Prof. Patrick Jenny is a Full Professor at the Department of Mechanical and Process Engineering and Head of the Institute of Fluid Dynamics at ETH Zurich. His research focuses on computational fluid dynamics (CFD), numerical methods for turbulent and multiphase flows, and reservoir simulation. He has held positions at ChevronTexaco and Cornell University, and received the National Latsis Prize 2005. PhD in CFD from ETH Zurich (1997) Postdoctoral work at Cornell University (1997–1999) Senior Researcher at ChevronTexaco (1999–2003) Research interests include: turbulent reactive flows, PDF modeling, multi-scale reservoir simulation, and data assimilation in engineering systems. He teaches courses on fluid dynamics, turbulence, and computational methods. Over 100 peer-reviewed publications span topics like fracture modeling, LES/RANS coupling, and particle-laden flows. His work bridges academia and industry, addressing challenges in energy systems, environmental engineering, and numerical algorithms. Winner: National Latsis Prize 2005 Led over 20 PhD projects and collaborates with institutions globally. His lab develops open-source tools for CFD and energy systems analysis.
Laura Bruckman is a Climo Associate Professor in the Department of Materials Science and Engineering at Case Western Reserve University's Case School of Engineering. Her research focuses on predictive lifetime modeling for materials degradation, quantitative spectroscopic characterization of materials, and applying statistical analytics and data science to solve challenges in photovoltaic systems and long-lived engineering materials. Her work emphasizes understanding degradation mechanisms in photovoltaic materials (e.g., backsheets, encapsulants, and silicon cells) under environmental stressors, with applications in improving reliability and service life through advanced data-driven approaches. Dr. Bruckman has contributed to the development of machine learning methods for material characterization (e.g., ToF-SIMS analysis) and spatiotemporal models for predicting degradation patterns in field-deployed PV systems. Her research also extends to curriculum design for applied data science, emphasizing industry-relevant training in statistical modeling and interdisciplinary problem-solving. Her expertise bridges materials science, data science, and energy systems, with over 50 peer-reviewed publications and a patent in classification using multivariate optical computing. Key technical contributions include analyzing environmental impacts on solar module performance, quantifying crack propagation in polymers, and developing predictive frameworks for material aging. Her work has been supported by collaborations with industry partners and federal research initiatives.
Ed Chien is an Assistant Professor at Boston University's Department of Computer Science within the College of Arts & Sciences. He specializes in applying differential geometry and topology to graphics, computational engineering, and machine learning. Previously, he was a postdoctoral researcher at MIT's CSAIL and Bar-Ilan University. His work focuses on mathematically rigorous solutions to problems in geometric data processing and optimal transport. Education PhD in Mathematics, Rutgers University (2015) A.B. in Mathematics & Physics, Dartmouth College (2009) Research Highlights Dr. Chien's research includes fundamental studies on hexahedral mesh topology for Finite Element Modeling, optimal transport applications in machine learning, and singularity-free geometric algorithms. His work bridges theoretical mathematics with computational tools for engineering and graphics. Publications span top venues like NeurIPS, Eurographics, and SIGGRAPH, reflecting contributions to geometry processing and machine learning intersections. Program committee roles include Eurographics SGP (2019) and AAAI (2020). Awards & Recognition No specific awards listed, but notable service includes program committee memberships and peer-reviewed contributions to leading conferences. Grants & Advising Active in mentoring through academic positions but no explicit grant details provided. Research focuses on advancing geometric algorithms and optimal transport methodologies.
Jonathan Weare is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He holds affiliations with the Faculty of Arts and Science and the Graduate School of Arts and Science. His academic journey includes roles as an Associate Professor at the University of Chicago (2014–2019) and Assistant Professor (2011–2014), following postdoctoral work as a Courant Instructor at NYU. He earned his Ph.D. in Mathematics from UC Berkeley in 2007. His research focuses on stochastic algorithms and models, with applications in astrophysics, biophysics, computational chemistry, and climate science. Key areas include Monte Carlo methods, rare event simulation, and machine learning-driven scientific analysis. Collaborations with domain experts ensure his work addresses real-world challenges in diverse fields. Recent publications emphasize advancements in trajectory stratification, rare event prediction using machine learning, and efficient algorithms for high-dimensional problems. Notable contributions include the BAD-NEUS framework and AI-based solar system instability predictions. His group’s interdisciplinary approach bridges computational methods with scientific inquiry. Weare has advised numerous students and mentored postdocs, fostering talent in applied mathematics and computational science. His work on Mercury’s orbital dynamics and extreme weather prediction showcases the societal impact of his research. Current projects explore AI applications in weather modeling and rare event analysis, leveraging cutting-edge machine learning techniques. Labs/Teams: His research group at Courant develops stochastic algorithms and collaborates with interdisciplinary teams in computational chemistry, climate science, and astrophysics. Key collaborations include the University of Chicago and Columbia University.
