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Ang LiView profile

Associate Professor

• Architectural Design
• Material Experiments
• Architectural Obsolescence
• +4 more

Ang Li is an Associate Professor with Tenure at the School of Architecture at Northeastern University. Her research focuses on architectural obsolescence, preservation strategies, and the role of material reuse in contemporary design practices. She holds a BA in Architecture from the University of Cambridge and a M.Arch. from Princeton University, where she also edited Pidgin Magazine. Before joining Northeastern, she served as a Visiting Artist at the School of the Art Institute of Chicago and a Peter Reyner Banham Fellow at the University at Buffalo. Her design practice emphasizes material experimentation and built interventions, exploring themes such as adaptive reuse and second-hand material economies. Notable projects include installations at the Venice Architecture Biennale and exhibitions at the Lisbon Architecture Triennale. Her work has been featured in journals like Log, Clog, and Thresholds, and she has collaborated with architectural firms including Adjaye Associates and Allies and Morrison Architects. Ang Li teaches courses such as ARCH 7130 Masters Research Studio and has been recognized for her innovative approaches to architectural education and sustainability. Her research bridges theoretical inquiry with practical applications, addressing pressing issues in architectural design and urban resilience.

Allen BradleyView profile

Professor

• Hydrology
• Hydrometeorology
• Flood Hydrology
• +2 more

Allen Bradley is a Professor in the Department of Civil and Environmental Engineering at the University of Iowa's College of Engineering. He serves as the Departmental Executive Officer and is affiliated with IIHR—Hydroscience and Engineering and the Iowa Flood Center. His research focuses on hydrology, hydrometeorology, flood hydrology, and extreme rainstorm analysis. Bradley joined the faculty in 1994 and holds a PhD from the University of Wisconsin-Madison (1992), an MS from Stanford University (1984), and a BS from Virginia Tech (1983). Bradley's work emphasizes flood risk forecasting, hydroclimatology, and the verification of hydrologic forecasts. He has contributed to projects like the Iowa Watersheds Project, which integrates community engagement with flood mitigation strategies. His expertise spans hydrometeorological data analysis, including the use of radar and numerical models for improving precipitation and streamflow predictions. He is a member of professional societies including the American Geophysical Union and American Society of Civil Engineers. Bradley's research also addresses groundwater quality, salinization processes in semi-arid regions, and the application of statistical methods like PCA for environmental data analysis. His publications span over three decades, reflecting a sustained focus on advancing hydrological science for practical water resource management.

Jan-Joachim RückmannView profile

Professor

• Non-linear Programming
• Mathematical Optimization
• Semi-infinite Programming
• +12 more

Jan-Joachim Rückmann is a Professor in the Department of Informatics at the University of Bergen, Norway. His research lies at the intersection of mathematical optimization, nonlinear programming, and their applications in finance and engineering. He is actively engaged in theoretical and applied research, with a focus on generalized semi-infinite programming and complementarity constraints. His research interests include non-linear programming, parametric optimization, robust optimization, data envelopment analysis, and the application of topological and differential geometric methods to optimization problems. He has made significant contributions to the theory of stability and stationarity in mathematical programs with complementarity constraints (MPCC) and semi-infinite programming. The recent publications highlight a strong trend in theoretical optimization, particularly in MPCC, stability of stationary points, and generalized semi-infinite programming. Several works explore duality, constraint qualifications, and convexification techniques. There is also interdisciplinary work applying optimization to mathematical finance, gas explosion modeling, and computational social choice using machine learning. While no scientific awards are listed, his extensive publication record in top journals such as Mathematical Programming , SIAM Journal on Optimization , and Optimization underscores his scholarly impact. He has served as guest editor for multiple special issues on parametric optimization, reflecting his leadership in the field. Though no students or grants are explicitly mentioned, his long-standing collaboration with researchers such as H. Günzel, D. Hernandez Escobar, F. Guerra Vazquez, and O. Stein indicates active research advising and team involvement. His work with the European Space Agency on trajectory analysis and space applications also points to applied research collaborations. Dr. Rückmann is affiliated with the Department of Informatics, contributing to both theoretical advances and practical applications of optimization methods. His work integrates deep mathematical analysis with real-world problem solving in engineering and economics.

