Associate Professor Wenhua Zhao is a globally recognized expert in offshore hydrodynamics and renewable energy technologies at The University of Queensland , School of Civil Engineering. With over 110 publications and 30 million AUD in secured research funding, his work bridges theoretical and practical advancements in marine engineering. Research focuses on Clean Energy , Artificial Intelligence , and Climate Change , specifically floating wind energy, floating solar, offshore aquaculture, and green hydrogen production. His 15 most recent articles (2024-2025) emphasize wave-structure interactions, gap resonance dynamics, and AI-driven wave prediction, published in top journals like Journal of Fluid Mechanics and Ocean Engineering . Scientific awards include the prestigious ARC Future Fellowship (2024-2028) and DECRA Fellowship (2019-2022) , recognizing his contributions to academia and industry. He teaches the 'Design of Offshore Energy Systems' course , training hundreds of students in coastal and ocean engineering, and serves as Deputy Editor for Ocean Engineering and Associate Editor for ASME's Journal of OMAE . Available for research supervision, Zhao actively collaborates with editorial boards of Applied Ocean Research and other Q1 journals.
Dr. Vincent Fortuin is a tenure-track Assistant Professor at the Technical University of Munich (TUM) and a research group leader at Helmholtz AI in Munich. He leads the Efficient Learning and Probabilistic Inference for Science (ELPIS) group and holds multiple prestigious fellowships including the Branco Weiss Fellowship. His academic affiliations include the TUM School of Computation, Information and Technology, the Konrad Zuse School of Excellence in Reliable AI, and the Munich Center for Machine Learning. Dr. Fortuin earned his BSc in Molecular Life Sciences from the University of Hamburg (2012-2015), followed by an MSc in Computational Biology and Bioinformatics from ETH Zürich (2015-2017), where he received the ETH Excellence Scholarship and the Willi Studer Prize. He completed his PhD in Machine Learning at ETH Zürich (2017-2021) under the supervision of Gunnar Rätsch and Andreas Krause, supported by a Swiss Data Science Center PhD Fellowship. Prior to joining TUM, he was a Research Fellow at St. John's College, University of Cambridge (2022-2023). His research focuses on the intersection of Bayesian statistics and deep learning, specifically developing methods for more robust, data-efficient AI systems with reliable uncertainty estimates. His work addresses critical limitations in standard deep learning approaches, particularly their tendency to be overconfident in predictions and require large datasets for training. He investigates better priors and more efficient inference techniques for Bayesian deep learning, deep generative modeling, meta-learning, and PAC-Bayesian theory, with applications in scientific and biomedical domains. Dr. Fortuin's recent publications demonstrate a consistent focus on improving uncertainty quantification in deep learning systems, with increasing emphasis on practical applications in scientific contexts. His work spans from theoretical foundations of Bayesian deep learning to practical implementations in protein design, materials science, and medical applications. A notable trend is his exploration of how to make Bayesian methods more scalable and applicable to modern large-scale AI systems while maintaining theoretical guarantees. Branco Weiss Fellowship (2023) St John's College Research Fellowship (2022) Swiss National Science Foundation Postdoc.Mobility Fellowship (2022) Swiss Data Science Center PhD Fellowship (2018) ETH Excellence Scholarship (2015) Willi Studer Award (2018) Dr. Fortuin actively supervises PhD and Master's students through his ELPIS research group at Helmholtz AI. He serves as a regular reviewer and area chair for major machine learning conferences and is an action editor for TMLR. He co-organizes the Symposium on Advances in Approximate Bayesian Inference (AABI) and the ICBINB initiative, demonstrating his commitment to advancing the field through community building. His research group receives funding from multiple sources including Helmholtz AI, the Branco Weiss Fellowship, and collaborations with international institutions. Dr. Fortuin leads the Efficient Learning and Probabilistic Inference for Science (ELPIS) group at Helmholtz AI, which focuses on fundamental machine learning research motivated by real-world scientific problems. The group collaborates extensively with researchers across Helmholtz centers and international institutions, particularly in biomedical applications where reliable uncertainty estimates are crucial.
