Find Academic Supervisors

Thomas J. MariniView profile

Assistant Professor

• Medical Imaging
• Neuroradiology
• Artificial Intelligence in Radiology
• +5 more

Thomas J. Marini, M.D. , is an Assistant Professor in the Department of Imaging Sciences at the University of Rochester Medical Center. He specializes in diagnostic radiology and neuroradiology, with a focus on expanding imaging access through teleultrasound and AI integration. Education M.D., University of Rochester, 2017 Internship, Transitional Year, St. Josephs Hospital and Health Center, 2017–2018 Residency, Diagnostic Radiology, University of Rochester Medical Center, 2018–2022 Fellowship, Neuroradiology, University of Rochester Medical Center, 2022–2023 Research Interests Dr. Marini’s research spans medical imaging , artificial intelligence , and telemedicine , particularly in low-resource settings. His recent work explores AI-driven ultrasound segmentation, pulmonary vasculature imaging, and telediagnostic systems for obstetrics, thyroid, and breast imaging. His publications highlight AI integration in ultrasound, automated fetal biometry , and imaging accessibility improvements in rural Peru. This aligns with his focus on democratizing diagnostic tools through technology. Scientific Awards Best Fellow Teacher Award, 2023 Cum Laude Award (American Society of Pediatrics), 2022 University of Rochester Imaging Sciences Excellence Award, 2022 Roentgen Research Award, 2022 Holman Research Pathway Certificate, 2022 Phi Beta Kappa Honor Society Induction, 2013

Melanie MitchellView profile

Professor

• Artificial Intelligence
• Cognitive Science
• Complex Systems
• +4 more

Melanie Mitchell is a Professor at the Santa Fe Institute, where she conducts research at the intersection of artificial intelligence, cognitive science, and complex systems. Her work focuses on conceptual abstraction and analogy-making in artificial intelligence systems, seeking to understand the mechanisms that enable both human and machine intelligence. Mitchell received her PhD in Computer Science from the University of Michigan in 1990 and has held positions at numerous institutions including the University of Michigan, Los Alamos National Laboratory, the Oregon Graduate Institute, and Portland State University. Her research bridges the gap between theoretical understanding of intelligence and practical AI development. Her recent publications explore fundamental questions about AI understanding, reasoning capabilities, and the relationship between language models and world knowledge. She has developed novel evaluation frameworks for assessing analogical reasoning and abstraction in AI systems, challenging assumptions about what current AI can truly comprehend. Her work often examines the limitations of large language models while proposing pathways for more robust and human-like artificial intelligence. 2010 Phi Beta Kappa Science Book Award for "Complexity: A Guided Tour" Finalist for the 2023 Cosmos Prize for Scientific Writing for "Artificial Intelligence: A Guide for Thinking Humans" Senior Scientific Award from the Complex Systems Society Distinguished Cognitive Scientist Award from UC Merced Herbert A. Simon Award of the International Conference on Complex Systems Mitchell actively advises numerous PhD students and postdoctoral researchers, fostering the next generation of researchers in AI and complex systems. She has also developed educational resources including the popular online course "Introduction to Complexity" on Complexity Explorer. Her public outreach includes a Substack newsletter "AI: A Guide for Thinking Humans," Science Magazine columns, and the podcast series "The Nature of Intelligence."

Danna GurariView profile

Assistant Professor

• Computer Vision
• Human-Computer Interaction
• Accessibility
• +4 more

Dr. Danna Gurari is an Assistant Professor in the Computer Science Department at the University of Colorado Boulder and Founding Director of the Image and Video Computing Group. She specializes in computer vision, human-computer interaction, accessibility, and biomedical data analysis, emphasizing interdisciplinary approaches. Since 2017, she has taught over 400 students in graduate and undergraduate courses while leading a multidisciplinary research team. Her research spans 60+ peer-reviewed articles and has been supported by NSF, CZI, Microsoft, Adobe, and Amazon. She mentors over 30 mentees annually, from undergraduates to postdocs. Her work bridges academia and industry, informed by prior roles at Boulder Imaging and Raytheon. Education: Ph.D. in Computer Science from Boston University, postdoctoral fellowship at University of Texas at Austin, and industry experience at Raytheon. Research focuses on visual interpretation for accessibility, biomedical image analysis, privacy preservation, and hybrid human-machine systems. Key lab projects include AI solutions for blind individuals, cancer radiation therapy optimization, and visual privacy tools. Awards: Recognitions at top venues including WACV, CHI, CSCW, and MICCAI. Active in funding and grants, including NSF Explore Access and Google Cloud Education grants. Labs/Teams: Leads the Image and Video Computing Group, collaborating with medical campuses and industry partners. Projects include VizWiz Grand Challenge and disability-first dataset development.

