جست‌وجوی استاد و پژوهشگر

Stan Sclaroffمشاهده پروفایل

استاد

• Computer Vision
• Pattern Recognition
• Machine Learning
• +۳ مورد دیگر

Stan Sclaroff is a Professor of Computer Science and Dean of the College of Arts & Sciences at Boston University. He holds affiliated faculty status in the Department of Electrical and Computer Engineering. His research focuses on computer vision, pattern recognition, and machine learning, with expertise in tracking, human motion analysis, and multimedia retrieval systems. He co-leads the Image and Video Computing research group and has contributed pioneering work like the ImageRover content-based image retrieval system. Education: PhD in Media Arts & Sciences from MIT (1995), SM from MIT (1991), and BS in Computer Science and English from Tufts University (1984). Research Interests: Human motion tracking, sign language analysis, deformable shape matching, and multimedia indexing. Notable contributions include early work on content-based image retrieval and foundational techniques in video analysis. Honors: IEEE & IAPR Fellowships, NSF CAREER Award (1996), ONR Young Investigator Award (1996), and BU's Mentor of the Year (2018). Over 40+ students advised, many now in academia and industry leadership roles. Key roles: Chair of BU Computer Science (2007–2013), Associate Dean for Mathematical & Computational Sciences (2015–2018), Interim Dean (2018–2019), and current Dean since 2019.

Ashish Venkatمشاهده پروفایل

دانشیار

• Computer Architecture
• Compilers
• Computer Security
• +۱۲ مورد دیگر

Ashish Venkat is an Associate Professor in the Department of Computer Science at the University of Virginia, part of the School of Engineering and Applied Science. He holds a Ph.D. from UC San Diego and has established himself as a leading researcher in computer architecture, compilers, and computer security. Research Interests: His research focuses on cross-disciplinary hardware and software techniques to build secure, high-performance computing systems. He investigates robust exploit mitigations that maintain energy efficiency and programmability, with a particular emphasis on speculative execution, memory safety, hardware security, and privacy-preserving computing. He also explores the application of machine learning to detect security threats and model execution behavior. Publication Trends: His recent publications (2020–2025) demonstrate a strong focus on hardware-based security, particularly microarchitectural vulnerabilities (e.g., micro-op cache attacks), memory safety via microcode capabilities, and secure accelerators for bioinformatics. There is a consistent trend of publishing in top-tier venues like ISCA, MICRO, IEEE S&P, and USENIX Security, often featuring novel hardware/software co-design solutions. Scientific Awards: NSF CAREER Award (2023) NSF CRII Award (2018) IEEE Micro Top Pick (2019) IEEE Design & Test Top Pick (2020, 2021) HPCA Best Paper Runner-Up (2019) DATE Best Paper Nominee (2023) UVA Research Achievement Award (2023) ISCA Prolific Author of the Decade (2013–2022) Advising and Grants: He actively mentors graduate and undergraduate students, many of whom have pursued advanced degrees or joined leading tech companies. He has secured significant funding as PI or co-PI from NSF, DARPA, SRC, and Intel, including a $4.9M DARPA HERCULES grant and an NSF CAREER award. His projects focus on holistic security solutions, speculative optimization, and privacy-preserving machine learning frameworks. Labs and Teams: He leads a research group focused on secure and efficient computing systems, collaborating with researchers at institutions like UC San Diego, UC Riverside, and UC Irvine, as well as industry partners including Intel and IBM.

Catia Pesquitaمشاهده پروفایل

دانشیار

• Semantic Web
• Biomedical Ontologies
• Knowledge Graphs
• +۷ مورد دیگر

Catia Pesquita is an Associate Professor in Computer Science at the Faculty of Sciences of the University of Lisbon , where she is also a Senior Researcher at LASIGE and leads the Health and Biomedical Informatics Research Line . With a multidisciplinary background in Biology and Computer Science, she focuses on Artificial Intelligence and Data Science applications in life and health sciences . Her research spans Semantic Web , Biomedical Ontologies , Knowledge Graphs , and Explainable AI , with significant contributions to ontology matching and semantic similarity . Education: PhD in Computer Science - Bioinformatics (2012) MSc in Bioinformatics (2008) Degree in Cell Biology and Biotechnology (2005) Current Projects: KATY (2021-2024): AI-Empowered Personalized Medicine for cancer treatments. BRAINTEASER (2021-2024): AI for ALS and MS disease progression models. Research Outputs: Developed tools like AgreementMakerLight (AML) , KGsim-benchmark , and the Epidemiology Ontology . Over 133 publications with significant citations (32,909 reads, 3,889 citations). Teaching: Lectures advanced topics in Databases , Data Integration , Bioinformatics , and Big Data . Advocacy: Vice-president of Biodata.pt , promoting biological data valorization in Portugal. Actively involved in initiatives to promote computer science careers to young women .

