جست‌وجوی استاد و پژوهشگر

• Project Management
• Database Management
• Information Systems
• +۴ مورد دیگر

Luisa Domingues is an Assistant Professor in the Department of Information Science and Technology at ISCTE - University Institute of Lisbon. She serves as an Integrated Researcher at ISTAR-Iscte - Research Center in Information Sciences, Technologies and Architecture, specializing in Information Systems research. Her academic qualifications include a PhD in Information Science and Technology from ISCTE-IUL (2013). Her primary research interests focus on: Project Management : Methodologies, risk assessment, and knowledge sharing Database Systems : Management, interoperability, and standardization e-Government Solutions : Public sector information systems and shared services Ontological Frameworks : Particularly for architectural heritage documentation Educational Technologies : Blended learning approaches in higher education Her recent publications (2016-2024) demonstrate a strong focus on project management methodologies, knowledge transfer, and information systems in public sector contexts. Key thematic trends include PMBOK framework applications, agile methodology adoption challenges, data science project risks, and technological solutions for cultural heritage preservation. Most works employ case study research and empirical validation methods. Dr. Domingues maintains an active advising portfolio with 1 doctoral candidate and 36 master's students, primarily focusing on topics in project management, information systems, and data science applications. She has held significant academic leadership positions including: 4th Year Coordinator for Bachelor's in Computer Science and Business Management (2020-2027) Program Director for the same degree (2017-2019) At ISTAR-Iscte research center, she contributes to projects involving information systems architecture, data standardization, and knowledge management frameworks.

• Stochastic processes
• Biodemography
• Survival analysis
• +۴ مورد دیگر

David Steinsaltz is an Associate Professor of Statistics at the University of Oxford, affiliated with Worcester College. His research focuses on stochastic processes, biodemography, survival analysis, and Bayesian methods, with applications to aging, mortality, and population dynamics. He holds a PhD in probability theory from Harvard University, followed by postdoctoral work at UC Berkeley. His work bridges theoretical probability and applied statistics, addressing questions in demography, ecology, and epidemiology. Education: PhD in Mathematics (Probability Theory), Harvard University (1996); Postdoctoral Research, UC Berkeley (Departments of Demography and Statistics). Research interests include stochastic flows, Markov processes, and statistical methods for longitudinal data. He contributes to interdisciplinary projects, such as earthquake impact modeling and vaccine efficacy analysis. His collaborations span fields like biostatistics, ecology, and machine learning. He advises students on topics including survival analysis and demographic modeling.

Vasant Honavarمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Causal Inference
• +۶ مورد دیگر

Dr. Vasant Honavar is a Professor of Computer Science and Informatics at Pennsylvania State University, holding the Edward Frymoyer Endowed Chair. He serves as Director of the Center for Artificial Intelligence Foundations and Scientific Applications and Associate Director of the Institute for Computational and Data Sciences. His expertise spans artificial intelligence, machine learning, causal inference, and bioinformatics. Honavar has led over $60M in research grants and mentored 36 PhD students, 30 MS students, and numerous undergraduates. He is a Fellow of the AAAS and recipient of NSF Director’s Awards. Education: Ph.D., Computer Science and Cognitive Science, University of Wisconsin–Madison (1990) M.S., Computer Science, University of Wisconsin–Madison (1989) M.S., Electrical and Computer Engineering, Drexel University (1984) B.E., Electronics Engineering, Bangalore University (1982) Research Interests: His work focuses on machine learning, causal inference, knowledge representation, health informatics, and algorithmic fairness. Notable contributions include scalable algorithms for big data analytics and predictive modeling, as well as computational infrastructure for interdisciplinary science. Awards: Fellow, AAAS (2018) ACM Distinguished Member NSF Director’s Award for Superior Accomplishment (2013) Edward Frymoyer Endowed Chair (2013) Leadership: Honavar co-founded the Penn State Center for Artificial Intelligence and led the NIH-funded Biomedical Data Sciences Ph.D. program. He is a Co-PI of the North East Big Data Innovation Hub and serves on editorial boards of journals like IEEE/ACM Transactions on Computational Biology and Bioinformatics. Lab & Teams: Directs the Artificial Intelligence Research Laboratory and the Center for Big Data Analytics and Discovery Informatics, fostering collaborations across computer science, life sciences, and health sciences.

