جست‌وجوی استاد و پژوهشگر

Wenwen Liمشاهده پروفایل

استاد

• Geographic Information Science
• Cyberinfrastructure
• Big Data
• +۸ مورد دیگر

Wenwen Li is a Professor at Arizona State University (ASU), holding roles as Director of the CyberInfrastructure and Computation Intelligence (CICI) Lab and Research Director at the Spatial Analysis Research Center (SPARC). She specializes in geographic information science, cyberinfrastructure, and geospatial big data. Her work focuses on developing intelligent cyberinfrastructure for environmental and urban studies, leveraging AI and semantic technologies. Li's research has been supported by NSF, USGS, and Google.org, among others. Education: Ph.D. in Earth System and Geoinformation Science (George Mason University, 2010), M.S. in Signal and Information Processing (Chinese Academy of Sciences, 2007), and B.S. in Computer Science (Beijing Normal University, 2004). Research Interests : Cyberinfrastructure, spatial-temporal data mining, semantic interoperability, GeoAI applications in climate science, and urban studies. Her lab, CICI, pioneers projects like the Arctic Permafrost Thaw analysis and disaster response systems. Awards : 2023 AAG and UCGIS Fellowships, 2021 NSF Mid-Career Award, 2015 NSF CAREER Award, and the 2024 Greg Leptoukh Lecture Award (AGU). She chairs AAG's Cyberinfrastructure Specialty Group and serves on editorial boards of key journals. Grants & Service : Leads NSF-funded projects on GeoAI, climate modeling, and Arctic science. Active in AAG leadership roles and global initiatives like the Polar Cyberinfrastructure Portal. Recruits Ph.D. students in Geography and Computer Science. Labs & Teams : Directs the CICI Lab, advancing interdisciplinary GeoAI research and training. Collaborates with global partners on environmental and computational geography projects.

Francesco Bulloمشاهده پروفایل

استاد

• Control Theory
• Network Systems
• Distributed Control
• +۵ مورد دیگر

Francesco Bullo is a Distinguished Professor of Mechanical Engineering at the University of California, Santa Barbara (UCSB), affiliated with the College of Engineering. He holds joint appointments in Electrical and Computer Engineering, Computer Science, and the Center for Control, Dynamical Systems, and Computation. His research focuses on distributed control, network systems, and neural networks, with notable contributions to contraction theory and social dynamics analysis. Education: Laurea (1994, University of Padova), PhD (1998, Caltech). Leadership roles: Former IEEE CSS President, SIAG CST Chair. Research interests include biological/artificial neural networks, distributed control of robotic networks, and synchronization in power grids. He authored books like Lectures on Network Systems and Contraction Theory for Dynamical Systems . His work spans 300+ publications, including impactful articles on Hopfield networks, power grid stability, and optimization. Awards include IEEE Fellow, ASME Fellow, and SIAM Fellow. Advising and grants: Mentored over 30 PhD students and led major projects like the NSF MURI on team behavior modeling. Current research includes neural synchronization and AI-driven control. Labs/teams: Directs the UCSB Center for Control, Dynamical Systems, and Computation, and collaborates on interdisciplinary initiatives like the Network Science for Medicine white paper.

