Find Academic Supervisors

Murat KantarciogluView profile

Professor

• Data Privacy
• Computer Security
• Machine Learning
• +5 more

Murat Kantarcioglu is an Ashbel Smith Professor of Computer Science at the University of Texas at Dallas within the Erik Jonsson School of Engineering and Computer Science. He holds visiting appointments at UC Berkeley and Harvard University, focusing on data privacy and security. With a Ph.D. in Computer Science from Purdue University (2005), he has made significant contributions to privacy-preserving data mining, blockchain analytics, and secure machine learning. Education: Ph.D. in Computer Science (Purdue, 2005) Current Roles: Ashbel Smith Professor (2021-present), Visiting Scholar at UC Berkeley (2020-present), Affiliate at Harvard (2013-present) Past Roles: Assistant (2005-2011), Associate (2011-2015), and Full Professor (2015-2021) at UTD His research focuses on data privacy , computer security , and machine learning , particularly addressing challenges in privacy-preserving distributed data mining , blockchain analytics , and adversarial machine learning . He has pioneered techniques for secure federated learning , topological analysis of blockchain networks , and privacy-utility tradeoffs in health data systems. Recent publications reveal a strong emphasis on IoT security , graph neural network vulnerabilities , and blockchain data structures . His work combines theoretical rigor with practical implementations using technologies like Intel SGX and homomorphic encryption. Notable Awards NSF CAREER Award (2009) IEEE Technical Achievement Award (2017) AMIA Homer Warner Best Paper Award (2014) Fellow of IEEE (2022), AAAS (2020), and ACM (2016) Key Projects Privacy-Preserving Genomics Data Sharing Adversarial Learning Frameworks Smart Contract Security Medical Data Protection Systems Labs Director of Data Security and Privacy Lab Collaborations with Vanderbilt, UC Berkeley (RISE Lab), and Harvard (Data Privacy Lab)

Fahad AhmadView profile

Lecturer

• Machine Learning
• Cybersecurity
• Medical Informatics
• +6 more

Fahad Ahmad is a Lecturer in the School of Computing within the Faculty of Technology at the University of Portsmouth. He holds affiliations with the Portsmouth AI and Data Science Centre, Centre for Cybercrime and Economic Crime, and Portsmouth Centre for Advanced Materials and Manufacturing. His research focuses on machine learning applications in healthcare, cybersecurity, and quantum computing. He supervises PhD students in topics like quantum machine learning for securing IoT medical devices. Key research areas include: Medical imaging diagnostics using deep learning (e.g., echocardiograms, X-rays) Cybersecurity for financial systems and SDN-NFV networks Quantum key distribution for post-quantum security AI-driven health management systems Recent work emphasizes: Human activity recognition through machine learning Cancer subtype classification using RNA expression data Emotional empathy modeling in intelligent agents His articles span healthcare technology, cybersecurity frameworks, and hybrid AI architectures. He actively contributes to international conferences and journals, with over 60 peer-reviewed publications. Research collaborations include institutions in Pakistan and the UK.

Shahana IbrahimView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +5 more

Shahana Ibrahim is a tenure-track Assistant Professor at the University of Central Florida under the AI Initiative, holding a joint appointment in the Department of Electrical and Computer Engineering and Computer Science. Her research develops provable methods for robust machine learning systems with applications in critical real-world scenarios. Education: Ph.D. in Electrical and Computer Engineering, Oregon State University (advised by Dr. Xiao Fu) Prior industry experience: System Validation Engineer at Texas Instruments (2012-2017) and NVIDIA GPU intern (2018) Her research spans machine learning, signal processing, and optimization with core expertise in weakly supervised learning, tensor decomposition, and stochastic algorithms. She focuses on enhancing AI reliability through theoretical guarantees for noisy data environments, particularly addressing label noise, incomplete annotations, and structured factorization challenges. Her work bridges signal processing theory with modern AI to solve practical problems in data quality and system robustness. Recent publications (2023-2025) reveal strong thematic consistency in handling imperfect supervision. Key trends include crowdsourced label modeling, instance-dependent noise characterization, and tensor/matrix completion techniques. Her approach uniquely integrates signal processing perspectives with deep learning, emphasizing identifiability conditions and geometric regularization to extract reliable patterns from corrupted data. Scientific Awards: Outstanding PhD Dissertation Award from EECS, Oregon State University (2024) Dr. Ibrahim actively mentors graduate researchers including Faizul and Grey, who co-authored her ICIP 2025 and IEEE CAMSAP 2023 publications. She secured the AI-BTO DARPA grant (December 2024) for physics-informed machine learning research on intrinsically disordered proteins. Current funding supports multiple RA/TA positions for PhD students in her lab. She serves on program committees for AISTATS, AAAI AI for Social Impact, and WiML at NeurIPS. Her research group develops end-to-end learning frameworks for noisy data environments, with active projects funded by DARPA focusing on biomedical applications and robust AI validation. The lab maintains strong industry connections through NVIDIA and Texas Instruments collaborations.