• Plasma Physics
• Nonlinear Dynamics
• Electromagnetic Theory
• +۳ مورد دیگر

Thomas M. Antonsen Jr. is a Distinguished University Professor at the University of Maryland, holding joint appointments in the Department of Electrical and Computer Engineering and the Department of Physics. He is affiliated with the Institute for Research in Electronics & Applied Physics (IREAP), Maryland Energy Innovation Institute, and the Institute of Physical Science and Technology. His research focuses on plasma physics, nonlinear dynamics, and high-power coherent radiation sources. Antonsen earned his B.S., M.S., and Ph.D. in electrical engineering from Cornell University (1973–1977) and has held visiting positions at institutions such as the University of California, Santa Barbara, and the École Polytechnique in France. **Education:** B.S., Electrical Engineering, Cornell University, 1973 M.S., Electrical Engineering, Cornell University, 1976 Ph.D., Electrical Engineering, Cornell University, 1977 **Research Interests:** Antonsen’s work spans magnetically confined plasmas, laser-plasma interactions, and advanced vacuum electronics. He has pioneered adjoint methods for optimizing beam-wave interaction systems and contributed to the development of high-power microwave amplifiers. His recent projects include wave chaos in complex systems and machine learning applications in nonlinear dynamics. **Awards & Honors:** James Clerk Maxwell Award (American Physical Society, 2023) IEEE Marie Sklodowska-Curie Award (2022) University of Maryland Distinguished University Professor (2017) IEEE Fellow (2012) **Teaching & Mentorship:** Antonsen teaches courses such as Physics 132 (Biophysics), Electrodynamics, and Plasma Physics. He mentors graduate students in plasma physics and vacuum electronics through his research groups at IREAP and the Bright Beams Collective. **Labs & Collaborations:** His research is supported by grants from the Department of Energy, NASA, and the Office of Naval Research. Key collaborations include the National Institute of Standards and Technology (NIST) and the European XFEL facility.

Jason Liمشاهده پروفایل

استادیار

• Fast Graph Algorithms
• Algorithm Design
• Minimum Cut Problems
• +۶ مورد دیگر

Jason Li is an Assistant Professor in the Department of Computer Science at Carnegie Mellon University's School of Computer Science. He teaches advanced algorithms courses including 15-754 Spectral Graph Theory (Spring 2025), 15-451 Design and Analysis of Algorithms (Fall 2024), and 15-850 Advanced Algorithms (Spring 2024). His research focuses on fast graph algorithms , particularly solving longstanding open problems through modern algorithmic techniques. Key research themes include preconditioning and locality , which serve as reductions from worst-case to well-behaved and local instances respectively. His work has produced breakthroughs in deterministic global minimum cut algorithms, all-pairs minimum cut (Gomory-Hu trees), and near-optimal parallel shortest path algorithms. Analysis of his recent publications reveals a consistent trend toward almost-linear time algorithms for fundamental graph problems, with significant contributions to dynamic graph algorithms, minimum cut variants, and parallel computation. His work frequently appears in top venues including STOC, FOCS, and SODA, often with multiple best paper recognitions. EATCS Distinguished Dissertation Award (2021) Best Paper Award at SODA 2024 Invited to HALG 2024 Invited to TALG and JACM for SODA 2024 paper Machtey Best Student Paper at FOCS 2019 Professor Li actively advises graduate students including Henry Fleischmann and George Li. His research is supported by collaborations with leading institutions and frequent invitations to present at major conferences. He maintains an open-door policy for CMU students and collaborators, though notes the high volume of research inquiries he receives weekly.

