Jonathan Ragan-Kelley is the Esther and Harold E. Edgerton Assistant Professor of Electrical Engineering & Computer Science at MIT and an Assistant Professor of EECS at UC Berkeley. He leads the Visual Computing group at CSAIL, focusing on high-efficiency visual computing, compilers, and architectures for image processing, machine learning, and 3D rendering. His research bridges systems, compilers, and hardware design, emphasizing scalable solutions for computational challenges. Education: PhD in Computer Science from MIT (2014), postdoc at Stanford University, and visiting researcher at Google. He co-created the Halide language and has developed multiple domain-specific languages (DSLs) and compiler systems. Research interests include compiler optimization, scheduling languages (e.g., Exo), and efficient computing frameworks. He has received awards such as the NSF CAREER Award and ACM SIGGRAPH’s Significant New Researcher Award. Awards: ACM SIGGRAPH Award, NSF CAREER, Intel Outstanding Researcher Award Key Contributions: Halide compiler framework, Exo scheduling language, machine learning acceleration techniques Labs/Teams: Visual Computing at MIT CSAIL
جستوجوی استاد و پژوهشگر
استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۷۳۶-۷۵۰ از ۱۱٬۵۷۵ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
- Machine Learning
- Data Mining
- Bioinformatics
- +۱۱ مورد دیگر
Andrea Passerini is a Full Professor in the Department of Information Engineering and Computer Science at the University of Trento, Italy, where he also serves as Coordinator of the PhD programme in Information Engineering and Computer Science (Ministerial Decree 45/2013). His academic footprint spans multiple departments including Mathematics, Sociology, Cellular Biology, and Industrial Engineering, reflecting deep interdisciplinary engagement across computational sciences and life sciences. His research centers on Machine Learning and Data Mining with specialized expertise in Neuro-Symbolic AI , Probabilistic Reasoning , and Statistical Relational Learning . He pioneers methods for graph-based learning, medical AI applications, and explainable systems, with significant contributions to bioinformatics (particularly RNA-protein interactions) and healthcare diagnostics. His work bridges theoretical rigor with practical implementations in critical domains. Analysis of his 2025 publications reveals dominant trends in neuro-symbolic integration for graph data, human-AI collaboration in medical decision-making, and robust recommender systems. His research increasingly focuses on interpretable AI for high-stakes applications like surgical planning and physician support, while advancing foundational techniques in graph neural networks and concept-based modeling. As PhD programme Coordinator, Professor Passerini mentors doctoral candidates across AI and computer science disciplines. His collaborative network extends to medical researchers at CIBIO (Cellular, Computational and Integrative Biology department) and industrial partners, though specific lab structures aren't documented in available materials. Current projects emphasize medical AI validation, temporal network modeling, and LLM integration with structured reasoning frameworks.
Leandros Tassiulas is the John C. Malone Professor of Electrical Engineering at Yale University, with additional appointments in Computer Science. His career spans faculty positions at the University of Thessaly, University of Maryland, University of Ioannina, and Polytechnic University. A Fellow of both IEEE (2007) and ACM (2020), he is renowned for contributions to network control theory, including the max-weight scheduling algorithm and back-pressure network policy. PhD in Electrical Engineering (1991) from the University of Maryland, College Park His research focuses on computer and communication networks , emphasizing mathematical models for complex networks , wireless system architectures , stochastic systems , and energy-efficient network design . Recent work explores quantum networking (Pant et al., 2019) and federated learning in edge environments (Jiang et al., 2022). Key publication trends include stability analysis (earlier works), mobile edge computing (2019), software-defined networking (2021), and smart grid optimization (2012-2013). The list includes monographs on network theory and patents for distributed bandwidth allocation (2011) and directional antenna protocols (2002). Scientific Awards ACM Fellow (2020) for network control contributions IEEE Koji Kobayashi Award (2016) for scheduling/stability analysis IEEE INFOCOM Achievement Award (2007) for resource allocation Bodossaki Foundation Prize (1999) for distributed systems NSF CAREER, ONR Young Investigator, and multiple best paper awards His work has been funded by the NSF, ONR, and IBM. Current projects bridge AI , quantum communication , and next-generation network architectures .