Giovanna Tinettiمشاهده پروفایل

استاد

• Extrasolar Planets
• Space Science
• Molecular Spectroscopy
• +۳ مورد دیگر

Giovanna Tinetti is a Professor of Astrophysics and Vice Dean (Research) at King's College London's Faculty of Natural, Mathematical & Engineering Sciences. She leads the European Space Agency's Ariel mission, a space telescope surveying exoplanet atmospheres, set to launch in 2029. As co-founder of the London Centre for Space Exochemistry Data and Blue Skies Space Ltd, she pioneers satellite technology for scientific data collection. She holds a PhD in Theoretical Physics from the University of Turin, with prior affiliations at Caltech/JPL, the Institute of Astrophysics in Paris, and University College London (UCL), where she was a Royal Society University Research Fellow. Her research focuses on exoplanetary atmospheres, molecular spectroscopy, and advanced data science techniques. With over 300 publications, her 2019 paper on water vapor in K2-18b's atmosphere achieved the highest altmetric score in Physical Sciences that year. She has delivered over 350 international talks and lectures. Education: PhD in Theoretical Physics (University of Turin) Affiliations: King's College London, UCL (past), ESA's Ariel Mission, Blue Skies Space Ltd Research Interests: Exoplanet atmospheres, molecular spectroscopy, space science, data-driven analysis methodologies, and atmospheric modeling. Her work bridges observational astronomy with computational chemistry to interpret exoplanet compositions and climates. Awards: Royal Society University Research Fellow Highest Altmetric Score (2019 Physical Sciences) Grants & Projects: Principal Investigator for ESA's Ariel mission Co-leader of the Ariel Data Challenge 2025 Labs/Teams: London Centre for Space Exochemistry Data, Blue Skies Space Ltd technical team, and the international Ariel collaboration network.

Federico Becattiniمشاهده پروفایل

استادیار

• Computer Vision
• Deep Learning
• Artificial Intelligence
• +۷ مورد دیگر

Federico Becattini is a Tenure-Track Assistant Professor at the Department of Information Engineering and Mathematics (DIISM), University of Siena, Italy. He is an active member of the Siena Artificial Intelligence Lab (SAILab), where he contributes to cutting-edge research in computer vision, deep learning, and artificial intelligence. His work spans multiple interdisciplinary domains, including autonomous driving, human behavior understanding, cultural heritage, neuromorphic vision, and fashion recommendation. His research interests center on memory-based neural networks , which he has applied in numerous publications at top-tier venues such as CVPR, ECCV, IEEE TPAMI, and ACM TOMM. He has also delivered tutorials on this topic at international conferences including ICIAP 2022 and ACM MM 2022, and taught a Ph.D. course at the University of Florence. His recent work is aligned with the Collectionless AI paradigm, which emphasizes continual learning and interaction with dynamic environments. The recent publications highlight a strong trend in human-centric AI , focusing on understanding people through multimodal analysis of face, body, and clothing, as well as generating 3D virtual avatars. There is also a clear emphasis on memory-augmented architectures for temporal reasoning, explainability, and adaptive learning. His editorial role as Associate Editor of the International Journal of Multimedia Information Retrieval further underscores his standing in the research community. Associate Editor, International Journal of Multimedia Information Retrieval (IJMIR) Organizer, Workshop on Facial and Body Expressions (ICPR2020) Co-organizer, T-CAP Workshop (ICIAP2021, ICPR2022) Co-organizer, MCFR Workshop (ACM MM 2022) Co-organizer, WCPA Workshop and Challenge (ECCV 2022) Federico Becattini actively advises students and researchers within SAILab, particularly in the context of Ph.D. theses and research projects related to Collectionless AI and memory-based models. While specific grants are not mentioned, his extensive publication record and leadership in workshops and editorial roles suggest involvement in funded research initiatives. He collaborates with both academic and international research communities, serving as a reviewer for top-tier conferences and journals. He is a core member of the SAILab research group, which is pioneering the Collectionless AI initiative—a framework for continual learning over time, interacting with humans and agents without relying on pre-built static datasets. This lab serves as a hub for innovation in adaptive and sustainable AI systems.

