Find Academic Supervisors

Mizuho IwaiharaView profile

Professor

• Database Systems
• Web Information Systems
• Text Mining
• +3 more

Professor Mizuho Iwaihara is affiliated with Waseda University's Faculty of Science and Engineering and Graduate School of Information, Production, and Systems. Her research focuses on database systems, web information retrieval, text mining, security/privacy, and social media analysis. She has led significant projects on Wikipedia edit history analysis, knowledge graph construction, and privacy-preserving frameworks. Key research areas: Database Query Processing, Web Information Systems, Text Mining, Knowledge Management, Social Media Her recent publications address semantic analysis of collaborative content, topic evolution tracking, and privacy behavior modeling. Over 85 papers with 349 citations reflect her impact in database and social media research. Scientific achievements include: Best Demo Award (2014) for WikiReviz Best Paper Award (2008) at IFIP e-Business Conference EC-Web2006 recognition Grants from Japan Society for the Promotion of Science (JSPS) span multiple projects on knowledge graph development, social content analysis, and privacy-preserving systems. She supervises numerous graduate students and leads the Data Engineering Laboratory at Waseda University.

Juan Pedro Dominguez-MoralesView profile

Assistant Professor

• Neuromorphic Engineering
• Spiking Neural Networks
• Audio Processing
• +11 more

Dr. Juan Pedro Dominguez-Morales serves as an Assistant Professor in the Department of Computer Architecture and Technology at the University of Seville, where he has maintained academic appointments since January 2019. He concurrently holds membership in the Robotics and Computer Technology Lab and serves on the Editorial Board for PeerJ Computer Science. His educational trajectory includes: B.S. in Computer Engineering (2014) from the University of Seville M.S. in Computer Engineering and Networks (2015) from the University of Seville Ph.D. in Computer Engineering (2018) from the University of Seville, specializing in neuromorphic audio processing and spiking neural networks Research activities center on Neuromorphic Engineering and Spiking Neural Networks , with significant contributions to audio processing and deep learning applications. His scholarly scope extends across Artificial Intelligence , Computer Vision , and Robotics , demonstrated through editorial work in Security and Privacy, Algorithms, and Data Mining. This interdisciplinary approach bridges theoretical neuromorphic computing with practical implementations in real-time systems. Recent editorial contributions reveal concentrated expertise in machine learning innovation, particularly in efficient neural network architectures, generative modeling for visual synthesis, and adversarial defense mechanisms. His work consistently intersects computer vision fundamentals with deep learning advancements, emphasizing computational efficiency and security robustness in applied contexts. He received doctoral research funding through a grant from the Spanish Ministry of Education and Science. As an active researcher within the Robotics and Computer Technology Lab , he contributes to multidisciplinary projects integrating computer architecture principles with robotic systems development.

• Data Analysis
• Recommendation System
• Responsible AI
• +6 more

Dr. Angela Huo is an Associate Professor at the School of Computer Science within the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS). She holds multiple leadership roles including Deputy Director of Transnational Education for the Faculty of Engineering and IT, Responsible Academic Officer for Teaching and Learning of the School of Computer Science, Course Director for the Bachelor of Computing Science (Honours) program, and site coordinator for the South Pacific Programming Contests region of the International Collegiate Programming Contest (ICPC). Dr. Huo's research spans several key areas in computer science, with a primary focus on Data Analysis, Recommendation Systems, and Responsible AI. She specializes in utilizing AI and data analysis techniques to improve the efficiency and security of modern software systems. Her recent research interests include recommendation systems, anomaly detection, and privacy-preserving through data mining and deep learning. She has made significant contributions across multiple domains including multimodal learning, federated learning, causal inference, and human behavior recognition. Dr. Huo's publication record demonstrates a strong trajectory in cutting-edge AI research, with recent work focusing on conditional diffusion models for trajectory prediction, hybrid recommendation systems, causal learning to mitigate echo chambers, and adaptive waste detection. Her research shows a consistent pattern of addressing real-world challenges through innovative AI approaches, often combining multiple techniques to achieve superior results. Dr. Huo has secured research funding for projects including "Churn prediction scoping study" and "Army sovereign Software-Defined Battlefield Network." She has been actively involved in teaching and curriculum development, having created Industrial sub-majors for Google, AWS and iOS, Information Security sub-major, Data Analytics for Cybersecurity Microcredentials, and Cybersecurity and Privacy major since 2019. She has taught programming and cybersecurity management courses at the undergraduate level and served as HDR Coordinator of the School of Computer Science during 2021-2022.