Corrado LoglisciView profile

Assistant Professor

• Temporal Data Mining
• Temporal Learning
• Textual Data Mining
• +13 more

Corrado Loglisci is an Assistant Professor at the Department of Computer Science, University of Bari Aldo Moro, Italy. His research focuses on Temporal Data Mining , Machine Learning , and Quantum Computing , with applications in bioinformatics, medical informatics, and cybersecurity. He earned his Ph.D. in Computer Science with a thesis on temporal projection in longitudinal data. Research Highlights : Temporal Learning, Textual Data Mining, Quantum-Classical Hybrid Systems Collaborations : IRSTEA Research Institute (France), Aristotle University of Thessaloniki (Greece) His publications address dynamic network analysis , emotion detection in social media , and quantum-enhanced classification . He contributes to program committees and journal editorial work, including a special issue on Mining Complex Patterns in the Journal of Intelligent Information Systems . Notable contributions include the jKarma framework for change detection and studies on concept drift robustness in intrusion detection systems. His work spans European/National research projects, leveraging machine learning for tasks like mobile crowd sensing trustworthiness prediction (2020) and investor behavior analysis (2023-2025).

Sean ChesterView profile

Assistant Professor

• Data Management
• Parallel Computing
• Algorithm Engineering
• +5 more

Sean Chester is an Assistant Professor in the Department of Computer Science at the University of Victoria, Canada. He is affiliated with the Faculty of Engineering and Computer Science and specializes in scalable data analytics, with a focus on data management, parallel computing, and algorithm engineering. His research interests include GPU-native algorithms, multicore optimization, spatio-temporal data processing, and graph-based analysis. He actively contributes to open-source projects and course materials on platforms like GitHub, emphasizing open science and education. Recent work highlights include advancements in skyline computation, GPU-accelerated algorithms, and efficient processing of large-scale datasets. His publications span topics such as kNN optimization, social network anonymization, and vectorized k-core decomposition. Sean is involved in teaching courses like CSC 485C/586C on data management on modern hardware and CSC 370 on database systems. No scientific awards or grants are explicitly mentioned in the provided texts. He collaborates with students and researchers through platforms like GitHub, where he maintains repositories related to algorithm engineering and educational materials.

Kiril GashteovskiView profile

Researcher

• Open Information Extraction
• Natural Language Processing
• Data Mining
• +3 more

Kiril Gashteovski is a PhD candidate at the Chair of Practical Computer Science I: Data Analytics within the School of Business Informatics and Mathematics at the University of Mannheim. He is supervised by Prof. Dr. Rainer Gemulla and affiliated with the Data and Web Science Group. His primary research focuses on open information extraction, natural language processing, and machine learning applications. He concurrently holds a position as Research Scientist at NEC Laboratories Europe GmbH in Heidelberg, Germany. Education: PhD candidate (University of Mannheim, ongoing), prior academic background includes studies and research roles at institutions in Bulgaria, France, and North Macedonia. Research interests emphasize developing efficient open information extraction systems, knowledge base construction, and text analytics applications. His work bridges theoretical advancements with practical implementations in areas like entity linking and corpus development. Publications span topics such as OPIEC corpus development, citation-centered extraction, and toolkit creation for entity aspect linking. He actively presents at venues like AKBC and EMNLP. Advising and supervision include leading team projects like 'Complaint Handling via Text Analytics for Roche' and 'Data analytics on a ticketing system' with Abbvie. He also teaches courses in data mining and machine learning. Labs/Teams: Core member of the Data and Web Science Group at University of Mannheim, collaborating with industry partners like NEC Labs Europe.