Daryl Fougnieمشاهده پروفایل

دانشیار

• Cognitive Psychology
• Visual Perception
• Visual Attention
• +۵ مورد دیگر

Daryl Fougnie is an Associate Professor of Psychology and Global Network Associate Professor at New York University Abu Dhabi. He is a core faculty member in the Department of Psychology within the College of Arts and Science, actively conducting research and teaching undergraduate courses in cognitive psychology. His research focuses on the fundamental limits of human cognition, particularly in visual perception, attention, and memory. Through psychophysics and computational modeling, the Fougnie Lab investigates how the brain processes and retains visual information, exploring questions about capacity limits, the nature of mental representations, and the interaction between attention and memory. Key areas include visual working memory, attentional selection, retro-cueing, and memory-guided behavior. The 15 most recent publications reflect a strong thematic focus on the probabilistic and resource-constrained nature of working memory, the strategic control of attention, and the neural and cognitive mechanisms underlying perception and memory. Research often employs innovative paradigms such as betting games and reproduction tasks to probe internal representations and decision-making processes. NYUAD Research Enhancement Fund NYUAD Research Enhancement Fund Daryl Fougnie mentors undergraduate students in the Fougnie Lab and supervises capstone research projects in psychology. His lab is actively funded, as evidenced by repeated Research Enhancement Fund awards, and collaborates widely with researchers such as Kartik Sreenivasan, Clay Curtis, and Edyta Sasin. The lab is currently recruiting postdoctoral researchers, indicating ongoing and expanding research activity. The Fougnie Lab at NYU Abu Dhabi serves as a hub for cognitive science research, integrating experimental psychology with computational approaches. It provides research opportunities for undergraduates and fosters collaboration across disciplines, contributing significantly to the understanding of human cognitive architecture.

Lee Ryanمشاهده پروفایل

استاد

• Memory and Aging
• Alzheimer's Disease Risk
• Functional MRI
• +۳ مورد دیگر

Lee Ryan is a Professor at the University of Arizona, serving as Director of the Cognition and Neuroimaging Laboratory and the Brain Imaging Center. She is also affiliated with the Evelyn F. McKnight Brain Institute and the Clinical Cognition & Neural Systems program. Ph.D. in Cognitive and Clinical Psychology, University of British Columbia (1992) Her research focuses on the neurological basis of memory and memory disorders, aging and memory, risk for Alzheimer’s disease, and functional magnetic resonance imaging. She actively investigates how life stress impacts aging, explores individualized approaches to healthy aging, and combines behavioral experiments with MRI to study age-related cognitive changes. Recent publications highlight her work on memory reconsolidation, white matter integrity in aging populations, and genetic factors influencing cognitive decline. Her studies often integrate neuroimaging techniques with psychological and physiological assessments. Lee Ryan teaches graduate courses in neuroanatomy and cognitive neuroscience, including PSY 502 (2024) and PSY 528 (2016). Her laboratory recruits participants for studies on aging, stress, and brain health, offering community engagement opportunities and compensation for participants.

Jingrui Heمشاهده پروفایل

استادیار

• Statistical Machine Learning
• Large Scale Data Mining
• Heterogeneous Machine Learning
• +۶ مورد دیگر

Jingrui He is an Assistant Professor in the Computer Science Department at Stevens Institute of Technology, with primary research focuses on statistical machine learning and large-scale data mining. Her work spans theoretical algorithm development and practical applications in diverse domains. University: Stevens Institute of Technology Department: Computer Science Department Academic Rank: Assistant Professor Research interests include: Heterogeneous machine learning techniques Rare category analysis and detection Social network analysis Public safety applications Traffic analytics Multimedia processing Virtual metrology in semiconductor manufacturing Publishing trends show consistent contributions to machine learning, data mining, and graph-based methods across multiple domains including semiconductor manufacturing, social networks, and multimedia. She has collaborated with researchers from Stevens Institute of Technology, IBM, and Carnegie Mellon University on both theoretical and applied problems.