Stephen A. Vavasis is a Professor in the Department of Combinatorics and Optimization at the University of Waterloo, part of the Faculty of Mathematics. He holds a PhD in Computer Science from Stanford University (1989) and has held academic positions at Cornell University (1989–2006) before joining Waterloo. His research focuses on continuous optimization, data science, first-order methods, scientific computing, and computational mechanics. Current teaching includes courses on convex optimization and portfolio optimization methods. He has served as Associate Dean of Computing (2017–2020) and Interim Director of Data Science graduate programs. His work is supported by NSERC grants. Notable awards include the Hertz Fellowship, Churchill Scholarship, and Guggenheim Fellowship. Vavasis's research emphasizes applications of optimization to clustering, machine learning, and fracture mechanics. His publications span convex optimization frameworks, algorithmic analysis of gradient methods, and numerical methods in mechanics. Recent work explores unifying analyses of first-order optimization algorithms and robust optimization techniques for high-dimensional data problems.
Arul Shankar is a Professor of Mathematics at the University of Toronto, with his primary appointment at the Mississauga campus (UTM). He holds an office at 215 Huron Street, Room 1023 and can be reached at ashankar@math.utoronto.ca. His research focuses on Number Theory and Arithmetic Statistics, with particular expertise in elliptic curves, Selmer groups, and class groups. Professor Shankar's research interests center on the arithmetic statistics of number fields and elliptic curves. His work has significantly advanced our understanding of the average rank of elliptic curves, the distribution of Selmer groups, and the statistics of class groups. He has developed innovative applications of the geometry of numbers to arithmetic problems, often in collaboration with Manjul Bhargava and other leading number theorists. His research bridges deep theoretical questions with statistical approaches to understand the behavior of arithmetic objects across families. His publications reveal a consistent focus on the statistical behavior of arithmetic invariants, with particular attention to bounding average ranks of elliptic curves and understanding the distribution of class groups. The work demonstrates sophisticated applications of geometry of numbers techniques to problems in arithmetic statistics, with results published in top journals including the Annals of Mathematics, Inventiones Mathematicae, and Journal of the American Mathematical Society. Scientific Awards: 2018 Sloan Research Fellowship from the Alfred P. Sloan Foundation Professor Shankar is currently on sabbatical as a Simons Fellow. His research has established foundational results in arithmetic statistics, particularly regarding the average rank of elliptic curves and the distribution of class groups across families of number fields. His work with Bhargava on bounding the average rank of elliptic curves represented a major breakthrough in the field. His research group focuses on extending geometry of numbers methods to new contexts in arithmetic statistics, with recent work examining coregular vector spaces and applications to class groups. The lab maintains strong collaborations with number theorists at leading institutions worldwide.
George Yin is a Professor in the Department of Mathematics at the University of Connecticut (since 2020). Previously, he held the position of Distinguished Professor at Wayne State University (2017–2020) and has been a faculty member there since 1988. He earned his Ph.D. in Applied Mathematics from Brown University in 1987, along with M.S. degrees in Applied Mathematics and Electrical Engineering, and a B.S. in Mathematics from the University of Delaware (1983). His research focuses on stochastic optimization, control theory, stochastic systems, and numerical methods, with applications to biology, finance, and engineering. He has held editorial roles at journals such as SIAM Journal on Control and Optimization and has received prestigious awards including SIAM Fellow (2015), IEEE Fellow (2002), and IFAC Fellow (2014–2017). Key funding includes continuous NSF support since 1989, grants from the Air Force Office of Scientific Research, and others. His work spans theoretical advancements in stochastic systems and practical applications in energy systems, control engineering, and data science. He has advised numerous students and maintains active collaborations internationally. Labs/Teams: Goldenson Center for Actuarial Research, Quantitative Learning Center. Grants: NSF, AFOSR, ARO, NSA, and multiple institutional grants.
Lucas Janson is an Associate Professor of Statistics and Affiliate in Computer Science at Harvard University. He leads the Harvard Statistical Consulting Service, supervising PhD students advising hundreds of researchers annually. His research focuses on high-dimensional inference, statistical machine learning, and applications in genetics, political science, and climatology. He teaches courses such as Statistical Inference I, Reinforcement Learning, and Statistical Machine Learning. His work bridges theoretical advancements with practical applications, including contributions to robotics motion planning and microbiome data analysis. Key research areas include variable importance inference, safe reinforcement learning, compositional data analysis, and robust paleoclimate reconstructions. His methodologies are implemented in software packages like Floodgate, EigenPrism, and Fast Marching Tree (FMT*). He advises a dynamic group of PhD students and has mentored alumni now in academia and industry roles. Notable contributions include the development of model-X knockoffs for controlled variable selection, conditional randomization tests, and optimization algorithms for adaptive control systems. His work emphasizes statistical rigor while addressing real-world challenges in healthcare, environmental science, and robotics.