Dr. James Saunderson is a Senior Lecturer and Director of Education in the Department of Electrical and Computer Systems Engineering at Monash University. He holds a PhD in Electrical Engineering and Computer Science from MIT and has held postdoctoral roles at Caltech and the University of Washington. His expertise spans convex optimization, semidefinite programming, and quantum information theory. Education : PhD in EECS, MIT (2015) MS in EECS, MIT (2011) Bachelor of Engineering (Honours) and Bachelor of Science (Honours), University of Melbourne (2008) Research Interests : Convex optimization, quantum information theory, signal processing, and algorithm design. Focuses on algebraic and geometric aspects of optimization, with applications in engineering and quantum systems. Recent Projects : Exploiting duality in quantum relative entropy optimization Hyperbolic programming and conic optimization Applications in nanotechnology and bioinformatics Teaching : Courses include Control System Design, Signals and Systems, and Optimization for Engineers. Awards : SIAM Optimization Best Paper Prize (2020) Grants and Collaborations : Australian Research Council Discovery Early-Career Research Fellow (2020–2024) Leading projects in quantum optimization and bioengineering applications.
Dr. Alfred Kume is a Senior Lecturer in Statistics at the University of Kent, affiliated with the School of Mathematics, Statistics and Actuarial Science. He has held this position since 2004 and has been involved in examining processes for the Institute of Actuaries. His research focuses on shape analysis, directional statistics, image analysis, and stochastic geometry. Kume obtained his PhD and postdoctoral training at the University of Nottingham after working as an actuary. He has supervised students including Theodoros Gkolias and Justyn Campbell-White. His work spans statistical methodology applied to astronomy (e.g., stellar light observations, HII regions) and computational statistics (e.g., holonomic gradient methods, clustering algorithms). His publications reflect expertise in probability distributions, algorithm development, and interdisciplinary applications. His office is located in Cornwallis South, Canterbury Campus. Research interests emphasize statistical techniques for shape and directional data, with applications in astronomy and biology. Key contributions include saddlepoint approximations for normalizing constants and statistical clustering methods. His work bridges theoretical statistics with practical problems in astrophysics and actuarial science. Publications highlight trends in statistical methodology (e.g., Bingham/Fisher-Bingham distributions), computational algorithms, and interdisciplinary collaborations. While no specific awards are listed, his extensive publication record and academic roles reflect scholarly recognition. Advising focuses on statistical shape analysis and Bayesian methods, with grants possibly tied to collaborative projects. He is part of research teams analyzing molecular clouds and astronomical phenomena. His lab or team activities are integral to interdisciplinary projects, though specific lab names are not mentioned.
Hyejin Ku is a Full Professor in the Department of Mathematics and Statistics at York University's Faculty of Science. Her research focuses on the intersection of Mathematical Finance and Machine Learning, addressing challenges in risk measurement, portfolio optimization, and quantitative finance. She develops advanced mathematical models to enhance decision-making through reinforcement learning and data analytics. Notable projects include novel algorithms for credit rating prediction using neural networks and sequence-based clustering for credit risk assessment. Her work integrates applied mathematics with real-world financial applications, such as systemic risk reduction in multi-layer networks and option pricing under liquidity constraints. She holds a prominent position in mathematical finance, contributing to both theoretical advancements and practical solutions for financial markets. Her research trends emphasize interdisciplinary approaches, combining machine learning techniques with financial modeling to solve complex problems in risk management and asset valuation. Her publications span over two decades, showcasing contributions to portfolio optimization, derivatives pricing, and computational finance. Dr. Ku is affiliated with York University’s Department of Mathematics and Statistics, where she contributes to academic leadership and research mentorship. Her office is located in DB 2025, and she can be reached at hku@yorku.ca.