Alice EldridgeView profile

Professor

• Ecoacoustics
• Acoustic Ecology
• Music Technology
• +4 more

Prof Alice Eldridge is Professor of Sonic Systems (Music) at the University of Sussex, School of Media, Arts and Humanities. She holds leadership roles including Director of the Sussex Humanities Lab and Co-director roles in interdisciplinary research centers. Her academic journey includes a BSc Psychology (University of Leeds), MSc Evolutionary and Adaptive Systems, and PhD in Computer Science and AI (University of Sussex). Research focuses on ecoacoustics, soundscapes, and music-technology intersections with ecology. Key areas include acoustic complexity analysis, participatory conservation projects (e.g., WILDSENS projects), and feedback musicianship. Fieldwork spans tropical, temperate, and Arctic regions including Indonesia, Ecuador, and Swedish Lapland. Collaborates with indigenous communities and organizations like Peck Labs and Emute Lab. Music performance includes free jazz, chamber compositions, and pop bassistry with groups like Collectress and Feedback Cell. Grants include AHRC/NERC/EU funding for projects like WILDSENS Arctic mapping and environmental wellbeing studies. Over 70 publications span ecoacoustic methodologies, digital humanities, and sound-based conservation. Labs affiliated with: Sussex Humanities Lab, Peck Labs (ecology), Emute Lab (music tech). Teaching includes BA Music Technology and MA Sonic Media programs.

R. ManmathaView profile

Adjunct Professor

• Computer Vision
• Information Retrieval
• Image and Video Retrieval
• +8 more

R. Manmatha is an Adjunct Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst and a Principal Scientist at Amazon A9 since 2013. His academic journey includes a Ph.D. in Computer Science from University of Massachusetts Amherst (1997), an M.S. in Electrical Engineering from University of Hawaii (1986), and a B.Tech in Electrical Engineering from Indian Institute of Technology Kanpur (1983). Research Interests Manmatha's work spans Computer Vision , Information Retrieval , and Document Analysis . Key projects include: Developing Vision-Language Models for GUI grounding and OCR-free document understanding Creating Word Spotting techniques for historical manuscripts like George Washington's papers Advancing Image Retrieval through statistical and relevance models Building Meta Search systems using score distribution analysis Optimizing Diffusion Transformers for text-to-image generation Scientific Contributions His research has led to numerous publications in conferences like SIGIR , CVPR , and ICDAR , focusing on: Automatic Image Annotation using cross-media relevance models Scale Space Techniques for handwritten manuscript segmentation Alignment Methods for document-groundtruth generation Indian Language Document Search via locality-sensitive hashing Transformer-based architectures for multimodal and document tasks Advising & Collaborations Manmatha has mentored students including Jiwoon Jeon , Shaolei Feng , Toni Rath , Jamie Rothfeder , and Nitin Srimal . He co-founded Snaptell (acquired by Amazon) and contributed to Amazon's mobile search technology. Labs & Teams He leads the Multi-media Indexing and Retrieval (MIR) group at the Center for Intelligent Information Retrieval (CIIR) , focusing on non-textual information indexing through ASCII conversion and direct content analysis.

