جست‌وجوی استاد و پژوهشگر

• Thermodynamics
• Energy Systems
• Cryogenics
• +۴ مورد دیگر

Andrew Rowe is a Professor in Mechanical Engineering at the University of Victoria (UVic), with affiliations to the Institute for Integrated Energy Systems (IESVic) and the Advanced Mechanical Research Laboratory (AMRLab). He holds a BEng from the Royal Military College of Canada, MASc and PhD from UVic, and is a licensed Professional Engineer (P.Eng). His research focuses on thermodynamics, energy systems, cryogenics, and heat transfer, with particular expertise in caloric cycles, hydrogen systems, and energy systems analysis. His research emphasizes decarbonization strategies, including hydrogen integration into natural gas networks, grid flexibility under electrification scenarios, and the optimization of magnetocaloric materials for energy-efficient cooling. AMRLab, led by Rowe, develops technologies for energy conversion, storage, and system optimization, addressing challenges in low-temperature thermal systems and integrated energy networks. Recent work explores the systemic impacts of electrification in transportation and buildings, leveraging open-source tools like the NExus Solutions Tool (NEST) for multi-scale energy-water-land system modeling. His projects often address regional energy challenges, such as British Columbia’s transition to renewable energy and grid infrastructure resilience. Key contributions include studies on gas system decarbonization via hydrogen blending, thermal-hydraulic modeling of energy systems, and experimental validation of magnetocaloric materials. Rowe’s publications highlight interdisciplinary approaches to climate mitigation, emphasizing the interplay between technological innovation and systemic policy frameworks.

Hector Geffnerمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Automated Planning
• +۵ مورد دیگر

Hector Geffner is an Alexander von Humboldt Professor at RWTH Aachen University, leading the Chair of Machine Learning and Reasoning. He specializes in automated planning, machine learning, and reasoning, with a focus on representation learning for acting and planning. His work bridges symbolic and model-based AI, emphasizing general policies and subgoal decomposition. Education & Background : PhD from UCLA (1989), prior roles at IBM Watson Research Center and Universidad Simón Bolívar. Former ICREA researcher and professor at Universitat Pompeu Fabra (2001–2022). Research Interests : Classical and probabilistic planning, reinforcement learning, knowledge representation, and applications in robotics. His ERC-funded RLeap project explores learning generalized policies and symbolic representations for effective decision-making. Teaching : Courses include 'Actions and Planning in AI' and 'Social and Technological Change', emphasizing interdisciplinary AI applications. Awards & Recognition : Alexander von Humboldt Professorship (2023), AAAI/EurAI Fellowships, and editor of influential works on Judea Pearl’s contributions to AI. Grants & Projects : Advanced ERC grant (2020–2025), Humboldt Foundation support, and RWTH funding for research on planning and reasoning. Labs & Teams : Heads the Chair of Machine Learning and Reasoning at RWTH, focusing on interdisciplinary research in AI, robotics, and planning algorithms.

• Conservation ecology
• Population ecology
• Landscape ecology
• +۳ مورد دیگر

Robert Fletcher is a Researcher at the University of Cambridge, affiliated with the Department of Zoology and the C-CLEAR Doctoral Training Partnership . His work focuses on applied ecology and conservation science, utilizing landscape and population ecology to address biodiversity challenges globally. Research Areas : Conservation biology, population ecology, landscape ecology, environmental informatics Collaborations : Partners in North America, Europe, Africa, and Southeast Asia Key Themes : Species extinction prevention, landscape conservation prioritization, and rapid biodiversity data delivery Email : rf497@cam.ac.uk Fletcher's interdisciplinary approach integrates fieldwork (e.g., Everglades endangered species, African elephants) with advanced modeling of habitat loss, fragmentation, invasive species, and climate change impacts. His recent work emphasizes: Drivers of species decline and recovery strategies Landscape management and restoration techniques Interdisciplinary collaborations with engineers, social scientists, and computer scientists His publications span topics like savanna ecosystem dynamics, community science applications, and conservation forecasting, reflecting a commitment to actionable science for global biodiversity preservation.