Yuan HongView profile

Associate Professor

• Security
• Privacy
• Trustworthy Machine Learning
• +8 more

Dr. Yuan Hong is an Associate Professor and Collins Aerospace Professor at the University of Connecticut's School of Computing, affiliated with the Connecticut Advanced Computing Center (CACC). He previously held an Assistant Professor position at Illinois Institute of Technology. His expertise spans Security, Privacy, and Trustworthy Machine Learning, with a focus on differential privacy, secure computation, adversarial attacks, and robustness in AI systems. He has received notable awards including the NSF CAREER Award and Cisco Research Awards, and his work has been published in top-tier venues like S&P, CCS, NeurIPS, and CVPR. Education: Ph.D. (Rutgers University), M.Sc. (Concordia University), B.Sc. (Beijing Institute of Technology). Research interests include adversarial machine learning, federated learning security, and privacy-preserving techniques. His recent work addresses challenges in machine unlearning, certified defenses, and robust watermarking for graph learning systems. He actively advises students (Ph.D., M.Sc., undergraduate) and leads the DataSec Lab. Current teaching includes Cybersecurity Lab, Computer Security, and Database Organization. Awards include the CCS Distinguished Paper Award (2024) and the Taylor L. Booth Graduate Fellowship for a student. He organizes conferences such as WPES'22 and DBSEC'22, and serves on editorial boards for IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Information Forensics and Security, and others. Key contributions include frameworks for certified adversarial robustness (e.g., UniCR), differential privacy mechanisms for federated learning, and defenses against poisoning attacks in video and code generation models.

Malachy EatonView profile

Associate Professor

• Genetic Algorithms
• Humanoid Robotics
• Artificial Intelligence
• +5 more

Malachy Eaton is an Associate Professor in the Department of Computer Science and Information Systems at the University of Limerick. His research focuses on evolutionary computation, humanoid robotics, and graph theory. Institution: University of Limerick Department: Computer Science and Information Systems Academic Rank: Associate Professor Email: Malachy.Eaton@ul.ie His work spans Genetic Algorithms , Humanoid Robotics , and Artificial Intelligence , with notable contributions to evolutionary robotics, graph layout optimization, and computational intelligence. Research includes: Evolutionary approaches to humanoid robot motion Simulated annealing for graph drawing Force-directed layout algorithms Bridging simulation-to-reality gaps Edge crossing reduction techniques Data mining to evolutionary robotics Key themes in his publications reflect strong expertise in Genetic Algorithms (100% usage), Humanoid Robots (66-100%), and Artificial Intelligence (100%).

Sharon Ihan HsiaoView profile

Assistant Professor

• Educational Technology
• Data-Driven Engineering
• Educational Data Mining
• +4 more

Dr. Sharon Ihan Hsiao is an Assistant Professor and David Packard endowed junior fellow in the Department of Computer Science & Engineering at Santa Clara University. She previously held positions at Arizona State University and Columbia University, where she established research projects and taught courses in computer science education. Her research focuses on educational technologies, computational modeling, and adaptive learning systems, with an emphasis on AI and HCI methodologies. She actively contributes to international conferences such as EDM, LAK, and AIED, and has held leadership roles including Program Chair of the 14th International Conference on Educational Data Mining. Education: Postdoctoral studies at Columbia University (2012-2014), PhD from the University of Pittsburgh (2006-2012), MS from Royal Holloway, University of London (2003-2004), and BA from National Central University (1999-2002). Research Interests: Dr. Hsiao's work centers on leveraging technology to enhance learning, including educational data mining, programming language learning analytics, and behavior modeling. Her projects include developing tools like WebPGA for programming assessment feedback, Ogmented for augmented reality learning, and Waste Genie for sustainability education. She explores distributed practice strategies, self-regulated learning behaviors, and immersive technologies to improve educational outcomes. Awards: Recognized with the FWA Outstanding Faculty Mentor Award (ASU) and the IEEE TCLT Young Researcher Award. Grants & Projects: Secured NSF funding for CatMapper (2-year grant) and led the Waste Genie project. Her research involves collaborations with industry partners and interdisciplinary teams, focusing on topics like sustainable waste management and computational thinking. Labs & Teams: Directs the Computer Science Innovation (CSI) Lab at Arizona State University and collaborates on projects like QuizIT and PiSA, which emphasize personalized learning and adaptive feedback systems.

