جست‌وجوی استاد و پژوهشگر

Sourav Medyaمشاهده پروفایل

استادیار

• Machine Learning
• Graph Mining
• Data Science
• +۳ مورد دیگر

Sourav Medya is an Assistant Professor in the Department of Computer Science at the University of Illinois at Chicago (UIC). Prior to joining UIC, he was a research assistant professor at the Kellogg School of Management and the Northwestern Institute on Complex Systems (NICO) at Northwestern University. He earned his Ph.D. in Computer Science from the University of California, Santa Barbara (2019) and a Master of Engineering in Computer Science and Automation from the Indian Institute of Science, Bangalore, India. Ph.D., University of California, Santa Barbara, 2019 M.Eng., Indian Institute of Science, Bangalore, India His research lies at the intersection of graphs, machine learning, and data science, with a focus on bias, fairness, and interpretability in AI. He also develops machine learning techniques with applications in healthcare, infrastructure, and computational social science. His recent publications address topics such as global counterfactual explainers for GNNs, game-theoretic node selection, and interpretable logical rule extraction from graph models. Medya has advised students in areas like multimodal learning, graph neural networks, explainable AI, and efficient AI. His scientific awards include the UIC Outstanding MS Thesis Award (2024) and Best Paper Award at the Machine Learning on Graphs (MLoG) Workshop at WSDM'23. He has reviewed for top conferences including NeurIPS, ICML, KDD, and AAAI, and recently taught courses on Machine Learning on Graphs (CS 519) and Introduction to Data Science (CS 418) at UIC.

• Adversarial Machine Learning
• Cybersecurity
• Data Mining
• +۵ مورد دیگر

Bowei Xi is an Associate Professor at the Department of Statistics at Purdue University. He holds a Ph.D. in Statistics from the University of Michigan (2004) and has made significant contributions to adversarial machine learning, cybersecurity, and metabolomics. Education: Ph.D. in Statistics, University of Michigan (2004) His research focuses on: Adversarial Machine Learning – Developing defenses against attacks on AI systems Cybersecurity – Integrating statistical methods with network security Metabolomics – Applying statistical analysis to biological data Big Data – Creating scalable analytical frameworks Differential Privacy – Balancing data utility with privacy preservation Recent publications highlight: Advancements in causal inference under privacy constraints Novel cyber deception strategies for battlefield IoT Applications of graph neural networks with dropout techniques Statistical defense mechanisms against adversarial examples Awards include: Faculty of 1000 Evaluation (2011) US Patents #7272707 and #7490234 for application server optimization He teaches STAT 514: Design of Experiments with a focus on hands-on learning and statistical software (SAS) integration. His office hours and contact details are publicly listed for student engagement.

Rada Mihalceaمشاهده پروفایل

استاد

• Natural Language Processing
• Text Mining
• Computational Linguistics
• +۵ مورد دیگر

Rada Mihalcea is a Professor at the University of Michigan in the Computer Science and Engineering department. She actively promotes Natural Language Processing (NLP) applications for social good and founded the ACL Year Round Mentorship program. University: University of Michigan School: College of Engineering Department: Computer Science and Engineering Academic Rank: Professor Research Interests include: Natural Language Processing for social good applications Text Mining and Sentiment Analysis Deception Detection and Multimodal Analysis Cross-Cultural Computational Linguistics Educational Technology and Gender Studies Recent research trends focus on: Fake news detection using multimodal features Lexicon-based cross-cultural value analysis Thermal imaging for affective computing Deception detection through linguistic and physiological cues Applications in healthcare and counseling analysis Scientific Awards : Sarah Goddard Power Award (2019) Best Paper Award at CICLING (2019) Best Paper Award at SocInfo (2018) Additional Contributions : Co-authored textbooks on Text Mining and Graph-based NLP Developed FunPython tutorial for children Collaborative work with the School of Music on the Ada Lovelace Opera Active in diversity initiatives in computing

• High-performance computing
• Graph algorithms
• Graph machine learning
• +۱ مورد دیگر