Dan Suciuمشاهده پروفایل

استاد

• Data Management
• Probabilistic Databases
• Query Optimization
• +۱۰ مورد دیگر

Dan Suciu is a Microsoft Endowed Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. His research focuses on data management, query optimization, probabilistic databases, parallel data processing, and information theory applications to databases. Awards : ACM Fellow (2011), American Academy of Arts and Sciences (2024), ACM SIGMOD Codd Innovation Award (2022), NSF Career Award (2001), Alfred P. Sloan Fellow (2001-2002). Research Trends : Recent work emphasizes cardinality estimation using Lp-norms, submodular width for query evaluation, dynamic query processing, and tensor program optimization. His publications highlight intersections between database systems and formal methods, driven by mathematical rigor. Key Collaborators : Mahmoud Abo Khamis, Dan Olteanu, Amir Shaikhha, Maximilian Schleich, Kyle Deeds, Moe Kayali. Advising : PhD students Gerome Miklau (2006), Christopher Re (2010), Paris Koutris (2016), Nilesh Dalvi (2008 runner-up), Yisu Remy Wang (2024 runner-up) have excelled in dissertation awards.

Uzi Vishkinمشاهده پروفایل

استاد

• Parallel Algorithms
• Computer Architecture
• Parallel Computing
• +۴ مورد دیگر

Uzi Vishkin is a Professor at the University of Maryland's Institute for Advanced Computer Studies (UMIACS) and Department of Electrical and Computer Engineering, with additional affiliation in the Department of Computer Science. His work focuses on parallel computing, including the PRAM-On-Chip vision to bridge parallel algorithms and hardware. He holds a D.Sc. from Technion (1981), M.Sc. and B.Sc. in Mathematics from Hebrew University (1975/1974). Research interests span parallel algorithms, PRAM (Parallel Random Access Machine) architecture, machine learning applications, and pattern matching. His PRAM-On-Chip project aims to create a coherent computing stack for many-core processors. Vishkin has contributed to theoretical foundations and practical implementations, including the XMT architecture and compiler. He has been recognized with ACM Fellow (1996), Highly Cited Researcher (2003), and National Academy of Inventors Fellow (2024). Awards highlight his pioneering work in parallel algorithms and computing systems. Teaching spans courses like Parallel Algorithms and Mathematical Foundations for Computer Engineering. He emphasizes parallel algorithmic thinking in education and has developed teaching materials for high school and university levels. Key projects include the XMT architecture, simulations, and tools for parallel programming. His work integrates algorithmic theory with hardware design to address programming challenges in many-core systems.

Bryan Wattsمشاهده پروفایل

استاد پژوهشی

• Conservation Biology
• Avian Ecology
• Coastal Ecosystems
• +۳ مورد دیگر

Bryan Watts is the Director of the Center for Conservation Biology and holds the Mitchell A. Byrd Research Professorship in Conservation Biology at William & Mary. He has a B.S. in Biology from Virginia Tech, an M.A. in Biology from William & Mary, and a Ph.D. in Ecology from the University of Georgia. His research focuses on avian conservation, wetland ecosystems, and the impacts of environmental changes on bird populations, particularly in coastal regions. He leads long-term monitoring programs for species like ospreys, red knots, and black rails, emphasizing habitat conservation and population dynamics. His work integrates citizen science, toxicology assessments, and climate adaptation strategies for vulnerable species. Watts collaborates with federal agencies and NGOs to inform policy decisions and habitat management. His projects include the Virginia Marshbird Survey, Whimbrel migration tracking, and assessments of contaminants in apex predators like peregrine falcons and bald eagles. Key areas of focus include the demographic responses of birds to fluctuating fish stocks, the effects of human activity on nocturnal roosting behaviors, and the development of conservation strategies for coastal wetlands under climate change. His research outputs span over three decades, addressing both applied and theoretical questions in conservation biology. Watts also advises on wildlife management policies, such as landfill use by bald eagles and wind energy siting to minimize bird collisions.

