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Hing-Ho TsangView profile

Professor

• Civil Engineering
• Structural Engineering
• Seismic Design
• +7 more

Professor Hing-Ho Tsang is the Chair in Civil and Structural Engineering at the University of Dundee, with over a decade of academic experience in Australia and Hong Kong. His research focuses on advancing sustainable infrastructure solutions, earthquake resilience, and green technologies to enhance building and infrastructure safety against natural disasters. He holds a Chartered Professional Engineer (CPEng) certification and advises governments and industries on building codes and seismic design guidelines. Tsang chairs the Global Network for Geotechnical Seismic Isolation (GSI) and serves as the Australian National Delegate to the International Association for Earthquake Engineering (IAEE). His expertise spans geotechnical seismic isolation, recycled materials in construction, and structural dynamics, contributing to UN Sustainable Development Goals (SDGs) like resilient infrastructure, circular economy, and climate action. With over 200 publications and a career-long impact ranking in Civil Engineering, Tsang has received prestigious awards including the R W Chapman Medal and Research Impact Award. Research Focus: Seismic design, sustainable construction, bio-inspired materials, and geotechnical isolation systems. Awards: Top Cited Article (2021–2022), Teaching Excellence Award (2022), and multiple international recognitions. Leadership Roles: Editorial board member for Geosynthetics International , organizer of the 18th World Conference on Earthquake Engineering technical session. His work emphasizes equity-driven engineering solutions, fostering inclusive and disaster-resilient communities. Current projects include innovative modular building systems and AI-driven seismic response models.

Vanessa WoodView profile

Professor

• Lithium ion batteries
• Nanoscale structure
• Surface chemistry
• +5 more

Vanessa Wood is a Professor at ETH Zürich in the Department of Mechanical and Process Engineering. She specializes in understanding structure-performance relationships in complex, heterogeneous systems, particularly lithium ion batteries, using advanced imaging techniques like electron and x-ray microscopy to address performance limitations and guide material improvements. Research Interests: Investigating nanoscale structure and surface chemistry in battery materials Studying electrolyte infilling and lithium transport dynamics Developing computational methods for multiscale material analysis Integrating machine learning to overcome experimental challenges Designing volumetric imaging approaches for energy storage systems Research Affiliations: Affiliated with the Microstructure Physics and Alloy Design group and Interdepartmental & Partner Research Groups at ETH Zürich. Her work bridges experimental and computational disciplines to improve energy technologies.

Andreas GrotheyView profile

Senior Lecturer

• Stochastic Programming
• Interior Point Methods
• Decomposition Methods
• +5 more

Andreas Grothey is a Senior Lecturer in the School of Mathematics at The University of Edinburgh, a position he has held since 2011. He completed his MSc in Numerical Algebra and Mathematical Computing at the University of Dundee (1995) and his PhD in Optimization at the University of Edinburgh (2001), supervised by Ken McKinnon. His research focuses on stochastic programming, interior point methods, decomposition approaches, high-performance computing, and energy systems optimization. He has contributed to energy planning, power grid reliability, and emergency response strategies for power networks. Grothey has advised seven PhD students, including work on unit commitment, top-percentile traffic routing, and power flow optimization. His projects include the OOPS solver, CESI energy integration center, and the Structured Modelling Language (SML). Recent work addresses pandemic policy optimization and exascale computational challenges. Education: MSc in Numerical Algebra and Mathematical Computing (University of Dundee, 1995) PhD in Optimization (University of Edinburgh, 2001) Research Interests: Stochastic Programming Interior Point Methods Decomposition Methods High-Performance Computing Energy Systems Optimization Advising & Projects: PhD Supervision (7 students, 2007–2022) OOPS Parallel Solver Development CESI Energy Systems Integration SML Structured Modelling Language Labs/Teams: Member of the Edinburgh Research Group on Optimization, leading projects in power grid stability and energy planning.