Univ.-Prof. Martin Pinzger is a Professor at the Department of Informatics Systems, Alpen-Adria-Universität Klagenfurt. He serves as Head of Department and Member of the Senate, actively contributing to academic governance. Research Focus: Automating Software Engineering Tasks, Mining Software Repositories, Program Analysis, Software Evolution and Visualization Recent Work: Developing tools for API evolution analysis, cybersecurity AI (CAI), robotics benchmarking (RobotPerf), and dependency validation His research combines empirical studies with tool development for software maintenance and security. Current projects address challenges in REST API breaking changes, cloud security certifications, and robotic system performance evaluation. Publications since 2023 demonstrate continued engagement with topics spanning AI-driven code segmentation, microservice API evolution, and cybersecurity tool development. Key trends include cross-disciplinary applications of NLP to software engineering and security-focused tool creation. Contact: martin.pinzger@aau.at
Thomas Ploetzمشاهده پروفایل
استاد مدعو
Thomas Ploetz is an Adjunct Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology. His work focuses on sensor-based human activity recognition, wearable computing, and computational behavior analysis with applications in healthcare, smart homes, and education. He leads interdisciplinary research integrating machine learning, IoT systems, and human-centered design. His research interests include improving activity recognition robustness through synthetic data generation, developing explainable AI for time-series analysis, and advancing healthcare technologies like diabetic foot ulcer monitoring systems. He explores ethical implications of wearable sensing in diverse populations, including underrepresented groups. Key contributions include IMUTube (virtual sensor data generation), ProxiCycle (cyclist safety monitoring), and DISCOVER (smart home activity recognition framework). His work addresses challenges in data scarcity, domain adaptation, and long-term system maintenance in pervasive computing environments. He has been awarded grants such as the GVU/IPaT Research and Engagement Grant (2020), and his research appears in top venues like UbiComp and ISWC. He actively contributes to the wearable computing community through conference organization and editorial work.
Laila Shereen Sakrمشاهده پروفایل
استادیار
Laila Shereen Sakr is an Assistant Professor of Film & Media Studies and Faculty Affiliate in Feminist Studies at the University of California, Santa Barbara. She co-founded Wireframe, a digital media studio focusing on critical game design, data visualization, VR/AR, and digital activism. Her research explores algorithmic culture, media theory/practice, and feminist Middle East studies through creative coding, data visualization, and immersive worldbuilding. Key projects include the VJ Um Amel data-body and R-Shief software system. Education: PhD in Media Arts + Practice (USC), M.F.A. in Digital Arts (UC Santa Cruz), M.A. in Arab Studies (Georgetown). Research interests span technocultural intersections, digital humanities, and global human rights. Her work examines social media's role in shaping virtual political identities and has been exhibited globally (e.g., SF MoMA, Jordan National Gallery of Art). She co-edits Media Theory and After Video , and collaborates with MIT's Global Media Technologies & Cultures Lab. Publications analyze Arab Spring media dynamics, digital dissidence in MENA, and feminist digital praxis. Her work has been featured in The Wall Street Journal , Science , and The Guardian . Her creative research bridges art and activism, emphasizing open-source Arabic localization. Current projects explore the ethical implications of data-driven narratives in post-revolutionary contexts.