Gabriel SpadonView profile

Assistant Professor

• Spatiotemporal Analytics
• Time-Series Forecasting
• Complex Network Mining
• +13 more

Gabriel Spadon is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He leads the MAPS Lab (Modeling and Analytics on Predictive Systems), focusing on spatiotemporal analytics, network science, and machine learning. His work is part of the Big Data Analytics, AI & Machine Learning research cluster. Education: PhD in Computer Science and Computational Mathematics, University of São Paulo (USP), 2021 MSc in Computer Science and Computational Mathematics, University of São Paulo (USP), 2017 BSc in Computer Science, São Paulo State University (UNESP), 2015 Research Interests: Gabriel Spadon’s research lies at the intersection of data science, machine learning, and geoinformatics . He specializes in spatiotemporal forecasting , complex network mining , and trajectory modeling , particularly in maritime and urban environments. His work integrates physics-informed AI, graph-based learning, and environmental data to improve predictive accuracy and decision support. Key domains include vessel movement prediction, environmental monitoring, and urban intelligence. Recent Publication Trends: His latest publications (2024–2025) focus on maritime mobility , using AIS data and physics-informed neural networks (PINNs) for vessel trajectory forecasting. He applies deep learning, clustering, and probabilistic fusion to model complex, multi-modal movement patterns. Other themes include fishing activity detection, port network analysis, and AI for public health and climate matching. Scientific Awards: Advising and Grants: Dr. Spadon supervises multiple graduate students and research assistants in the MAPS Lab, working on mobility data mining, physics-informed AI, and geospatial analytics. His research is supported by major grants from the Ocean Frontier Institute (OFI) , Canadian Space Agency (CSA) , Mitacs , and IMT Atlantique . He leads projects such as smartWhales , AISViz , and Physics-Informed Mobility Forecasting , involving international collaborations with institutions in Brazil and France. Labs and Teams: He is the Director of the MAPS Lab , which develops cutting-edge computational methods for predictive systems. The lab collaborates with the smartWhales initiative (DHI, WSP, Fisheries and Oceans Canada), MERIDIAN , and the Institute for Big Data Analytics . His team includes PhD and MCS students working on AI, data mining, and signal processing for real-world ocean and urban challenges.

Louis BoucherieView profile

Researcher

• Network Science
• Machine Learning
• Artificial Intelligence
• +3 more

Louis Boucherie is a Postdoctoral Researcher at the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His work focuses on network science, machine learning, and mathematical modeling. Current Role: Postdoc at DTU Department: Applied Mathematics and Computer Science Research Group: Cognitive Systems His research explores the intersection of complex networks, social network analysis, and artificial intelligence. Notably, he has developed methods for modeling citation networks and analyzing polarization patterns in social networks. Boucherie's recent publications include a PhD thesis on the geometry of complex networks and peer-reviewed conference papers on topics such as signed relational modeling and policy analysis in economic systems. Contact: louibo@dtu.dk | Website

James HendersonView profile

Researcher

• Natural Language Processing
• Machine Learning
• Deep Learning
• +3 more

James Henderson is a Senior Researcher at Idiap Research Institute where he heads the Natural Language Understanding group. He currently serves as Action Editor for Transactions of the Association for Computational Linguistics (TACL) and was recently awarded an ERC Advanced Grant for his project 'Interpretable Beliefs and Programmable Knowledge with Bayesian Attention in Large Language Models' (BALM). Previously, Henderson held positions as Chargé de Cours at University of Geneva's Department of Computer Science and Principal Scientist at Xerox Research Centre Europe (now Naver Labs Europe). Henderson's research focuses on machine learning methods for natural language processing, with pioneering work on recurrent neural networks for syntactic and semantic parsing. His current investigations include representation learning for language semantics, graph-to-graph deep learning models, entity induction, and variational-Bayesian attention-based representation learning. His research bridges Bayesian inference, transformer architectures, and structured prediction for NLP tasks. His publication portfolio demonstrates consistent contributions to core NLP methodologies, with recent emphasis on transformer optimization, Bayesian neural methods, efficient model architectures, and graph-based language representations. Research frequently appears in top venues including ACL, EMNLP, ICLR, and NeurIPS. Honors: ERC Advanced Grant (2023) Henderson leads the Natural Language Understanding group at Idiap, currently recruiting PhD students and postdoctoral researchers for his ERC project. He obtained his PhD and MSc from University of Pennsylvania and BSc from Massachusetts Institute of Technology, all in computer science.