Philip R.O. PayneView profile

Professor

• Artificial Intelligence in Healthcare
• Biomedical Informatics
• Clinical Decision Support Systems
• +7 more

Philip R.O. Payne holds multiple prestigious roles at Washington University in St. Louis and the BJC Health System. As the Janet and Bernard Becker Professor , he leads the Institute for Informatics, Data Science and Biostatistics (I2DB) . He also serves as Vice Chancellor for Biomedical Informatics and Data Science at WashU Medicine and Chief Health AI Officer for BJC Health System and WashU Medicine. Additionally, he is the founding director of the Center for Health AI , a joint initiative between the two institutions. His academic appointments include professorships in General Internal Medicine and Computer Science and Engineering . Dr. Payne’s research focuses on four key areas: AI-driven biomolecular and clinical phenotype analysis , interventional applications of EHRs and clinical decision support systems , human factors and workflow optimization in healthcare IT , and data sharing and analytics platforms for healthcare systems and research initiatives . His work emphasizes leveraging advanced technologies to enhance healthcare outcomes and operational efficiency, such as developing the M3NetFlow AI model for multi-omic data analysis and the Cumulus federated EHR learning system. In terms of awards, he is an elected fellow of the American College of Medical Informatics (ACMI) American Medical Informatics Association (AMIA) American Institute for Medical and Biological Engineering (AIMBE) International Academy of Health Sciences Informatics (IAHSI) He currently serves as president-elect of AMIA, the leading professional organization in Biomedical and Health Informatics. Dr. Payne’s advising and grants narrative involves leading a research group with over 300 publications and fostering interdisciplinary collaboration. While no formal advisees or grant details are explicitly listed, his entrepreneurial ventures include founding digital health companies and advising health/life sciences organizations and venture capital firms. He emphasizes translating academic innovations into real-world solutions. He is affiliated with key labs/teams like the Institute for Informatics (I2DB) , Center for Health AI , and the Collaborative Resource for Intensive care Translational science, Informatics, Comprehensive Analytics, and Learning (CRITICAL) . These teams focus on AI-driven healthcare solutions, data integration, and clinical informatics advancements.

Xiaokui XiaoView profile

Professor

• Data Management
• Data Analytics
• Data Privacy
• +5 more

Prof. Xiaokui Xiao is a Professor and Deputy Head (Research & Graduate Matters) at the Department of Computer Science, National University of Singapore (NUS). He previously held an Associate Professor position at Nanyang Technological University (NTU). His academic journey includes a PhD from the Chinese University of Hong Kong (2008) and a postdoctoral stint at Cornell University. His research focuses on data management and analytics, with emphasis on large data algorithms, privacy (e.g., differential privacy), and data mining. Notable contributions include the PrivBayes system for private data release and scalable network embedding techniques. Awarded the 2024 ACM SIGMOD Test-of-Time Award, IEEE Fellowship (2022), and ACM Distinguished Membership (2021), Xiao is also a trustee of the VLDB Endowment and Editor-in-Chief of Proceedings of the VLDB Endowment . He has advised over 30 PhD students and researchers, many of whom hold academic or industry leadership roles. His work bridges theoretical foundations with practical systems, addressing challenges in privacy, scalability, and machine learning integration.

Frank WoodView profile

Associate Professor

• Probabilistic Programming
• Deep Generative Models
• Reinforcement Learning
• +4 more

Frank Wood is an Associate Professor of Computer Science at the University of British Columbia and a Canada CIFAR AI Chair at AMII. He directs the Pacific Laboratory for Artificial Intelligence (PLAI) research group and co-founded Inverted AI, a spin-out focused on advanced simulation for autonomous vehicles. His research focuses on probabilistic programming, deep generative models, and reinforcement learning, with applications in autonomous driving, robotics, and vision. Wood teaches courses such as Machine Learning and Data Mining (CPSC 340) and Topics in Artificial Intelligence (CPSC 532W). He has been on academic leave from January to December 2025. His work integrates theoretical advancements with practical applications, including projects like PLAICraft, a large-scale embodied AI dataset. Key awards include the ICML Best Paper Honourable Mention and contributions to probabilistic programming systems like Anglican. His research group collaborates on tools such as TorchDriveEnv for autonomous driving benchmarking and explores cutting-edge methods in diffusion models and continual learning.

