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نمایش ۱۶-۳۰ از ۲۹۱ استاد راهنما

اطلاعات پروفایل‌ها از صفحات عمومی دانشگاه‌ها گرفته شده و ممکن است به‌روز نباشد.

Adam Ruleمشاهده پروفایل

استادیار

University of Wisconsin-MadisonUnited States

Adam Rule is an Assistant Professor of Information Science at the University of Wisconsin–Madison’s Information School. He also holds honorary and affiliate roles in the Department of Family Medicine and Community Health, the Department of Medicine (Division of General Internal Medicine), and the Department of Biostatistics and Medical Informatics. His research focuses on human-computer interaction, medical informatics, and human-centered data science, particularly in the context of electronic health records (EHRs). Education: PhD in Cognitive Science, University of California, San Diego (2013–2018) MS in Human-Centered Design & Engineering, University of Washington (2011–2013) BS in Industrial Engineering, University of Illinois Urbana-Champaign (2008–2011) Research Interests: EHR workflow analysis and usability Clinician documentation practices Computational notebooks and collaborative data science Impact of technology on healthcare workflows Awards & Fellowships: National Library of Medicine Postdoctoral Training Grant (2019–2021) Best Paper, 2nd Place, MedInfo 2021 CHI 2018 Honorable Mention Grants & Advising: Co-Investigator on grants examining EHR workload and team support in primary care Mentored over 20 students across MS, BS, and MD programs Labs & Teams: Collaborative Computing Group (UW–Madison Information School), with interdisciplinary ties to clinical and informatics teams.

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Oliver Kennedyمشاهده پروفایل

دانشیار

State University of New York at BuffaloUnited States

Dr. Oliver Kennedy is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo's School of Engineering and Applied Sciences. He serves as Co-Director of Graduate Studies and leads the Online Data Interactions (ODIn) Lab. His research focuses on databases, programming languages, and user interfaces for data science, with particular emphasis on scalable compilers and managing uncertainty in data. Kennedy holds a PhD in Computer Science from Cornell University (2011), MS from Cornell (2008), and dual BS degrees in Computer Science and Computer Engineering from NYU and Stevens Institute of Technology (2005). His work bridges theoretical computer science with practical data management challenges. His recent publications demonstrate a strong focus on improving database query processing, uncertainty management in data systems, and developing practical tools for data integration and exploration. Awarded the NSF CAREER Award in 2018, Kennedy's research has significant implications for efficient data processing in scientific and commercial applications.

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Anand Padmanabhanمشاهده پروفایل

دانشگاهی

University of Illinois Urbana-ChampaignUnited States

Anand Padmanabhan is a Research Associate Professor in the Department of Geography & Geographic Information Science at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the School of Earth, Society & Environment within the College of Liberal Arts & Sciences. He holds a Ph.D. in Computer Science from the University of Iowa, alongside an MS in Computer Science (University of Iowa) and a BE in Computer Engineering (University of Mumbai). His research focuses on advanced cyberinfrastructure, cyberGIS, geospatial data science, and high-performance computing. He leads the spatial algorithms and systems team at the CyberGIS Center for Advanced Digital and Spatial Studies, developing cyberGIS capabilities to leverage advanced computing for geospatial innovation. His work emphasizes scalable geocomputation, cloud-based frameworks, and reproducible research environments, with contributions to tools like CyberGIS-Compute and EasyScienceGateway. Recent publications highlight advancements in science gateway frameworks, middleware systems, and geospatial education platforms. He serves as Online MS Program Adviser and has secured NSF and EPA grants for interdisciplinary projects. His work spans transdisciplinary training programs, urban sensing analytics, and integration of social media with geospatial data.

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Philippe Schwallerمشاهده پروفایل

استادیار

Swiss Federal Institute of Technology in LausanneSwitzerland

Philippe Schwaller is a Tenure Track Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL), School of Basic Sciences, within the Institute of Chemical Sciences and Engineering. He leads the Laboratory of Artificial Chemical Intelligence (LIAC), a research group focused on leveraging artificial intelligence to accelerate molecular discovery and sustainable chemistry. He is also a core Principal Investigator of the NCCR Catalysis, a national Swiss research center. His research lies at the intersection of chemistry, materials science, and computer science, with a strong emphasis on developing machine learning models for molecular design and synthesis. LIAC's work is driven by real-world sustainability challenges, aiming to reduce the time and cost of discovering new functional molecules and materials. The recent publications and projects from his lab highlight a strong trend in generative AI for chemistry, including memory-augmented models, hypergraph neural networks, and large language models tailored for scientific discovery. These efforts are complemented by educational initiatives such as the 'AI for Chemistry' course and practical programming resources for chemists. He actively supervises a diverse group of PhD students and contributes to multiple doctoral programs at EPFL, including EDCH and EDPY. His teaching portfolio includes courses on computational chemistry, AI applications in chemistry, and scientific machine learning. Philippe Schwaller is deeply involved in advancing AI-driven scientific discovery through both research and education, positioning his lab at the forefront of artificial chemical intelligence. The lab maintains active open-source contributions on GitHub, fostering collaboration and transparency in scientific AI development.