• Causality
• Statistical Machine Learning
• Probabilistic Modeling
• +12 more

Bryon Aragam is an Associate Professor and Topel Faculty Scholar at the Booth School of Business , University of Chicago . His work bridges causality , statistical machine learning , and probabilistic modeling , with applications in AI systems like ChatGPT and DALL-E. Key research themes include: Causal Structure Learning : Extracting latent causal graphs from multimodal data using nonparametric methods. Deep Generative Models : Analyzing overparametrization and variational inference for representation learning. Latent Variable Discovery : Using Markov boundaries and convex subset lattices to uncover hidden dependencies. Algorithm Design : Developing scalable methods like DAGMA for DAG learning and theoretical guarantees for GES/PC algorithms. His paper trends reveal a focus on nonparametric statistics , graphical models , and neural network theory , with recent work on transformer memory dynamics and identifiability in deep latent models . Papers frequently appear in top venues like NeurIPS , JMLR , and AOS , emphasizing theoretical rigor and practical validation.

• Data Management
• Big Data Systems
• Knowledge Graphs
• +12 more

Philippe Cudré-Mauroux is a Full Professor in the Department of Computer Science at the University of Fribourg, affiliated with the Faculty of Science and Medicine. His research spans data management, big data systems, knowledge graphs, semantic web, and human-AI collaboration. His primary research interests include Data Management , Big Data Systems , Knowledge Graphs , Time Series Analytics , Database Systems , Human-AI Collaboration , and Machine Learning for Data Cleaning . His work integrates theoretical database research with practical applications in smart cities, social media, and healthcare analytics. The recent publications reflect a strong trend toward knowledge graph embeddings , large language models for data quality , time series benchmarking , and human-in-the-loop systems . His research combines symbolic and neural methods, emphasizing schema awareness, explainability, and real-world deployment. He actively supervises numerous PhD and Master’s students and collaborates widely across institutions. His group contributes to open-source tools and benchmarking frameworks for database and AI systems.

Lauren M. ChildsView profile

Associate Professor

• Mathematical Biology
• Infectious Disease Modeling
• Computational Biology
• +3 more

Lauren M. Childs is an Associate Professor in the Department of Mathematics at Virginia Tech , where she has been since 2016. She serves as the Cliff and Agnes Lilly Faculty Fellow and Associate Director of the VT Center for the Mathematics of Biosystems . Her research focuses on mathematical and computational models for biologically motivated questions, particularly in infectious diseases , immunology , and ecology . Education: Ph.D. in Applied Mathematics, Cornell University (2010) M.S. in Applied Mathematics, Cornell University (2007) B.S. in Mathematics and Chemistry, Duke University (2004) Her work combines analytical and numerical approaches to address challenges in disease transmission dynamics , including malaria , dengue , and COVID-19 . She has developed models for multi-scale epidemiological systems , explored the role of mosquito life history traits in vector control, and investigated human behavioral responses in epidemic settings. Key contributions include: Scientific Awards: 2023-2024 Michler Award Cliff and Agnes Lilly Faculty Fellowship NSF CAREER Award Grants and Collaborations: NSF EEID Grant VT Pandemic Preparedness Destination Area Funding VT Seed Grant for transdisciplinary research

• Micro-Electrical Discharge Machining (micro-EDM)
• Machine Learning Applications in Manufacturing
• Edge Computing
• +5 more

Zequan Yao is a doctoral researcher at KU Leuven within the Manufacturing Processes and Systems (MaPS) unit. His work focuses on advancing micro-EDM and nano-manufacturing processes through digital monitoring and machine learning integration. Research Emphasis: Surface quality prediction, edge computing, and deep learning for precision machining Key Technologies: RF radiation sensing, multiphysical process analysis, and adaptive pulse classification Collaborations: Works with researchers like Ming Wu, Jie Qian, and Dominiek Reynaerts Recent publications highlight trends in machine learning applications for manufacturing diagnostics, CFRP composite machining challenges, and RF-based process signatures . His work bridges traditional machining with intelligent systems for quality control. Labs/Teams: Micro- & Precision Engineering group at KU Leuven Future Directions: Expanding edge computing for in-process optimization and interpretable AI frameworks

