Dr. Carlos Ventura is a Professor of Structural & Earthquake Engineering at the University of British Columbia (UBC), serving as Director of the Earthquake Engineering Research Facility (EERF). With over 30 years of experience, his research focuses on structural dynamics, seismic retrofit of buildings, and infrastructure resilience. He has published over 400 papers and led projects for seismic safety guidelines in schools and bridges. Research Interests: Earthquake engineering Structural vibration testing Performance-based design Regional seismic risk assessment Structural health monitoring Awards: Lieutenant Governor’s Award of Excellence (2013) Fellow, Canadian Academy of Engineering (2010) Champions of Earthquake Resilience Award (2015) Multiple best paper awards in international journals Labs & Projects: Director of UBC Earthquake Engineering Research Facility (EERF) Lead researcher in BC School Seismic Retrofit Program Principal investigator in structural health monitoring networks Contributions to bridge and dam safety evaluations
Fabian Bastin is a Full Professor in the Department of Computer Science and Operational Research (IRO) at Université de Montréal. He holds a prestigious academic position within the university's research and teaching community. His work focuses on optimization, stochastic programming, simulation, and their applications in transportation, energy systems, and finance. Teaching Responsibilities: Bastin teaches advanced courses such as IFT-2505 (Linear Optimization), IFT-3515 (Nonlinear Programming), and IFT-6512 (Stochastic Programming). He also contributes to graduate-level courses on dynamic programming and simulation techniques. His courses emphasize theoretical foundations alongside practical applications, often using tools like MATLAB and the ORATIO library he helped develop. Research Interests: Bastin's research spans stochastic optimization, simulation methodologies, and decision-making under uncertainty. Key areas include air traffic management optimization, hydroelectric reservoir scheduling, and synthetic population generation using copula-based models. He has pioneered work on scenario tree generation for multistage stochastic programming and developed algorithms for efficient mixed logit model estimation. Research Contributions: His publications highlight advancements in stochastic models for transportation systems, energy planning, and financial engineering. Notable works include contributions to the progressive hedging algorithm, recursive logit models for route choice analysis, and Monte Carlo methods for option pricing. Bastin is also actively involved in software development, notably the ORATIO simulation library used in discrete-event modeling. Professional Engagements: He has co-organized conferences on optimization and simulation, and his work has been supported by grants from NSERC and other funding bodies. Despite no explicit mention of awards in the text, his extensive publication record and methodological innovations suggest significant recognition in his field.
Christopher G. Fletcher is an Associate Professor cross-appointed between the Department of Geography and Environmental Management and the Department of Applied Mathematics at the University of Waterloo, where he has been faculty since 2010. He serves on the governing body for the Waterloo Climate Institute and holds affiliations with the Computational Mathematics program. Previously, he was an Assistant Professor at Waterloo (2010-2017), SHARCNET Research Chair (2010-2012), and held postdoctoral positions at the University of Toronto. His education includes a PhD from University College London (2005), MSc from University of Reading (2001), and BSc from University of Manchester (2000). Fletcher leads a research group studying climate dynamics using global climate models to analyze variability from seasonal to centennial scales. Key research areas include: Arctic climate processes and snow-albedo feedbacks Machine learning applications to improve Earth System Models Extratropical teleconnections and land-ocean-atmosphere interactions Satellite remote sensing of snowfall and soil moisture His publications (87+ articles) demonstrate consistent focus on Arctic climate processes, model validation, and machine learning applications in climate science. Recent work emphasizes high-resolution snow modeling, climate tipping points, and geoengineering impacts. Honors include the SHARCNET Research Chair (2010-2012). Major grants support his research: NSERC Discovery Grant (2020-2024): 'Using statistical learning to build better Earth System Models' Microsoft AI for Social Good program funding Canadian Space Agency project on Arctic snowfall measurement (2018-present) NSERC Climate Change and Atmospheric Research networks (CanSISE, NETCARE) Fletcher actively advises graduate students, with 4 current PhD candidates and 21+ alumni from doctoral and master's programs. His lab develops novel observational datasets to constrain climate simulations, particularly for Canada's northern regions.