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Paolo Prandoniمشاهده پروفایل

مدرس

Swiss Federal Institute of Technology in LausanneSwitzerland

Paolo Prandoni is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences (IC). He serves as a Scientist in the Audiovisual Communications Laboratory (LCAV) and teaches in the SSC-ENS and SIN-ENS units, focusing on signal processing theory and practical applications in audiovisual communications. He earned his PhD from EPFL after completing all prior education there, driven by childhood fascination with long-distance telephony. His doctoral work established foundations in communication systems that continue to inform his research. Prandoni's research spans audio/image processing, machine learning for media analysis, and DSP education. Key areas include computational photography (e.g., spectral imaging, stained glass rendering), speech quality assessment via transfer learning, music information retrieval (e.g., fingering prediction), and audience analytics through his company Quividi. His work consistently bridges theoretical signal processing with real-world implementation. Recent publications reveal a strategic shift toward machine learning integration in signal processing tasks, particularly non-intrusive speech assessment and lensless imaging reconstruction. Simultaneously, he advances DSP pedagogy through MOOC development and hands-on teaching tools using off-the-shelf hardware, emphasizing accessibility and practical skill development. No scientific awards are documented in the provided materials. He has advised PhD student Thanikachalam Niranjan (thesis: Image Based Relighting of Cultural Artifacts , 2016) and teaches Communication Systems and Computer Science courses. His educational impact extends through the open-access textbook Signal Processing for Communications (2008) and tools like MultiPub for maintainable online classes. Industry engagement includes Quividi co-founding (2006) and ongoing CSO role in attention analytics. As a core LCAV laboratory member, he collaborates on interdisciplinary projects including cultural heritage digitization, embedded signal processing systems, and real-time audience measurement, leveraging EPFL's infrastructure for both academic and commercial applications.

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Miguel Mahechaمشاهده پروفایل

استاد

Leipzig UniversityGermany

Miguel Mahecha is Professor of Environmental Data Science and Remote Sensing at the University of Leipzig, where he serves as Institute Head of the Institute for Earth System Science and Remote Sensing. He is also affiliated with the Remote Sensing Centre for Earth System Research, a collaboration between Leipzig University and the Helmholtz Centre for Environmental Research (UFZ). Mahecha is a member of the German Centre for Integrative Biodiversity Research (iDiv) and serves as Principal Investigator in the Centre for Scalable Data Analytics and Artificial Intelligence. Additionally, he is a Fellow of the European Laboratory for Learning and Intelligent Systems and co-spokesperson for the National Research Data Infrastructure for Earth System Sciences (NFDI4Earth). Full Professor for Modelling Approaches in Remote Sensing, University of Leipzig (since 03/2020) Research Group Leader: Empirical Inference in the Earth System, Max Planck Institute for Biogeochemistry, Jena (12/2012 - 03/2020) PostDoc, Max Planck Institute for Biogeochemistry, Jena (10/2009 - 11/2012) PhD in Environmental Sciences, ETH Zürich (06/2006 - 09/2009) Diploma in Geoecology, Bayreuth University (10/2000 - 04/2006) Mahecha's research focuses on understanding ecosystem responses to climate extremes and human-environment relationships during these events. He investigates macro-ecological dynamics and ecosystem functioning using data-driven methods and high-dimensional Earth observations. A key contribution is his co-development of the Earth System Data Cube concept, which integrates empirical methods with theoretical understanding to analyze complex Earth system interactions. His work spans biogeography, ecosystem functioning, and advanced data science methodologies for environmental monitoring. His recent publications demonstrate a strong emphasis on analyzing compound climate extremes, particularly heatwaves and droughts, and their impacts on ecosystems. Mahecha has pioneered methods using Earth System Data Cubes to integrate diverse environmental datasets, enabling novel insights into biosphere-atmosphere interactions. His research increasingly incorporates artificial intelligence and machine learning approaches to understand spatiotemporal patterns in ecological systems, with applications in real-time forest monitoring and biodiversity assessment. Fellow of the European Laboratory for Learning and Intelligent Systems Co-spokesperson for NFDI4Earth (National Research Data Infrastructure for Earth System Sciences) Mahecha leads multiple significant research projects including Digital Forest (real-time forest monitoring), NFDI4BioDiversity, and XAIDA (extreme events: AI for Detection and Attribution). His work receives funding from diverse sources including EU, DFG, and Stiftungen Inland. He collaborates extensively with the German Centre for Integrative Biodiversity Research (iDiv) and the Centre for Scalable Data Analytics and Artificial Intelligence. His research group, Earth System Data Science (ESDS), focuses on developing methods to extract valuable information from long-term environmental observations to understand coupled Earth system dynamics. At the Remote Sensing Centre for Earth System Research, Mahecha's ESDS group investigates how ecosystem functions respond to climate extremes, societal vulnerability to environmental hazards, and nonlinear interactions in coupled Earth systems. The group leverages citizen science data, remote sensing observations, and advanced computational methods to address pressing environmental questions.