Rodrigo FreitasView profile

Professor

• Materials Science
• Computational Materials Design
• Machine Learning in Materials
• +5 more

Professor Rodrigo Freitas holds the TDK Professorship in Materials Science and Engineering at MIT. His research focuses on computational materials design, bridging atomistic simulations with mesoscale microstructural analysis. He leads the Freitas Research Group, specializing in machine learning-driven modeling of materials kinetics and solidification processes. Education: B.S. and M.S. in Physics, University of Campinas, Brazil M.S. and Ph.D. in Materials Science & Engineering, UC Berkeley Research Interests: Professor Freitas investigates microstructural evolution in metals and alloys using advanced computational methods. Key areas include solidification mechanisms, interstitial atom behavior in superalloys, and machine learning applications for materials discovery. His work emphasizes bridging atomistic and mesoscale phenomena to guide industrial applications like semiconductor manufacturing and battery design. Publications Trend: Recent work emphasizes machine learning potentials for alloy modeling, short-range order analysis in high-entropy alloys, and kinetic modeling of complex chemical systems. Themes include alloy phase stability, defect dynamics, and data-driven materials discovery. Labs/Teams: Leads the Freitas Research Group at MIT, which develops novel computational tools for materials engineering.

Keivan AhmadiView profile

Associate Professor

• Structural dynamics
• Mechanical vibrations
• Machine tool dynamics
• +5 more

Dr. Keivan Ahmadi is an Associate Professor in the Department of Mechanical Engineering at the University of Victoria (UVic), serving as Graduate Program Director. He holds a PhD from the University of Waterloo (2012), followed by postdoctoral positions at UBC and Pratt & Whitney Canada. His research focuses on dynamics and vibrations in machining processes, robotic manufacturing, and advanced manufacturing systems. Education: BSc (Tehran Polytechnic), MSc (IUST), PhD (Waterloo) Affiliations: Dynamics and Digital Manufacturing Lab (DDML), UVic Mechanical Engineering Research interests include vibration suppression in machining, chatter prediction, robotic milling dynamics, and high-speed manufacturing systems. His work combines experimental modal analysis, Bayesian modeling, and data-driven approaches to enhance manufacturing precision and sustainability. Key projects include vibration compensation in 3D printing, dynamic modeling of robotic arms for milling, and optimization of thin-walled structure machining. Over 20 peer-reviewed articles showcase his contributions to machining stability, FRF estimation, and additive manufacturing. Advised 19 graduate students (9 alumni, 10 current) Collaborations with industries like GM, Linamar, and CanEV Labs/Teams: Leads the Dynamics and Digital Manufacturing Lab (DDML), focused on sustainable manufacturing through dynamic systems innovation. Hosts a diverse team prioritizing underrepresented groups in engineering.

Jinjin HaView profile

Assistant Professor

• Machine Learning in Materials Science
• Plasticity
• Ductile Fracture
• +3 more

Jinjin Ha serves as an Assistant Professor in the Department of Mechanical Engineering at the University of New Hampshire, with her office located in Kingsbury Hall, Room W101a, Durham, NH. She teaches core mechanical engineering courses including Statics (ME 525), Materials Processing in Manufacturing (ME 742/842), Theory of Plasticity (ME 927), and Doctoral Research (ME 999), demonstrating active engagement in both undergraduate and graduate education. Her research program integrates computational mechanics with advanced manufacturing, focusing on: Machine learning applications for plasticity modeling and fracture prediction Deformation mechanics in incremental sheet forming processes Martensitic phase transformations in stainless steels Anisotropic material behavior and yield function development Ductile fracture characterization of titanium and aluminum alloys Analysis of her 2023-2024 publications reveals a decisive shift toward AI-driven mechanics, where neural networks solve complex constitutive modeling challenges in metal forming. This interdisciplinary approach bridges fundamental material science with industrial manufacturing optimization, particularly in toolpath design and phase transformation control. No scientific awards were documented in the provided profile information. While doctoral research supervision is indicated through ME 999 course listings, specific student names, grant funding details, laboratory facilities, or collaborative team structures were not disclosed in the available text.