Rudi A. Pendavinghمشاهده پروفایل

استادیار

• Combinatorics
• Matroid Theory
• Graph Theory
• +۳ مورد دیگر

Rudi A. Pendavingh is an Assistant Professor at Eindhoven University of Technology's Mathematics and Computer Science department, specializing in Combinatorial Optimization. He earned his PhD at the University of Amsterdam in topological graph theory and has since contributed to diverse areas of combinatorics and geometry, with recent focus on matroid theory. His teaching spans mathematical programming topics including linear, integer, and semidefinite optimization. Education: PhD in Topological Graph Theory (University of Amsterdam) His research integrates combinatorial structures with geometric and algebraic methods, particularly in matroid theory, graph embeddings, and tropical geometry. Key publication themes include optimization algorithms, excluded matroid minors, and geometric invariants. Collaborations extend to institutions in mathematics and applied sciences, with notable academic output (67 research items) and external partnerships. Recent research outputs (2022-2025) highlight his work on the Colin de Verdière parameter, Dressian bounds, and tropical amoebas. While no explicit awards are mentioned in the provided text, his contributions to combinatorial optimization and network collaborations underscore his academic impact. He has supervised 29 students and contributed to 7 courses, including Discrete Optimization Modeling and Mathematics II.

• Graph Representation Learning
• Deep Learning
• Reinforcement Learning
• +۷ مورد دیگر

Jian Tang is an Assistant Professor at HEC Montreal and the Montreal Institute for Learning Algorithms (MILA), as well as an Associate Professor at the Department of Computer Science and Operations Research (DIRO) at Université de Montréal. He is also affiliated with IVADO (Institut de valorisation des données) as a member. His research spans multiple institutions including collaborations with leading biology labs worldwide and access to extensive computational resources through industry partners. Ph.D. in Computer Science, Peking University (2009-2014) Visiting Ph.D. student, University of Michigan (2011.10-2013.8) B.S. in Mathematics, Beijing Normal University (2005-2009) Professor Tang's research focuses on the intersection of deep learning and graph theory, with particular emphasis on geometric deep learning, knowledge graph reasoning, and applications in drug discovery. His work bridges symbolic and neural approaches to create robust reasoning systems that can handle complex structured data. He has pioneered techniques in graph representation learning that have significantly advanced the field of molecular property prediction and protein design. His publication record shows a clear trajectory toward applying geometric deep learning to biological problems, with a growing emphasis on protein design, molecular conformation generation, and multi-omics analysis. Recent work demonstrates sophisticated integration of 3D geometry with deep learning architectures to model complex biomolecular interactions. Canada CIFAR Artificial Intelligence Chairs (CCAI Chair) Tencent AI Lab Rhino-Bird Gift Fund Amazon Faculty Research Award Microsoft-Mila collaboration grant National Research Council Canada (NRC) Collaborative Research and Development Grant Professor Tang actively mentors doctoral and master's students, with six recent graduates working on cutting-edge topics including graph neural networks for reasoning, protein design, and molecular representation learning. His research is supported by substantial funding from industry partners including Microsoft, Amazon, and Tencent, as well as government agencies like NRC. He collaborates extensively with biology labs worldwide, applying AI to solve real-world biomedical challenges. He leads a research group focused on geometric deep learning for drug discovery, with active projects in protein design using geometric-aware models and large language models for multi-omics analysis. The group has access to thousands of GPUs through industry collaborations, enabling large-scale experiments in molecular simulation and generative modeling.