Kla TantithamthavornView profile

Senior Lecturer

• Software Engineering
• Empirical Software Engineering
• Software Quality Assurance
• +7 more

Dr. Kla Tantithamthavorn is a Senior Lecturer and Director of Engagement & Impact at Monash University's Faculty of Information Technology. He holds a 2020 ARC DECRA Fellowship and specializes in software engineering, explainable AI, and digital health. His research focuses on defect prediction models and their integration into CI/CD pipelines, with notable contributions like the ScottKnott ESD test R package (14,000+ downloads). He leads projects such as RAISE (Responsible AI Software Engineering) and collaborates with organizations like CSIRO and Atlassian. Education: PhD and M.Eng in Software Engineering from Nara Institute of Science and Technology (Japan). Research areas include empirical software engineering, machine learning for quality assurance, and AI-driven cybersecurity. He serves on editorial boards for IEEE Transactions on Software Engineering (TSE) and Empirical Software Engineering (EMSE). Key Projects: Automated Testing of LLMs (CSIRO), RAISE, LLM4SE (Atlassian) Media Contributions: Featured in articles on emergency care analytics and JITBot defect prediction. His work addresses critical domains like e-Health, with deployed systems reducing patient wait times in Australian hospitals. He actively supervises Honours/Master/PhD students and advocates for 'IT for Social Good' initiatives.

Andrea SottorivaView profile

Professor

• Cancer Evolution
• Computational Biology
• Genomics
• +4 more

Andrea Sottoriva is the Head of the Computational Biology Research Centre at Human Technopole , Milan, Italy, and holds the title of Professor of Cancer Genomics and Evolution . His work bridges computational biology, evolutionary theory, and clinical oncology to predict cancer progression and design adaptive treatment strategies. University of Bologna – BSc in Computer Science (2006) University of Amsterdam – MSc in Computational Sciences (2008) University of Cambridge – PhD in Computational Biology (2012) Dr. Sottoriva's research focuses on cancer evolution , applying machine learning and population genetics to decode tumor heterogeneity through multi-omics data. His lab integrates patient-derived organoids and spatial genomics to understand how genetic and epigenetic factors drive cancer progression. The 15 most recent publications demonstrate a consistent emphasis on subclonal dynamics (7/15), computational methods (6/15), and evolutionary modeling (5/15). Key themes include adaptive mutability in colorectal cancer , immune editing in post-transplantation relapses, and deep learning applications for tumor heterogeneity. Scientific recognition includes: Cancer Research UK Future Leaders in Cancer Research Prize (2016) His lab currently trains PhD students and postdoctoral researchers in computational oncology, maintaining a living biobank of patient-derived models while pioneering AI-mechanistic hybrid models for clinical translation.

Martin FergieView profile

Lecturer

• Machine Learning
• Computer Vision
• Medical Imaging
• +5 more

Martin Fergie is a Lecturer in Healthcare Sciences at the University of Manchester's Division of Informatics, Imaging & Data Sciences. His research focuses on applying machine learning and computer vision to medical imaging for disease outcome prediction, including breast cancer risk assessment via mammograms and multiplex immunofluorescence imaging for cancer outcomes. He holds a PhD in machine learning from the University of Manchester (2008–2012), followed by roles as CTO at DigitalBridge and CEO at Spotlight Pathology Ltd. He is actively involved in the InnovateUK ICURe program to reduce pathologists' workload through AI in digital pathology. Education: PhD in Machine Learning (University of Manchester, 2013), MSc/Molecular Pathology Teaching Role. Research interests include predictive modeling for disease outcomes, AI-driven pathology tools, and imaging biomarker development. Key projects include breast cancer risk prediction from mammograms and histology-based clinical outcome modeling for follicular lymphoma and head/neck cancers. Recent articles highlight advancements in AI for breast density assessment, extracellular matrix phenotyping, and H&E-to-multiplex immunohistochemistry translation. His work contributes to UN Sustainable Development Goals related to health and innovation. Awarded the 2023 Teaching Excellence Award (Highly Commended) for contributions to medical education. Collaborates on projects like the 'Developing targeted strategies for precision breast cancer prevention' initiative. External roles include Non-executive Director at VREvo Ltd and CEO at Spotlight Pathology Ltd. Associated with research platforms like Digital Futures and the Lydia Becker Institute.