• Data-efficient deep learning
• Adversarial attacks
• Deep learning interpretations
• +4 more

Anshul Thakur is a Departmental Lecturer in Clinical Machine Learning at the University of Oxford's Institute of Biomedical Engineering. His research focuses on advancing data-efficient deep learning techniques, adversarial attacks, and interpretable AI frameworks for healthcare applications. He holds a PhD from IIT Mandi (2020), where his thesis explored audio signal analysis using dynamic kernels and deep learning. Education: PhD in Computing & Electrical Engineering, Indian Institute of Technology Mandi (2020) Research concentrated on bioacoustic signal pattern analysis and ML frameworks for acoustic classification. Research Interests: His work emphasizes clinical AI applications, including federated learning for medical data, multimodal diagnosis systems, and mitigating class imbalance in healthcare datasets. He develops interpretable models for medical practitioners and explores ethical AI deployment in clinical settings. Recent Trends in Publications: Recent work addresses federated learning optimization, multimodal clinical diagnosis, and early disease prediction using biomarker patterns. His studies highlight innovations in EHR analysis, privacy-preserving techniques, and cross-domain medical model adaptation. Labs & Teams: Active in the Institute of Biomedical Engineering, collaborating on projects like the RapiD_AI framework for pandemic preparedness and Continuous Patient State Attention Models for irregular EHR data analysis.

George KolliosView profile

Professor

• Temporal Indexing
• Spatio-Temporal Data Management
• Data Mining
• +4 more

George Kollios is a Professor and Chair of the Computer Science Department at Boston University. He holds a PhD in Computer Science from Polytechnic University, New York (2000), an M.Sc. from the same institution (1998), and a Diploma in Electrical and Computer Engineering from the National Technical University of Athens, Greece (1995). His research focuses on temporal/spatio-temporal indexing, database security, privacy-preserving technologies, and large-scale data management. His work is supported by NSF (including an NSF CAREER Award) and IARPA. Education: PhD, Polytechnic University, New York (2000) M.Sc., Polytechnic University, New York (1998) Diploma in Electrical and Computer Engineering, National Technical University of Athens (1995) Research Interests: His expertise spans data mining , secure database systems , and privacy-preserving algorithms . He develops methods for efficient query processing on encrypted data, spatio-temporal data management, and scalable clustering techniques. Recent Research Trends: His publications emphasize differential privacy , secure encryption schemes , and large-scale graph algorithms . Notable contributions include optimizing privacy in database queries and evaluating encryption protocols for secure data access. Awards: NSF CAREER Award Grants & Collaborations: Recipient of NSF-funded projects on access pattern privacy and IARPA-supported research. He collaborates on interdisciplinary initiatives to bridge theoretical foundations with practical security frameworks.

Paula BrancoView profile

Assistant Professor

• Machine Learning
• Data Mining
• Data Science
• +8 more

Paula Branco is an Assistant Professor at the School of Electrical Engineering and Computer Science, University of Ottawa (since 2020). Previously, she was a PostDoctoral Fellow at Dalhousie University's Faculty of Computer Science (2019). She holds a PhD in Computer Science from the University of Porto, alongside a MSc in Data Mining and a Mathematics degree. She is an external researcher at INESC TEC's LIAAD laboratory. Her research focuses on Machine Learning, particularly utility-based learning and imbalanced domains, with applications in cybersecurity, healthcare, and environmental monitoring. Key areas include anomaly detection, fraud detection, rare event prediction, and spatio-temporal data analysis. Recent work emphasizes resampling techniques for imbalanced regression (e.g., SMOGN, ImbalancedLearningRegression Python package), evaluation metrics for imbalanced domains, and cybersecurity applications like intrusion detection and malware analysis. She has supervised seven graduate students and contributed to open-source projects like UBL (R package) and ImbalanceMetrics. Her teaching includes courses on databases and AI for cybersecurity. She actively publishes in top venues and collaborates on tools like SMOGN-LIDTA17 and NeighborhoodBiasResamplingRegression.