Yuan DingView profile

Professor

• Software Systems
• Distributed Systems
• Large-scale Software Reliability
• +3 more

Professor Yuan Ding is a Canada Research Chair in Systems Software at the University of Toronto , affiliated with the Faculty of Applied Science and Engineering and serving as Chair of the Computer Engineering Group . He joined the Department of Electrical and Computer Engineering in 2013 and was promoted to Professor in 2023. Education: PhD in Computer Science (2012) from University of Illinois at Urbana-Champaign, B.E. in Computer Science and Engineering and B.S. in Mathematics (2006) from Beihang University Research Interests focus on improving the reliability and performance of large-scale software systems , particularly distributed and systems software. His work addresses critical issues like upgrade failures , log compression , performance profiling , and non-intrusive debugging . Key methodologies include empirical studies of real-world failures, static analysis, and runtime optimization. Publication Trends show expertise in operating systems , distributed systems , and software engineering . Recent work includes μSlope (log compression) and Relational Debugging (root-cause analysis), while earlier studies investigated JVM performance and distributed failure patterns . Scientific Awards : Canada Research Chair (2023), McCharles Prize (2018), NetApp Faculty Fellowship (2013–2016), ACM SIGSOFT Distinguished Paper (2011), Saburo Muroga Fellowship (2006–2007), University Gold Medal (2005) Advising & Collaborations : Supervised 15+ PhD and Master’s students, including Yongle Zhang (Purdue), Xu Zhao (Facebook), and Xiang Ren (Northeastern). Founded startup YScope to commercialize log compression tools like CLP , now deployed at Uber.

Olaf PoschView profile

Professor

• Macroeconomics
• Dynamic Stochastic General Equilibrium (DSGE) Models
• Asset Pricing
• +12 more

Olaf Posch is a Professor of Economics, specializing in Methods of Economics, at the Department of Economics within the Faculty of Economics and Social Sciences at the University of Hamburg. His research integrates macroeconomic theory, econometrics, and computational methods to analyze dynamic equilibrium models, asset pricing, and policy design. Research Interests: His primary research areas include Macroeconomics, Dynamic Stochastic General Equilibrium (DSGE) models, Asset Pricing, Monetary and Fiscal Policy, Econometrics, Structural Estimation, and Continuous-Time Modeling. He investigates the implications of uncertainty, rare disasters, heterogeneous agents, and public policy on macroeconomic dynamics and financial markets. Recent Research Trends: His recent publications demonstrate a consistent focus on advancing the estimation and solution methods of macroeconomic models. He explores the role of uncertainty (e.g., Peso problems, Poisson shocks), the impact of fiscal policy and debt maturity (e.g., FTPL), and the importance of risk in model approximations. His work often bridges theoretical innovation with empirical application, using mixed-frequency data and structural estimation techniques. Scientific Grants: DFG Project No. 268475812: 'Dynamic equilibrium models' – Developing a continuous-time New Keynesian model. DFG Project No. 446166239: 'Fiscal Sustainability'. DFG Project No. 510995673: 'Fiscal policy in models with heterogeneous agents'. Advising and Grants: While specific advisees are not listed, he frequently collaborates with co-authors on research projects and working papers, indicating an active role in mentoring junior researchers. He is the principal investigator on multiple DFG-funded projects, demonstrating significant grant acquisition and research leadership. Laboratories and Research Teams: He leads a research team at the University of Hamburg, including Corinna Kienle, Réka Rátfai, and Josie Oetjen, focused on dynamic equilibrium models and macroeconomic policy analysis.