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Abbas Heydarnooriمشاهده پروفایل

استادیار

Bowling Green State UniversityUnited States

Abbas Heydarnoori is an Assistant Professor in the Department of Computer Science at Bowling Green State University (USA) since 2022, and previously held a faculty position at Sharif University of Technology (Iran) from 2012 to 2022. He earned his Ph.D. in Computer Science from the University of Waterloo (Canada, 2009), and M.Sc. and B.Sc. in Software Engineering from Sharif University of Technology (2001 and 1999). His research focuses on AI-driven software engineering (AI4SE/SE4AI), leveraging data science and AI to address challenges like fault localization, bug prediction, and code comprehension. He analyzes software repositories (e.g., GitHub, Stack Overflow) to improve developer productivity and software quality. He has contributed to tools like CrowdSummarizer and ExceptionTracer, and his work spans topics such as microservices architecture, API usage analysis, and code summarization. Teaching includes graduate/undergraduate courses on AI for Software Engineering, Database Systems, and Software Engineering. His service roles include editorial board membership at Science of Computer Programming , and PC membership in conferences like MSR, SANER, and FSE. His research group actively publishes on automated code analysis, documentation generation, and developer productivity tools, with a focus on empirical and data-driven approaches.

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Thomas Degueuleمشاهده پروفایل

پژوهشگر

InriaFrance

Thomas Degueule is a CNRS researcher in the Software Engineering group at LaBRI (Bordeaux, France). His research focuses on software evolution , modularity , and comprehension , with a particular emphasis on software libraries , APIs , software ecosystems , and domain-specific languages (DSLs) . Prior to his current role, he worked as a postdoctoral researcher at CWI (Amsterdam) and earned his PhD at IRISA (Rennes, France). Research Themes : Software evolution, API analysis, dependency management, empirical software engineering, DSL engineering Projects : ANR JCJC ALIEN (PI), H2020 CROSSMINER (WP leader), ALE (associate member), LEOC Clarity (member), ANR INS GEMOC (member), ITEA2 MERgE (member) Software Tools : Roseau, Maracas, BreakBot, Melange, Eclipse GEMOC Studio, K3, Alex, Scava His recent publications highlight trends in API breaking changes , dependency evolution , DSL interoperability , and automated software analysis (see articles list for details). He also contributes to software education through tools like MOON and Immediate Feedback for Jupyter Notebooks. Thomas has supervised PhD students including Gustave Monce (University of Bordeaux), Lina Ochoa (now assistant professor at Eindhoven University), Corentin Latappy (post-doc at University of Bordeaux), and Christophe Casseau (lecturer at University of Bordeaux). He actively participates in academic service, serving on program committees for conferences such as ICSE , ASE , ICSME , ISSTA , and SLE , and organizing workshops like SLEBoK and MLE 2021 .