Basil EllView profile

Researcher

• Semantic Web
• Natural Language Processing
• Knowledge Base Population
• +5 more

Basil Ell is a Researcher at the University of Oslo's Department of Data and Knowledge Management, affiliated with SIRIUS labs. He holds a PhD from Karlsruhe Institute of Technology (KIT) and has held postdoctoral positions at Bielefeld University's CITEC. His work bridges Semantic Web technologies and Natural Language Processing, focusing on knowledge base population, semantic parsing, and information extraction from text/tables. He splits his time between Oslo and Bielefeld, contributing to projects like semantically enhanced Virtual Research Environments and table understanding systems. Education: B.Sc./M.Sc. in Computer Science from Mannheim University of Applied Sciences, Pohang University of Science and Technology, and IIT Madras. PhD (2015) from KIT under Prof. Rudi Studer. Research Trends: Recent publications emphasize semantic data integration (e.g., materials informatics), link prediction in knowledge graphs, and clinical trial evidence synthesis. His work often combines RDF data structures with NLP techniques. Labs/Teams: Active in SIRIUS labs (Oslo) and previously at CITEC/Bielefeld's Semantic Computing group.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +9 more

Han Chen is an active academic researcher in the fields of artificial intelligence, machine learning, computer vision, and biomedical informatics, with extensive publications in top-tier venues such as CVPR, ICLR, IEEE Transactions, and Bioinformatics. The research spans diverse applications including medical image analysis, deepfake detection, federated learning, and multimodal affective computing. The research interests of Han Chen include artificial intelligence, machine learning, computer vision, medical image analysis, graph neural networks, and cybersecurity. The work emphasizes deep learning architectures, multimodal fusion, and robustness in AI systems, particularly in healthcare and security applications. Key themes include disentangled representation learning, contrastive learning, and transformer-based models. The recent articles demonstrate a strong trend toward multimodal AI, with applications in medical diagnostics (e.g., mammography, nasopharyngeal imaging), trustworthy AI (e.g., deepfake detection, backdoor defense), and efficient learning paradigms (e.g., federated learning, weak supervision). There is a consistent focus on enhancing model interpretability, robustness, and real-world applicability across domains. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: There is no explicit information about students or advising. However, the high volume of collaborative research suggests active involvement in research teams and likely supervision of graduate students. Funding sources are not mentioned, but the scope of work implies support from national or institutional research grants in AI and health informatics. Labs and Teams: While no specific lab or team is named, the collaborative nature of the work—especially with researchers in medical imaging and federated learning—suggests membership in a multidisciplinary AI research group, possibly affiliated with a medical school or engineering institute.

Jingxia WangView profile

Lecturer

• Urban Green Infrastructure Planning
• Environmental Analysis
• Sustainability Transformations
• +11 more

