Find Academic Supervisors

Ralf BrownView profile

Researcher

• Machine Translation
• Computational Linguistics
• Digital Forensics
• +5 more

Ralf Brown is a Principal Systems Scientist at Carnegie Mellon University's School of Computer Science, affiliated with the Language Technologies Institute (LTI). His research focuses on machine translation , language identification , and digital forensics , with notable contributions like the open-source CMU-EBMT system and the LTI LangID Corpus for 2000+ languages. Academic Roles: Research faculty since 1993, teaching courses like Coding & Algorithms Bootcamp and MIIS Capstone Project . Research Interests: Multilingual processing, example-based translation, low-resource languages, and text mining. Scientific Contributions: Authored 15+ publications on EBMT, corpus indexing, and context-sensitive translation. Developed tools for digital forensics (corrupted ZIP recovery) and language identification at scale. Awards: IJCAI Distinguished Paper Award Allen Newell Award for Research Excellence Open-Source Contributions: Key developer of darktable image editor Maintainer of the Interrupt List and other technical resources

Roger D. EastmanView profile

Professor

• Computer Vision
• Robotics
• Immersive Media Design
• +3 more

Roger D. Eastman is a Professor of the Practice in the Department of Computer Science at the University of Maryland, College Park, and serves as Director of the Immersive Media Design Program. He holds a Ph.D. (1990) and B.A. (1979) in Mathematics and Computer Science from the University of Maryland and University of Missouri-Columbia, respectively. His research focuses on computer vision applications in robotics, remote sensing, and immersive media. Notable projects include work with NASA-Goddard on satellite imagery registration, NIST on robotics perception standards, and JHU on medical imaging for glaucoma diagnosis. Current efforts emphasize XR visualization for Earth science and algorithmic art. Teaching priorities include introductory computer science, non-major courses in computer art with Processing/Arduino, and graphics/HCI. He has supervised undergraduate research leading to advanced academic and industry careers in VFX/game design. Publications span image registration techniques, sensor ontology development, and XR-based scientific visualization. Collaborations include Google Scholar profiles and a Cambridge Press book on remote sensing registration.

Doug OardView profile

Professor

• Information Retrieval
• Multilingual Information Access
• Speech Retrieval
• +3 more

Doug Oard is a Professor at the University of Maryland, College Park, holding joint appointments in the College of Information Studies and the Institute for Advanced Computer Studies (UMIACS). His research focuses on enhancing information retrieval across languages, speech, and sensitive content, with notable contributions to cross-language information retrieval (CLIR), eDiscovery, and evaluation design for information systems. He leads efforts in developing test collections for evaluating retrieval systems, including work on the TREC and CLEF evaluation forums. Key research areas include multilingual information access, speech retrieval, and computational social science. Oard has pioneered methods for evaluating machine translation, automated speech recognition, and the retrieval of historical or archival content. He has contributed to projects like the Apollo Mission Control Archive and the Multilingual TEDx Corpus, advancing accessibility to spoken and written materials across languages. His work emphasizes practical applications, such as improving legal eDiscovery processes and developing tools for searching sensitive or informal text. Oard has authored over 200 peer-reviewed publications and led major international evaluation initiatives, driving advancements in information retrieval systems' reliability and cross-cultural utility.

Anne HorakView profile

Academic

• Problem-Based Learning
• Gifted Education
• Educational Policy
• +1 more

Dr. Anne Horak is a Research Assistant Professor and Principal Investigator for federally funded Jacob K. Javits grants (Project ExCEL and Project E-Ignite) at George Mason University's Center for International Education, part of the College of Education and Human Development. Her work focuses on Problem-Based Learning (PBL) to support high-ability low-income students, address achievement gaps, and enhance teacher perspectives through professional development. She previously served as an Advanced Academic Program Specialist for Fairfax County Public Schools, leading curriculum development and supporting open enrollment transitions. Dr. Horak holds a PhD in Education from George Mason University, an M.Ed in Advanced Studies in Teaching and Learning, and a B.A. in Secondary Education from SUNY Oswego. Her research interests include Gifted Education, Educational Policy, and Teacher Professional Development. Recent publications emphasize PBL methodologies and strategies for identifying gifted English Language Learners. As a PI, she leads initiatives funded by major grants, demonstrating her impact in bridging educational equity gaps. Dr. Horak has also taught middle school English and Social Studies, contributing to her practical insights in classroom settings. Advising and grants: Dr. Horak directs projects funded by Jacob K. Javits grants, emphasizing teacher growth and curriculum innovation. She collaborated with Fairfax County Schools on a College Partnership Laboratory School, showcasing her commitment to academic-community partnerships.