Mark D. Smucker is a Professor in the Department of Management Science and Engineering at the University of Waterloo, cross-appointed with the David R. Cheriton School of Computer Science (Faculty of Mathematics). His research focuses on interactive information retrieval systems, including search engines and recommendation systems, aiming to enhance evaluation methods for better prediction of human search performance. He co-organized the TREC Health Misinformation Track (2019–2022) and currently co-leads the TREC DRAGUN Track, addressing health misinformation and trustworthiness assessment in news. Education: PhD (Computer Science, UMass Amherst, 2008), MSc (Computer Science, UW-Madison, 1996), BSc (Physics & Computer Science, Iowa State, 1994). Research interests include design/analysis of interactive IR systems, evaluation frameworks, and human-computer interaction. He has been recognized with the ACM SIGIR 2012 Best Paper Award and teaching excellence awards from the University of Waterloo. Teaching includes courses like Search Engines (MSCI/MSE 541/720) and Databases/Software Design (MSCI 245). Active in organizing TREC tracks and publishing over 50 peer-reviewed articles.
Fotios Petropoulos is a Professor at the University of Bath, holding the Management Chair in Management Science within the School of Management's Information, Decisions & Operations department. He also served as the Spyros Makridakis Chair in Forecasting at the University of Nicosia (2023–2023). His research focuses on time series forecasting, judgmental approaches, and integrating statistical and human judgment in decision-making processes. He has contributed to improving forecasting accuracy through temporal aggregation and hierarchical methods. Petropoulos holds a Doctor of Engineering (2012) and Bachelor of Engineering (2007) from the National Technical University of Athens. Editor of the International Journal of Forecasting (2020–present) Associate Editor of Foresight: The International Journal of Applied Forecasting (2015–2022) Director of the International Institute of Forecasters (2016–2018) His research interests emphasize forecasting processes, model selection, and the role of judgment in statistical models. Key areas include temporal aggregation, forecast reconciliation, and behavioral operations analytics. He has published over 100 peer-reviewed articles, focusing on topics like computational cost optimization, probabilistic forecasting, and scalable reconciliation methods. His work contributes to Sustainable Development Goals related to education and innovation. Recent articles highlight advancements in univariate forecasting efficiency, forecast selection criteria, and dynamic reconciliation. Petropoulos is a member of the Smart Warehousing and Logistics Systems group and actively participates in editorial boards of leading forecasting journals. His academic and professional roles bridge theoretical research and practical applications in operational decision-making.
- Memristor
- Non-volatile memories
- VLSI
- +۷ مورد دیگر
Dr. Patrick W. C. Ho is a Lecturer in the Department of Electrical & Computer Systems Engineering (ECSE) at Monash University Malaysia School of Engineering. He holds a PhD in Electronics Engineering from the University of Nottingham Malaysia Campus (2016), with research focusing on non-volatile FPGA architectures using memristors. His academic journey includes roles as a Scholarly Teaching Fellow and unit coordinator for courses like ECE2131 Electrical Circuits and ECE4063 Large Scale Digital Design. He has industry experience with Intel Microelectronics and Altera Corporation, alongside teaching A-level Physics at Methodist College Kuala Lumpur. Education: BEng (First Class Honours) in Engineering (2009) MSc in Science (2012) PhD in Electronics Engineering (2016) Research Interests: Dr. Ho specializes in memristor-based non-volatile memory systems, VLSI design, and FPGA architectures. His work bridges hardware design with emerging materials, as seen in his Q1 journal article on memristive LUTs. Collaborations with CAD-IT expand his focus into AI, image processing, and object recognition. Recent projects include studies on memristor substrate performance (2023–2026) and UAV communication reliability (2021–2024). Teaching and Industry Engagement: As ECSE’s Industrial Training Advisor and IAP representative, he actively connects academic curricula with industry needs. His teaching spans foundational engineering courses and advanced digital design modules. Labs and Collaborations: Active in CAD-IT partnerships for student FYP co-sponsorship. Research groups focus on nanotechnology, machine learning integration in UAV systems, and memristor material analysis.