Dr. Jingxia Wang is a Lecturer in GIS at the School of Geography and Planning, University of Sheffield, UK. She joined the university in 2022 after working as a full-time postdoctoral researcher at the Institute of Geography, Ruhr University Bochum (RUB), Germany. Her academic journey includes a PhD (Dr.rer.nat.) from the Technical University of Munich (TUM), Germany, awarded in October 2020 for her dissertation on urban green infrastructure planning and development. Dr. Wang's research spans urban ecology, spatial planning, and environmental analysis, with a strong focus on sustainability transformations. Her work integrates concepts from urban ecology and spatial planning with advanced methods in computational landscape ecology, GIS, and remote sensing. She has conducted extensive research on land use planning and management in German metropolitan regions (Ruhr and Leipzig) and Chinese cities including Beijing, Inner Mongolia, Tianjin, Kunming, and Lijiang. Her research methodology combines traditional GIS and remote sensing with emerging technologies such as environmental sensors, Internet of Things, point cloud, digital twins, and other smart technologies for urban nature applications. This interdisciplinary approach enables comprehensive analysis of urban green infrastructure connectivity across spatiotemporal scales. Dr. Wang's publication record demonstrates a clear trajectory in ecosystem services assessment, green infrastructure planning, and urban sustainability. Her recent work shows increasing focus on equity in green infrastructure distribution, biodiversity conservation, and the development of decision-support tools for sustainable urban planning. The geographic scope of her research spans Europe and Asia, with particular emphasis on comparative urban studies between German and Chinese metropolitan regions. Doctoral project certified by HIGRADE doctoral program with distinction Extensive international research experience in both European and Chinese urban contexts Guest Editor for Remote Sensing Special Issue Active reviewer and editorial contributions to academic journals As an educator, Dr. Wang teaches several GIS-related modules including Advanced GIS Methods, Spatial Analysis, Sustainable Cities, GIS for Built Environment Professionals, and The Professional GIS Project. She serves as a personal tutor for undergraduate students (level 2) and supervises dissertations. Her teaching combines theoretical knowledge with practical applications in urban green infrastructure planning and environmental analysis. Dr. Wang maintains active research collaborations with EU and Chinese universities and institutions, reflecting her transnational research approach to urban sustainability challenges. Her work bridges disciplinary boundaries between geography, ecology, urban planning, and environmental science to address complex urban sustainability issues.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +7 more

Dr. Basil Ell is a faculty member at Bielefeld University's Faculty of Engineering, where he is affiliated with the Semantic Databases Group within the Center for Cognitive Interaction Technology (CITEC). He teaches several courses including Applied Optimization, Programming, Deep Learning, and International Courses in Data Science. His research focuses on the intersection of artificial intelligence, semantic web technologies, and data science. Dr. Ell's work spans multiple areas including knowledge graph representation, ontology engineering, natural language processing, and machine learning applications for dataset retrieval and question answering systems. He has made significant contributions to the fields of link prediction, relation extraction, and semantic data spaces. Dr. Ell's research output demonstrates a consistent focus on improving how machines understand and process semantic information. His recent work has explored the integration of numerical literals in knowledge graphs, advanced dataset retrieval systems, and the application of graph neural networks for relation extraction. He has also contributed to domain-specific applications in materials science and clinical evidence synthesis. ACORDAR: A test collection framework for dataset retrieval OTTR-centric ontology engineering methodologies Human-machine collaborative annotation systems Semantically accessible data spaces for specialized domains Dr. Ell maintains an active research agenda with numerous recent publications that bridge theoretical advances with practical applications. His work often involves interdisciplinary collaboration, particularly with the Center for Cognitive Interaction Technology at Bielefeld University.

