learn about the mechanical behavior of rock materials, tunnel excavation and support design, planning and management of underground works, and risk assessment in tunnel construction.
Dr. Xinqun Zhu is an Associate Professor at the University of Technology Sydney (UTS) in the School of Civil and Environmental Engineering . He has held academic positions at Western Sydney University (2016-2017), University of Western Australia (2005-2009), and University of Manchester (2001-2005). His research spans structural health monitoring, steel-concrete composite structures, physics-informed machine learning, and advanced sensor systems.
Shaun Truelove is an Associate Research Professor at Johns Hopkins University , affiliated with the Bloomberg School of Public Health and the Department of International Health . He is also a joint member of the Infectious Disease Dynamics group and the International Vaccine Access Center . Research interests focus on infectious disease dynamics , epidemiology , vaccination strategies , and humanitarian/refugee health . His work spans Measles transmission modeling in Zambia COVID-19 impact assessments Diphtheria epidemiology Mobile phone data applications Supplementary immunization activities Scenario modeling for pandemics . Article trends highlight expertise in infectious disease modeling across multiple pathogens (Measles, MERS-CoV, Influenza), with emphasis on Vaccine effectiveness Human mobility patterns Real-time forecasting Public health policy Global health disparities . Current projects include the flepiMoP modeling pipeline for pandemic response, collaborations with the CDC, and field studies in Zambia/India. Contact: shauntruelove@jhu.edu | trueloves@jhu.edu
Caglar Oskay is an Associate Professor in the Department of Civil and Environmental Engineering at Vanderbilt University, where he has held academic positions since 2006. He specializes in multiscale computational mechanics, materials modeling, and failure analysis of heterogeneous materials. His research integrates advanced numerical methods such as the Extended Finite Element Method (XFEM), reduced-order homogenization, and variational multiscale enrichment to study composite materials, viscoelastic systems, and polycrystalline structures under extreme conditions. Dr. Oskay has been recognized with awards including the Chancellor Faculty Fellow (2016–2018) and ASCE ExCEEd Fellow (2011). Education: PhD (Civil Engineering, Rensselaer Polytechnic Institute, 2003), M.S. (Civil Engineering, Rensselaer Polytechnic Institute, 2000), M.S. (Applied Mathematics, Rensselaer Polytechnic Institute, 2000), B.S. (Civil Engineering, Middle East Technical University, 1998). Research focuses on predictive computational models for material behavior under mechanical, thermal, and chemical loading. Key areas include fatigue life prediction, damage accumulation in composites, and coupled transport-deformation phenomena. Recent work addresses multiscale modeling of nickel-based superalloys, polyurea-coated composites, and energetic materials under dynamic loading. His contributions span 100+ peer-reviewed publications, including seminal studies in International Journal for Multiscale Computational Engineering and Acta Materialia . His articles emphasize multiscale frameworks for heterogeneous materials, with trends in reduced-order methods, uncertainty quantification, and interdisciplinary applications (e.g., biology, energy systems). Awards highlight his educational and technical leadership. Advising and grants include collaborative projects on composite durability and energetic material simulation. Dr. Oskay leads the Multiscale Computational Mechanics Lab (MCML), advancing computational tools for engineering materials research.
Denis Mestivier serves as a Professor of Bioinformatics at the University of Paris-Est Créteil, where he leads the bioinformatics platform of the Mondor Institute of Biomedical Research (IMRB) under the Faculty of Health. His multidisciplinary expertise bridges computational and biological sciences, with a career-long focus on health-related applications of data-intensive methods. Education: Master's degree in Molecular Genetics PhD in Biomathematics Research Interests: Mestivier develops computational models and bioinformatics pipelines for large-scale biological datasets, specializing in microbiome-cancer interactions and molecular biomarker discovery. His work integrates statistical analysis, metagenomics, and epigenetic modeling to address clinical health problems, particularly in gastrointestinal and liver diseases, through active collaboration with experimental research teams. Publication Trends (2011-2021): His research demonstrates a clear trajectory from metabolic modeling (2011) to translational microbiome studies in colorectal cancer (2019-2021), emphasizing bias mitigation in metagenomics and epigenetic mechanisms linking microbiota to oncogenesis. All works showcase applied bioinformatics in health contexts, with consistent focus on clinical biomarker identification and computational solutions for complex biological systems. Scientific Awards: No awards, fellowships, or medals are documented in the provided text. Teaching and Leadership: Since the 1990s/2000s, Mestivier has pioneered biology-computing curriculum development at the university level, intensifying health-focused teaching after his Faculty of Health appointment. He directs IMRB's bioinformatics platform but specific advising roles or grant funding details are not disclosed. Labs and Teams: As head of IMRB's bioinformatics platform, he oversees computational infrastructure for biomedical research, facilitating cross-disciplinary projects with experimental teams at the Mondor Institute while specializing in health data analysis.
