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Michael FrenchView profile

Associate Professor

• Supercell and Tornado Dynamics
• Doppler Weather Radar Applications
• Mesoscale Meteorology
• +3 more

Michael French is an Associate Professor at Stony Brook University's School of Marine and Atmospheric Sciences (SoMAS), Department of Atmospheric Sciences. His research focuses on using Doppler radar data to study severe weather phenomena, particularly tornado dynamics and mesoscale processes. He holds a Ph.D. in Atmospheric Sciences from the University of Oklahoma (2012). His expertise includes analyzing phased array and dual-polarization radar data to improve understanding of supercell thunderstorms, tornado formation, and operational forecasting techniques. He has contributed to major field campaigns like VORTEX2 and has published extensively on radar signatures, hydrometeor analysis, and tornadic processes. Key research themes include radar-based tornado prediction, mesoscale snow banding, and improving short-term forecasting methods. His work integrates advanced radar technologies and GIS tools to study environmental impacts on severe weather events. Dr. French collaborates with institutions like NOAA and the University of Oklahoma, focusing on advancing radar meteorology and severe storm dynamics. His recent studies explore the influence of radar scanning strategies on thunderstorm observations and the role of hydrometeor size sorting in distinguishing tornadic supercells.

• Signal Processing
• Machine Learning
• Audio Engineering
• +7 more

Archontis Politis is an Assistant Professor in the Department of Computing Sciences at Tampere University's Faculty of Information Technology and Communication Sciences. His research focuses on signal processing, machine learning, and their applications in audio engineering, particularly in spatial audio, sound source separation, and parametric audio coding. He explores topics such as Ambisonics, reverberation control, and neural network-based approaches for audio processing. His work emphasizes spatial audio reproduction, including six degrees of freedom (6DOF) rendering, microphone array processing, and efficient compression techniques for higher-order Ambisonics. He also investigates sound event localization and detection, leveraging machine learning for real-world acoustic scenarios. His contributions span theoretical advancements in spherical harmonics and practical implementations of spatial audio systems. Recent research highlights include developing datasets for music source separation, improving synthetic-to-real generalization in classical music, and creating neural encoding models for irregular microphone arrays. His methodologies often integrate deep learning with traditional signal processing to address challenges in multi-speaker environments and dynamic acoustic scenes.

• Post-Quantum Cryptography
• Lattice-Based Cryptographic Schemes
• Homomorphic Encryption
• +3 more

Dr. Rachel Player is a Senior Lecturer in the Department of Information Security at Royal Holloway, University of London . Her research focuses on post-quantum cryptography, lattice-based cryptographic schemes, homomorphic encryption, and quantum algorithm applications in cryptanalysis. She holds a PhD in Information Security from Royal Holloway, supervised by Prof. Carlos Cid and Prof. Sean Murphy. Education: PhD in Information Security, Royal Holloway, University of London (supervisors: Prof. Cid & Prof. Murphy) Research Interests: Rachel explores cutting-edge areas in cryptography with a focus on privacy-preserving technologies. Her work bridges theoretical advancements and practical implementations, particularly in homomorphic encryption and post-quantum security protocols. Recent efforts emphasize applying quantum algorithms to cryptanalytic challenges. Professional Activities: Rachel actively contributes to standards development in cryptography, including organizing HomomorphicEncryption.org meetings and participating in ISO/IEC JTC 1/SC 27/WG 2 (cybersecurity standards). She also serves as an editor for the Designs, Codes and Cryptography journal. Labs/Teams: Collaborations include the PolSys team at Sorbonne Université (Paris) and the EU H2020 PROMETHEUS project. Current research is anchored in Royal Holloway's Information Security Department.