Yue NingView profile

Assistant Professor

• Machine Learning
• Graph Neural Networks
• Deep Learning
• +7 more

Dr. Yue Ning is an Assistant Professor in the Department of Computer Science at Stevens Institute of Technology, where she conducts cutting-edge research at the intersection of machine learning, graph mining, and data analytics. Her work focuses on social informatics, healthcare, and financial technologies, with an emphasis on developing predictive and generative methods that capture spatio-temporal, dynamic, and interpretable patterns in large-scale datasets. Dr. Ning is affiliated with the Stevens Institute for Artificial Intelligence (SIAI), Center for Research Toward Advancing Financial Technologies (CRAFT), and Semcer Center for Healthcare Innovation (CHI). PhD in Computer Science (2018), Virginia Tech Dr. Ning's research spans several interconnected domains in artificial intelligence. Her work in Graph Neural Networks focuses on designing new architectures for dynamic and heterogeneous graph data, with applications in continual learning and knowledge graph reasoning. In healthcare AI, she develops methods for personalized care, epidemic forecasting, and medical representation learning. Her work on socially responsible AI addresses fairness in finance and medicine, fake news detection, and hate speech identification. She also explores transfer learning and federated learning frameworks for domain adaptation and bias mitigation in imbalanced data scenarios. Analysis of Dr. Ning's recent publications reveals a strong focus on bridging graph neural networks with causal inference for robust event prediction. Her work increasingly integrates large language models with structured graph data for healthcare applications, particularly in medical coding and clinical record analysis. A notable trend is her growing emphasis on fairness and privacy in machine learning systems, reflected in multiple publications on graph fairness, certified unlearning, and bias mitigation in recommender systems. Scientific Awards: NSF CAREER Award (2021-2026) for deep interpretable predictions for multi-scope temporal events NSF CRII Award (2020-2024) for learning dynamic graph-based precursors for event modeling Early Career Award for Research Excellence from Stevens Institute of Technology Research fellowship from NIH-sponsored AIM-AHEAD program Dr. Ning actively mentors PhD and undergraduate students, with several successful PhD graduates including Songgaojun Deng and Chang. Her research is supported by multiple significant grants, including three NSF awards totaling over $1.5 million, NVIDIA GPU grants, and institutional support from Stevens Institute of Technology. She has served as Principal Investigator on projects exploring dynamic graph learning for event prediction, interpretable temporal event modeling, and domain-informed generative frameworks for medical knowledge learning. Dr. Ning leads research within the Stevens Institute for Artificial Intelligence, where she contributes to initiatives in healthcare AI and socially responsible machine learning. Her lab focuses on developing frameworks that integrate domain knowledge with deep learning for applications in healthcare, social science, and finance. Current projects include deep graph learning for dynamic and heterogeneous data, transfer learning for domain adaptation, and machine learning methods for healthcare applications including personalized care and epidemic forecasting.

Aaron BeckerView profile

Assistant Professor

• Robotics
• Medical Engineering
• Swarm Intelligence
• +3 more

Aaron Becker is an Assistant Professor in the Department of Electrical Engineering at the Cullen College of Engineering, University of Houston. His research focuses on robotics, particularly using swarms of robots and magnetic manipulation for biomedical and engineering applications. His lab develops solutions involving particle relocation, surgical assist systems, and magnetic modular robots. He is affiliated with the Hewlett Packard Enterprise Data Science Institute (HPE DSI) and the Research Computing Data Core (RCDC). Research interests include swarm robotics, magnetic control systems, human-robot interfaces, and medical robotics applications such as clot removal and endovascular interventions. His work integrates algorithmic foundations with practical implementations, often validated through experiments and simulations. Recent work emphasizes magnetic rotating swimmers for biomedical uses, collaborative research on modular cubes, and interface design for robotic scope systems. His publications span topics from network optimization to surgical tool development, reflecting interdisciplinary approaches to robotics challenges. No scientific awards are explicitly mentioned in the provided materials. His research activities include grant-funded projects and partnerships with institutions like HPE DSI. The lab's innovations aim to advance programmable matter and medical robotics through novel control strategies and modular systems design.

Daniel GildeaView profile

Professor

• Natural Language Processing
• Machine Translation
• Semantic Parsing
• +3 more

Professor Daniel Gildea is a faculty member in the Department of Computer Science at the Hajim School of Engineering & Applied Sciences, University of Rochester . He holds a PhD from the University of California at Berkeley. His research focuses on statistical approaches to natural language processing, including machine translation, semantic parsing, and algorithms for NLP. He explores the intersection of formal linguistics and computational methods, with notable contributions to AMR (Abstract Meaning Representation) parsing and semantic role labeling. Education : PhD in Computer Science from UC Berkeley. Research Interests : Machine Translation Semantic Parsing Statistical NLP Parsing Algorithms (e.g., AMR Parsing) Formal Semantics Graph-Based NLP Models Research Trends : Gildea’s work emphasizes structured prediction, graph decomposition techniques for NLP, and the application of formal methods to linguistic problems. Recent projects include optimizing AMR-to-text generation, exploring semantic underspecification, and developing efficient parsing strategies for context-free grammars. Awards : None explicitly mentioned in the provided texts. Advising & Grants : No formal advisees or grant details listed. His research collaborations likely involve NLP-focused projects at the University of Rochester. Labs/Teams : Engaged with computational linguistics research groups, though specific lab affiliations are not detailed here.