Mohsin Malikمشاهده پروفایل
دانشیار
Dr. Mohsin Malik is an Associate Professor of Project Management at Swinburne University of Technology (since 2023) in Melbourne, Australia. Previously, he held roles as Senior Lecturer (2018–2022) at Swinburne and Lecturer (2016–2018) at the University of Melbourne. He earned his PhD in Technology Management from the University of Western Australia (2012). His research focuses on organizational dynamics for innovative outcomes in digital contexts, sustainability in supply chains, and project management practices aligned with UN Sustainable Development Goals (SDG-9, SDG-12, SDG-3). Key areas include digital transformation, lean management, agile project management, and supply chain resilience. Dr. Malik’s work has been published in top-tier journals like the International Journal of Project Management, Information & Management, and IEEE Transactions on Engineering Management. He currently serves as Senior Associate Editor of the International Journal of Physical Distribution & Logistics Management and has secured grants totaling over AUD 1M, including a 2025–2028 project on digital transformation in Indian/Australian organizations funded by DFAT. His research explores topics such as socio-cognitive dynamics in digital transformation, lean socio-technical systems, and agency theory in international development projects. Recent projects include analyzing digital platform interactions for Australian artists and evaluating virtual healthcare barriers during the pandemic. He actively supervises doctoral and master’s students focusing on digital governance, social sustainability, and project monitoring frameworks. Dr. Malik is a globally recognized scholar, with over 23 years of academic and industry experience. He collaborates with organizations like the Victorian Music Development Office and the Australian Academy of Science, emphasizing real-world impact through research.
Paul Fischer is a Professor at the University of Illinois, holding dual appointments in the Siebel School of Computing and Data Science and the Mechanical Science and Engineering department. His research focuses on advanced numerical methods for fluid dynamics, particularly leveraging spectral element techniques and high-performance computing. He is a core contributor to the Nek5000/NekRS computational frameworks. Recent work emphasizes turbulence modeling, exascale CFD simulations, and multiphase flow dynamics in complex systems like pebble bed reactors. His research interests span spectral methods, large eddy simulation (LES), direct numerical simulation (DNS), and parallel computing architectures. Key projects include developing scalable algorithms for Reynolds-averaged Navier-Stokes (RANS) models and exploring non-conforming domain decomposition approaches for reacting flows. His contributions bridge computational methodology and engineering applications, with a focus on exascale-ready solutions. Publications from 2024-2025 highlight advancements in energy-efficient CFD simulations, turbulence transition mechanisms in granular media, and reduced order modeling for turbulent flows. Collaborations involve cross-disciplinary teams focusing on combustion, fluid-structure interaction, and high-fidelity flow analysis. He maintains active involvement in computational fluid dynamics communities and contributes to open-source software tools critical for industrial and academic research. Current efforts prioritize scalability, accuracy, and adaptability in numerical methods for next-generation supercomputing platforms.
Kevin Vinsenمشاهده پروفایل
پژوهشگر ارشد
Kevin Vinsen is a Senior Research Fellow at the University of Western Australia, working in the Data Intensive Astronomy (DIA) Program of the International Centre for Radio Astronomy Research (ICRAR) since 2009. He is also affiliated with the UWA Defence and Security Institute and holds an ORCID ID of 0000-0001-5332-3784. His work focuses on translating ICRAR software capabilities into practical industry applications across diverse domains. His research interests include: Peta-scale systems High-performance Computing Machine Learning applications in multiple fields Wave and weather forecasting Digital Assistive Technologies Agricultural applications of ML Large language models Vinsen heads the Translation and Impact work of the DIA team and leads the development of Machine Learning systems. His current projects include ML for wave forecasting on the NW shelf, wind and temperature forecasting, honey traceability and provenance, and digital assistive technology for people with disabilities. His work contributes to UN Sustainable Development Goals related to industry, oceans, agriculture, food, disability, and defense. His research output demonstrates a strong trend toward applying machine learning techniques to solve real-world problems across astronomy, environmental science, agriculture, and disability support. This interdisciplinary approach showcases the versatility of his computational expertise across scientific and social domains. Vinsen has an h-index of 11 with 621 citations across 33 research outputs. As the ICRAR/UWA Summer Studentship Co-ordinator, he mentors emerging researchers and contributes to building research capacity. His collaborative network spans multiple institutions and research areas, reflecting his ability to bridge academic research with practical applications.