Xinyu Zhao serves as an Assistant Professor in the Department of Mathematical Sciences at the New Jersey Institute of Technology (NJIT), where his research focuses on applied mathematics and scientific computing with emphasis on nonlinear wave phenomena. His academic journey includes: Undergraduate studies at Beijing Normal University Ph.D. in Mathematics from the University of California, Berkeley under advisor Jon Wilkening Postdoctoral fellowship at McMaster University's Department of Mathematics and Statistics with Bartosz Protas Dr. Zhao's research program centers on advanced mathematical analysis of nonlinear wave systems, investigating critical phenomena such as traveling wave dynamics, finite-time singularity formation in fluid equations, and solution stability properties. His methodology integrates rigorous analytical techniques with high-performance scientific computing to address fundamental challenges in mathematical physics. Based in Cullimore Hall 609 at NJIT, he maintains active research collaborations though specific grant details and laboratory infrastructure are not documented in the available materials.
Allan R. Willms is a Professor in the Department of Mathematics and Statistics at the University of Guelph, Ontario, Canada. He received his B.Math and M.Math from the University of Waterloo and his Ph.D. from Cornell University in 1997. After working as a Visiting Scientist at Cornell and as a Lecturer at the University of Canterbury in New Zealand (1998-2003), he joined the University of Guelph faculty in 2003 where he continues to maintain an active research program. Dr. Willms' research focuses on dynamical systems models of biological and physical processes, with particular emphasis on: Simple models of climate change, including paleoclimate transitions and Arctic climate bifurcations Parameter range reduction techniques for ordinary differential equation models Bifurcation theory with symmetry, including work on Huygens' clocks Mathematical biology applications spanning neuronal ion channels, disease transmission, fluid dynamics in biological systems, and veterinary medicine His recent publications demonstrate a diverse research portfolio spanning mathematical biology, climate modeling, and complex analysis. The analysis of his work reveals a consistent theme of applying dynamical systems theory to solve practical problems across multiple disciplines, with a recent emphasis on veterinary applications and climate change modeling. His publications show a progression from fundamental mathematical theory to increasingly applied interdisciplinary work. Dr. Willms has received multiple NSERC Discovery grants (2004, 2010, 2015, 2020) and has served as Editor for Biosystems (2014-present) and the International Journal of Applied Nonlinear Science (2012-2016). His research has been recognized with features as NPG Paper of the Month for 'Anthropocene Climate Bifurcation' and in DSWeb Magazine for 'Breathing Torus Near Double Hopf Bifurcation'. He actively mentors students and maintains available positions for graduate students, undergraduates, and postdoctoral fellows. His research group develops computational tools for parameter estimation and dynamical systems analysis, including the PRRMD (Parameter Range Reduction using Monotonic Discretizations) software package and NEUROFIT for Hodgkin-Huxley model fitting.
Pengfei Li is an Assistant Professor in the School of Information at the Rochester Institute of Technology (RIT), where he leads research at the intersection of machine learning, sustainability, and social equity. Previously, he completed his Ph.D. in Computer Science at the University of California, Riverside under Prof. Shaolei Ren, with additional collaborations at Caltech with Adam Wierman and an internship at Nokia Bell Labs. His educational background includes an M.S.E. in Robotics from Johns Hopkins University and a B.E. in Electrical Engineering from Zhejiang University. Dr. Li's research focuses on three interconnected pillars: developing trustworthy online algorithms with strict robustness guarantees, creating sustainable AI systems that minimize environmental impact, and addressing environmental and social inequities through algorithmic solutions. Analysis of his recent publications reveals a strong emphasis on the environmental consequences of AI systems, particularly water consumption ('Making AI Less 'Thirsty'') and geographical distribution of environmental burdens ('Towards Environmentally Equitable AI'). His work bridges theoretical computer science with practical sustainability challenges, often incorporating learning-augmented approaches to traditional online optimization problems. Scientific Recognition: Dissertation Completion Fellowship Award (DCFA) from the Graduate Program in Computer Science (February 2025) 'Making AI Less 'Thirsty'' in Communications of the ACM has received 15 citations and over 17,000 downloads Organizer of the workshop on learning-augmented algorithms at SIGMETRICS 2025 Dr. Li actively seeks to build a research group focused on societal fairness, reliable generative AI, and decision-focused learning. His work has established important connections between theoretical computer science and critical societal challenges, particularly around AI's environmental footprint and equitable resource distribution. Current research directions include developing algorithmic solutions for environmental and social fairness, with applications in water infrastructure, energy systems, and equitable AI deployment.