Ming YuanView profile

Professor

• High-dimensional statistics
• Machine learning
• Tensor methods
• +4 more

Ming Yuan is a Professor in the Department of Statistics at Columbia University and serves as Associate Director of the Data Science Institute. His research focuses on high-dimensional statistics, machine learning, and statistical methodology with applications in genomics, finance, and imaging. Yuan holds a Ph.D. in Statistics from the University of Wisconsin-Madison (2004) and a B.S. in Electrical Engineering from the University of Science and Technology of China (1997). Education: 2004 Ph.D., Statistics, University of Wisconsin-Madison 2003 M.S., Computer Science, University of Wisconsin-Madison 2000 M.S., Probability and Statistics, University of Science and Technology of China 1997 B.S., Electrical Engineering, University of Science and Technology of China Research Interests: Dr. Yuan’s work bridges theoretical and applied statistics, emphasizing scalable methods for high-dimensional data. Key areas include tensor decomposition, covariance estimation, and statistical machine learning. His contributions to methods like sparse inverse covariance estimation and matrix/tensor completion have found applications in finance, genomics, and image analysis. Publications: His recent work explores tensor-based methods for high-dimensional analysis and develops optimal algorithms for compressed sensing. Articles often address statistical theory and computational challenges in modern data science, reflecting a balance between foundational and applied research. Awards: 2025 JASA Theory & Method Invited Discussion Paper 2024 William F. Sharpe Award (JFQA) 2018 Medallion Lecturer (Institute of Mathematical Statistics) 2014 Guy Medal in Bronze (Royal Statistical Society) 2007 Leo Breiman Junior Award Professional Activities: Yuan has served as Co-Editor of The Annals of Statistics (2019–2021) and Program Secretary for the Institute of Mathematical Statistics (2018–2021). His work integrates interdisciplinary collaborations, particularly in biomedical imaging and financial econometrics.

Ka Ho ChowView profile

Assistant Professor

• Machine Learning
• Cybersecurity
• Artificial Intelligence
• +6 more

Ka Ho Chow is an Assistant Professor in the Department of Computer Science at the University of Hong Kong, part of the School of Computing and Data Science. He holds a PhD from Georgia Institute of Technology and was previously a research scientist at IBM Research. His research focuses on the intersection of machine learning, cybersecurity, and scalable systems, emphasizing trustworthy AI and defense against security/privacy threats in federated learning, large language models, and visual recognition systems. Key achievements include IBM PhD Fellowship (2022) and Croucher Scholarship (2021). Education: PhD in Computer Science from Georgia Tech (2020), advised by Prof. Ling Liu. His work spans algorithmic optimization, infrastructure resilience, and adversarial machine learning. Current research explores attack-resilient solutions for centralized/federated learning and AI system vulnerabilities. Recent articles highlight innovations in federated learning security, gradient inversion attacks, backdoor detection, and privacy-preserving techniques. He has openings for PhD students interested in AI security and trustworthy systems. His lab collaborates on projects involving blockchain fraud detection (ZipZap), facial recognition privacy (Personalized Masks), and graph neural network robustness. Awards: IBM PhD Fellowship (2022), Croucher Scholarship (2021). Active in guiding PhD candidates and advising on microservices cloud migration (Atlas/SCAD systems). Research outputs include over 30 peer-reviewed papers spanning cybersecurity, AI ethics, and distributed learning frameworks.

• Big Data Management
• Data Mining
• Graph Learning
• +9 more

Dr. YANG, Renchi is an Assistant Professor in the Department of Computer Science at Hong Kong Baptist University, Faculty of Science. He earned his BEng in Software Engineering from Beijing University of Posts and Telecommunications and his PhD in Computer Science from Nanyang Technological University, followed by a postdoctoral fellowship at the National University of Singapore. His research is centered on developing efficient algorithms and systems for large-scale data management and analysis. His research interests include: Big Data Management and Analysis Graph Learning and Network Embedding Databases and Data Management (especially graph query processing and similarity search) The Web and Information Retrieval (search, ranking, recommendation, web mining) Data Mining and Machine Learning (social network analysis, text mining, large language models) Dr. Yang’s recent publications span top conferences such as KDD, SIGMOD, WWW, ICDE, and AAAI, focusing on scalable graph clustering, network embedding, GNNs, and LLM integration. His work emphasizes algorithmic efficiency, scalability, and practical applications in real-world graph data. Scientific honors include: VLDB 2021 Best Research Paper Award 2022 ACM SIGMOD Research Highlight Award Best Paper Award Nominee in WWW 2022 Honorable mention as best PC member in WWW 2022 Dr. Yang actively mentors PhD and research students, currently supervising several RPg students including LIN Xiaoyang, LAI Yurui, and ZHENG Haoran. He has secured research funding enabling PhD scholarships and research assistant positions. He serves on the program committees of major conferences like VLDB, KDD, WWW, and SIGIR, and reviews for journals including TKDE and VLDBJ. He is a key member of the Database Research Group at HKBU, which has published extensively in top venues, including 8 papers at SIGMOD 2023. His research lab, the LAGAS Group, focuses on large-scale graph analytics and systems. The team is actively working on projects involving graph clustering, embedding, GNNs, and integration with large language models. Dr. Yang is currently recruiting PhD students for 2026 and research assistants for 2025, indicating active and expanding research operations.