Kishlay Jhaمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Natural Language Processing
• +۴ مورد دیگر

Kishlay Jha is an Assistant Professor at the University of Iowa's College of Engineering in the Department of Electrical and Computer Engineering. He is also a researcher at the Center for Bioinformatics and Computational Biology and the Iowa Initiative for Artificial Intelligence. PhD in Computer Science from University of Virginia (2022) Email: kishlay-jha@uiowa.edu Office: 3320 Seamans Center, Iowa City, IA 52242 Phone: (319) 467-0096 His research focuses on data science and artificial intelligence with emphasis on data mining, machine learning, and their applications in biomedical domains. He develops methodologies for transforming heterogeneous clinical, genomic, and bibliographic data into actionable knowledge for scientific advancement. Recent work includes: Semantic knowledge integration in biomedical language models Dynamic representation learning for evolving systems Hypergraph-based contrastive learning for healthcare applications Continual learning frameworks for time-sensitive domains Knowledge-guided representation learning Biomedical hypothesis generation He leads the Data Mining and Machine Learning Laboratory, where his team develops innovative tools for both biomedical discovery and general AI applications.

• Computational Humanities
• Music Information Retrieval
• Audio Analysis
• +۷ مورد دیگر

Christof Weiß is a Professor for Computational Humanities at the CAIDAS / Institute of Computer Science, Julius-Maximilians-Universität Würzburg (JMU), Germany. He serves as Head of the DFG-funded Emmy Noether group on Computational Analysis of Music Audio Recordings: A Cross-Version Approach. His academic journey includes previous positions as Visiting Researcher at University Télécom Paris (2021), Visiting Lecturer at Karlsruhe University of Music (2020, 2021), and Research Assistant at International Audio Laboratories Erlangen (2015-2022) and Fraunhofer Institute for Digital Media Technology (2012-2015). His educational background encompasses a PhD in Media Technology from University of Technology Ilmenau (2017), Concert Diploma in Composition from Würzburg University of Music (2012), Physics Diploma from University of Würzburg (2012), and Music Diploma in Composition from Würzburg University of Music (2011). This unique combination of technical and artistic training forms the foundation of his interdisciplinary research approach. Weiß's research operates at the critical intersection of computer science and musicology, developing novel computational methods for analyzing musical structures in audio recordings. His work bridges technical audio processing with musicological insights, creating methodologies for tonal analysis, key estimation, and cross-version comparison of musical performances. His approach combines deep learning techniques with music theory to extract meaningful patterns from large music corpora, enabling new forms of musicological corpus studies that were previously impossible. His recent publications reveal a clear research trajectory toward integrating advanced machine learning with fundamental musicological questions. The consistent theme across his work involves analyzing classical music structures through computational lenses, with particular emphasis on cross-version consistency in performances, tonal complexity measurement, and developing datasets that support computational musicology. His publications span both highly technical audio processing journals and musicology-focused venues, demonstrating his commitment to bridging these disciplines. Best paper award at the 4th conference on Computational Humanities Research (CHR), 2023 KlarText award for science communication of the Klaus Tschira Foundation, 2018 2nd prize at Festival Pablo Casals composition competition, Prades (France), 2013 Youth Cultural Advancement Award (Kulturförderpreis) of the city of Amberg, Germany, 2011 As principal investigator of the DFG Emmy Noether group, Weiß leads a multidisciplinary research team investigating computational analysis of music audio recordings through a cross-version approach. His research has secured significant funding including the prestigious Emmy Noether program, supporting doctoral and postdoctoral researchers working on various aspects of music information retrieval and computational humanities. His collaborative network spans institutions across Europe, including University Télécom Paris, Queen Mary University of London, and multiple German research centers. Weiß leads the Computational Humanities research group at CAIDAS, which focuses on developing computational methodologies for music analysis with particular emphasis on classical repertoire. The lab creates specialized datasets (including the Wagner Ring Dataset and Schubert Winterreise Dataset), develops algorithms for structural music analysis, and applies these tools to address musicological questions that require computational scale and precision. Their work bridges the gap between technical audio processing capabilities and humanities research questions, creating new pathways for understanding musical structure and evolution.