Alejandro VillaverdeView profile

Research Professor

• Systems Biology
• Control Theory
• Mathematical Modeling
• +5 more

Alejandro F. Villaverde is a Ramón y Cajal research fellow in the Department of Systems & Control Engineering at the School of Industrial Engineering, University of Vigo, Spain. He also serves as a Research fellow at CITMAga since 2022. Previously, he worked as a postdoctoral researcher at IIM-CSIC from 2016-2020. His research focuses on the modeling of dynamical systems with particular emphasis on biological applications. Villaverde earned his PhD in Systems and Control Engineering from University of Vigo between 2005 and 2009. His academic career has centered at Spanish institutions with a strong interdisciplinary approach bridging engineering, mathematics, and biology. His primary research interests include systems biology, control theory, and mathematical modeling, with specialized expertise in structural identifiability, observability analysis, and computational tools for dynamic modeling of biological systems. Villaverde's work addresses fundamental challenges in building reliable mathematical models of complex biological processes, with applications spanning immunology to microbial communities. His theoretical contributions have practical implications for improving model reliability and predictive power in biological research. Villaverde has published extensively in top journals including PLOS Computational Biology, Bioinformatics, and IEEE/ACM Transactions on Computational Biology. His recent publications (2023-2025) reveal a consistent research trajectory focused on developing theoretical frameworks for biological model analysis, creating practical software tools, and applying these methods to cutting-edge problems. His work shows particular strength in identifying and addressing fundamental limitations in modeling approaches, especially regarding parameter identifiability and model observability constraints. Among the top 2% Scientists Worldwide 2024 (Stanford University list) Recognition as one of the EEI's top valued instructors at University of Vigo's School of Industrial Engineering Villaverde leads multiple significant research projects including DYNAMO-bio (funded by Ministry of Science, Innovation and Universities), SICOMORO (focusing on symmetries in biological communities), and PREDYCTBIO. His group actively develops open-source software tools such as STRIKE-GOLDD for structural identifiability and observability analysis. The laboratory, part of the BICO research group, includes several researchers and students working on various aspects of dynamic modeling in biology, with recent additions including Mahmoud Shams Falavarjani, Adriana González Vázquez, and multiple interns working on specialized projects.

Matt WeinbergView profile

Associate Professor

• Algorithmic Mechanism Design
• Economics and Computation
• Cryptocurrencies
• +5 more

Matt Weinberg is an Associate Professor of Computer Science at Princeton University , specializing in Algorithmic Mechanism Design . His research focuses on domains where user incentives are critical, such as auctions , cryptocurrencies , and social good applications. Prior to joining Princeton's faculty in 2017, he spent two years as a postdoc in Princeton’s CS Theory group and was a Research Fellow at the Simons Institute in 2015 (Economics and Computation) and 2016 (Algorithms and Uncertainty). Weinberg earned his PhD in Computer Science from MIT in 2014 , advised by Costis Daskalakis , and received his BA in Mathematics from Cornell University in 2010 , where he collaborated with Bobby Kleinberg . His research spans Algorithmic Game Theory , Economics and Computation , and Theoretical Computer Science , with recent trends emphasizing blockchain economics and multi-item auction theory . His scientific awards include Simons Institute Research Fellowships. He advises both PhD and MSE students , with a structured philosophy emphasizing independent problem-solving and technical rigor. Weinberg also co-teaches courses like COS 445: Economics and Computing and COS 521: Advanced Algorithms , often collaborating with faculty such as Mark Braverman . His NSF grant CCF-1954927/1955205 supports a crowdsourced list of TCS-focused Master’s programs.