Nicolaj Søndberg-JeppesenView profile

Part-Time Lecturer

• Bayesian Networks
• Cyber-Physical Systems
• Healthcare Informatics
• +4 more

Nicolaj Søndberg-Jeppesen is a Part-Time Lecturer at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design. His research focuses on Bayesian networks, cyber-physical systems, and their applications in healthcare, industrial diagnostics, and decision support systems. Key research interests include: Development of Bayesian network models for predictive maintenance and health monitoring in industrial systems Integration of operational data into continuous improvement frameworks Application of probabilistic graphical models in healthcare logistics and emergency management Design of smart manufacturing architectures for diagnostics and predictive maintenance Notable projects include: Animated Computer Games (ongoing since 2010) Extending the scope of graphical models for decision problems (ongoing since 2010) His recent work emphasizes: Patient flow prediction in emergency departments Risk assessment in home care deliveries Object-oriented Bayesian networks for component-level diagnostics Publications span applications in healthcare informatics, industrial engineering, and game theory, with a focus on practical implementations of probabilistic models.

Xiao WuView profile

Assistant Professor

• Biostatistics
• Causal Inference
• Climate and Health Research
• +7 more

Xiao Wu is an Assistant Professor of Biostatistics at the Mailman School of Public Health, Columbia University. He is also a verified member of the Data Science Institute (DSI) and affiliated with the Computational Social Science Committee, Foundations of Data Science, and Health Analytics initiatives. His work bridges statistical methodology and public health applications, particularly in climate and environmental health. Education: Ph.D. in Biostatistics, Harvard University (advised by Dr. Francesca Dominici and Dr. Danielle Braun) Postdoctoral Fellow in Data Science, Stanford University (Department of Statistics, with Dr. Trevor Hastie, 2021–2022) Xiao Wu's research focuses on developing advanced statistical and causal inference methods to assess the health impacts of environmental factors amid climate change. His work addresses methodological challenges in handling error-prone and time-series exposures, with applications in air pollution, extreme weather, and global health. He integrates data science techniques with public health questions, emphasizing real-world evidence, Bayesian modeling, and meta-analysis. His methodological innovations support rigorous evaluation of environmental policies and health interventions. His recent publications span high-impact journals such as Science Advances , The Lancet Planetary Health , and Annals of Applied Statistics . Collectively, his articles emphasize causal frameworks, scalable algorithms, and integrative modeling for environmental health. Key trends include the use of satellite data, hierarchical Bayesian models, and real-world data to estimate health risks under climate variability. Topics frequently involve exposure-response relationships, measurement error correction, and health disparity assessment. Scientific Awards: Forbes 30 Under 30 Xiao Wu has advised doctoral students and postdoctoral researchers in biostatistics and data science. His collaborative projects include designing Bayesian clinical trials and conducting meta-analyses using real-world evidence. While specific grants are not listed, his affiliations and research scope suggest active involvement in federally and foundation-funded studies at the intersection of climate, health, and data science. He is actively involved in interdisciplinary research teams, including the Data Science Institute and Health Analytics Center at Columbia. His work leverages large-scale datasets and computational methods to address pressing public health challenges related to climate change. Through these collaborations, he contributes to building robust statistical frameworks for policy-relevant environmental health research.

Francesco CauteruccioView profile

Assistant Professor

• Social and Complex Network Analysis
• Data Mining and Data Science
• Internet of Things (IoT)
• +3 more

Francesco Cauteruccio is a Tenure-Track Assistant Professor at the Department of Information Engineering, Electrical Engineering and Applied Mathematics (DIEM) at the University of Salerno, Italy. He is affiliated with the CORE and S 3 labs, focusing on interdisciplinary research at the intersection of social networks, IoT, and hybrid AI models. Ph.D. in Mathematics and Computer Science, University of Calabria (thesis: "Generalizing Identity-Based String Similarity Metrics: Theory and Applications") Italian National Scientific Qualification (ASN) for Associate Professor in Computer Science and Computer Engineering Visiting researcher at University of Klagenfurt (AI & Cybersecurity) and University of Lyon 1 (CERMEP/CREATIS) His research spans: Social & Complex Networks: Modeling cross-blockchain ecosystems, Reddit community dynamics, and sentiment propagation IoT & Hybrid Intelligence: Scope formalization, anomaly detection in MIoT networks, and air quality prediction systems Logic Programming: Integrating Answer Set Programming with machine learning for medical informatics and pattern mining Algorithm Design: Multi-Parameterized Edit Distance (MPED) for bioinformatics and network analysis Recent publications address network theory applications, hybrid reasoning frameworks, and high-utility pattern mining with formal constraint specification. He serves as guest editor for journals like Big Data and Cognitive Computing and Frontiers in Medical Engineering , and organizes international workshops on hybrid intelligence (HYDRA, VIPERC). Academic distinctions: Italian National Scientific Qualification for Associate Professor positions (2024) Guest Editor for 12+ special issues in data science and IoT journals Program/General Chair for 8 international workshops (2022-2024) His teaching portfolio includes courses on algorithms, distributed systems, and data analytics at University of Salerno, Polytechnic University of Marche, and University of Calabria.