Gregory Paradisمشاهده پروفایل

استادیار

• Sustainable Forest Management
• Operations Research
• Forest Economics
• +۷ مورد دیگر

Gregory Paradis is an Assistant Professor in the Department of Forest Resources Management at the University of British Columbia (UBC) Faculty of Forestry. His research focuses on sustainable forest management, integrating operations research, mathematical optimization, and systems modeling to address complex interactions between ecosystems, industries, and society. He works with the FRESH Lab and collaborates with the Integrated Remote Sensing Studio, emphasizing ecological and economic integration in forest planning. Sustainable Forest Management Operations Research Forest Economics Data Science Risk Assessment GIS-based Methods His research spans forest inventory optimization, climate change adaptation strategies, wildfire risk modeling, and decision support systems for invasive species. He develops computational frameworks to enhance wood supply planning, carbon management, and ecological resilience. Recent work includes machine learning applications for fire safety in timber structures and automated road planning tools for wildlife conservation. Paradis’s publications highlight trends in applying optimization methods to sustainable forestry, with a focus on biodiversity, climate adaptation, and value chain innovation. He advocates for interdisciplinary approaches that bridge silviculture, industrial engineering, and data science to tackle emerging challenges in forest ecosystems. As an educator, he seeks motivated students with quantitative and creative problem-solving skills. His lab collaborates on remote sensing integration, risk assessment models, and policy-relevant forest management strategies, ensuring plans account for uncertainties like insect infestations or windthrow events.

• Computational Mathematics
• Numerical Analysis
• Tsunami Modelling
• +۵ مورد دیگر

Stephen Roberts is a Professor at the Australian National University (ANU) in the College of Science, Department of Mathematics. He is the lead developer of the ANUGA open-source hydrodynamic modeling software, which simulates dam breaks, floods, and tsunamis for governments and engineers. Roberts has made significant contributions to computational mathematics, particularly in numerical methods for partial differential equations, sparse grid data fitting, and finite element approximations scaling to millions of data points. MSc, Flinders University (1980) PhD, University of California, Berkeley (1985) His research interests include: Computational methods for shallow water wave equations Development of Python-based scientific computing frameworks Global sensitivity analysis and uncertainty quantification Adaptive mesh algorithms for fluid dynamics Recent publications focus on energy-stable numerical schemes, multiscale flood simulation, and convergence analysis in sensitivity methods. Roberts actively collaborates with environmental agencies and has led major computational science education programs at ANU. Stephen serves as Treasurer of the Computational Mathematics Group (ANZIAM) and leads projects in: Parallelization of hydrodynamic models CO2 leak detection via atmospheric measurements Optimization of sparse grid combinations His work combines theoretical advancements with real-world applications in disaster risk reduction and climate policy.

Michael J. Freedmanمشاهده پروفایل

استاد

• Distributed Systems
• Networking
• Security
• +۴ مورد دیگر

Michael J. Freedman is the Robert E. Kahn Professor of Computer Science at Princeton University and co-founder/CTO of Timescale. He received his Ph.D. from NYU’s Courant Institute and degrees from MIT. Current roles: Professor, Co-founder & CTO Affiliations: Princeton University, SNS Group, CITP Associate Education: Ph.D. (NYU), S.B./M.Eng. (MIT) His research spans distributed systems, networking, and security, with innovations like CoralCDN, DONAR, and Ethane. His work impacts decentralized content delivery, software-defined networking, and privacy-enhancing technologies. His recent publications address scalable fusion algorithms, GPU acceleration for data systems, and distributed GPU resource management. These works intersect with cloud infrastructure, network optimization, and security. Scientific honors include: Presidential Early Career Award for Scientists and Engineers (PECASE) Sloan Fellowship NSF CAREER Award Office of Naval Research Young Investigator Award Test of Time Award (Theory of Crypto Conference) ACM SIGOPS Mark Weiser Award He advises graduate students like Sam Ginzburg and Ashwini Raina, who joined Meta AI and Timescale post-PhD. His projects have secured substantial grants, including $110M Series C funding for Timescale. Key labs/teams: Princeton SNS Group Co-founder, Timescale (enterprise data platform) Co-founder, iobeam (IoT analytics, acquired by Timescale) Collaboration with FCC on Consumer Broadband Test Contributions to OpenFlow/SDN standardization