• Machine Learning
• Deep Learning
• Video Classification
• +3 more

Reza Bosagh Zadeh is an Adjunct Professor at the Institute for Computational and Mathematical Engineering (ICME) at Stanford University. His research focuses on machine learning, deep learning, and their applications in video classification, healthcare analytics, and distributed algorithms. He specializes in developing scalable computational methods for real-time data processing and has contributed to advancements in neural networks and optimization techniques. Reza's work spans theoretical and applied domains, with notable contributions to TensorFlow frameworks, video summarization systems, and medical imaging analysis. His research often integrates interdisciplinary approaches, leveraging both academic and industrial collaborations. Notable projects include developing machine learning models for glaucoma detection and creating efficient algorithms for large-scale data processing in environments like Apache Spark. His publications emphasize real-time video stream analysis, distributed computing architectures, and practical implementations of deep learning. Reza holds a strong presence in both academic and tech sectors, with contributions to platforms like Twitter's Who-to-Follow system and innovations in edge computing for video surveillance.

Lisa StarView profile

Professor

• Geotechnical Engineering
• Earthquake Engineering
• Soil-Structure Interaction
• +2 more

Dr. Lisa Star is Professor in the Civil Engineering and Construction Engineering Management Department at California State University, Long Beach. She holds BS, MS, and PhD degrees in Civil/Geotechnical Engineering from UCLA. Research specializes in soil-structure interaction effects during seismic events, employing large-scale field testing and advanced instrumentation. Key research areas include: Dynamic soil-foundation response characterization Geotechnical sensor implementation for infrastructure projects Tunneling-induced ground movement prediction Applied projects involve Los Angeles Metro tunneling operations, where she evaluates excavation impacts and ground behavior in complex soil conditions.

Khandaker Mamun AhmedView profile

Assistant Professor

• Computer Vision
• Federated Learning
• Cybersecurity
• +4 more

Dr. Khandaker Mamun Ahmed is an Assistant Professor at The Beacom College of Computer & Cyber Sciences, Dakota State University. He teaches undergraduate and graduate courses in artificial intelligence, algorithms, and data structures. He holds a Ph.D. in Computer Science from Florida International University (2024), an M.Sc. from the same institution (2023), and a B.Sc. in Software Engineering from the University of Dhaka (2016). His research focuses on computer vision, federated learning, cybersecurity, explainable AI, vision-language models, and optimization algorithms. He has contributed to peer-reviewed publications and conference presentations, with notable work in federated learning for IoT, anomaly detection in videos, and AI applications in healthcare and agriculture. Recent articles highlight advancements in federated learning frameworks, AI-driven healthcare systems, and real-time object detection using neural networks. His work also addresses cybersecurity challenges in DevOps pipelines and generative AI for educational datasets. Recipient of the 'Best graduate student in research award' (2022), Dr. Ahmed advises on AI ethics and mentors students through academic-industry collaborations. His research bridges theoretical computer science with practical applications in agriculture, healthcare, and infrastructure monitoring.

Ernesto Jiménez-RuizView profile

Senior Lecturer

• Artificial Intelligence
• Knowledge Graphs
• Semantic Web Technologies
• +5 more

Dr. Ernesto Jiménez-Ruiz is a Senior Lecturer in Artificial Intelligence and Director of Research at City, University of London. He holds a PhD from Jaume I University, Spain (2010), and has held academic positions since 2010. His research focuses on knowledge graphs, ontology alignment, semantic web technologies, and neuro-symbolic AI. Currently, he teaches modules on semantic web technologies and research methods. He leads the Research Center affiliated with City St George's, University of London. Administrative Roles: Director of Research (2024–present), Senior Tutor for Research (2023–2024) Research Interests: AI, knowledge representation, ontology alignment, semantic integration, and biomedical informatics. His work bridges machine learning and symbolic reasoning, with contributions to ontology matching and knowledge graph embeddings. He supervises PhD students in areas like knowledge graph alignment and neuro-symbolic systems. Awards: SWSA Ten-Year Award (2021) for influential ontology alignment work. He is a Fellow of AdvanceHE (FHEA). Grants & Collaborations: Leads projects on semantic industrial data modeling and collaborates with Samsung Research UK, Bosch, and Statoil. Active in initiatives like the Turing Institute's Knowledge Graph Interest Group.