Fenglong MaView profile

Associate Professor

• Data Mining
• Health Care Informatics
• Machine Learning
• +5 more

Fenglong Ma is an Associate Professor at Pennsylvania State University, affiliated with the Institute for Computational and Data Sciences and the Center for Socially Responsible Artificial Intelligence. His research focuses on data mining, healthcare informatics, machine learning, natural language processing, and multimodal learning. He holds a Ph.D. from the University at Buffalo (2019) and degrees from Dalian University of Technology. His work addresses challenges in federated learning, medical AI, adversarial robustness, and multimodal systems. Key contributions include innovations in quantization for large language models, federated knowledge injection, and medical vision-language benchmarking. Recent publications explore topics like collaborative fairness in federated learning, robust medical vision-language models, and adversarial attack mitigation. His research bridges theory and practical applications in healthcare, cybersecurity, and personalized recommendation systems. He leads the PSU Data Science Lab and collaborates on projects involving AI ethics, multimodal data integration, and scalable medical foundation models.

Kevin GaryView profile

Associate Professor

• Software Architecture and Design
• Open Source Software
• Agile Methods
• +3 more

Kevin Gary is an Associate Professor in the School of Computing and Augmented Intelligence (SCAI) within the Ira A. Fulton Schools of Engineering at Arizona State University (ASU). He joined ASU in 2004 after prior industry experience and faculty work at the Catholic University of America. His research focuses on software agility, open source software, and applications in healthcare and e-learning. He has contributed to mHealth platforms addressing pediatric chronic conditions and adaptive e-learning systems. Education: Ph.D. in Computer Science from Arizona State University (1999). Research Interests: Software Architecture, Agile Methods, Open Source Software, Healthcare Informatics, and Educational Technology. His recent work explores agile impact on regression testing and lean metrics in open source software. He has developed mobile health apps for asthma, epilepsy, and anxiety, leveraging agile principles and AI. Teaching & Innovation: Created the Software Enterprise program, an industry-aligned pedagogy integrated into ASU’s software engineering curriculum. This initiative earned the President’s Award for Innovation in 2011. He has taught courses in software engineering, web applications, and secure software systems. Grants & Projects: Led projects funded by NSF, industry partners (e.g., UNICON, GEORGETOWN UNIV MED CTR), and foundations (Children’s National Medical). Notable projects include the Image-Guided Surgical Toolkit and the ATIC-funded Software Enterprise pedagogy model. Service: Reviewed for journals/conferences, served as Associate Chair of computing programs, and contributed to professional organizations (IEEE, ACM, ASEE).

Hanan SametView profile

Professor

• Spatial Databases
• Data Structures
• Geographic Information Systems (GIS)
• +5 more

Hanan Samet is a Distinguished University Professor at the University of Maryland's Computer Science Department, affiliated with the Institute for Advanced Computer Studies (UMIACS) and the Center for Automation Research. He holds a Ph.D. from Stanford University (1975) and specializes in spatial databases, data structures, and geographic information systems. His research bridges computer science and geospatial analytics, with applications in image databases, computer vision, and spatio-temporal data management. Education: Ph.D., Computer Science, Stanford University, 1975 Research Interests: Focuses on spatial data structures, GIS, spatio-textual systems like NewsStand and CoronaViz, trajectory analysis, and metric indexing. His work emphasizes scalable algorithms for spatial networks and multimedia databases. Notable Projects: CoronaViz : Tracks disease spread via spatio-temporal data visualization NewsStand : Maps news articles geospatially SAND: Spatial browser for digital government Awards: ACM Paris Kanellakis Award (2014), IEEE McDowell Award (2015), UCGIS Research Award, and Fellowships in ACM/IEEE/AAAS. Recognized for advancing spatial database theory and practice. Grants/Advising: Leads NSF-funded projects on spatio-textual extraction and similarity search. Advises graduate students (e.g., Nicole Schneider, Montana Hoover) and undergraduate researchers. Labs/Teams: Active in UMIACS and the Center for Automation Research, collaborating on projects like VASCO (spatial visualization tools) and MARCO (image database systems).

• Multimedia Modeling
• Machine Learning
• Computer Vision
• +7 more

Dr. Stevan Rudinac is a Researcher at the University of Amsterdam's Faculty of Economics and Business , Section Business Analytics . His work focuses on interactive learning systems and multimodal data analysis, particularly in urban contexts and multimedia modeling. Education: PhD in Multimedia and Information Retrieval from Delft University of Technology (2013). Research Interests: Stevan specializes in multimedia modeling , hypergraph learning , and interactive video search . He develops frameworks for scalable analysis of social networks, urban imagery, and large multimodal datasets, bridging machine learning with practical applications in city planning and financial social media. Recent Trends: His 2024-2025 publications highlight large language model optimization , diffusion model evaluation , and dynamic graph embedding for meme stocks. Collaborative projects include the CASTLE 2024 dataset and Exquisitor , a system for 100 million image exploration. Labs & Teams: He contributes to the Business Analytics group at UvA, collaborating with Prof. Marcel Worring and Dr. Björn Þór Jónsson. He co-organized the UrbanMM'21 workshop and participates in ACM Multimedia and MMM conferences.