Lili Qiuمشاهده پروفایل

استاد

• Internet Networking
• Wireless Networking
• Mobile Systems
• +۶ مورد دیگر

Lili Qiu is a Professor in the Department of Computer Science at The University of Texas at Austin, where she has been a faculty member since January 2005. She is an active member of the Wireless Networking and Communications Group (WNCG) and has made significant contributions to the field of networking research. Dr. Qiu previously spent 2001-2004 as a researcher at Microsoft Research in Redmond, WA, before joining UT Austin. Dr. Qiu's research spans Internet and wireless networking with a current focus on wireless network management and content distribution in mobile networks. Her work extends into diverse applications including acoustic imaging, metasurface applications, healthcare sensing technologies, and AI systems. She has pioneered research in areas such as acoustic motion tracking, passive RFID sensing, and wireless network optimization. Her research demonstrates a consistent pattern of innovation that bridges theoretical networking concepts with practical real-world applications, particularly in mobile and wireless systems. Her extensive publication record shows a clear evolution from fundamental networking research to increasingly interdisciplinary work that combines wireless systems with healthcare applications, AI, and novel sensing technologies. Recent publications demonstrate growing integration of machine learning techniques with traditional networking problems, as well as expansion into healthcare applications like Parkinson's disease modeling and non-invasive glucose monitoring. ACM Fellow IEEE Fellow National Academy of Inventors (NAI) Fellow ACM Distinguished Scientist NSF CAREER award Google Faculty Research Award Best paper award at ACM MobiSys'18 Best paper award at IEEE ICNP'17 Dr. Qiu has supervised numerous students, including a PhD dissertation that won the SIGMOBILE best dissertation award in 2020. She has served in significant leadership roles including chair of ACM SIGMOBILE, General co-chair for ACM MobiCom 2025, and various conference chair positions for IEEE ICNP, ACM CoNEXT, and other major networking conferences. Her research has been supported by substantial grants from NSF, Google, and other organizations, enabling her to lead cross-disciplinary research teams. As a member of the Wireless Networking and Communications Group at UT Austin, Dr. Qiu leads research efforts that combine networking expertise with innovations in sensing technologies, metasurfaces, and AI systems. Her lab has produced numerous influential results in mobile networking, wireless sensing, and network management, with applications spanning healthcare, consumer electronics, and communication infrastructure.

Arnab Nandiمشاهده پروفایل

استاد

• Database Systems
• Human-Computer Interaction
• Human-in-the-Loop Data Analytics
• +۵ مورد دیگر

Arnab Nandi is a Professor of Computer Science and Engineering at The Ohio State University, with a courtesy appointment in Biomedical Informatics. He holds leadership roles including Steering Committee Member for the Human-in-the-Loop Data Analytics (HILDA) Workshop and has served as Workshops co-chair for SIGMOD 2025-26 and Demonstrations co-chair for SIGMOD 2024. His research focuses on bridging human interaction and data infrastructure, spanning database systems, human-in-the-loop data analytics, and next-generation query interfaces. Nandi's work emphasizes interactive data exploration through projects like DICE (Distributed Interactive Cube Exploration), GestureDB (Querying Beyond Keyboards), and Omni (Multimodal Data Exploration). His recent research explores integrating LLMs into database education, augmented reality interfaces for data analytics, and multimodal approaches to video querying. Nandi has received numerous honors including the NSF CAREER Award, Google Faculty Research Award, IEEE TCDE Early Career Award, and the University's Alumni Award for Distinguished Teaching. He was also named to Columbus Business First's '40 under 40' and became an ACM Distinguished Member in 2024. As an educator, he teaches courses including CSE 3241 (Introduction to Database Systems), CSE 5242 (Advanced Database Systems), and CSE 5251 (Introduction to Software Startups). His educational innovations include DBTutor, which integrates LLMs into database systems education. At Ohio State, Nandi co-founded the OHI/O Program, which fosters tech culture through hackathons, and The STEAM Factory, an interdisciplinary research collaboration network. Prior to academia, he was founder and CEO of Mobikit, a connected vehicles data analytics startup acquired by Azuga Inc. (a Bridgestone company). His research has been supported by the NSF and industry partnerships, with applications spanning precision agriculture (CropFusion), clinical data pipelines (ICARUS), and interactive visualization systems (Perceptvis).