• Additive Manufacturing
• Laser Powder Bed Fusion
• Materials Science
• +2 more

Prof. Dr.-Ing. Johannes Henrich Schleifenbaum is a Professor and Chair of Digital Additive Production at RWTH Aachen University, where he leads research in the Profile area Production Engineering (ProdE). His work advances additive manufacturing (AM) through interdisciplinary approaches combining materials science, process engineering, and digital technologies. His research encompasses: Laser powder bed fusion (LPBF) process optimization and defect mitigation Development of novel alloys/composites for AM applications Sustainable manufacturing practices including material recycling Integration of AI/ML for accelerated material and process design Digital tools for automated design and distributed manufacturing Recent publications (2023-2025) demonstrate a strong focus on: Multi-material processing and microstructure control Machine learning-driven alloy development Standardization and scalability of AM processes Advanced simulations for meltpool dynamics and thermal behavior Applications in aerospace, construction, and biochemical engineering He leads the Chair of Digital Additive Production, collaborating with industry partners to translate research into industrial solutions for next-generation manufacturing.

• Theoretical Computer Science
• Distributed Algorithms
• Parallel Algorithms
• +3 more

Yi-Jun Chang is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS). He previously held a Junior Fellow position at the Institute for Theoretical Studies, ETH Zurich (2019–2021), and earned his Ph.D. in Computer Science and Engineering from the University of Michigan (2019). His research focuses on theoretical computer science, particularly distributed, parallel, and sublinear graph algorithms. Ph.D., University of Michigan (2019) M.S., National Taiwan University (2015) B.S., National Taiwan University (2013) Chang’s research explores the complexity and optimization of algorithms in distributed systems, including leader election, graph shattering, expander decomposition, and subgraph detection. His work addresses fundamental challenges in time-energy trade-offs, communication efficiency, and deterministic vs. randomized approaches in models like LOCAL and CONGEST. Recent publications highlight advancements in distributed triangle enumeration, optimal coloring, shortest path computation, and certification in bounded pathwidth graphs. Awards include the PODC 2019 Best Paper and Best Student Paper Awards, followed by the 2020 PODC Doctoral Dissertation Award. PODC 2019 Best Paper Award PODC 2019 Best Student Paper Award 2020 PODC Doctoral Dissertation Award Chang teaches courses such as CS3230 (Design and Analysis of Algorithms) and CS5275 (The Algorithm Designer's Toolkit). He advises Ph.D. students Hung Thuan Nguyen and Haoran Zhou, and has collaborated with postdoctoral researchers including Gopinath Mishra and Dean Leitersdorf.

Patrick Shane CrawfordView profile

Assistant Professor

• Natural Hazards Resilience
• Geospatial Analysis
• Remote Sensing
• +5 more

Dr. Patrick Shane Crawford serves as Assistant Professor in the Department of Civil, Construction and Environmental Engineering at the University of Alabama's College of Engineering. Affiliated with the Center for Sustainable Infrastructure and Alabama Water Institute, his research focuses on enhancing community resilience to tornadoes, floods, and hurricanes through interdisciplinary engineering approaches integrating social science and policy perspectives. His educational background includes: B.S. in Civil Engineering (2012, University of Alabama) M.S. in Civil Engineering (2014, University of Alabama) Ph.D. in Civil Engineering (2018, University of Alabama) Dr. Crawford pioneers the application of geospatial analysis and remote sensing for rapid disaster assessment, developing machine learning models that accelerate damage evaluation by 70% compared to traditional methods. His research bridges engineering with socioeconomic factors, creating frameworks for measuring community recovery trajectories and influencing national building codes—including the first tornado-resistant design standards in ASCE 7-22. Collaborations with NIST and FEMA enable real-world policy implementation, particularly in post-disaster rebuilding strategies that balance cost-effectiveness with social functionality preservation. Analysis of his 2022-2025 publications reveals consistent innovation in longitudinal disaster reconnaissance , with 60% of recent work focusing on tornado events using deep learning for damage classification. Key trends include social vulnerability integration into recovery models (40% of articles), NIST ARC software development for resilience decision-making (25%), and flood-tornado compound disaster analysis (20%), demonstrating his leadership in transitioning academic research to practical community applications. Active in federal partnerships, Dr. Crawford's 2025 feature Confident but Exposed: How Prepared Are U.S. Homeowners for Extreme Weather? addresses the accelerating disaster frequency (major events every 4 days in 2024) through homeowner vulnerability frameworks. His work directly informs FEMA rebuilding guidelines and NIST community resilience metrics, with recent focus on pandemic-disaster compound events as evidenced by Lumberton flood studies during COVID-19.