Ariful Azad serves as an Associate Professor in the Department of Computer Science & Engineering at Texas A&M University, where he leads research at the intersection of high-performance computing and graph analytics. His work focuses on developing scalable algorithms for graph machine learning with applications in bioinformatics and security informatics. Educational Background: Ph.D. in Computer Science, Purdue University (2014) B.S. in Computer Science and Engineering, Bangladesh University of Engineering and Technology (2006) Research Focus: Dr. Azad specializes in high-performance graph algorithms , particularly for distributed-memory systems. His pioneering work includes the Combinatorial BLAS library and novel approaches for graph neural networks (GNNs), with emphasis on explainability through Shapley values and optimization of sparse matrix operations. His bioinformatics research tackles large-scale metagenomics challenges through projects like Exabiome. Publication Trends: Recent publications (2023-2025) reveal three dominant themes: (1) Scalable GNN explanation frameworks using distributed Shapley values, (2) High-performance sparse linear algebra for graph embeddings and knowledge graphs, and (3) Bioinformatics applications in metagenomics and network alignment. His work consistently bridges theoretical algorithm development with practical implementations for exascale systems. Scientific Recognition: NSF CAREER Award (2024) for foundational contributions to scalable graph algorithms Indiana University Trustee's Teaching Award (2024) U.S. Department of Energy Early Career Award (2021) Research Leadership: As principal investigator for multiple federal grants, Dr. Azad directs projects advancing graph analytics at extreme scales. His work on Weapons of Mass Destruction knowledge graphs demonstrates applied security research, while Exabiome represents significant contributions to computational biology. He actively develops open-source tools like PLANETALIGN for network analysis benchmarking, fostering reproducibility in computational science.

Onet-Marian Zsuzsannaمشاهده پروفایل

مدرس

• Software Engineering
• Machine Learning
• Data Mining
• +۳ مورد دیگر

Dr. Onet-Marian Zsuzsanna is a Lecturer in the Department of Computer Science at Babes-Bolyai University in Cluj-Napoca, Romania. Her research focuses on applying machine learning techniques to software engineering challenges, particularly software defect prediction and restructuring. Academic Rank: Lecturer Affiliation: Babes-Bolyai University Department: Computer Science Email: zsuzsanna.onet@ubbcluj.ro Research Interests : Dr. Marian specializes in developing machine learning models (clustering, association rules, reinforcement learning) for software defect detection, package-level restructuring, and test order optimization. She also explores applications of Formal Concept Analysis in text summarization and music pattern discovery. Recent Publications highlight her work in source-code embeddings, unsupervised learning for software analysis, and comparative studies of online/traditional learning environments. Her methods often integrate domain-specific metrics and AI-driven optimization. Contact : zsuzsanna.onet@ubbcluj.ro | Office: Teodor Mihaly street, Room 440

Krzysztof J. Kochutمشاهده پروفایل

استاد

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

Krzysztof J. Kochut is a Professor in the School of Computing at the University of Georgia. His career at UGA spans from Limited Term Assistant Professor (1987–1988) to his current professorial role since 2001. He holds a Ph.D. in Computer Science from Louisiana State University (1987) and an M.S. from the University of Warsaw (1982). Education: Ph.D., Computer Science, Louisiana State University (1987) M.S., Computer Science, University of Warsaw (1982) His research focuses on knowledge graphs, ontologies, semantic web technologies, bioinformatics, distributed systems, and computational intelligence. He has developed frameworks for knowledge graph embedding, entity summarization, and semantic resource federation. Recent publications highlight knowledge graph mining, random walk-based embeddings, and relation prediction using large language models. Funded by NIH and NSF, his work addresses challenges in biomedical data integration and dark proteome annotation. Scientific Awards: Student Career Success Influencer Award 2024 Student Career Success Influencer Award 2022 Outstanding Faculty Service Award 2016 Best Paper Award 2015 Professor Kochut has received grants for projects like Annotating dark ion-channel functions using knowledge graph mining (NIH, 2021–2025) and Integrated Technology Resource for Biomedical Glycomics (NIH, 2002–2008). He leads the development of tools such as GTXplorer for glycosyltransferase analysis and participates in semantic web initiatives.