Yicheng Songمشاهده پروفایل

استادیار

• Personalized Recommender Systems
• Digital Consumer Analytics
• Charity Analytics
• +۳ مورد دیگر

Yicheng Song serves as an Assistant Professor in the Department of Information & Decision Sciences at the Carlson School of Management, University of Minnesota. His research develops personalization technologies using machine learning and structural economic models to enhance user satisfaction and business revenue through data-driven solutions. His educational background includes: PhD in Business (2017), Questrom School of Business, Boston University PhD in Computer Science (2012), Institute of Computing Technology, Chinese Academy of Sciences BA in Computer Science (2006), Wuhan University Dr. Song's research specializes in Personalized Recommender Systems, Digital Consumer Analytics, and Charity Analytics. He develops advanced machine learning frameworks to address information overload in digital platforms, with applications spanning e-commerce, customer journey optimization, and nonprofit donation systems. His work uniquely bridges technical innovation in AI with practical business impact through structural economic modeling. Analysis of his publication trajectory (2011-2024) reveals consistent focus on personalization algorithms, evolving from multimedia geolocation to reinforcement learning for customer interventions. Recent work emphasizes explainable AI in donor matching systems and multi-category utility modeling, demonstrating increasing sophistication in handling complex consumer behavior data across digital and charitable contexts. His research excellence has been recognized with prestigious awards: Best Paper Award at INFORMS CIST 2018 Best Paper Award at WITS 2014 Runner-up for Best Paper Award at INFORMS CIST 2023 While specific grant details and student advising records aren't publicly listed, his publications indicate active collaboration with researchers in marketing, operations, and computer science. The absence of laboratory information suggests integration within the Carlson School's existing research infrastructure rather than standalone facilities.

Steven O. Kimbroughمشاهده پروفایل

استاد

• Artificial Intelligence
• Computational Rationality
• Decision Support Systems
• +۶ مورد دیگر

Steven O. Kimbrough is a Professor of Operations, Information and Decisions at the Wharton School, University of Pennsylvania. His research spans artificial intelligence, computational rationality, and strategic optimization with applications to political science, economics, and service innovation. He teaches courses like Agents, Games, and Evolution and Thinking With Models , focusing on experimental approaches to bounded rationality and uncertainty in decision-making. Primary Email: kimbrough@wharton.upenn.edu Office: 3730 Walnut Street, 565 Jon M. Huntsman Hall, Philadelphia, PA 19104 His research interests include: Artificial intelligence and metaheuristics for constrained optimization Evolutionary computation in electoral redistricting Agent-based modeling of market dynamics Logic modeling for normative reasoning Text mining applications in event analysis Publications demonstrate expertise in computational economics, political modeling, and service analytics. Recent work focuses on: Empirical validation of electoral compactness Strategic learning in oligopolies Multi-objective matching algorithms Feasible-infeasible solution spaces Service network optimization Teaching emphasizes: Game-theoretic approaches to strategic behavior Modeling life-cycle for energy sustainability Computational experiments in social science

• Gravitational Physics
• Astrophysics
• High-Energy Physics
• +۳ مورد دیگر

Juno Chan is a Research Fellow at the Niels Bohr Institute , University of Copenhagen , specializing in Theoretical High Energy, Astroparticle and Gravitational Physics . Holding a permanent position since 2025, Chan contributes to advanced research in gravitational wave astronomy and neutron star dynamics. Role: PhD Fellow (converted to Research Fellow) Location: Blegdamsvej 17, Copenhagen Ø Contact: chun.lung.chan@nbi.ku.dk , +45 35 32 87 21 Chan's research focuses on gravitational wave detection , neutron star magnetohydrodynamics , and gravitational lensing in astrophysical contexts. Key contributions include: Developing gravitational wave detection algorithms in lensed systems Modeling magnetized rotating neutron stars with relativistic MHD simulations Curating the LensCAT catalog for known gravitational lenses Multi-messenger lensing studies through LIGO-Virgo-KAGRA collaborations Publications and Research Trends Chan's 15 most recent publications (2023-2025) demonstrate expertise in gravitational wave astronomy , neutron star physics , and computational astrophysics . The work involves: Waveform analysis for lensed events MHD simulations of compact objects Multi-messenger lensing frameworks Signal processing for detector networks Cosmological implications of lensing Collaborations span international institutions in gravitational wave networks, with frequent contributions to Physical Review D and Monthly Notices of the Royal Astronomical Society . No formal awards or student mentorship details are publicly available.