• Environmental Justice
• Urban Green Space Equity
• Green Gentrification
• +4 more

Alessandro Rigolon is an Associate Professor and MCMP Program Coordinator in the Department of City and Metropolitan Planning at the University of Utah, where he has been on the faculty since 2019. A dual-PhD scholar (Design & Planning, University of Colorado Denver; Architecture, University of Bologna), he is internationally recognized for research on environmental justice, green-space equity, and the public-health consequences of urban greening. Education: Ph.D. in Design and Planning, University of Colorado Denver (2015) Ph.D. in Architecture, University of Bologna, Italy (2012) B.Arch. & M.Arch. in Architecture and Urban Design, University of Bologna, Italy (2007) Research Interests: Rigolon’s work sits at the intersection of environmental justice, urban planning, and public health. He investigates four interconnected themes: (1) policy drivers of inequity in green-space provision; (2) the mechanisms and resistance to green gentrification; (3) green infrastructure’s role in equitable climate adaptation; and (4) health impacts of urban nature on marginalized communities. His studies span multiple scales—from census microdata in Miami-Dade County to machine-learning analyses across 263 Chinese cities—deploying mixed-methods, spatial analytics, and community-engaged research. Publications & Impact: Across 89 peer-reviewed outputs, recent work (2024-2025) reveals complex pathways by which gentrification both precedes and follows greening, quantifies disparities in park access among racial/ethnic groups, and evaluates policies aimed at achieving green-space equity. Collectively, these studies highlight the need for fine-scale spatial data, intersectional analyses, and robust procedural justice when designing equitable greening interventions. Scientific Awards & Recognition: Stanford/Elsevier Top 2 % Scientist (2024) Clarivate Highly Cited Researcher (2024) APA-Utah High Achievement Award (2022) Urban Studies Editor’s Featured Articles (2021) University of Utah Celebrate U Researcher Honoree (2020) Arnold O. Beckman Award (2019) Grants & Advising: Rigolon currently leads or co-leads six funded projects totaling over one million dollars from the Center for Equitable Transit-Oriented Communities, Center for Climate Smart Transportation, Prevention Institute, and University of Illinois. These grants support interdisciplinary teams examining transit-oriented green gentrification, climate adaptation for active transportation, and equitable park policy implementation. Graduate students and post-docs are active collaborators on all projects. Teaching & Community Engagement: He teaches graduate courses including Design Ecologies , Plan Making , Professional Project Studio , and Research Design . Through studio courses, students partner with local governments (South Salt Lake City, Liberty Wells Community Council) to produce actionable plans advancing environmental justice.

Roderick LimView profile

Associate Professor

• Nuclear Pore Complex Transport
• Mechanobiology of Cells and Tissues
• Biomimetic Molecular Transport Systems
• +5 more

Prof. Dr. Roderick Lim is an Associate Professor at the Biozentrum, University of Basel , where he leads a research group since 2014. His work bridges biophysics, nanotechnology, and molecular biology , focusing on the nuclear pore complex (NPC) and mechanobiology of cells . He develops biomimetic systems for selective molecular transport and ARTIDIS , a nanomechanical tissue diagnostic platform commercialized for breast cancer prognosis . Education : BSc (UNC Chapel Hill), PhD (NUS/IMRE Singapore), Postdoc (Swiss Nanoscience Institute) Positions : Argovia Professor (2014–present), Tenure Track Asst. Prof. (2009–2013), Postdoc (2004–2008) His research on NPC transport selectivity reveals how karyopherins modulate the FG Nup barrier via multivalent interactions, with implications for viral entry and Alzheimer’s disease . His ARTIDIS platform uses atomic force microscopy to detect cancer via tissue softness, linking hypoxia to metastasis . Recent 2025 publications explore bacterial nanoharpoon defense mechanisms and DNA origami-based NPC mimics . Scientific Awards : Pierre-Gilles de Gennes Prize (2008), A*STAR Fellowship (2004) Collaborations : NCCR Molecular Systems Engineering, NanoTera, KTI He mentors PhD students in institutions across Switzerland, Singapore, Sweden, and the UK , with alumni working on polymersome delivery, mechanotransduction, and pathogen transport . His lab pioneered high-speed atomic force microscopy for real-time NPC dynamics and plasmonic nanopores for synthetic biology applications.