• Graph Theory
• Data Mining
• Network Analysis
• +۲ مورد دیگر

Aristides Gionis is a Research Professor Fellow at Aalto University specializing in advanced network analysis and data mining. His work bridges theoretical graph algorithms with practical applications in dynamic and signed networks. His research focuses on graph theory and temporal network analysis , particularly in discovering dense subgraphs, community structures, and event patterns in evolving networks. Key methodologies include contrastive subgraph mining for explainable AI in brain networks and polarization detection in social systems. His publication trends reveal consistent contributions to top-tier venues like KDD, PAKDD, and ECML PKDD, with emphasis on algorithmic solutions for network segmentation, correlation mining, and interpretable classification. Primary application domains include social networks, neuroscience, and temporal data streams. As a Fellow at Aalto University, he leads research in computational network science without explicit departmental affiliation stated in available sources.

Roy E. Welschمشاهده پروفایل

استاد

• Statistics
• Data Science
• Statistical Learning
• +۱۰ مورد دیگر

Dr. Roy E. Welsch is the Eastman Kodak Leaders for Global Operations Professor of Management and a Professor of Statistics and Data Science at the MIT Sloan School of Management and the MIT Center for Statistics and Data Science . He currently serves as Director of the MIT Center for Computational Research in Economics and Management Science . Education : AB in Mathematics from Princeton University (1969), MS and PhD in Mathematics from Stanford University Dr. Welsch's research spans advanced statistical methodologies and their interdisciplinary applications. His work focuses on: Robust statistical methods for regression and covariance modeling Applications in financial markets, biomedical imaging, and drug repurposing Machine learning algorithms for high-dimensional data analysis Statistical computing and computational finance Uncertainty quantification in experimental design His recent publications emphasize cross-domain applications of machine learning, particularly in financial forecasting, biomedical diagnostics, and network behavior modeling. Notable trends include: Development of robust statistical frameworks Integration of NLP techniques in financial analysis Biomedical image processing algorithms for disease quantification Time-series and high-content analysis Portfolio optimization under uncertainty Scientific Recognition : Fellow of the Institute of Mathematical Statistics Fellow of the American Statistical Association Fellow of the American Association for the Advancement of Science Eastman Kodak Leaders for Global Operations Professorship As a dedicated educator, Dr. Welsch teaches Data Analysis and Applied Statistics courses focusing on regression modeling, experimental design, and quality control with applications in finance and marketing.

• Artificial Intelligence
• Explainable AI
• Computational Argumentation
• +۳ مورد دیگر

Antonio Rago is a Researcher in the Department of Computing at Imperial College London. He specializes in Explainable Artificial Intelligence (XAI) with a focus on computational argumentation frameworks and their integration with data-driven AI systems. His work bridges symbolic AI and machine learning to enhance transparency and societal benefit in AI applications. Education: PhD in Computing (2019) from Imperial College London, supervised by Prof. Francesca Toni and Dr. Marco Aurisicchio, with an MEng in Automotive Engineering from Loughborough University (2012). Research: Explores explainable AI through argumentation semantics, counterfactual reasoning, and hybrid symbolic-statistical methods. Application domains include e-learning, Formula One race strategy, healthcare, and mechanical engineering. Workshops: Organizer of international workshops like Arg&App 2025 and ArgXAI-25, and co-organizer of previous events at KR, ECAI, and COMMA conferences. Publications: Active in top AI venues (KR, IJCAI, AAAI, AAMAS) with over 25 publications since 2016, emphasizing argumentation-based explanations and robust AI systems. Industry Experience: Former Race Strategy Engineer at Mercedes AMG Petronas F1 Team (2012-2014) and Project Manager at Green Lifting Ltd. (2014-2017).