Joseph Lewisمشاهده پروفایل

پژوهشگر ارشد

• Computational Archaeology
• Geographic Data Science
• Spatial Modelling
• +۴ مورد دیگر

Joseph Lewis is a Research Associate at the McDonald Institute for Archaeological Research within the Department of Archaeology at the University of Cambridge. He completed his PhD in Archaeology at Cambridge (2020-2024) following an MSc in Geographical Information Science with Distinction from the University of Leicester and a BSc in Applied Geology from the University of Plymouth. Dr. Lewis specializes in Computational Archaeology, with research interests spanning Geographic Data Science, Spatial Modeling, Spatial Uncertainty, and Bayesian Data Analysis. His work focuses on developing quantitative methods for analyzing archaeological landscapes through spatial statistics and computational approaches to understanding ancient movement and connectivity across Britain, Europe, and the Mediterranean. His publication record demonstrates expertise in Roman road networks, least cost path analysis, and uncertainty quantification in archaeological modeling. Lewis has developed innovative methodological approaches that combine spatial statistics with archaeological theory to better understand past human movement patterns and landscape use, with particular focus on Roman Britain and Mediterranean connectivity. Scientific Awards: Open-Oxford-Cambridge AHRC DTP - Judy and Nigel Weiss Studentships at Robinson College Match-funded Studentship Vice-Chancellor's & King's College Scholarship (now Honorary) CASA Prize for best paper on Spatial Analysis - GISRUK 2018 2nd Prize GISCRG Dissertation Prize 2017 Royal Institution of Chartered Surveyors Prize 2017 - Best Dissertation 2016 Lewis is the author and creator of the R package 'leastcostpath,' which provides tools for calculating Least Cost Paths and networks while incorporating barriers and uncertainty through probabilistic modeling. His technical proficiency spans R, Python, QGIS, ArcGIS, and SQL, enabling sophisticated spatial analyses of archaeological data across multiple periods and geographical regions.

• Biometrics
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Raghavendra Ramachandra is a Professor at the Department of Information Security and Communication Technology (IIK) , within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU) in Gjøvik. His research focuses on biometric systems, particularly in face, fingerprint, and finger vein recognition, with emphasis on presentation attack detection, morphing attack detection, and deep learning applications. Current research projects include: SALT (2022-2026) : Developing privacy-preserving facial biometric authentication systems. OffPAD (2022-2025) : Creating cryptographic tools and presentation attack detection for fingerprint biometrics. SWAN (2015-2020) : Developing biometric countermeasures against presentation attacks. His recent publications demonstrate technical expertise in: Face morphing attack detection using vision transformers and point cloud networks Image fusion techniques for multispectral biometrics GAN-based synthetic data generation for security evaluation Explainable AI approaches for biometric verification Professor Ramachandra also supervises PhD and Master’s students, and has extensive experience in leading national and EU research initiatives.

Jonathan Klassenمشاهده پروفایل

دانشیار

• Microbial Community Ecology
• Symbiosis
• Genomics
• +۴ مورد دیگر

Jonathan Klassen is an Associate Professor in the Department of Molecular and Cell Biology / Microbiology at the University of Connecticut . His research focuses on microbial community ecology, particularly using the fungus-growing ant symbiosis as a model system to study the evolution of microbial interaction networks. He couples genomics and chemical biology to understand molecular mechanisms in symbiosis and explore drug discovery opportunities. PhD : Microbiology and Biotechnology, University of Alberta Postdoctoral Study : Bacteriology, University of Wisconsin-Madison His work spans microbial ecology, symbiosis, secondary metabolite discovery, and comparative genomics. Recent publications highlight studies on microbial exclusion dynamics in ant symbiosis, ITS2 metabarcode biases, and chemical defense mechanisms in insect-associated microbes. Scientific Awards : “Highly Accessed” article in BMC Genomics (2012) Contact : Email: jonathan.klassen@uconn.edu Phone: 860-486-6890 Lab: @klassenlab