Julie Boissonneault serves as a Sessional Lecturer in the French Studies Department at Laurentian University since 2003, specializing in sociolinguistics and French language didactics within minority contexts. With over 30 years of post-secondary teaching experience, she leads the Observatoire de la langue française en Ontario (OLFO) and serves as editor of the Revue du Nouvel-Ontario. Her institutional affiliations include accreditation with Laurentian's Ph.D. Program in Human Sciences and M.Sc. program in Speech Pathology. Her research focuses on Francophone linguistic security , digital media in academia , and educational transitions in minority communities . Key projects include analyzing oral corpora of Northern Ontario workers, studying French-to-English school transitions (funded by Ontario Ministry of Education), and investigating cultural nuclei in Francophone communities (supported by Canadian Heritage). Her methodology combines sociolinguistic corpus analysis with policy-relevant qualitative research. Publications demonstrate consistent interdisciplinary output since 2004, with recent work emphasizing linguistic security dynamics (2013), digital technology integration in education (2012), and cross-cultural lexical studies (2010). Her edited book series Language and Territory represents significant scholarly contributions to minority language studies. Ontario Ministry of Education research grant (2013-2016) on school transitions Fédération culturelle canadienne-française/Canadian Heritage funding (2013-2016) Laurentian University support for OLFO oral history collections As supervisor for Ph.D. and M.Sc. programs, she offers opportunities in sociolinguistic analysis and educational interventions. Her editorial role at Revue du Nouvel-Ontario provides additional scholarly engagement pathways. The OLFO research group maintains unique oral and literary corpora essential for studying language evolution in minority contexts.
Dr. Mirella Stroink serves as Associate Professor of Psychology and Dean of the Faculty of Health and Behavioural Sciences at Lakehead University, where she has held faculty positions since August 2004. Her dual role positions her at the nexus of academic leadership and interdisciplinary research in complex human-ecological systems. Her academic foundation includes: PhD in Psychology, York University (2005) MA in Psychology, York University (1998) BA (Honours), Mount Allison University (1996) Dr. Stroink's research program integrates complexity science to analyze human behaviour within interconnected social-ecological contexts. Her work emphasizes nonlinear dynamics in environmental behaviour , food systems , and identity processes , revealing how psychological factors interact with ecological constraints. Key contributions examine resilience mechanisms in food security contexts and meaning-making processes within complex adaptive systems. As director of the CCR Lab, she advances methodological innovation in systems psychology. Her leadership as Dean provides unique insights into institutional dynamics while maintaining active research programs that bridge psychological science with sustainability challenges through complexity frameworks.
Dr. Henrique Correa da Cunha serves as an Associate Professor in the Global Management Studies program at the Ted Rogers School of Management, Ryerson University, where he has taught undergraduate and MBA courses since 2009. His academic credentials include: BSBA from Old Dominion University, Norfolk, VA, USA MBA from HULT International Business School, Boston, MA, USA Academic Master in Business Administration from Universidade Regional de Blumenau (FURB), Brazil PhD in Business Administration and Management Accounting from FURB, Brazil PhD in Innovation Sciences from Halmstad University, Sweden Dr. Correa da Cunha's research centers on International Business dynamics in Latin America, with emphasis on how cultural frameworks and formal institutions shape multinational firms' internationalization strategies. His work bridges theoretical institutional analysis with practical cross-border management challenges, addressing critical gaps in understanding subsidiary performance under asymmetric institutional environments. This focus positions him at the forefront of emerging markets research, particularly regarding cultural adaptation and risk mitigation in volatile economic landscapes. His recent publication surge (8 papers in 2022-2023) reveals evolving research trajectories toward geopolitical risk assessment in trade patterns, advanced cultural distance measurement, and outward investment determinants from Latin American firms. These studies consistently employ rigorous quantitative methodologies across multiple Latin American contexts, demonstrating methodological sophistication while maintaining regional specificity. Key accolades include: Best PhD Dissertation Paper award at the 16th SGBED conference (2019) Nomination for AIB-Lat 2018 best papers Nomination for Best Student Reviewer at the AIB-SE conference (2015) Full International MBA Scholarship from Bunge Alimentos (2007/2008) Best Business Plan award by OSRAM Sylvania (2008) Dr. Correa da Cunha teaches core courses including GMS 200 (Introduction to Global Management) and GMS 693 (The Latin American and Caribbean Business Environment). His industry experience in multinational firms prior to academia informs practical classroom applications. While specific grant details aren't provided, his sustained publication output in top journals indicates active research funding and supervision of graduate students, as evidenced by his dissertation award recognition. Though no dedicated research lab is mentioned, his extensive international co-authorship network—spanning Brazilian, Swedish, and other Latin American institutions—demonstrates robust collaborative research infrastructure focused on emerging market dynamics.