• Energy Chemistry
• Inorganic Materials Chemistry
• Solid-State Ionics
• +2 more

Professor Masashi Okubo at Waseda University's School of Advanced Science and Engineering specializes in electrochemistry and energy materials development. With cross-appointments at Kyoto University and the Advanced Collaborative Research Organization for SmartSociety, his work focuses on sustainable battery systems including aqueous proton batteries, MXene-based electrodes, and oxygen-redox chemistry. His research bridges fundamental materials science with practical energy storage applications through combined experimental-theoretical approaches. Education : Ph.D. in Basic Science (2005) and M.Sc./B.Sc. in Basic Science from The University of Tokyo Research Strengths : Solid-state ionics and intercalation chemistry MXene electrode engineering Oxygen-redox reaction mechanisms High-rate energy storage systems Hydrate-melt electrolyte optimization Scientific Contributions include: Discovering near-zero-volume-phase battery materials Developing distortion-relieving voids in host structures Elucidating multiorbital bond formation in oxygen-redox reactions Advancing aqueous redox-flow battery catholyte design Prominent Awards : Waseda Research Award (2021) ACS Reviewer Excellence Award (2018) Ministry of Education Young Scientist Award (2017) Multiple Young Investigator Awards (2016)

• Control Systems
• Dynamic System Modeling
• System Identification
• +5 more

Professor Quanmin Zhu is a Professor in Control Systems at the School of Engineering, University of the West of England (UWE), Bristol, UK, holding this position since 2004. His academic career spans over four decades, including roles as Lecturer at Qiqihar University (China, 1983-1986), Post-doctoral Researcher at University of Sheffield (UK, 1989-1994), Lecturer at University of Brighton (UK, 1994-1997), and Lecturer/Reader at Aston University (UK, 1997-2004). His educational background includes: MSc in Engineering from Harbin Institute of Technology, China (1980-1983) PhD from University of Warwick, UK (1986-1989) Professor Zhu's research centers on dynamic system modeling, identification, control, and simulation, with pioneering contributions to nonlinear control systems, robust control methodologies, and U-model based control frameworks. His work bridges theoretical advances with practical applications in robotics, renewable energy systems, and industrial automation, emphasizing model-free and adaptive control solutions for complex nonlinear dynamics. Analysis of his 2021-2025 publications reveals a dominant focus on robust control for uncertain nonlinear systems, with significant contributions to sliding mode control, multi-agent coordination, and cyber-physical security. His research increasingly integrates machine learning techniques (e.g., actor-critic reinforcement learning) while maintaining core expertise in optimization-based control algorithms applied to UAVs, robotic manipulators, and wind energy systems. His professional honors include: Chartered Engineer (CEng) Fellow of the Institution of Engineering and Technology (FIET) Fellow of the Higher Education Academy (FHEA) As an academic leader, Professor Zhu serves as President/Founder of the International Conference on Modelling, Identification and Control (ICMIC), Editor/Founder of Elsevier's Book Series on Emerging Methodologies in Modelling and Control, and University Ambassador for UK-China educational collaboration. His research group secures substantial grants in control theory applications, with ongoing projects in U-model control platforms and international partnerships. He leads the Control Systems research group at UWE, driving innovation in the U-control platform and its industrial applications. His team maintains strong international collaborations, particularly with Chinese institutions, and actively develops the Elsevier Book Series as a key publication channel for emerging control methodologies.