El Mahdi Chayti is a doctoral researcher at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences and the Department of Computer Science. He works under the Machine Learning and Optimization (MLO) lab, focusing on advanced optimization techniques and machine learning theory. Contact: el-mahdi.chayti@epfl.ch . Current Roles: Doctoral Assistant and PhD Student in Computer and Communication Sciences Research Interests: Machine Learning, Optimization Algorithms, Meta-learning, Second-order Optimization, Energy Forecasting His publications highlight expertise in stochastic and cubic Newton methods, hybrid deep learning models, and personalized collaborative learning. Recent works include Improving Stochastic Cubic Newton with Momentum (2025) and Hybridization of Deep Learning with Physical Knowledge for Energy Forecasting (2019) . Key trends in his research span optimization efficiency , meta-learning frameworks , and integration of domain knowledge into AI models. He emphasizes theoretical guarantees and practical scalability in algorithm design.
Dominik Schnaus is a PhD Student at the Computer Vision Group within the School of Computation, Information and Technology at the Technical University of Munich. His research focuses on computer vision and deep learning, particularly in vision-language correspondence and uncertainty estimation in neural networks. Research Interests: 3D/4D reconstruction, vision-language models, neural network uncertainty, robotics Contact: dominik.schnaus@tum.de
Professor Josef Dick serves as a Professor and Deputy Head in the School of Mathematics & Statistics at the University of New South Wales (UNSW). With a distinguished career in computational mathematics, he has established himself as a leading researcher in numerical integration methods and quasi-Monte Carlo theory. His work bridges theoretical mathematics with practical computational applications across various scientific domains. Dr. Dick earned his PhD in Mathematics from UNSW in 2004 and his MSc in Mathematics from the University of Salzburg in 2001. His academic journey reflects a strong foundation in both theoretical and applied mathematics, which has informed his subsequent research contributions. Professor Dick's research primarily focuses on numerical integration and quasi-Monte Carlo rules , employing techniques from number theory , abstract algebra (particularly finite fields), discrepancy theory , wavelet theory , and statistics . His work provides rigorous analysis of practical algorithms for computational problems, with implementations often provided in Matlab to bridge theory and application. His research has successfully addressed point distributions on the unit cube for numerical integration, completely uniformly distributed sequences for Markov chain quasi-Monte Carlo algorithms, and explicit constructions of uniformly distributed points on the sphere. Analysis of his recent publications (2022-2025) reveals a consistent focus on advancing quasi-Monte Carlo methods, with increasing integration of machine learning techniques and applications to complex computational problems. His work demonstrates strong interdisciplinary connections between pure mathematics, computational science, and practical engineering applications, particularly in uncertainty quantification and high-dimensional numerical integration. Discovery project from Australian Research Council (2012-2014): "Mathematics in the round - the challenge of computational analysis on spheres" Queen Elizabeth II Fellowship from Australian Research Council (2010-2014): "Algebraic methods for Markov Chain Monte Carlo and quasi-Monte Carlo" UNSW Vice Chancellor Fellowship (2006-2009) Professor Dick has supervised numerous PhD and Honours students working on topics including Quasi-Monte Carlo methods, Discrepancy Theory, Markov chain Monte Carlo, and Uncertainty Quantification. His research has been supported by significant grants from the Australian Research Council, including serving as Chief Investigator on multiple projects. Beyond his research, he serves as an Editor for the Journal of Complexity and Journal of Approximation Theory, demonstrating his leadership in the mathematical community. He teaches courses in Algebra and Mathematical Computing for Finance at UNSW.