Yizhi Wangمشاهده پروفایل

استادیار

• Financialization
• Econometrics
• Climate Finance
• +۵ مورد دیگر

Dr Yizhi Wang, FHEA, is a Lecturer (Assistant Professor) in Finance at Cardiff Business School, Cardiff University, UK. He serves as Co-Director of the Cardiff Fintech Research Group and is the Editor-in-Chief of Elsevier’s Finance Research Letters . He is also Associate Editor for International Review of Financial Analysis and Research in International Business and Finance , and Guest Editor for International Review of Economics & Finance and Journal of Chinese Economic and Business Studies . Research Interests: Financialization and Econometrics Climate finance and carbon markets Cryptocurrency and FinTech indices Energy economics and risk management Green finance and ESG investing Behavioral and institutional finance Dr Wang has published extensively in world-leading journals such as Risk Analysis , European Journal of Operational Research , Journal of Economic Behavior & Organization , Energy Economics , and Journal of International Money and Finance . His work spans theoretical modeling, empirical econometrics, and policy-oriented studies. Scientific Awards: Shanghai Institute of International Finance and Economics Best Paper Award Teaching & Supervision: As a Fellow of the UK Higher Education Academy (FHEA), Dr Wang is module leader for undergraduate BS2514: Financial Markets and Institutions and postgraduate BST158: Big Data Analytics for Finance , earning consistently high teaching evaluations (4.8–4.9/5). He is available to supervise doctoral students in Financialization and has mentored graduates now employed at the University of Oxford, Morgan Stanley, and JPMorgan Chase. Labs & Research Groups: Co-Director, Cardiff Fintech Research Group Member, Cardiff Sustainable Finance Research Group Member, Environmental, Ecological, Extinction Accounting, Governance and Economics Research Group Member, Cardiff Business School Research Committee Dr Wang also curates multiple quarterly-updated indices (UCRY, ICEA, CBDCAI, NFTsAI, ACCC, FinTech Index) publicly available at https://sites.google.com/view/cryptocurrency-indices/home .

• intelligent systems
• high performance distributed computing
• resource management
• +۸ مورد دیگر

Professor Omer Rana serves as Professor of Performance Engineering and International Dean for the Middle East at Cardiff University's School of Computer Science and Informatics. He also holds the prestigious position of Cross-Council Research Director for the UK National Edge AI Hub, demonstrating his leadership in national research initiatives. Previously, he led the Complex Systems research group and served as Dean of International for the Physical Sciences and Engineering College at Cardiff University. Professor Rana is a Fellow of both the Learned Society of Wales and the Higher Education Academy, and serves on the Advisory Board of the Welsh Ethnic Minority Professors Initiative (WEMPI). His research expertise centers on the intersection of intelligent systems and high performance distributed computing, with particular focus on applying intelligent techniques to resource management in distributed systems. His scholarly contributions span edge computing, cloud computing, Internet of Things (IoT), artificial intelligence, cybersecurity, federated learning, privacy-preserving systems, and sustainable computing. Professor Rana has published extensively in top-tier journals and conferences, with recent work emphasizing practical applications in industrial automation, smart buildings, and sustainable computing solutions. Professor Rana's publication record demonstrates consistent leadership in edge computing and distributed systems research, with a growing emphasis on practical implementations across diverse application domains. His work bridges theoretical computer science with real-world technological challenges, particularly in industrial automation, smart environments, and sustainable infrastructure. Fellow of the Learned Society of Wales Fellow of the Higher Education Academy Professor Rana actively supervises postgraduate students and has secured significant research funding for projects related to edge computing, IoT, and distributed systems. His international collaborations span Europe, Asia, and the Middle East, reflecting his global influence in the field. He serves as a sought-after keynote speaker, workshop chair, and panel moderator at major international conferences including IEEE/ACM Utility and Cloud Computing (UCC) and IEEE Edge Computing. He leads multiple research initiatives, most notably as Cross-Council Research Director for the UK National Edge AI Hub, where he shapes national research directions in edge computing and AI. His work has practical applications across industrial automation, smart building management, electric vehicle infrastructure, and sustainable computing solutions.