Felix Naumannمشاهده پروفایل

استاد

• Data Profiling
• Data Quality
• Data Integration
• +۴ مورد دیگر

Professor Felix Naumann is Chair for Information Systems at the Hasso Plattner Institute (HPI) at the University of Potsdam in Germany, where he leads the Information Systems research group. He is also Coordinator for MSc. Data Engineering and for the Data and AI track for MSc. Computer Science, and Speaker of the Research School on Data Science and Engineering. His extensive academic career includes visiting positions at CIRES Centre in Brisbane (2024-2025), SAP's Innovation Center (2020), AT&T Research (2016), and QCRI (2012). Professor Naumann's research focuses on data profiling, data cleansing, data integration, and data quality assessment with over 200 scientific publications. His work spans theoretical foundations and practical applications, with significant contributions to data quality metrics, metadata extraction, and AI-driven data preparation techniques. His research group develops prominent systems like Metanome for data profiling and Metis for data quality assessment. His recent publications demonstrate continued innovation in data management, with a growing intersection between traditional database research and AI applications. The 15 most recent papers show increasing focus on data quality for AI applications (KITQAR), multimodal data analysis (MELArt), and practical data cleaning frameworks that bridge database systems with machine learning pipelines. GI Dissertationspreis 2000 for best computer science PhD thesis IBM Research Division Award, 2002 Distinguished ACM member since 2021 Distinguished Reviewer Award - SIGMOD 2023 Best paper award at EDBT 2024 for Tasheeh paper Professor Naumann has successfully advised over 30 PhD students who now hold prominent positions at institutions like MIT, Google, Snowflake, and universities worldwide. His research has been funded by major grants including DFG Nachwuchsforschergruppe (2003-2008), IBM SUR Grant (2007), and DFG Forschergruppe Stratosphere (2010-2016). He leads the Information Systems research group at HPI, which includes PostDocs, PhD students, and student assistants working on projects like Metanome, Metis, KITQAR, and Janus, focusing on data profiling, quality assessment, and change exploration in data systems.

• Partial Differential Equ
• Wave Propagation
• Numerical Analysis
• +۵ مورد دیگر

Eliane Becache is a permanent researcher at ENSTA Paris within the Applied Mathematics Unit (UMA) and serves as Director of the POEMS laboratory (CNRS/ENSTA/INRIA) and Deputy Director of the EDMH doctoral school. Her research focuses on partial differential equations for wave propagation, numerical methods including mixed finite elements and perfectly matched layers (PML), and unbounded domain resolution techniques. Education: Numerical analysis of PDEs and finite element methods Teaching: Courses on finite elements, complex variables, and wave equations at ENSTA and Orsay master's programs Her publications from 2010-2023 address topics like: Stability analysis of PMLs in anisotropic/disersive media Numerical methods for convective/acoustic wave propagation Wave scattering in unbounded domains Time-domain and frequency-domain wave simulations Elastodynamic and electromagnetic wave modeling Scientific Outreach: Co-author of educational films on wave propagation, including the award-winning Digital modeling of the acoustic guitar (Henri Poincaré Prize, 2003). Organized WAVES conferences and scientific events.

Sonia Flissمشاهده پروفایل

استاد

• Wave Propagation
• Mathematical Analysis
• Numerical Analysis
• +۵ مورد دیگر

Sonia Fliss is a Full Professor at ENSTA Paris , affiliated with the Department of Applied Mathematics and the POEMS laboratory (UMR CNRS-INRIA-ENSTA) . She teaches applied mathematics courses on partial differential equations (PDEs) , finite element methods , and periodic homogenization to undergraduate and graduate students. Doctor in Applied Mathematics (2009) Authorized to supervise research (2019) Research Interests : Sonia Fliss specializes in the modeling and numerical analysis of wave propagation in periodic, quasi-periodic, and random media . Her work includes transparent boundary conditions , guided waves , and asymptotic methods for acoustic, electromagnetic, and elastic wave phenomena . Recent Publications highlight her contributions to the Half-Space Matching Method , edge states in honeycomb structures , and scattering problems in unbounded domains . Her numerical techniques address multi-scale waveguides and time-harmonic propagation . Laboratory : As a member of the POEMS team, she collaborates on interdisciplinary projects involving mathematical analysis , computational physics , and engineering applications in domains like defence, energy, and transport .

Andreas Bjerre-Nielsenمشاهده پروفایل

دانشیار

• Educational Public Policy
• AI for Public Policy
• Social Networks
• +۶ مورد دیگر

Andreas Bjerre-Nielsen is an Associate Professor at the Department of Economics and Copenhagen Center for Social Data Science (SODAS) within the Faculty of Social Sciences at the University of Copenhagen. His work bridges economics and data science to analyze education-related behavior and policies. Research Focus: School choice, digital technology in education, predictive analytics for interventions, and social network effects. Methodology: Combines econometrics with machine learning techniques to evaluate policy impacts. Research Trends: Recent publications emphasize algorithmic fairness in college admissions, socioeconomic impacts of school boundary policies, and behavioral insights from large-scale datasets. His 2025 Scientific Reports study reveals nation-scale social network dynamics. Awards and Grants: Tietgen Prize (2021) for young social science researchers 2024: Independent Research Fund Denmark grant for 'Coded Clues' project 2023: Major grant for school choice research Collaborations: Works with Danish Ministry of Children and Education through UDDanKvant unit, and collaborates with multidisciplinary researchers including Sune Lehmann and David Dreyer Lassen.