Jason Rightsمشاهده پروفایل

دانشیار

• Quantitative Methods
• Multilevel/Hierarchical Data Analysis
• Statistical Methods
• +۴ مورد دیگر

Dr. Jason Rights is an Associate Professor in the Department of Psychology within the Faculty of Arts at the University of British Columbia. His office is located in Kenny Room 2017 at 2136 West Mall, Vancouver, BC. He leads The Rights Lab, a quantitative methods research group dedicated to improving statistical practice in scientific research. His educational background includes: B.S. in Psychology and Mathematics from the University of North Carolina at Chapel Hill (2011) M.S. in Psychology (Quantitative Methods) from Vanderbilt University (2015) Ph.D. in Psychology (Quantitative Methods) from Vanderbilt University (2019) Dr. Rights' research focuses on addressing methodological complexities in multilevel/hierarchical data contexts where observations are nested (e.g., patients within clinicians, students within schools). His work spans several interconnected programs including developing R-squared measures for multilevel models, addressing issues with level-specific effects, exploring connections between multilevel and mixture models, and advancing latent variable model selection techniques. Analysis of his publication record reveals a consistent focus on methodological innovations in quantitative psychology, with particular emphasis on improving statistical techniques for hierarchical data structures. His work bridges theoretical statistical development with practical applications across psychology and related fields. Dr. Rights actively develops open-source software in R to implement his methodological contributions, making advanced statistical techniques accessible to researchers. The Rights Lab serves as the hub for his ongoing research program in quantitative methods development.

Ali Ghodsiمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Natural Language Processing
• +۳ مورد دیگر

Ali Ghodsi is a Professor at the University of Waterloo and Director of the Data Science Lab, with affiliations at the Vector Institute. His research spans machine learning, deep learning, and artificial intelligence, with applications in natural language processing, bioinformatics, and computer vision. His group develops theoretical frameworks and algorithms for analyzing large-scale datasets, focusing on neural network architectures, knowledge distillation, and model efficiency. Current projects include deep learning for identity control, computational antibody design, and generative AI/large language models. Ghodsi has authored influential tutorials on diffusion models, graph neural networks, and large language models. Notable research contributions include computational methods for de novo peptide sequencing from mass spectrometry data, green simulation-assisted reinforcement learning, and efficient natural language processing models. His lab maintains collaborations with industry partners including Google, Amazon, and Roche.

Bo Zhuمشاهده پروفایل

استادیار

• Computer Graphics
• Computational Physics
• Scientific Machine Learning
• +۳ مورد دیگر

Bo Zhu is an Assistant Professor in the School of Interactive Computing at Georgia Institute of Technology. His research focuses on computational approaches for complex physical systems, including fluid dynamics, topology optimization, and robotics control. He holds a Ph.D. from Stanford University and completed postdoctoral research at MIT CSAIL. He has been recognized with the NSF Career Award (2022) and multiple best paper awards at SIGGRAPH conferences. Education: B.E.-M.S., Software Engineering, Shanghai Jiao Tong University Ph.D., Computer Science, Stanford University Postdoc, EECS, MIT Research Interests: Develops numerical algorithms and machine learning techniques to simulate fluidic systems, soft materials, and multi-scale phenomena. His work emphasizes vorticity preservation, real-time simulation, and physics-based AI integration. Key Contributions: Pioneered Particle Flow Map (PFM) methods for fluid simulation, developed open-source libraries like SimpleX and PFM Hub, and contributed to projects like Genesis physics engine. Over 50 peer-reviewed publications in top venues (SIGGRAPH, NeurIPS, IEEE TVCG). Awards: NSF Career Award (2022) Best Paper Honorable Mention (SIGGRAPH 2025) Best Paper Award (SIGGRAPH Asia 2024) Grants & Projects: Leads NSF-funded research on Physical AI Design, collaborating with Sandia National Labs on real-time CFD solvers. Active in open-source software development for computational physics and graphics.