• Machine Learning
• Information Fusion
• Data Clustering
• +۳ مورد دیگر

Julien Ah-Pine is a lecturer at Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS) under Université Clermont Auvergne , with affiliations at Institut national polytechnique Clermont Auvergne and École des Mines de Saint-Étienne . He also holds a Researcher position at CNRS. Research Interests His work spans machine learning , information fusion , aggregation functions , and multi-criteria decision support , with a focus on complex data types like graphs , functional data , and multi-view datasets . Recent publications emphasize anomaly detection in spectral data streams , online learning , and interpretable AI for industrial applications. Selected Publications 2025 work on OnlineBootKNN introduces a novel framework for real-time spectral anomaly detection, while 2024 research explores multiple kernel methods in functional data classification. Earlier studies cover graph-based clustering , relational data mining , and linguistic network models for NLP tasks. Laboratory & Collaborations Works within LIMOS laboratory at Université Clermont Auvergne, collaborating with institutions like Mines Saint-Étienne and CNRS. Key partnerships include Nicolas Rojas Varela and Engelbert Mephu Nguifo on data stream analysis projects.

Len Dawsمشاهده پروفایل

استاد مدعو

• Machine Learning
• Adversarial Machine Learning
• Noisy Label Learning
• +۳ مورد دیگر

Len Daws serves as an Adjunct Professor in the Department of Computer Science at Rowan University's College of Science & Mathematics while concurrently holding a Sr. Engineering Manager position at Lockheed Martin in Moorestown, NJ. With over 12 years of teaching experience across New Jersey state and community colleges, he is currently in his sixth year instructing the Computers & Society course at Rowan University, focusing on large-scale software system development and integration. His research specializes in machine learning robustness under adversarial conditions, particularly addressing noisy label learning challenges and cybersecurity threats in AI systems. Key contributions include modeling label noise dependencies across multiple annotators, developing contributor-aware defenses against backdoor attacks, and analyzing fundamental trade-offs between attack strength and detectability in adversarial settings. Analysis of his publication history reveals a consistent trajectory toward securing machine learning pipelines against sophisticated threats, with recent work emphasizing labeler-dependent noise modeling and spectral ranking techniques for truth extraction from unreliable sources. His scholarship bridges theoretical machine learning with practical defense mechanisms for real-world AI deployment.

Shen-Shyang Hoمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Artificial Intelligence
• +۷ مورد دیگر

Shen-Shyang Ho is a Full Professor in the Department of Computer Science at Rowan University's College of Science & Mathematics. His work spans machine learning, data mining, and edge computing with applications in urban mobility, precision agriculture, and data privacy. He leads NSF-funded research projects on dynamic graph analysis and spatiotemporal anomaly detection. Ph.D. in Computer Science, George Mason University Post-Doctoral Associate, Caltech & NASA JPL B.S. in Mathematics with Computational Science, National University of Singapore Research expertise includes graph-based machine learning, conformal prediction, cooperative inference, and privacy-preserving ML. Current work focuses on federated learning for edge devices and anomaly detection in evolving systems. He has developed tools like SplitTracer for cooperative inference evaluation and ParkGauge for urban mobility monitoring. Recent publications highlight his contributions to 2026 Pattern Recognition journal (martingale-based graph analysis), 2025 IEEE ICAIC conference (blockchain gas optimization), and 2024 ACM SAC symposium (shared mobility systems). His work integrates machine learning with real-world constraints across energy grids, transportation, and agricultural technology. NSF Grant (2022) for Dynamic Graph Anomaly Detection NSF Grant (2018) for Spatiotemporal Analysis Google Scholar Classic Paper Recognition (2017) for 2006 Radar Micro-Doppler Study Professional memberships include the Association for Computing Machinery (ACM). His teaching portfolio ranges from introductory programming to advanced ML courses. He previously held positions at Nanyang Technological University before joining Rowan in 2016.