Dr. Jingjing Qiu is an Associate Professor in the Department of Mechanical Engineering at Texas A&M University (TAMU), leading the Advanced Materials & Manufacturing (AM²) Lab. Her research focuses on advanced manufacturing, nanomaterials, multifunctional composites, sustainable materials, energy harvesting, and healthcare applications. She holds a Ph.D. in Industrial & Manufacturing Engineering from Florida State University (2008), and M.S./B.S. degrees in Materials Science from Beihang University (2004/2001). Prior to academia, she gained 1 year of industrial experience as a Quality Engineer at SAIC Motor. Research interests include AI-driven nanomaterials synthesis, low-carbon manufacturing processes, and biomedical innovations such as drug delivery systems for brain tumors. The AM² Lab emphasizes interdisciplinary collaboration in materials science, data science, and sensor integration for energy and medical devices. Recent work highlights include AI-assisted microplastics removal, thermoelectric energy harvesting via graphene aerogels, and neuromorphic computing systems. Her team actively pursues postdoctoral and PhD candidates for research in energy/healthcare materials, requiring expertise in nanomaterials characterization (e.g., SEM, XPS, electrochemical techniques) and interdisciplinary problem-solving. Lab facilities support cutting-edge fabrication and testing of functional materials. Publications span over 15 years, with recent trends in sustainable manufacturing, soft robotics, and bio-inspired materials. Ongoing projects include DOE-funded initiatives on rare earth recycling and low-carbon ceramic production. No scientific awards are explicitly listed in the provided texts.
Ryan Murray is an Assistant Professor in the Department of Mathematics at North Carolina State University (NC State). His research focuses on developing mathematical tools to address problems in applied analysis, including calculus of variations, partial differential equations (PDEs), and their applications to machine learning, fluid dynamics, and control theory. He holds a PhD in Mathematics from Carnegie Mellon University (2016). His expertise spans regularization methods for machine learning, singular perturbations in materials science, algorithms for distributed optimization, and singularity formation in fluid dynamics. His work is supported by the National Science Foundation (NSF) and the Simons Foundation. He actively collaborates with researchers in data science, PDE analysis, and optimization. Key research areas include adversarial training in classification, geometric data analysis via statistical depths, and the analysis of vortex sheet singularities. His teaching experience includes courses on partial differential equations, optimal control theory, and linear control systems. Ryan has published extensively in journals such as SIAM Journal on Mathematics of Data Science , Archive for Rational Mechanics and Analysis , and Journal of Machine Learning Research . His articles explore topics ranging from graph-based learning to fluid dynamics instabilities.
Dr. Somayeh Allahyari is an Assistant Professor in Operations and Supply Chain Management at the Birmingham Business School, University of Birmingham. She holds a PhD in Industrial Engineering (2021) and has academic qualifications including an MSc (2013), BSc (2011), and a Diploma in Mathematics and Physics (2006). Her research focuses on Operations Research methodologies applied to Logistics, Network Design, and Supply Chain Management. Key interests include Optimization, Heuristics, Decision Support Systems, and Business Analytics. She has led industry projects such as the Drone Medical Logistics project for the Solent Future Transport Zone Programme, exploring multi-modal logistics solutions. Teaching responsibilities include modules on Supply Chain Management (UG), Operations Management (MSc), and Business Analytics (MSc Singapore). She actively supervises PhD candidates in areas like Logistics, Blockchain Technology, and Digital Transformation. Her publications appear in top journals like Transportation Research Part E and European Journal of Operational Research , with conference contributions at INFORMS Transportation Science. She serves as a peer reviewer for major journals and conferences in her field.