Dominique Orban is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He holds a Ph.D. from FUNDP Namur and INP Toulouse and has established himself as a leading researcher in numerical optimization. His academic affiliations include the Institute for Data Valorization (IVADO) and the Decision Analysis Study and Research Group (GERAD). Professor Orban's research focuses on numerical mathematics, particularly continuous nonlinear optimization, nonlinear systems of equations, and numerical linear algebra. His work involves designing specialized numerical algorithms for optimization problems, with particular interest in degeneracy and ill-posed problems. His research spans theoretical development of algorithms, their implementation in software, and applications to real-world problems such as image reconstruction, optimal structure design, and optimization under differential constraints. Analysis of his recent publications (2021-2025) reveals a strong focus on developing practical optimization algorithms with theoretical guarantees. His work spans multiple areas including nonsmooth optimization, iterative methods for linear systems, regularization techniques, and software implementation in Julia. A notable trend is his increasing focus on developing open-source software tools that make advanced optimization methods accessible to practitioners. Over 160 publications including journal articles, conference papers, and technical reports Multiple publications in top optimization journals each year through 2025 Strong emphasis on both theoretical foundations and practical implementation Increasing focus on Julia-based optimization software development Professor Orban has successfully supervised 9 doctoral students and 13 master's students to completion, demonstrating his commitment to mentoring the next generation of researchers. His supervision style appears to balance theoretical depth with practical implementation skills, preparing students for both academic and industry careers. His research has been supported through various institutional and collaborative grants, enabling him to maintain an active research program with multiple ongoing projects. His contributions to the field include significant software developments such as Krylov.jl, JSOSuite.jl, and DCISolver.jl, which have made advanced optimization techniques more accessible to the broader scientific community. These tools reflect his philosophy of bridging theoretical optimization with practical computational implementation.
Marc Laforest is an Associate Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He holds a B.Sc. from UQAM and M.Sc. and Ph.D. degrees from SUNY at Stony Brook. His academic career at Polytechnique Montréal spans multiple decades, during which he has established himself as a researcher in computational mathematics and numerical analysis. Professor Laforest's research interests focus on continuum mechanics, hyperbolic conservation laws, kinetic equations, numerical analysis, multi-scale analysis, a posteriori error estimation, and mesh adaptivity . His work bridges theoretical mathematics with practical applications, particularly in wave propagation and gas mechanics. Recent publications demonstrate increasing integration of machine learning techniques with traditional numerical methods, reflecting evolving trends in computational science. Analysis of his 15 most recent publications reveals a strong trajectory in computational mathematics with growing emphasis on machine learning applications. His research spans pure mathematical theory to practical engineering applications, particularly in superconductivity modeling. The consistent publication rate (2 papers in 2025, 2 in 2024, 2 in 2023, and 5 in 2022) indicates active research programs and likely multiple ongoing grants. Professor Laforest has supervised 5 doctoral students to completion (most recent in 2024) and 9 master's students (most recent in 2023). His students' thesis topics reflect the breadth of his research interests, ranging from wave equation approximation to contact detection methods and superconductivity modeling. This extensive supervision record demonstrates his commitment to graduate education and mentorship. His teaching responsibilities include courses in differential equations, analysis, and numerical analysis , connecting his research expertise with classroom instruction. While specific grant information isn't provided in the available materials, his consistent publication record across multiple high-impact journals suggests successful grant acquisition and management of research funding.