Yelva Roustan is a Lecturer at CEREA (joint laboratory between École Nationale des Ponts et Chaussées and EDF R&D) since 2005, specializing in air quality modeling . Her research focuses on multiscale modeling approaches, source-concentration relationships, and pollutant deposition processes. She teaches at the National School of Bridges and Roads, sharing knowledge from her research in atmospheric transport and environmental science. Her work addresses urban pollution challenges, including the impact of low-emission vehicles on air quality, urban tree effects, and pollutant dynamics in street networks. She has developed the MUNICH street-network model and contributed to projects like EURODELTA for multi-pollutant analysis across Europe. Her methods integrate data assimilation, inverse modeling, and Bayesian techniques to improve emission source reconstruction and model accuracy. Key contributions include studies on radioactive release scenarios (e.g., Fukushima, 106Ru event) and urban infrastructure impacts on pollution. Her research bridges atmospheric science with engineering solutions for sustainable urban environments. She holds a PhD in Environmental Engineering from École Nationale des Ponts et Chaussées (2005) and an HDR in Numerical Modeling of Atmospheric Pollutants (2020).
Eric TOTEL is a Professor at Telecom SudParis, specializing in cybersecurity and network security. His research focuses on intrusion detection systems, graph-based anomaly detection, machine learning applications in security, and data confidentiality in distributed systems. He has contributed to projects such as DAMS (DDoS mitigation using deep reinforcement learning), Sec2Graph (novelty detection on graph-structured data), and DAEMON (dynamic autoencoder-based anomaly detection). His work emphasizes scalable solutions for multi-step attack detection and privacy-preserving infrastructure for encrypted DNS logs. Key contributions include developing correlation engines for distributed systems, formalizing invariant-based attack detection in web applications, and exploring static analysis for information flow control. He has authored over 50 peer-reviewed publications and served on program committees for conferences like RAID, CRiSIS, and EuroS&P. His HDR (2012) formalized error-detection techniques applied to intrusion detection. Advising and grants: He collaborates on projects funded by French national research agencies and has mentored students in cybersecurity, AI for defense (CAID conferences), and cloud infrastructure security. His research often bridges theoretical models and practical implementations, with tools like STARLORD for 3D graph visualization of security data.
Eileen Martinمشاهده پروفایل
دانشیار
Eileen Martin is an Associate Professor in the Department of Geophysics and Applied Math and Statistics at the Colorado School of Mines. Her research focuses on near-surface geophysics, environmental monitoring, and the application of distributed acoustic sensing (DAS) technology. She leads projects involving fiber-optic sensing for permafrost degradation, urban seismic monitoring, and mining safety. Martin has developed open-source tools like DASCore and contributes to scalable computational methods for geophysical data analysis. Education: PhD (2018) in Computational and Mathematical Engineering from Stanford University; MS (2017) in Geophysics from Stanford; BS (2012) in Mathematics and Physics from UT Austin. Research interests include fiber-optic sensing systems, seismic imaging, data-intensive computing, and applications in environmental science. Her work bridges geophysics with computational methods, emphasizing real-world deployment in challenging environments like arctic permafrost sites and underground mines. Her recent work explores DAS for glacier monitoring, mine seismicity detection, and urban infrastructure assessment. Collaborative projects include Arctic permafrost monitoring and developing public datasets for geoscience research (PubDAS repository). Grants and lab activities include NSF CAREER funding for scalable computational seismology and partnerships with industry on fiber-optic monitoring solutions.
فقط ۷۵۰ نتیجه اول نمایش داده میشود. برای رسیدن به نتایج دقیقتر، جستوجو را با فیلترها محدودتر کنید.