Dr. Olesya Zhupanska is a Professor in the Department of Aerospace and Mechanical Engineering at the University of Arizona, where she holds a faculty position and is a member of the Graduate Faculty. Her research focuses on the mechanics of composite materials, especially under extreme multi-field conditions involving mechanical, thermal, and electromagnetic loads. Education: PhD in Mechanics of Solids and Applied Mathematics, Taras Shevchenko National University of Kyiv, Ukraine, 2000 BS/MS in Mechanics and Applied Mathematics (with Highest Honors), Taras Shevchenko National University of Kyiv, Ukraine, 1996 Her research interests span mechanics of composites, impact and damage, micromechanics, multi-field effects, and structural health monitoring, with applications in aerospace, wind energy, and smart materials. She has made significant contributions to understanding lightning strike damage, electrified composites, and thermostructural response of advanced materials. Her work integrates experimental, analytical, and computational methods to solve complex engineering problems. The 15 most recent publications highlight a strong trend in composite materials under electrical and thermal loads, with a focus on damage mechanisms, contact mechanics, and predictive modeling. Her research bridges mechanics, materials science, and electromagnetics, with increasing integration of machine learning for damage detection. Applications span aerospace structures, hypersonic vehicles, and wind turbine blades. Scientific Awards and Honors: DARPA Young Faculty Award (2011) Elsevier Young Composites Researcher Award (2008) ASME/Boeing Structures & Materials Award (2007) Multiple ASC Best Paper Awards National Research Council Senior Research Associateship Award (2022, 2015) Air Force Summer Faculty Fellowships (multiple years) Woman of Impact Award, University of Arizona (2022) Fellow, ASME Associate Fellow, AIAA ASME Dedicated Service Award (2023) Dr. Zhupanska has advised numerous graduate students, many of whom have won prestigious awards such as the DoD SMART Scholarship and NASA Fellowships. Her research has been funded by DARPA, NSF, NASA, AFOSR, AFRL, and industry partners. She has served on technical review boards including ARL and actively promotes engineering education and inclusion through NSF-funded initiatives. She holds leadership roles in professional societies, currently serving as President of the American Society for Composites (ASC) and as a member of the ASME IMECE Steering Committee Senate. She also serves as a Topic Editor for Composites and Advanced Materials and on the editorial board of Applied Composite Materials.
Fahiem Bacchus is a Professor in the Department of Computer Science at the University of Toronto, within the Faculty of Arts and Science. His research is centered on foundational problems in Artificial Intelligence, particularly in reasoning, representation, and algorithm design. Institution: University of Toronto School: Faculty of Arts and Science Department: Department of Computer Science Email: fbacchus@cs.toronto.edu His work spans key areas including constraint satisfaction, satisfiability (SAT), automated planning, Bayesian inference, and constraint optimization. He focuses on developing algorithms that exploit domain-specific knowledge and structural properties to improve performance. His research has led to significant contributions such as the TLPlan planning system, which won the AIPS2002 international planning competition, and the 2clseq SAT solver, which demonstrated that extensive binary clause reasoning can dramatically improve solver efficiency. His work on preprocessors like Hypre further advanced formula simplification techniques. The recent articles reflect a strong focus on improving search algorithms through richer reasoning mechanisms, particularly in SAT solving and non-clausal logic. His publications show a consistent trend toward enhancing DPLL-based solvers with advanced inference techniques, reducing search space through preprocessing, and leveraging structural knowledge in logical theories. No scientific awards are explicitly mentioned in the provided text. Bacchus has supervised research and developed educational materials, with involvement in teaching and academic conference organization. While specific grants are not listed, his software releases (2clseq, Hypre, NoClause) suggest externally supported research activity. He has contributed tutorials, talks, and online teaching resources, indicating an active role in academic dissemination and mentoring. His research group has produced several software systems available for non-commercial research use, including 2clseq, Hypre, and NoClause, reflecting a strong applied and experimental component to his work. These tools are used in SAT solving, preprocessing, and non-clausal reasoning, and are documented with detailed technical information and usage instructions.
Myrto Limnios is a Bernoulli Instructor at the Institute of Mathematics at École Polytechnique Fédérale de Lausanne (EPFL), holding dual appointments as Lecturer in the School of Basic Sciences - Mathematics Section and Scientist in the Mathematics Department - Geometry section. Her office is located at CM 1 618 (Centre Midi), Station 10, 1015 Lausanne, Switzerland. Dr. Limnios's academic journey began with her PhD at Centre Borelli, ENS Paris-Saclay, Université Paris-Saclay, under the supervision of Prof. Nicolas Vayatis and Ioannis Bargiotas. Her doctoral thesis, Rank Processes and Statistical Applications in High Dimension , established her expertise in statistical methodology. Following her PhD, she completed a postdoctoral fellowship at the Copenhagen Causality Lab of the University of Copenhagen. Her research program focuses on nonparametric statistics, statistical learning theory, and stochastic processes with biomedical applications. She specializes in causal learning methods for event processes using conditional local independence testing (collaborating with Niels Richard Hansen) and concentration results for k-sample Rank and U-processes (with Prof. Stephan Clémençon of Télécom Paris). Her work bridges theoretical advances with practical implementations in epidemiology, neuroscience, and medical diagnostics. Analysis of her recent publications reveals a strong trend toward developing ranking-based statistical methods with applications to anomaly detection, two-sample testing, and causal inference. Her work demonstrates increasing sophistication in handling high-dimensional event processes while maintaining theoretical rigor in statistical guarantees. Among her notable achievements is the Bernoulli Instructorship at EPFL and organizing the Women in Mathematics conference at EPFL in May 2025, which hosted over 60 participants from Switzerland, France, and Spain. She was also honored with a research visit to the Simons Institute for the Theory of Computing at UC Berkeley in November 2024, hosted by Prof. Peter Bartlett. Dr. Limnios teaches advanced statistical methods at EPFL, including Regression Methods (covering linear regression, analysis of variance, diagnostics, and variable selection) and Empirical Processes (focusing on controlling nonasymptotic behavior of estimator collections). Her GitHub activity shows active development of statistical software for independence testing and anomaly ranking. She maintains active collaborations with the Copenhagen Causality Lab, Télécom Paris, and biomedical research groups, particularly through her work on postural control analysis for Parkinsonian syndromes and epidemiological modeling of infectious diseases.