Miguel Anjos is Professor and Chair of Operational Research at the School of Mathematics, University of Edinburgh. He serves as Deputy Head of School and leads the Data and Decisions research theme. His professional roles extend to Chair of the Mathematical Optimization Society (2023–2025) and Vice-President (International Activities) of INFORMS. He holds Fellowships from the Humboldt Foundation, EUROPT, and the Canadian Academy of Engineering. Research Focus: Mathematical optimization for large-scale nonlinear problems in engineering applications, particularly conic optimization in facility layout and power grid optimization. Collaborations: National Grid ESO, Hydro-Québec, Schneider Electric, Rio Tinto, EDF, and Yeji Data Lab. Datasets: Maintains QAPLIB, FLPLIB, and Jones Benchmark for optimization research. His work addresses smart grid challenges: maximizing grid efficiency, integrating renewables, optimizing energy storage, and enabling customer participation. Former students have secured positions at universities (University of Tennessee, Western Ontario) and tech companies (Amazon, Shopify). Scientific Awards: Fellow of the Humboldt Foundation Fellow of EUROPT Fellow of the Canadian Academy of Engineering He has supervised PhD researchers including Elspeth Adams (2014 MOPTA runner-up), Christian Bingane (2019 CORS Student Paper Competition winner), and Mathieu Tanneau (2018–2021 FRQNT Merit Scholar). His postdocs have contributed to bilevel attacks in AI, energy storage scheduling, and electric vehicle infrastructure planning.
Vasilisa Shramchenko serves as Full Professor and Associate Director of Graduate Studies and Research in the Department of Mathematics at the University of Sherbrooke. Her work bridges advanced algebraic geometry with mathematical physics, focusing on structural frameworks governing complex systems. Educational background: Master of Science in Mathematics, St. Petersburg State University (2000) Ph.D. in Mathematics, Concordia University (2005) Her research program integrates Frobenius manifolds, integrable systems, and Riemann surface theory through the lens of isomonodromic deformations and Riemann-Hilbert problems. Key contributions include foundational work on Hurwitz spaces, Painlevé equations, and Eynard-Orantin topological recursion, revealing deep connections between algebraic curves, moduli spaces, and differential equations. This interdisciplinary approach extends to combinatorial enumeration and quantum field theory applications. Publication trends indicate sustained focus on algebro-geometric methods for solving nonlinear systems, with recent work emphasizing cluster algebras and superelliptic curve structures. Her output consistently appears in high-impact journals spanning mathematical physics and pure mathematics. Scientific recognition: No awards explicitly documented in source material Administrative leadership in graduate studies complements her research role, though specific grant funding or advisee information remains unreported. She actively contributes to the Algebraic and Geometric Structures Research Team and the Sherbrooke Mathematics Circle, fostering collaborative exploration of mathematical frontiers.
Clayton Pettit serves as an Assistant Professor in the Department of Civil and Environmental Engineering within the Faculty of Engineering at the University of Alberta, appointed in 2023. He concurrently holds the role of Associate Director of Experiential Learning for the Civil, Environmental, Mining, and Petroleum (CEEMP) Department, where he integrates hands-on learning experiences into the engineering curriculum. His academic credentials include: Doctor of Philosophy (Ph.D.) in Structural Engineering, University of Alberta, 2023 Master of Science (M.Sc.) in Structural Engineering, University of Alberta, 2020 Bachelor of Science (B.Sc.) in Civil Engineering, University of Alberta, 2017 Pettit's research program focuses on structural engineering through experimental and computational methodologies. Key areas include masonry wall systems (flexural rigidity of slender walls, in-plane shear capacity of squat walls), numerical modeling of long-distance pipelines, and thermal analysis of masonry veneer envelopes. His contributions to industry standards are evident through active participation in TMS 402/602-28 technical committees for general masonry design and seismic considerations. As an educator, he teaches foundational courses including Engineering Mechanics (ENGG 130), Structural Analysis, Continuum Mechanics (CIV E 398), and the Finite Element Method (CIV E 665). He has successfully transitioned all courses to online formats and maintains an educational YouTube channel with over 25,000 subscribers. Pettit currently accepts undergraduate students for research supervision in structural engineering projects. Scientific Awards: No scientific awards, fellowships, or medals were documented in the source material. Pettit oversees experiential learning initiatives department-wide and conducts research utilizing structural engineering laboratories for large-scale testing. His technical committee work and pipeline modeling projects indicate collaborative industry-academic partnerships, with future research directions emphasizing infrastructure resilience and advanced computational modeling.