Elisa Donatiمشاهده پروفایل

پژوهشگر

• Neuromorphic Engineering
• Biomedical Signal Processing
• Wearable Healthcare Technologies
• +۵ مورد دیگر

Dr. Elisa Donati is a researcher and independent group leader at the Institute of Neuroinformatics , affiliated with both the University of Zurich and the Swiss Federal Institute of Technology Zurich . Her work bridges neuromorphic engineering, biomedical signal processing, and wearable healthcare technologies, with a focus on creating brain-inspired systems for neuroprosthetics and rehabilitation. Affiliation: Institute of Neuroinformatics, University of Zurich & ETH Zurich Email: elisa@ini.uzh.ch Elisa’s research emphasizes developing neuromorphic signal processing strategies for wearable and embedded systems, enabling real-time closed-loop interactions with the nervous system. She specializes in translating neuroscience insights into energy-efficient technologies for digital health applications, including neuroprosthetics and personalized biomedical devices using neuromorphic hardware. Her recent publications (2024–2025) highlight advancements in gesture recognition via EMG and event-based systems, neuromorphic heart rate monitoring , and spiking neural network architectures . These works span biomedical signal processing, low-power computing, and adaptive algorithms, reflecting her commitment to robust, real-time, and brain-inspired solutions for healthcare. Elisa’s contributions to neuromorphic computing are evident in her exploration of heterogeneous population encoding , event-driven processing , and ultra-low-power microcontrollers . Her projects often integrate wearable systems with neuroscience, aiming to improve prosthetic control and rehabilitation technologies .

• Artificial Intelligence
• Machine Learning
• AI Alignment
• +۲ مورد دیگر

Michael Riegler is a Researcher at the AI Department, Simula Research Laboratory , focusing on interdisciplinary applications of Artificial Intelligence in healthcare, sports analytics, and multimedia systems. His work bridges Machine Learning , AI Alignment , and Applied AI across clinical and real-world domains. Key Affiliations: Simula Research Laboratory (AI Department Head) Research Themes: Explainable AI in medicine, multimodal data analysis, and AI-driven health monitoring Research Interests include: Developing AI/ML algorithms for medical imaging (e.g., polyp detection, embryo analysis) Addressing missing data challenges in healthcare through novel imputation techniques Creating multimodal virtual avatars for investigative interview training Designing edge AI systems for sports analytics and sustainable fishing Recent Publications highlight collaborations with institutions in Norway and globally, with a focus on: Medical Applications: Polyp segmentation, ECG analysis, and explainable models for disease detection Sports Analytics: Athlete performance prediction and soccer video processing Data Infrastructure: Lifelogging datasets (ScopeSense), semantic representation frameworks Labs & Teams include leadership in Simula’s AI Department and participation in projects like Medico Multimedia Task , ImageCLEF , and MediaEval workshops. His work emphasizes responsible AI innovation in public sectors and privacy-preserving systems for edge environments.