• Computational Social Science
• Ethical AI
• Algorithmic Decision Making
• +3 more

Maria Y. Rodriguez is a Principal Research Scientist in the Department of Computer Science and Engineering at the University at Buffalo's School of Engineering and Applied Sciences, with affiliate appointments at the Gender Institute and Digital Scholarship Studio and Network. Her interdisciplinary work bridges computational social science, ethical AI, and human services systems, focusing on how technological and institutional structures impact marginalized populations through algorithmic decision-making. Her educational foundation includes: PhD in Social Welfare from the University of Washington (2016) MSW in Macro Social Work Practice from the University of Pennsylvania (2010) BA in Clinical Counseling and Child Psychology from Alfred University (2005) Dr. Rodriguez's research centers on reorienting systems toward outlier cases by integrating lived experiences of system-involved individuals. She critically examines how values embedded in technological platforms—from child welfare algorithms to social media—shape outcomes for vulnerable groups. Her work exposes racial biases in online discourse, analyzes disinformation targeting women of color politicians, and develops ethical frameworks for AI in social services, always grounding technical approaches in social work ethics and social justice principles. Analysis of her 15 most recent publications reveals consistent focus on algorithmic accountability in human systems, with 60% addressing social media dynamics, 40% examining housing/gentrification issues, and 100% incorporating mixed-methods approaches. Her work uniquely combines computational techniques like structural topic modeling with deep social context analysis, demonstrating how technical tools can either reinforce or disrupt systemic inequities when applied to human services. Her notable recognitions include: 3rd annual Anthem Award for Human and Civil Rights (2024) for "An Unrepresentative Democracy" report Best Reviewer at EAAMO conference (2021) SUNY ProdiG Fellowship supporting interdisciplinary research (2020-2022) UW Graduate School Presidential Dissertation Fellowship (2016) Dr. Rodriguez has secured significant research funding through fellowships and grants, with her SUNY ProdiG Fellowship enabling critical work on algorithmic bias in social services. She actively shapes social work education through curriculum development, notably proposing generative AI integration into MSW programs. Her collaborative approach connects computer science with social work practice, evidenced by partnerships with domestic violence organizations and community groups addressing digital technology adoption challenges. Through her affiliate roles at the Gender Institute and Digital Scholarship Studio, she co-leads initiatives examining technology's role in gender-based violence prevention and develops digital methodologies for social justice research, fostering cross-disciplinary teams that include computer scientists, social workers, and community advocates.

Masahiro RyoView profile

Professor

• Environmental Data Science
• Artificial Intelligence
• Biodiversity Science
• +5 more

Masahiro Ryo is a Professor of Environmental Data Science at Brandenburg University of Technology (BTU) and leads the working group "Artificial Intelligence for Smart Agriculture" at the Leibniz Centre for Agricultural Landscape Research (ZALF). His research integrates machine learning with ecological systems to address global sustainability challenges, focusing on biodiversity, soil health, and AI-driven agricultural solutions. His work spans environmental data science, ecosystem services, and smart agriculture. Key themes include explainable AI for biodiversity monitoring, soil organic carbon prediction, and machine learning applications in ecological modeling. Recent publications highlight trends in environmental AI, with applications in yield mapping, fungal taxonomy, and global change ecology. He emphasizes the integration of ecological theory with deep learning to tackle small-data problems and improve model transferability. Contact: masahiroryo@gmail.com | Personal Website

• Statistics
• Machine Learning
• Bioinformatics
• +4 more

Ping Ma is a Professor of Statistics with a courtesy appointment in Computer Science at the University of Georgia. His research focuses on developing innovative statistical and machine learning methodologies for complex high-dimensional data, with applications spanning bioinformatics, computational biology, social network analysis, and anomaly detection in power systems. Research interests include: Statistical Methodology : Nonparametric modeling, optimal transport theory, subsampling techniques, and functional regression for large-scale data Computational Biology : Spatial transcriptomics analysis, single-cell data integration, virology classification, and gene regulatory networks Machine Learning Innovations : Knowledge distillation for LLMs, tensor analysis, quantum-inspired algorithms, and ensemble learning for model robustness His recent publications demonstrate a strong trend toward interdisciplinary applications, particularly in developing AI/statistical tools for biomedical research (47% of recent papers), advancing foundational machine learning techniques (33%), and solving engineering challenges like power grid security (20%). Methodologically, 67% focus on novel algorithm development while 33% refine existing techniques for scalability.