Tianyi Lin serves as an Assistant Professor in the Department of Industrial Engineering and Operations Research (IEOR) at Columbia Engineering, Columbia University, a position he assumed in 2024. He holds dual affiliations as a verified Data Science Institute (DSI) Member and an Affiliated Member of both the Financial and Business Analytics Center and the Foundations of Data Science Center. His academic credentials include: Ph.D. in Electrical Engineering and Computer Science, UC Berkeley Postdoctoral Researcher, Laboratory for Information & Decision Systems (LIDS), MIT (2023-2024) M.S. in Operations Research, UC Berkeley M.S. in Pure Mathematics and Statistics, University of Cambridge B.S. in Mathematics, Nanjing University Dr. Lin's research spans optimization theory , game-theoretic models , and machine learning algorithms , with emphasis on nonconvex minimax problems , variational inequalities , and data science applications . His work bridges theoretical guarantees with practical implementations in high-dimensional settings, particularly focusing on convergence properties and computational efficiency in complex systems. Analysis of his 15 most recent publications (2022-2025) reveals dominant themes in high-order optimization methods , no-regret learning in games , and optimal transport algorithms . His contributions demonstrate consistent innovation in developing doubly optimal algorithms for monotone games, spectral regularization techniques for policy optimization, and structure-driven approaches for nonconvex problems, reflecting strong interdisciplinary connections between operations research, computer science, and applied mathematics. No scientific awards or honors were documented in the provided source material. Information regarding student advising and research grants remains unspecified in the current documentation, though his center affiliations suggest active participation in collaborative research initiatives. Dr. Lin maintains significant interdisciplinary engagement through his affiliations with Columbia's Data Science Institute and specialized research centers, positioning his work at the intersection of theoretical optimization and real-world data science applications.
Pedro Jorge Martins Coelho is a Professor in the Mechanical Engineering Department at Instituto Superior Técnico, University of Lisbon, Portugal. His academic career spans several decades with a focus on advanced thermal sciences and computational methods. His research has significantly contributed to the understanding of radiative heat transfer phenomena in complex systems. Dr. Coelho's educational background includes a Ph.D. in Mechanical Engineering, which has provided the foundation for his extensive research in thermal sciences. His work demonstrates a strong theoretical foundation combined with practical applications across various engineering domains. His primary research interests encompass radiative heat transfer, turbulence-radiation interaction, combustion modeling, and numerical methods for thermal systems. Recent work has expanded into biomedical applications of thermal radiation, particularly in laser-tissue interactions for cancer detection and treatment. His publications reveal a consistent focus on developing and refining computational methods for solving complex heat transfer problems, with particular emphasis on the radiative transfer equation in various media and geometries. Analysis of his recent publications shows a clear evolution toward more complex and interdisciplinary applications, including biomedical thermal applications, advanced turbulence modeling, and thermal management of electrical systems. His work consistently bridges fundamental theoretical developments with practical engineering applications, particularly in combustion systems, energy recovery, and thermal management. Dr. Coelho has served on editorial boards for prestigious journals including Heat Transfer Research, Computational Thermal Sciences, and International Journal of Energy for a Clean Environment, demonstrating his standing in the thermal sciences community. His research collaborations span numerous institutions and researchers worldwide, as evidenced by his extensive publication record with various co-authors across different countries. He has also been involved in conference organization, serving as Associate Editor for major international heat transfer conferences.