Tong WuView profile

Assistant Professor

• Applied Mathematics
• Numerical Analysis
• Computational Mathematics
• +9 more

Tong Wu is an Assistant Professor in the Department of Mathematics at The University of Texas at San Antonio (UTSA), part of the College of Sciences. He specializes in applied and computational mathematics, with a focus on developing robust numerical methods for nonlinear partial differential equations and data assimilation. Ph.D. in Mathematics, Tulane University B.S. in Mathematics and Applied Mathematics, Xi'an Jiaotong University His research lies at the intersection of numerical analysis, fluid dynamics, and mathematical biology. He develops innovative numerical schemes—such as adaptive moving mesh central-upwind and upwind methods—for solving hyperbolic systems, shallow water equations, chemotaxis models, and stiff ordinary differential equations. His work emphasizes properties like well-balancing, positivity preservation, and high resolution in capturing shocks and complex structures. His publications reveal a consistent focus on improving numerical accuracy and efficiency in modeling physical and biological systems. Key themes include the development of algorithms for shallow water models with wet/dry fronts and friction, moving-water equilibria preservation, and adaptive mesh refinement. He also contributes to computational epidemiology by designing bipartite network generation algorithms that preserve joint degree distributions, enabling realistic modeling of disease spread. Tong Wu has not received any explicitly mentioned scientific awards or fellowships in the provided text. He is actively involved in teaching core mathematics courses such as Calculus I–III, Linear Algebra, and Scientific Computing. While no formal advising roles or grant funding are listed, his collaborative research with leading figures in applied mathematics indicates an active academic trajectory. He does not appear to lead a formal lab or research team, but his work is deeply collaborative and computational in nature.

Gary CantrellView profile

Academic

• Digital Forensics
• Computer Security
• Software Engineering
• +3 more

Gary Cantrell, PhD, is an Associate Teaching Professor and Director of Computing Programs (Portland) at Northeastern University’s Khoury College of Computer Sciences . Since summer 2022 he has been based at the Roux Institute in Portland, Maine, where he teaches in the Align Master’s program and delivers electives in software engineering, security, and digital forensics. Education PhD, Computer Science and Engineering (Digital Forensics emphasis), Mississippi State University, 2012 MS, Computer Science (Computer Security & Software Engineering emphasis), Mississippi State University, 2004 BS, Computer Science, Mississippi State University, 2000 Research Interests Dr. Cantrell’s work centres on digital forensics , computer security , digital forensics education , steganography , and software engineering . He has designed novel digital triage process models that blend automation with human expertise to accelerate evidence prioritisation for law-enforcement agencies, and he investigates both offensive (data-hiding) and defensive (data-recovery) techniques in contemporary file systems and mobile devices. Recent Publication Themes His 2019–2020 papers foreground pedagogical innovation —introducing data-carving and data-recovery concepts through real-world mobile-device dumps and classroom-friendly destruction paradigms—while earlier works (2011–2013) concentrate on automated crime-specific triage and steganographic concealment within FAT32 and office-document structures. Awards & Recognition CSIS Teacher of the Year 2019–2020, Southern Utah University Honorary Sheriff Award, Utah Sheriff’s Association, October 2015 Teaching, Advising & Outreach Across three universities he has taught core CS courses (Java, C++, algorithms, operating systems), digital-forensics sequences (file-system analysis, mobile forensics, tool mastery), and capstone projects. He has also led middle- and high-school cybersecurity camps in Portland and delivered professional workshops for law-enforcement officers (2004-2009). While no individual student names are listed in the provided text, his tutorials, quizzes, and mentorship resources are openly shared with all advisees. Labs & Professional Memberships At Dixie State University he founded and directed the DSU Computer Crime Institute (DSU-CCI) , offering forensic casework support and training to regional agencies. He maintains memberships in the Association for Computing Machinery (ACM) and the American Society of Digital Forensics and eDiscovery .

• Combustion Science
• Hydrogen Technology
• Thermal Energy Storage
• +5 more

Dr. Michael Evans is a Program Director at UniSA STEM , University of South Australia, with a focus on combustion science, thermal energy storage, and machine learning applications. He actively supervises research degrees and contributes to media commentary on clean energy systems. University: University of South Australia Academic Unit: UniSA STEM / Future Industries Institute Email: Michael.Evans@unisa.edu.au His research spans combustion dynamics in low-oxygen environments, hydrogen jet autoignition, and thermal energy storage systems for solar power and refrigeration. Key interests include flame acceleration , phase change materials , and structural integrity under vibration . Recent publications highlight his work on bluff-body obstructions in hydrogen combustion (2024), machine learning for flame diagnostics (2024), and fatigue life analysis of latent heat storage tubes (2025). He collaborates with institutions like Stanford University, University of Adelaide, and CRC Press. Grants from the Australian Research Council (ARC), AOARD, and National Computational Infrastructure (NCI) support his projects. Notable affiliations include the Future Industries Institute and the UniSA STEM college.