• Cryptography
• Security
• Blockchain Technology
• +۴ مورد دیگر

Elaine Shi is a Professor at Carnegie Mellon University's Computer Science Department and Electrical and Computer Engineering Department, with an Adjunct Professor appointment at the University of Maryland. Her research spans cryptography, security, blockchain technology, algorithms, and privacy-enhancing techniques. Co-founder of Oblivious Labs, Inc. Co-developer of cryptographic protocols adopted by Signal, Meta, and Google Co-founder of CyLab's crypto seminar series Her work has been recognized with prestigious awards including the Packard Fellowship, Sloan Research Fellowship, ACM Fellow, and IACR Fellow. She has advised numerous PhD students and postdocs, many of whom now hold academic or industry positions. 2023 ACM CCS Test of Time Award 2020 CyLab Distinguished Alumni Award 2016 ONR YIP Award Recent publications focus on advancing cryptographic protocols, privacy-preserving algorithms, and blockchain security, with key contributions in garbled RAM, oblivious computation, and differentially private mechanisms.

Paolo Prandoniمشاهده پروفایل

مدرس

• Signal Processing
• Audio Processing
• Image Processing
• +۵ مورد دیگر

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.

Amir Aryaniمشاهده پروفایل

دانشیار

• Data Science
• Social Data Analytics
• Public Health Informatics
• +۵ مورد دیگر

Amir Aryani is an Associate Professor at the School of Business, Law and Entrepreneurship , Swinburne University of Technology . He leads the Social Data Analytics (SoDA) Lab within the Social Innovation Research Institute , focusing on data-driven solutions for health and social challenges. His work involves large-scale cross-institutional projects with international collaborators including the British Library , ORCID , and NIH . Research Focus: Data modeling, real-time analytics, and information retrieval for social-good initiatives Collaborations: CERN, Data Archiving and Networked Services (DANS), and Global Information Systems (GESIS) His research explores data science applications in mental health, community resilience, and sustainable development. Key trends in his publications include: Mapping research to United Nations Sustainable Development Goals Developing hybrid expert-finding models using NLP and graph algorithms Creating interoperable research graphs for cross-platform discovery Assessing social impact of data projects in non-profit sectors Amir is actively involved in PhD supervision and has secured funding from Australian Research Council , National Health and Medical Research Council , and philanthropic foundations . He also contributes to community data projects through the SoDA Lab , which builds tools for social connection analysis and humanitarian response optimization.

• Chemical modeling of planetary atmospheres
• Exoplanetary atmospheres
• Astrochemistry
• +۷ مورد دیگر

Dr. Éric Hébrard is a Senior Lecturer in Astrophysics at the University of Exeter since 2018, with prior academic roles including NASA Goddard Senior Research Fellow and CNRS Research Associate. His work bridges planetary atmospheres, astrochemistry, and combustion modeling with expertise in 3D chemical simulations. PhD in Physics and Chemistry of Planetary Atmospheres, Université Paris 7 (2006) Magna cum laude Magistère Interuniversitaire de Chimie, ENS Paris (2003) Research focuses on: Exoplanetary atmosphere modeling (hot Jupiters, TRAPPIST-1e) Photochemical kinetics and UV absorption Coupling of atmospheric circulation and chemistry Cross-disciplinary combustion-atmosphere analogs Chemical validation strategies for model accuracy Scientific contributions include: NASA-funded research on organic-rich habitable zones Development of KIDA kinetic database for astrochemistry STFC Consolidated Grant for multi-dimensional chemical models Quantum chemistry integration for Titan atmosphere studies Awards: Higher Education Academy Fellowship (ASPIRE program) NASA Postdoctoral Fellowship (2015-2017) CNES Postdoctoral Fellowship (2007-2009)