Zhiqiang Yu serves as an Associate Professor in the Department of Modern Languages and Comparative Literature at Baruch College's Weissman School of Arts and Sciences, City University of New York. With over two decades of teaching experience, he has established himself as a dedicated educator specializing in Chinese language, cinema, and civilization courses. Education: Ph.D. in Chinese, University of Washington M.A. in Asian Civilization, University of Iowa B.A. in Chinese Literature, Fudan University (Shanghai) Professor Yu's research focuses on innovative approaches to Chinese language pedagogy, with particular interest in applying economic principles and artificial intelligence to language teaching. His work bridges traditional linguistic scholarship with modern educational technology, examining how efficiency, resource allocation, and technological advancements can enhance language learning outcomes. His research spans Chinese linguistics, dialectology, cinema studies, and cultural elements in language teaching. His publication record reveals a consistent trajectory toward optimizing language instruction through systematic analysis. Recent work increasingly focuses on AI applications in language education and the economic framework of pedagogical efficiency. His scholarship demonstrates a progression from traditional linguistic analysis toward innovative teaching methodologies that incorporate modern technological and theoretical frameworks. Scientific Awards: Award of Excellent Academic Research Reviewer from Journal of International Chinese Education (2016) Graduate Teaching Fellowship from University of Washington (1993) Graduate Teaching Fellowship from University of Iowa (1989) Graduate Research Fellowship from University of Iowa (1988) Student Excellency Award from Fudan University (1984) Professor Yu has served extensively on departmental committees including as Department Assessment Coordinator and Secretary of the Asian and Asian-American Studies Committee. He has organized numerous academic events featuring prominent Chinese cultural figures and has contributed to developing Chinese language curriculum resources including online placement tests. His professional service extends to editorial work for CUNY publications and Chinese language training for the New York Police Academy. He maintains active involvement with multiple professional organizations including the American Name Society, American Oriental Society, American Society of Geolinguistics, Association for Asian Studies, and Chinese Language Teachers Association, frequently presenting at international conferences on Chinese language pedagogy.
Professor David Sarpong is a leading academic in Strategy and Organisation at Aston Business School, part of Aston University's College of Business and Social Sciences. He currently serves as Head of the Marketing and Entrepreneurship Department and Director of Research for the Marketing and Strategy Subject Group. His research focuses on strategy-as-practice, relationalism, innovation management, and temporality in organizational processes. He holds adjunct roles at KNUST Business School and Bristol Business School, and is actively involved in professional organizations like EURAM and CABS. His work employs qualitative methods including ethnographic interviews and microstoria approaches. Professional Activities: He serves as Editor-in-Chief of the Journal of Strategy and Leadership, and on editorial boards for Technology Analysis & Strategic Management and European Management Journal. His external roles include UK Country Representative for EURAM and Board Membership at The Milestones Trust. He has held prior academic leadership positions at Brunel University London and visiting roles at HSE Moscow and University of KwaZulu-Natal. Research Interests: Prioritizes cross-level management problems using Heideggerian-Wittgensteinian frameworks. Key themes include second-order technology management, business tournament rituals, strategic foresight, and discursive practices in European cosmopolitan marketplaces. Methodologically innovative in combining narratives, digital datasets, and performative routines analysis. Collaborations: Active in global research networks with scholars across Africa, Europe, and Asia. Recent work addresses mining-industry partnerships, gender dynamics in extractive economies, and ethical dimensions of corporate governance. His research outputs number over 110 publications in top journals like Technovation and Journal of Business Research. Awards/Affiliations: Recognized through editorial leadership roles and visiting professorships. His research has policy implications for sustainable development, innovation ecosystems, and organizational resilience in global contexts.
Mahdi S. Hosseini is an Assistant Professor in the Department of Computer Science and Software Engineering at Concordia University and a faculty member of the Applied AI Institute. He holds a PhD from the University of Toronto (2016) and completed a postdoctoral fellowship at UofT, supported by MITACS-Elevate and NSERC fellowships. His research focuses on advancing deep learning and computer vision for computational pathology and healthcare technologies, aiming to develop AI tools for clinical diagnosis. He currently supervises graduate students and has published over 30 papers and two patents. Education: PhD in Electrical and Computer Engineering from the University of Toronto (2016), postdoctoral training at UofT collaborating with Huron Digital Pathology Inc. (Waterloo, Ontario). Research interests include deep learning, computer vision, computational pathology, medical imaging, and AI ethics (P4AI project). His work emphasizes developing explainable AI systems for clinical pathology, biomarker discovery, and efficient learning algorithms. Professional service includes serving as Area Chair for NeurIPS 2023, CVPR 2023-2024, and ECCV 2024. He reviews grants for CIHR, NSERC, and serves on program committees for key conferences (ICCV, CVPR, NeurIPS). Teaching includes courses on applied AI, machine learning, and deep learning for computational pathology at both graduate and undergraduate levels. Awards: MITACS-Elevate Fellowship (postdoc), NSERC Research Funding (2016-2017). His work has led to patents in diagnostic systems and has collaborated with hospitals and pathologists to advance clinical applications. Labs/Teams: Active collaborations with the Applied AI Institute at Concordia, Huron Digital Pathology, and healthcare institutions. Research emphasizes interdisciplinary approaches between computer science and clinical medicine.