Oleg LashininView profile

Researcher

• Recommender Systems
• Machine Learning
• Artificial Intelligence
• +3 more

Oleg Lashinin is an active researcher in the field of Recommender Systems , with a focus on Machine Learning , Temporal Modeling , and User Behavior Analysis . He has contributed to 15 recent publications spanning 2021–2025, including conference papers at ECIR, SIGIR, RecSys, and workshops like KaRS@RecSys and ORSUM@RecSys. His work explores advanced techniques such as Self-Attention Models , Time-Aware Item Weighting , and Cost-Constrained Recommendations . Key research trends in his publications include Deep Learning for sequential recommendation tasks, Crowdsourcing for explanation evaluation, and Temporal Dynamics in user behavior. Notable projects include the GPT3RecBot Telegram chatbot and the RecBaselines2023 dataset for benchmarking recommender systems.

• Machine Learning
• Statistical Methodology
• Computational Biology
• +5 more

Eric P. Xing is a Professor at the Language Technologies Institute of Carnegie Mellon University , and currently serves as President of the Mohamed bin Zayed University of Artificial Intelligence . His work bridges machine learning methodology with computational biology and large-scale AI systems . Research Focus: Developing machine learning theory for high-dimensional, dynamic data Building foundation models for biology (AIDO, scLong, ProteinAligner) Designing scalable AI architectures (Pollux, LLM360, PAN) Advancing interpretable and controllable NLP systems Scientific Leadership: Founded the SAILING Lab at CMU Co-chaired ICML 2014 and ICML 2019 Recipient of the Jay Lepreau Best Paper Award (OSDI 2021) Education & Mentorship: Advises PhD students across machine learning and computational biology Alumni include faculty at ETH Zurich, University of Chicago, and UC San Diego

• Machine Learning
• Quantum Computing
• Medical Data Analysis
• +7 more

Luka Radic is a Researcher in the Machine Learning Section at the Department of Computer Science, University of Copenhagen. His work bridges theoretical and applied research in machine learning, with a focus on quantum machine learning , large language models , and fairness in AI systems.

• Digital Humanities
• Digital Musicology
• History of Technology
• +3 more

Eamonn Bell is an Assistant Professor in the Department of Computer Science at Durham University, with affiliations to the Institute of Advanced Study. His research focuses on digital humanities, applying computational methods to musicology and examining the history of digital technology in music production. He leads projects like CCP-AHC and DISKAH to enhance digital infrastructure for arts researchers. Previously, he was a postdoctoral Research Fellow at Trinity College Dublin, studying the cultural impact of the Compact Disc. He holds a PhD in Music Theory from Columbia University (2019) and a joint degree in Music and Mathematics from Trinity College Dublin (2013). His educational background includes advanced studies in music theory, digital humanities, and mathematics. He teaches courses on algorithms, software engineering, and music processing within Durham’s Computer Science curriculum. Supervision includes postgraduate students focusing on music technology and computational analysis. Research interests span digital musicology, the history of technology, and accessibility of computational tools for non-specialists. Awards include the Government of Ireland Postdoctoral Fellowship (2019–2021). His work addresses ethical issues in genetic studies of musicality and explores emerging digital tools for music analysis, such as time-coded YouTube comments and CD data extraction techniques. Key initiatives include designing DRI projects for UK-based researchers and advancing digital skills through collaborative frameworks. He actively participates in academic conferences and has published widely on topics ranging from media archaeology to interdisciplinary computational research.

Latifur KhanView profile

Professor

• Data Mining
• Cybersecurity
• Machine Learning
• +2 more

Dr. Latifur Khan is a Professor in the Department of Computer Science at the University of Texas at Dallas' Erik Jonsson School of Engineering and Computer Science. He directs the Database and Data Mining Laboratory and conducts research in data mining, cybersecurity, and semantic web technologies. Research domains include: Large language models for threat detection Fairness in machine learning Vulnerability analysis in software systems Graph-based information retrieval Recent publications focus on AI security applications in transportation systems, political conflict analysis using NLP, and federated learning for IoT security. His work consistently bridges theoretical algorithms with practical cybersecurity implementations. Research grants include funding from NSF, NASA, Raytheon, Nokia, and SUN Microsystems. Teaching includes courses in Plant Breeding (PBG 450/550) and Breeding Clonal Crops (PBG 551).