Pedro Miguel Sánchez SánchezView profile

Researcher

• IoT Cybersecurity
• Machine Learning
• Deep Learning
• +3 more

Pedro Miguel Sánchez Sánchez is a Senior Researcher at the University of Murcia's Faculty of Computer Science, holding a Ph.D. in Computer Science. His research focuses on IoT cybersecurity, machine learning, and federated learning. He leads projects like DEFENDER (enhancing IoT/5G lab cybersecurity), EU-GUARDIAN (AI-based cyber defense automation), and 5GZORRO (5G security frameworks). He co-developed Fedstellar, a privacy-preserving federated learning platform, and contributed to datasets like LwHBench for hardware benchmarking. His work addresses adversarial attacks, trustworthy AI, and decentralized systems. Education: Ph.D. in Computer Science, University of Murcia Key Projects: DEFENDER (INCIBE-funded), EU-GUARDIAN (€13M), CDL-TALENTUM (cybersecurity training) Research interests span federated learning robustness, IoT device fingerprinting, and AI trustworthiness. He has published over 40 papers on topics like malware detection, hardware authentication, and decentralized frameworks.

John McNamaraView profile

Professor

• Educational Psychology
• Child Development
• Learning Disabilities
• +3 more

Dr. John McNamara is a Professor and Department Chair in the Department of Child and Youth Studies at Brock University. His research focuses on educational psychology, particularly studying young children with learning and reading disabilities. He leads the LD Research Team and is the principal investigator on a SSHRC Insight Grant for the 'Reading Rocks' literacy intervention program aimed at supporting vulnerable readers. His work emphasizes early identification, prevention of reading problems, and understanding neurological/environmental risk factors associated with dyslexia. Current projects include collaborations with provincial agencies to develop literacy programs and studies on developmental health in children with reading disabilities. Recent publications highlight his contributions to literacy support strategies, interactionist approaches to learning disabilities, and longitudinal studies on reading outcomes for at-risk children. Dr. McNamara’s research has been published in journals such as the American Journal of Applied Psychology and the Journal of Learning Disabilities. He is actively involved in applied research projects and has led initiatives to close summer learning gaps and sustain family literacy programs for vulnerable learners. His work bridges theory and practice, aiming to improve educational outcomes for children facing developmental challenges.

Chengkai LiView profile

Professor

• Artificial Intelligence
• Data Science
• Data Management
• +9 more

Chengkai Li is a Professor and Associate Chair in the Department of Computer Science and Engineering at the University of Texas at Arlington (UTA), directing the Innovative Data Intelligence Research (IDIR) Lab and co-directing the Center for Artificial Intelligence and Big Data (CARIDA). He holds adjunct roles in the Multi-Interprofessional Center for Health Informatics (MICHI). His academic journey includes a Ph.D. from UIUC (2007) and faculty positions at UTA since 2007, progressing from Assistant to Full Professor (2019). Education: Ph.D. in Computer Science (UIUC, 2007), M.E. and B.S. from Nanjing University (2000, 1997). Research focuses on AI-driven data systems for social good, including computational fact-checking, knowledge graphs, and graph data usability. Key projects include ClaimBuster (end-to-end fact-checking), FactWatcher (automated fact monitoring), and Maverick (exceptional fact discovery). His work spans 30+ news features and collaborations with organizations like Knight Foundation and Google. Publications (over 100) appear in top venues (SIGMOD, KDD, VLDB), with awards like the 2017 SIGMOD Most Reproducible Paper Award. He advises over 30 students and leads grants totaling millions from NSF, Knight Foundation, and industry partners. Labs/Teams: IDIR Lab (40+ researchers), CARIDA (AI/Big Data initiatives), and partnerships with Duke Tech & Check Cooperative.