Christiane Barzمشاهده پروفایل

استاد

• Operations Research
• Revenue Management
• Stochastic Dynamic Optimization
• +۳ مورد دیگر

Christiane Barz is a Professor of Mathematics at the University of Zurich's Institute for Business Administration since 2016. Previously, she held academic roles at the UCLA Anderson School of Management, the Chicago Booth School of Business, and the Technical University (TU) Berlin. Her research focuses on stochastic dynamic systems, Markov decision processes, and their applications in revenue management. She emphasizes making mathematical tools accessible and practical for real-world problem-solving, particularly in optimizing decision-making under uncertainty. Education includes a degree in industrial engineering and a doctorate from the University of Karlsruhe (TH), Germany. Her career path includes postdoctoral research at the University of Chicago's Booth School of Business and roles as an Assistant Professor at UCLA. She combines academic excellence with balancing family life, advocating for gender equity in STEM fields. Her research explores risk-sensitive decision-making frameworks, dynamic pricing models for transportation and healthcare, and optimizing resource allocation in complex systems. Recent work includes applications in FlixBus, air cargo networks, and improving patient admission scheduling in hospitals. Barz's teaching philosophy prioritizes demystifying mathematics for students, encouraging critical engagement rather than fear of complexity. She collaborates with industry partners to apply operations research methods to real-world challenges, emphasizing both theoretical rigor and practical relevance.

Jason Schweinsbergمشاهده پروفایل

استاد

• Probability Theory
• Stochastic Processes
• Mathematical Biology
• +۵ مورد دیگر

Jason Schweinsberg is a Professor in the Department of Mathematics at the University of California, San Diego (UCSD), where he has been a faculty member since Fall 2004. His academic journey began with a Ph.D. in Statistics from the University of California, Berkeley in 2001, followed by a three-year NSF Postdoctoral Fellowship at Cornell University. His research focuses on probability theory with applications to evolutionary biology and population genetics. Schweinsberg's work centers on stochastic processes involving coalescence, branching Brownian motion, and their connections to biological phenomena. He has made significant contributions to understanding population models undergoing selection, cancer evolution, and spatial mutation processes. Recent publications reveal a strong emphasis on coalescent theory (particularly Λ-coalescents and nested coalescents), branching processes with absorption, and spatial evolutionary models. His work often bridges rigorous mathematical analysis with biological applications, especially in population genetics and cancer modeling. The 15 most recent articles demonstrate consistent focus on asymptotic analysis of stochastic processes, genealogical structures, and mutation dynamics in evolving populations. Scientific Awards: Fellow of the Institute of Mathematical Statistics NSF Postdoctoral Research Fellowship While specific student names aren't listed in the source material, Schweinsberg has delivered numerous lecture series at international institutions including the Indian Institute of Science (Bangalore), Centre de Recherches Mathématiques (Montreal), and the Isaac Newton Institute (Cambridge), indicating active mentorship and academic leadership. His collaborations span multiple institutions, with frequent co-authorship with researchers like Julien Berestycki, Nathanaël Berestycki, and Rick Durrett. Though no formal lab structure is mentioned, Schweinsberg participates in interdisciplinary research communities through workshops at institutions like BIRS (Banff International Research Station), where he presented on mutation patterns in spatially structured populations in May 2025. His work connects probability theory with biological applications through sustained collaborations across mathematics, statistics, and computational biology fields.

Michael Gubanovمشاهده پروفایل

استادیار

• Big Data Management
• Scalable Machine Learning
• Knowledge Graphs
• +۱ مورد دیگر

Dr. Michael Gubanov is an Assistant Professor in Computer Science at Florida State University and founder of BigLab!, specializing in scalable data systems for scientific knowledge discovery. Research: Develops hybrid polystore/LLM systems for cancer research (CancerKG.ORG), COVID-19 knowledge graphs (COVIDKG.ORG), and aging studies (AgingGraph.ORG). Focuses on metadata classification, tabular embeddings, and web-scale knowledge extraction. Funding: Secured $1.8M+ from NSF, Florida Department of Health, and AWS for projects bridging data management and AI. Awards: IEEE ICDE Best Paper (2017), ACM SIGMOD Research Highlight (2018), CACM Research Highlight (2020). Elected to Sigma Xi. Education: PhD in Computer Science (University of Washington); Postdoc at MIT CSAIL.