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Lisa Yanمشاهده پروفایل

استاد آموزشی

University of California, BerkeleyUnited States

Lisa Yan serves as a Teaching Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, appointed in Spring 2022. She teaches core computer science education courses including CS 195 (Social Implications of Computer Technology), CS H195 (Honors variant), CS 294-189 (Teaching Process Design), and CS 375 (Teaching Techniques), holding regular office hours in Soda Hall for student engagement. Her academic credentials include: PhD in Electrical Engineering from Stanford University (2019) MS in Electrical Engineering from Stanford University (2015) BS in Electrical Engineering and Computer Science from UC Berkeley (2013) Dr. Yan's research centers on data-driven analysis of student learning in large-scale computer science courses, with significant contributions to computing ethics pedagogy and teaching assistant development programs. Her work develops innovative methodologies for assessing student earnestness in interactive lectures, creating flexible learning extensions, and designing integrity-focused assessments. Earlier research focused on software-defined networking and network switch performance optimization, demonstrating technical depth before her pivot to educational innovation. Current projects emphasize scalable teaching techniques and mastery learning frameworks that address challenges in modern CS education. Analysis of her 14 publications (2013-2024) reveals a strategic shift from computer networking (pre-2018) to computer science education research (2018-present). Recent work (2020-2024) dominates in venues like SIGCSE, featuring tools such as Otter-Grader for Jupyter notebook grading and the Earnest Insight Toolkit for lecture participation analysis. This evolution highlights her commitment to solving practical educational challenges through data analysis and tool development, particularly for large undergraduate courses. She received recognition through: The Faculty Award for Outstanding Mentorship of GSIs (2024) Lisa actively mentors Graduate Student Instructors and collaborates with educational technology initiatives. Her research team includes dedicated support staff like Taylor Kaserman (taylor.kase@berkeley.edu), reflecting structured collaboration in developing teaching innovations. She contributes to curriculum design committees within EECS, focusing on assessment integrity and scalable pedagogical methods for growing student populations. Her work operates through the EECS department's educational infrastructure, utilizing Soda Hall resources for both teaching coordination and research development, with strong connections to Berkeley's broader computing education ecosystem.

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Tobias Hoßfeldمشاهده پروفایل

استاد

Julius-Maximilians-Universität WürzburgGermany

Tobias Hoßfeld is a full Professor at the Chair of Communication Networks (Informatik III) within the Faculty of Mathematics and Computer Science at the University of Würzburg, Germany, since 2018. He completed his PhD in 2009 and habilitation in 2013, both at the University of Würzburg, and previously held the Chair of Modeling of Adaptive Systems at the University of Duisburg-Essen (2014–2018). His research focuses on network technologies, including 5G/6G, Quality of Experience (QoE), Green Communication Networks, and Software Defined Networking. His work has been recognized with awards such as the Fred W. Ellersick Prize (2013), multiple best paper and reviewer awards, and the GI/ITG MMB 2010 PhD Award. He contributes to editorial boards of journals like IEEE Communications Surveys & Tutorials and serves on committees including the ITG/VDE expert group on Communication Networks and Systems. Research Interests: Network and Service Management, Green Communication Networks, 5G/6G, QoE, IoT, and Queueing Theory. Awards: 10+ major awards, including IEEE Fred W. Ellersick Prize and multiple best paper awards. Leadership Roles: Head of Chair of Communication Networks (2018–present), Chair of Modeling of Adaptive Systems (2014–2018), and TPC Co-Chair for international conferences. Education: Habilitation (2013), PhD (2009), and Diplom in Computer Science (2003), all at University of Würzburg. Tools & Datasets: Authored an open-access textbook on performance modeling with interactive notebooks for numerical implementations.

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Andreas Weberمشاهده پروفایل

دانشیار

University of TwenteNetherlands

Andreas Weber is an Associate Professor at the University of Twente's Digital Society Institute , specializing in the Knowledge, Transformation & Society (KiTeS) research group . His work examines the long-term historical and global relationship between Science, Technology, and Society , with particular focus on colonial histories of natural history, chemistry, and sustainability , as well as computational technologies for contextualizing digitized archives . He leads the HAICu project (2023–2029) on digital cultural heritage and coordinates STS PhD training for the Netherlands Graduate Research School (2019–2024). MA & PhD in History (2005 & 2012), Leiden University Assistant Professor (2017–2023), University of Twente Andreas' research integrates digital humanities with colonial science history , emphasizing global histories of minerals , digital humanism , and AI's societal context . His 15 most recent publications (2016–2025) explore topics like colonial bias in natural history collections , FAIR data implementation , and semantic annotation of handwritten archives , spanning disciplines from history of science to computer science and museum studies . He has received 7 scientific awards , including multiple Best Teacher Awards (2020–2023) and the IEEE eScience Best Poster Award (2018). Andreas supervises PhD and postdoctoral projects while engaging in media commentary on colonial heritage issues and co-organizing international conferences like Hydrogen Pasts and Futures (2024).

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Rei Sanchez-Ariasمشاهده پروفایل

استاد آموزشی

University of North Carolina at Chapel HillUnited States

Rei Sanchez-Arias is a Teaching Professor and Director of the Master of Applied Data Science (MADS) program at UNC Chapel Hill's School of Data Science and Society. His expertise includes data mining, machine learning algorithm development, and data science pedagogy. He previously held positions at Florida Polytechnic University and St. Thomas University. Research interests span educational tools, health informatics, and optimization methods. Recent work involves AI for endoscopic surgery evaluation, meta-analysis of MLOps tools, and curriculum design for data science programs. Awards include the Excellence in Teaching Award from Florida Polytechnic. Student mentorship focuses on data wrangling and analytics projects.