Prof. Dr.-Ing. David E. Rival is a full Professor at the Institute of Fluid Mechanics within the Faculty of Mechanical Engineering at Technische Universität Braunschweig. His research spans interdisciplinary domains at the intersection of experimental fluid dynamics, data assimilation, network science, and bio-inspiration, with applications in renewable energy systems and bio-mimetic engineering. Former Associate Professor at Queen’s University, Canada Doctoral work on dragonfly flight aerodynamics at TU Darmstadt Alexander von Humboldt research fellowship recipient (2020) Postdoctoral associate at MIT studying shape morphing in nature Research chair at University of Calgary on atmospheric sensing His work focuses on unsteady flow phenomena, bio-inspired design, and advanced measurement techniques. Key projects include: Co-chairing NATO AVT task group on flow separation International collaborations with AFOSR, NATO, and ONR Development of cost-effective flow-tracking sensors for natural environments Investigations into shear-thinning suspension dynamics and vortex ring behavior Recent publications demonstrate a strong emphasis on: Large-scale particle tracking with natural light and UAVs Machine learning for sparse data reconstruction in fluid flows Soft coastal protection methods and ecohydraulics Advanced sensing techniques for atmospheric and industrial applications Scientific Awards: 2020: Alexander von Humboldt Research Fellowship Notable research achievements include textbook authorship on Biological and Bio-Inspired Fluid Dynamics (Springer) and media features in The Nature of Things (David Suzuki) and Discovery Channel’s Daily Planet .
Kyla Pohl is a Visiting Assistant Professor of Mathematics at Colby College and an ABD PhD candidate at the University of Oregon, where she is advised by Ben Young. Her academic background includes a Bachelor of Arts degree in Mathematics with a concentration in Japan Studies from St. Olaf College and a Master's degree in Mathematics from the University of Oregon. Her research focuses on algebraic and enumerative combinatorics, with primary emphasis on Jack symmetric functions, hook length formulas, and probabilistic methods. She employs experimental approaches using SageMath and maintains an active GitHub repository showcasing implementations of combinatorial algorithms. Pohl's publications demonstrate expertise in both combinatorics and algebra, with recent work exploring probabilistic aspects of symmetric functions. Her research trajectory shows increasing focus on combinatorial algorithms and computational approaches to partition theory. She contributes to academic service through seminar organization and maintains educational resources including Jupyter notebooks demonstrating Markov Chain Monte Carlo methods. Her teaching experience includes courses in mathematics and mentorship through the Directed Reading Program.
Professor Pascal Fua is a distinguished faculty member at EPFL (Swiss Federal Institute of Technology) in the School of Computer and Communication Science. He joined EPFL in 1996 and currently serves as Head of the Computer Vision Laboratory (CVLAB). His extensive research spans multiple cutting-edge areas in computer vision and geometric deep learning, with applications ranging from 3D reconstruction to medical imaging and aerodynamic optimization. Dr. Fua's research interests encompass Computer Vision, 3D Reconstruction, Shape Modeling, Geometric Deep Learning, Medical Image Analysis, Augmented Reality, Motion Recovery, Surface Mesh Processing, and Aerodynamic Shape Optimization. His work demonstrates a remarkable ability to bridge theoretical computer vision with practical applications across diverse domains. His research has evolved from traditional geometric computer vision techniques to incorporating deep learning approaches for 3D modeling, with recent focus on differentiable rendering, implicit surface representations, and applications in medical imaging and engineering design. His publication record shows a consistent trajectory of high-impact research, with recent work focusing on differentiable iso-surface extraction, geometric deep learning for aerodynamic shape optimization, and novel approaches to 3D reconstruction. His work spans both theoretical advances in computer vision algorithms and practical applications in medical imaging, autonomous driving, and computational fluid dynamics. IEEE Fellow Multiple ERC Grants recipient Associate Editor of IEEE Transactions for Pattern Analysis and Machine Intelligence Throughout his career, Professor Fua has mentored numerous PhD students who have gone on to make significant contributions in computer vision and related fields. His laboratory has established collaborations across multiple disciplines, including medical imaging, aerospace engineering, and neuroscience, demonstrating the broad applicability of his research. His current work continues to push the boundaries of geometric deep learning and 3D vision, with particular emphasis on making these techniques more practical and applicable to real-world engineering and medical problems.
Ashish Khisti is an Associate Professor at the University of Toronto's Department of Electrical and Computer Engineering (ECE), where he directs the Signals, Multimedia and Algorithms Laboratory (SMA Lab). He holds the Canada Research Chair (Tier II) and maintains affiliations with the Vector Institute for Artificial Intelligence. His research bridges communication systems, information-theoretic security, and machine learning, with a focus on real-time streaming and privacy-preserving algorithms. Research Trends: Recent publications emphasize streaming codes for latency-sensitive networks , machine learning-driven compression , and privacy mechanisms in federated learning . Scientific Recognition: Canada Research Chair (Tier II), 2012 and 2017 renewal Cisco Research Center Award, 2017 Ontario Early Researcher Award, 2012 Best Paper at NeurIPS 2021 Deep Generative Models Workshop Academic Contributions: Supervised PhD students Ahmed Badr, Farrokh Etezadi, and Si-Hyeon Lee. Served as Associate Editor for IEEE Transactions on Communications (2012-2015) and IEEE Transactions on Information Theory (2015-2018). Labs & Collaborations: Leads the Signals, Multimedia and Algorithms Laboratory, collaborating with institutions like KAUST, Texas A&M University (Qatar), and the Vector Institute. Organized workshops at BIRS and IEEE conferences.