• Data Visualization
• Visual Data Analysis
• Data Science
• +۳ مورد دیگر

Univ.-Prof. Torsten Möller, PhD is a Professor at the University of Vienna and serves as Head of the Research Group Visualization and Data Analysis and Head of the Research Network Data Science. His work spans data visualization, visual analytics, and human-computer interaction, with a focus on biomedical, environmental, and societal data applications. Academic rank: Professor Research group: Visualization and Data Analysis Network: Data Science Email: torsten.moeller@univie.ac.at Research interests include: Visual data analysis for complex systems Interdisciplinary applications in climate science and medicine Human-computer interaction in data exploration Image processing and computer graphics Recent publication trends show expertise in: Visualizing climate change and pandemic data Multi-volumetric and network analysis Algorithmic transparency and user-centered design Interdisciplinary collaborations (e.g., astrophysics, medical imaging) Statistical and uncertainty visualization Design frameworks for visualization recommendation Teaching includes courses in: Computer graphics and visualization Image processing and analysis Human-computer interaction Data analysis projects Doctoral research seminars

Arunima Singhمشاهده پروفایل

استادیار

• Materials Science
• Computational Physics
• Surface Science
• +۷ مورد دیگر

Arunima Singh is an Assistant Professor in the Department of Physics at Arizona State University (ASU), with graduate faculty status in the Materials Science and Engineering Department. Her work focuses on computational materials discovery, leveraging first-principics simulations and data science to accelerate the design of materials for energy applications. She leads research at the Computational Materials Design Lab and co-leads a thrust at ULTRA, a DOE-Energy Frontier Research Center, and has received the 2023 Department of Energy Early Career Research Program Award. Ph.D., Cornell University (2014) B.Tech., Indian Institute of Technology Kharagpur (2009) Her research bridges materials science , surface science , and renewable energy , with a strong emphasis on 2D materials , nanostructures , and machine learning for materials design. She also explores electronic properties at material interfaces and phonon behavior at grain boundaries. The 2025–2023 articles highlight her expertise in heterostructures , wide bandgap materials , and data-driven discovery , with recurring themes in solar energy conversion , nanoengineering , and first-principles simulations . These works often involve machine learning and high-throughput workflows for materials optimization. Scientific Awards 2023 Department of Energy (DOE) Early Career Research Program Award She teaches courses such as Quantum Theory of Solids I , University Physics I: Mechanics , and research/dissertation sections (PHY 792, MSE 792, etc.). Her service includes expertise in computational modeling , solar materials , and nanoscience .

Tianyi Zhangمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۴ مورد دیگر

Tianyi Zhang is a Tenure-Track Assistant Professor in the Department of Computer Science at Purdue University, part of the College of Science. He leads the Human-Centered Software Systems Lab, focusing on AI-driven systems that synergize human expertise with machine intelligence to enhance programming productivity and software reliability. Prior to Purdue, he was a Postdoctoral Fellow at Harvard University under Dr. Elena Glassman and earned his Ph.D. from UCLA (2019) and B.Sc. from Huazhong University of Science and Technology (2013). Education: Ph.D. in Computer Science, University of California, Los Angeles (2019) Bachelor's in Computer Science, Huazhong University of Science and Technology (2013) Research Interests: His work spans Software Engineering, Human-Computer Interaction, and AI. Key areas include program synthesis, interactive debugging tools, autonomous driving system testing, and mitigating biases in AI models. He develops systems like Interpretable Program Synthesis and SQLucid to bridge human and machine intelligence. Recent Trends in Publications: Recent work emphasizes human-in-the-loop AI, including mixed-initiative systems for data wrangling (Dango), interactive program repair, and bias analysis in text representations (STILE). He also explores challenges in autonomous driving testing and LLM-based code generation errors. Awards & Grants: NSF Career Award (2024) Amazon Research Award Showalter Trust Research Award for pre-diabetes research $1.5M NSF grant for software supply chain security Best Paper Honorable Mentions at CHI and VAHC Advising & Teams: Supervises 12+ PhD/Master's students and 30+ research interns. Notable advisees include Bonan Kou (API misuse studies) and Yuan Tian (text-to-SQL systems). Collaborates with Harvard Medical School on healthcare data analysis. Labs & Initiatives: Directs Purdue's Human-Centered Software Systems Lab. Co-founded the Societal Impact Fellows program. Active in open-source projects like Examplore for API usage visualization and JShrink for Java debloating.