Gianvito PioView profile

Associate Professor

• Data Mining
• Bioinformatics
• Social Network Analysis
• +3 more

Gianvito Pio is an Associate Professor at the Department of Computer Science, University of Bari Aldo Moro, Italy. His research spans data mining, bioinformatics, social network analysis, multi-relational data mining, and big data analytics. He holds a PhD in Computer Science from the University of Bari (2015) and has taught courses such as Big Data Management and Analysis and Security in Blockchain Technology . As an expert in heterogeneous network analysis, he develops methods for anomaly detection in cryptocurrency trends, microbiome data interpretation, and legal judgment clustering. He leads the local research unit of the PRIN 2022 project COCOWEARS and contributes to journals like the Machine Learning Journal and Expert Systems with Applications as an associate editor. His recent work includes multi-view learning for risk identification in dynamic networks, spatially-aware models for energy forecasting, and biclustering algorithms for biological data. Collaborations with researchers such as Michelangelo Ceci and Antonio Pellicani highlight his interdisciplinary approach. Gianvito Pio also organizes academic events like the Discovery Science conference and contributes to open-source software tools including HOCCLUS2 and GENERE .

• Cybersecurity
• Applied Cryptography
• Identity & Access Management
• +4 more

Dr. Amna Qureshi is an Assistant Professor in Cybersecurity at the Department of Computer Science, University of Bradford. Previously, she served as a Senior Researcher at Universitat Oberta de Catalunya in Barcelona. Her research focuses on secure cryptographic protocols, distributed systems, IoT security, privacy preservation, and cyberbullying detection. She holds a Fellowship from the Higher Education Academy (FHEA) and received a 2018 grant from Spain’s INCIBE for cybersecurity research. Her teaching includes modules like Ethical Hacking (COS7029-B) and ISO27000 Framework (COS7030-B). Research highlights include over 25 peer-reviewed publications in systems security and privacy, with a focus on Zero Trust Architecture, blockchain integration in finance, and AI-driven cybersecurity solutions. She actively reviews for JCR-indexed journals and serves on conference program committees. Key contributions include frameworks for multi-layered DDoS defense in 5G networks, AI-based fake news detection, and Urdu-language cyberbullying identification. Her work bridges theoretical cryptography and practical applications in IoT, cloud computing, and financial systems. Grants: Advanced Cybersecurity Team Excellence Grant (INCIBE, 2018) Awards: FHEA Fellowship Her publications emphasize Zero Trust models, blockchain applications, and privacy-preserving techniques. Current research trends focus on integrating AI with cybersecurity to address emerging threats in distributed systems and social media.

Ramanathan SubramanianView profile

Associate Professor

• Human-centered computing
• AI systems
• Non-verbal behavioral analysis
• +7 more

Ramanathan Subramanian is an Associate Professor in the Department of Information Technology & Systems, AI and Robotics at the University of Canberra. His past affiliations include IIT Ropar and UIUC, Singapore. He has received notable awards including the IEEE Transactions on Affective Computing Best Paper Award (2019) and Multimedia Rising Star nomination (2015). His research focuses on Human-centered computing, AI systems leveraging non-verbal behavioral cues, and applications in health analytics and virtual reality. He actively supervises PhD and Master’s students in areas like Human-Computer Interaction, Data Science, and VR for healthy aging. Research interests: Human perception modeling, intelligent interfaces, health-focused VR, and applied machine learning Key projects: Driver sentiment prediction, distraction awareness campaigns, and robotics for waste management His work contributes to UN SDGs related to health and well-being, particularly through technologies improving aging care and mental health monitoring. Recent projects include developing explainable AI for emotion inference and multimodal data fusion systems. Awards: IEEE Best Paper Award, Multimedia Rising Star Grants: Lead or Co-Investigator on three major grants totaling over AUD 4M Labs/Teams: Collaborates extensively with the University's robotics and health tech groups, contributing to interdisciplinary projects in AI ethics and medical signal processing.

Dean FalkView profile

Professor

• Biological Anthropology
• Paleoneurology
• Hominin Evolution
• +3 more

Dean Falk is the Hale G. Smith Professor of Anthropology and Distinguished Research Professor at Florida State University's Department of Anthropology. Her research focuses on paleoneurology, hominin evolution, and the origins of language/music. She earned her Ph.D. in Anthropology from the University of Michigan (1976) and has contributed significantly to understanding brain evolution through fossil endocasts. Notable work includes challenging microcephaly claims about the 'Hobbit' species Homo floresiensis , analyzing Albert Einstein's brain, and exploring the role of motherese in language origins. Falk has published extensively and lectures globally. Her recent projects include collaborating on Hans Asperger's historical letters and advancing studies on bipedalism's impact on fetal development. Her research interests span biological anthropology, fossil hominins, and the evolutionary links between cognition and creativity. She has been featured in Discover magazine and media worldwide, though she is currently not actively recruiting students. Upcoming talks include a 2025 lecture series in Austria and collaborative work on translating Asperger's correspondence.