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Christopher Brooksمشاهده پروفایل

استادیار

University of Michigan-Ann ArborUnited States

Christopher Brooks is an Assistant Professor at the University of Michigan's School of Information, specializing in educational technologies and data science education. He directs the Educational Technology Collective (etc), a multidisciplinary research group focused on learning analytics, educational data mining, and collaborative learning systems. His work bridges computer science and education, with a focus on improving teaching methods through AI-driven tools and platforms. Research Interests: Development and impact assessment of educational technologies Predictive modeling for student success Data science pedagogy Privacy in smart home technologies Publications reflect a focus on learning analytics, MOOC design, and educational AI, with notable contributions to conferences like CHI, LAK, and AIED. Awards include multiple best paper recognitions. Teaching includes applied data science courses at UMich and Coursera. He leads the Master of Applied Data Science (MADS) program and collaborates with institutions like Microsoft to build AI-driven educational tools.

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Andrea Volkamerمشاهده پروفایل

استاد

Leibniz-Institut für Molekulare PharmakologieGermany

Andrea Volkamer is a computational chemist and active principal investigator in the field of computer-aided drug design (CADD), with a focus on kinase targets, druggability prediction, and machine learning applications. She has published extensively in journals such as the Journal of Chemical Information and Modeling and Journal of Medicinal Chemistry , with recent work up to 2025 indicating an ongoing academic research program. Her research group, referred to as 'volkamerlab,' develops open-source tools including DoGSite, KiSSim, KinFragLib, and TeachOpenCADD, which are widely used in both academic and industrial drug discovery settings. Her research interests span computational drug discovery , structural bioinformatics , kinase inhibitor design , off-target and polypharmacology prediction , and educational platforms for CADD . She emphasizes open science and reproducibility, particularly through the TeachOpenCADD initiative, which provides interactive Jupyter Notebooks and KNIME workflows for teaching cheminformatics concepts. The 15 most recent publications reflect a strong trend toward integrating machine learning and deep learning (e.g., transformers, graph neural networks) with structure-based methods such as molecular docking, free energy calculations, and binding site comparison. Her work increasingly addresses real-world challenges in drug discovery, including kinase mutation resistance, selectivity optimization, and in vivo toxicity prediction using conformal and hybrid models. Scientific Contributions and Awards: Development of key computational tools: DoGSite, KiSSim, KinFragLib, TeachOpenCADD. Leadership in open-source and open-education initiatives in cheminformatics. Active publication record in top-tier journals with interdisciplinary impact. Advising and Grants: While specific student names and grant details are not mentioned in the provided text, her role as a corresponding author on numerous publications and the existence of a dedicated research lab ('volkamerlab') imply that she mentors students and postdoctoral researchers. She likely secures competitive funding to support her research in computational drug discovery and method development. Labs and Teams: She leads the Volkamer Lab ('volkamerlab'), which focuses on developing and applying computational methods for drug discovery. The lab collaborates with both academic and pharmaceutical partners and emphasizes open-source software development and educational outreach.

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Yalong Yangمشاهده پروفایل

استادیار

Georgia Institute of TechnologyUnited States

Yalong Yang is an Assistant Professor at the School of Interactive Computing at Georgia Institute of Technology. His research focuses on immersive analytics, virtual reality (VR), and augmented reality (AR) interfaces, with a particular emphasis on spatial interaction, hybrid user interfaces, and data visualization techniques. He explores how embodied interactions and immersive environments enhance understanding in domains like education, sports analytics, and collaborative decision-making. Key research themes include hybrid immersive systems (combining VR/AR with physical devices), asymmetric collaboration in mixed environments, and AI-driven tools for programming education. His work spans both theoretical frameworks and applied systems, such as SPHERE for scalable personalized feedback in coding classrooms and VizGroup for collaborative learning analytics. Yang’s recent publications highlight trends in spatial hybrid interfaces, navigation in immersive environments, and the integration of generative AI in educational technologies. His projects often involve evaluating interaction techniques (e.g., label placement in AR) and comparing performance across desktop and VR platforms. Notable contributions include the SportsXR initiative for immersive analytics in sports, and systems like CompositingVis for creating complex visualizations in 3D spaces. His work addresses challenges in situated analytics, wearable technologies for outdoor activities, and audience analysis in VR exhibitions.

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