Hannah Dugdaleمشاهده پروفایل

دانشیار

• Evolutionary Biology
• Ecology
• Genetics
• +۳ مورد دیگر

Prof. Hannah Dugdale is an Associate Professor at the Faculty of Science and Engineering , University of Groningen, leading the Dugdale group - Evolutionary Medicine . Her research bridges evolutionary genetics, animal behavior, and microbiome ecology, focusing on wild vertebrate populations. University: University of Groningen School: Faculty of Science and Engineering Academic Rank: Associate Professor Research Interests: Telomere dynamics and aging Gut microbiome evolution Cooperative breeding mechanisms Environmental stress impacts Population genetics Behavioral ecology Article Trends: Her recent work examines telomere length as a biomarker for survival, gut microbiome effects on fitness, and climate/predation impacts on avian populations. Studies often employ long-term ecological datasets and genomic analyses. Grants & Projects: Involved in 14 funded projects, including environmental buffering hypotheses and microbiome research. Collaborates extensively with Prof. Terry Burke (University of Sheffield) and Prof. Jan Komdeur (University of Groningen). Labs & Teams: Leads the Dugdale group in Evolutionary Medicine, collaborating with international teams on vertebrate population studies. Research spans fieldwork, genetic analysis, and ecological modeling.

Bo Xiongمشاهده پروفایل

پژوهشگر

• Machine Learning
• Knowledge Graphs
• Graph Neural Networks
• +۴ مورد دیگر

Bo Xiong is a researcher at the University of Stuttgart in the Analytic Computing group. His research focuses on machine learning and knowledge graphs , with a particular emphasis on geometric embeddings and hyperbolic neural networks. His research interests include: Knowledge graph embeddings Hyperbolic and pseudo-Riemannian geometry in AI Temporal knowledge graph reasoning Structured multi-label prediction Recent publications highlight his work on geometric relational embeddings, complex query answering, and temporal fact reasoning using advanced manifold-based techniques.

Bryan Kian Hsiang Lowمشاهده پروفایل

دانشیار

• Probabilistic Machine Learning
• Bayesian Optimization
• Multi-Agent Systems
• +۴ مورد دیگر

Bryan Kian Hsiang Low serves as Associate Professor in the Department of Computer Science at the National University of Singapore's School of Computing, while simultaneously holding leadership positions as Director of AI Research at AI Singapore and Deputy Director of the NUS AI Institute. His academic journey includes a B.Sc. (2001) and M.Sc. (2002) in Computer Science from NUS, followed by a Ph.D. in Electrical & Computer Engineering from Carnegie Mellon University (2009). His research spans probabilistic machine learning, multi-agent systems, and trustworthy AI, with particular focus on Bayesian optimization , federated learning , and data-efficient methodologies . The Low Lab develops frameworks for collaborative AI, automated machine learning, and AI applications in scientific domains through the Group of Learning and Optimization Working in AI (GLOW.AI), which maintains a multi-disciplinary approach bridging computer science, mathematics, and engineering disciplines. Analysis of his recent publications reveals a consistent emphasis on data valuation , privacy-preserving collaborative learning , and robust optimization techniques , with increasing integration of large language models into his research framework. His work demonstrates strong theoretical foundations coupled with practical applications in computational sustainability and robotics. Andrew P. Sage Best Transactions Paper Award (2006) NUS Overseas Graduate Scholarship (2004-2009) Faculty Teaching Excellence Award (2017-2018) IEEE RAS Distinguished Lecturer (2019) World Economic Forum Global Future Councils Fellow (2016-2018) Dr. Low actively mentors PhD students including Rachael Sim, Quoc Phong Nguyen, and Zhongxiang Dai, while leading major initiatives like the AI Phenome Platform for plant breeding optimization. His research group GLOW.AI operates at the intersection of theory and practice, with strong industry engagement through AI Singapore. Current projects focus on scalable AI systems for scientific discovery and developing frameworks for equitable collaborative machine learning with robust privacy guarantees.