• Theoretical Computer Science
• Computational Complexity
• Randomness Extraction
• +7 more

David Zuckerman is a prominent theoretical computer scientist known for his foundational contributions to computational complexity, pseudorandomness, and randomness extraction. His research spans over two decades, with consistent publication in top-tier venues under the Electronic Colloquium on Computational Complexity (ECCC). Although his current institutional affiliation is not specified in the text, his work is widely recognized and frequently collaborative with leading figures in the field. His research interests include: Randomness extraction and derandomization Computational complexity and circuit lower bounds Pseudorandom generators and hitting set constructions Error-correcting codes and combinatorial constructions Communication complexity and Boolean function analysis Applications in cryptography and distributed protocols Analysis of his recent publications reveals a sustained focus on improving the efficiency and scope of randomness extractors, especially for weak or structured sources such as Chor-Goldreich sources, interleaved sources, and algebraic sources. His work often bridges hardness and randomness, leveraging lower bounds to construct pseudorandom objects. A recurring theme is the derandomization of space-bounded and distributed computation, with implications for cryptography and coding theory. David Zuckerman has received significant recognition in the theoretical computer science community, though specific awards are not listed in the provided text. His collaborations with researchers like Salil Vadhan, Raghu Meka, and Eshan Chattopadhyay suggest leadership in major research initiatives. He has also contributed to foundational results in derandomization, such as optimal pseudorandomness from hardness assumptions and improved bounds for coin-flipping protocols. There is no mention of advising students or managing labs in the provided data, but his long-standing research output and mentorship through collaboration imply an academic leadership role. His continued publication into 2025 indicates active engagement in research.

• Database Systems
• Data Mining
• Machine Learning
• +7 more

K. Selçuk Candan is a Professor at Arizona State University, Tempe, Arizona, with an extensive research career spanning over two decades in database systems, data mining, and machine learning. His work demonstrates deep expertise in tensor decomposition, causal inference, spatio-temporal analysis, and time series modeling. Candan has maintained a prolific publication record with significant contributions to premier venues including SIGMOD, ICDE, VLDB, and CIKM. His research interests focus on the intersection of data management and machine learning, particularly in developing novel methods for tensor decomposition, causal discovery, and efficient data representation. Candan's work addresses critical challenges in handling high-dimensional data, building robust predictive models, and developing systems for complex decision-making scenarios. Recent research directions include spatio-causal modeling for environmental systems, causal benchmarks, and learned data mapping techniques that bridge traditional database systems with modern machine learning approaches. Analysis of Candan's recent publications reveals a strong trend toward causal inference applications across multiple domains including hydrology, environmental science, and healthcare. His work increasingly integrates deep learning techniques with traditional data management principles, as evidenced by research on diffusion models for time series imputation and learned data mapping for compression. The research demonstrates practical applications in wetland conservation, streamflow forecasting, building fault detection, and medical diagnostics, showing a commitment to solving real-world problems through advanced data science techniques. Candan has established significant collaborative relationships, particularly with Maria Luisa Sapino, Huan Liu, and several other researchers across multiple institutions. His work often appears in top-tier conferences and journals, reflecting the high impact and quality of his research contributions. While specific grant information isn't detailed in the available text, the scope and depth of his research suggest substantial research funding supporting his work in data science and causal inference.