Yifan SunView profile

Assistant Professor

• Optimization Algorithms
• Machine Learning
• Scientific Computing
• +10 more

Yifan Sun is an Assistant Professor in the Department of Computer Science at Stony Brook University. She is also affiliated with the AI Institute and the Institute of Advanced Computational Science (IACS) at Stony Brook. Dr. Sun received her PhD in Electrical Engineering from UCLA in 2015, with research focusing on convex optimization and semidefinite programming. Prior to joining Stony Brook, she worked at Technicolor Research and Innovation on machine learning applications and completed postdoctoral research at the University of British Columbia in Vancouver and INRIA in Paris. Her research centers on the design and analysis of optimization algorithms, particularly those arising in large-scale machine learning and scientific computing. Dr. Sun studies how structural properties like sparsity, curvature, and decomposability can be exploited to design faster, more stable, and more interpretable optimization methods. Her work spans both theoretical foundations and practical applications, covering first-order methods, quasi-Newton techniques, and algorithms for convex programming to nonconvex deep models. She also investigates how optimization theory, particularly insights from linear algebra and geometry, can improve understanding of deep learning models, regularization techniques, and representation learning. Dr. Sun's recent publications demonstrate strong trends in large-scale graph learning, advanced optimization techniques (particularly Frank-Wolfe variants), and applications to natural language processing. Her work often bridges theoretical guarantees with practical implementations, as evidenced by her open-source code repositories. She leads the OptML Research lab at Stony Brook, focusing on optimization methods for machine learning challenges. Her publications appear in top venues including NeurIPS, ICML, CVPR, and various optimization journals, reflecting her interdisciplinary approach that connects computer science, applied mathematics, and engineering disciplines.

Prasad A. KulkarniView profile

Professor

• Compilers
• Computer Architecture
• Runtime Systems
• +4 more

Prasad A. Kulkarni is a Professor in the Department of Electrical Engineering and Computer Science (EECS) at the University of Kansas. He leads the Compilers, Architectures, and Runtime Systems (CARS) research group, focusing on compiler optimizations, runtime systems, and cyber-security in embedded systems. His work addresses challenges like efficient parallel execution, compiler phase ordering, and secure software execution on multi-core and heterogeneous architectures. Education: Ph.D. in Computer Science from Florida State University (2007). Awards include the NSF CAREER Award (2010) and the Harry Tally Excellence in Teaching Award (2012, 2017). He has advised numerous graduate students in areas such as compiler transformations, virtual machine optimization, and binary analysis. Research interests span compiler design, computer architecture, and runtime systems. Recent work emphasizes binary analysis for security (e.g., detecting security checks in binaries) and improving performance in embedded systems. His publications explore topics like dynamic binary translation, memory management, and decompiler accuracy. Teaching includes courses on compilers, computer systems design, and security. He actively recruits students interested in solving efficiency and security challenges in modern computing systems.

Brian DavisView profile

Assistant Professor

• Natural Language Processing
• Data-to-Text Generation
• Bias Detection
• +2 more

Brian Davis is an Assistant Professor and Assistant Head for Teaching & Learning in DCU's School of Computing. As an SFI ADAPT Centre member, his research focuses on Natural Language Processing, including multilingual systems, bias detection, and cyberbullying identification. He leads projects on Irish language technology and ethical NLP applications. Recent work examines LLMs in content moderation and job market fairness.

Ricard GavaldàView profile

Professor

• Machine Learning
• Data Mining
• Healthcare Analytics
• +3 more

Ricard Gavaldà is a Professor in the Department of Computer Science at Universitat Politècnica de Catalunya (UPC), affiliated with the LARCA research group. Since February 2020, he has been on academic leave to work full-time at Amalfi Analytics, focusing on healthcare data analytics. His research interests include Machine Learning, Data Mining, and their applications to healthcare and social good. He has supervised numerous PhD students and contributed to impactful projects in clinical decision support, traffic prediction, and energy-efficient systems. Education details are not explicitly mentioned in the provided texts. His work spans data stream mining, algorithmic frameworks (e.g., MOA), and healthcare informatics. He has co-founded Amalfi Analytics to develop AI-driven solutions for healthcare management. Notable recent activities include co-chairing the Nectar Track at ECML PKDD 2025 and organizing workshops on Data Science for Social Good. Key technical contributions include methods for adaptive learning, probabilistic modeling, and interpretable AI systems. His research bridges theoretical foundations and real-world applications, with a focus on improving healthcare outcomes and sustainable practices.