Dr. Edouard Boujo is a Scientist and Lecturer at the Swiss Federal Institute of Technology Lausanne (EPFL) , affiliated with the School of Engineering (STI) and working in the Institute of Mechanical Engineering (IGM) and Laboratory of Fluid Mechanics and Instabilities (LFMI) . He also teaches in the SGM-ENS department of the School of Engineering. Scientist at EPFL STI IGM LFMI Lecturer at EPFL STI-SGM SGM-ENS His research focuses on Fluid Dynamics with expertise in Flow Stability , Flow Control , Aeroacoustics , Thermoacoustics , Fluid-Structure Interaction , and Coating Flow Dynamics . He employs advanced mathematical modeling and computational methods to study complex fluid behaviors. Recent publications highlight his work on stochastic modeling of fluid instabilities, adjoint-based optimization of flow systems, and nonlinear dynamics of coating flows. His 15 most recent papers cover topics ranging from symmetry-breaking bifurcations to spin coating optimization and noise-induced transitions in fluid systems. Dr. Boujo actively collaborates with institutions across Europe and New Zealand, mentoring PhD student Atharva Lagwankar . He has received research funding from the Swiss National Science Foundation for two PhD theses and contributes to major fluid dynamics conferences like the European Fluid Dynamics Conference and APS Division of Fluid Dynamics meetings. His laboratory work at LFMI involves experimental and computational studies of fluid instabilities, with applications in aerospace, mechanical engineering, and industrial coating processes. He develops adjoint-based control methods for optimizing flow systems and reducing drag in various fluid configurations.
Dr. Martha Sidury Christiansen is a Professor of Applied Linguistics/TESOL at the University of Texas at San Antonio , where she serves in the College of Education and Human Development . She is the Principal Investigator of Project RESPETO , a NSF-funded initiative exploring racial equity in engineering education. Her work spans sociolinguistics, digital literacies, and raciolinguistic analysis, with a focus on transnational multilingual communities. Born in Veracruz, Mexico Ph.D. in Foreign/Second Language Education (Ohio State University, 2013) M.A. in English Composition (Indiana University, 2007) B.A. in English Language Teaching (Universidad Veracruzana, 2002) Her research examines how transnational youth navigate digital spaces through multiliteracies , challenges Western academic writing norms via Mexican decolonial methodologies , and investigates raciolinguistic intersections in identity formation. The 15 most recent publications reveal a strong focus on digital communication , transnationalism , and critical pedagogy across journals like TESOL Quarterly and Language@Internet . 2023-2025: Expanding raciolinguistic frameworks in digital contexts 2021-2022: Analyzing multimodal identity construction 2019-2020: Exploring Mexican bilinguals' online language use She has received multiple honors including ACUE Fellow (2023), Fulbright Scholar (2017), and Faculty Leadership Fellow (2021-2022). Her presentations at conferences like ICOLLITE and DDVM Lab highlight her expertise in critical sociolinguistic awareness and digital discourse analysis . Dr. Christiansen actively consults for nonprofits on linguistic equity and multilingual education .
Professor Khac Duc Do is a faculty member at Curtin University, holding a position in the School of Civil and Mechanical Engineering within the Faculty of Science and Engineering. He serves in the Office of the Provost and is based at Curtin Perth campus. His research focuses on advanced control systems, nonlinear dynamics, and robotics applications in marine, aerospace, and mechanical systems. He earned a PhD with distinction in 2003 and has held prestigious fellowships including ARC Postdoctoral Fellow (2004) and ARC Australian Research Fellow (2009). His teaching includes courses like Advanced Control and Mechatronics, Navigation and Marine Control Systems, and Advanced Control Engineering. Key research interests encompass control of nonlinear systems, stochastic systems, formation control of mobile agents, fluid-structure interaction, and boundary control of PDE-governed systems. His funded projects include wave-energy converter development (2023-2026), inerter-based damper research (2019-2021), and ocean vehicle control systems. Scientific awards include ARC grants totaling over AUD 2 million. Current opportunities include scholarships in control systems/fluid-structure interaction and a postdoc position in wave-energy conversion.