• Supply Chain Analytics
• Logistics Management
• Operations Research
• +۵ مورد دیگر

Professor Ying Xie is a leading academic at Cranfield University's School of Management, holding the position of Professor of Supply Chain Analytics. Her work bridges engineering, data science, and business management across diverse sectors including maritime logistics, healthcare, and disaster response. Her educational background includes a BEng, MSc, and PhD in Management Science from Coventry University. Key research interests focus on supply chain digitalization and decarbonization , renewable energy supply chain configuration , and AI applications in operational resilience . Her work consistently integrates machine learning with real-world business challenges, particularly in port management and sustainability. Recent publications reveal strong trends in maritime decarbonization (25%), AI-driven supply chain optimization (30%), and healthcare systems resilience (20%). Her research increasingly addresses climate adaptation through circular economy models and clean energy transitions. Principal Fellow of the Higher Education Academy (HEA) Expert reviewer for UKRI MRC, British Academy, and EU Horizon Professor Xie has supervised numerous PhD completions and secured two dedicated scholarships for port management and digital twin research. Her £20m+ grant portfolio includes major projects with EPSRC (£7.6m Net Zero Aviation CDT), Innovate UK (£174k Sustainability Platform), and EU Horizon (£5m MEDiate disaster management). She actively collaborates with the Port of Dover, Ocado Technology, and NHS England. She leads the Co-Innovation Group in the UK National Clean Maritime Research Hub and directs academic activities for the EPSRC Centre for Doctoral Training in Net Zero Aviation, shaping national maritime strategy through the Department for Transport's 'UK Ports the Future' report.

Rainer Schnellمشاهده پروفایل

استاد

• Survey Methodology
• Privacy-Preserving Record Linkage
• Health Data Analysis
• +۲ مورد دیگر

Prof. Dr. Rainer Schnell is a Professor at the Institute of Sociology, University of Duisburg-Essen. His research focuses on advanced survey methodologies, privacy-preserving data linkage techniques, and statistical analysis of health and social data. He leads the Chair of Empirical Social Research and contributes to interdisciplinary studies combining sociology, computer science, and public health. Research interests span: Survey Methodology : Innovations in web surveys, non-response analysis, and data quality assurance Data Privacy : Cryptographic techniques for secure record linkage and vulnerability assessments Health Analytics : Vaccination behavior studies, health data governance, and pandemic-related research Recent publications demonstrate a strong emphasis on: Privacy-enhancing technologies for sensitive data integration Methodological critiques of survey and data linkage practices COVID-19-related behavioral research using large-scale population data

Michael Dinitzمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Approximation Algorithms
• Online Algorithms
• +۵ مورد دیگر

Michael Dinitz is an Associate Professor in the Department of Computer Science at Johns Hopkins University with a secondary appointment in the Department of Applied Mathematics and Statistics . He is a member of the Algorithms and Complexity group and the Mathematical Institute for Data Science . His research focuses on Theoretical Computer Science , with emphasis on approximation algorithms , online algorithms , and distributed algorithms , particularly in applications to computer networking , distributed systems , and machine learning . His recent work involves differential privacy , dynamic networks , and resilient network design . His publications cover diverse topics in graph theory , privacy-preserving algorithms , and online optimization , with a focus on practical applications of theoretical results. He has received multiple NSF grants including a CRII award and Algorithms in the Field funding, and was honored with the Professor Joel Dean Excellence in Teaching Award . He advises a research group with PhD, MSE, and undergraduate students , and serves on program committees for leading theory conferences like FOCS , STOC , and ICALP . Education PhD in Computer Science, Carnegie Mellon University (2010), advised by Anupam Gupta AB in Computer Science, Princeton University (2005), advised by Sanjeev Arora Grants & Awards NSF CRII Grant (2015) NSF Algorithms in the Field Grant (2016) Multiple NSF Algorithmic Foundations grants (2019, 2022, 2025) NSF Graduate Research Fellowship (2005-2010) ARCS Foundation Scholarship (2005-2010) Best Paper Awards at ICDCS (2014) and DISC (2017)