Find Academic Supervisors

• Machine Learning
• Statistical Learning Theory
• Optimization
• +3 more

Professor Yiming Ying is a faculty member in the Faculty of Science at the University of Sydney, where he joined in December 2023. Previously, he held tenured positions at SUNY Albany (Departments of Mathematics & Statistics and Computer Science) and was a Lecturer at the University of Exeter. He completed his PhD in Mathematics at Zhejiang University (2002) and postdoctoral training at CityU Hong Kong, UCL, and University of Bristol. Research Focus His research spans statistical learning theory, optimization algorithms, trustworthy AI, and data science mathematics. Key applications include cancer informatics for early detection. His work aligns with Faculty research strengths in Data and Decisions and Decision-Making for a Sustainable Future. Recent Research Trends Analysis of recent publications shows strong focus on theoretical foundations of machine learning: differential privacy, fairness algorithms, optimization methods for AUC maximization, generalization guarantees, and robust learning techniques for adversarial settings and biological data. Awards and Honors SUNY Chancellor’s Award for Excellence (2023) University at Albany Presidential Research Award (2022) University of Exeter Merit Award (2012) Grants and Advising Significant funding includes current ARC DP250101359 (2025-2028) and multiple past NSF grants. He founded the UALBANY Machine Learning Group and currently advises PhD student Peilin LIU on operator learning.

Fabio MirandaView profile

Assistant Professor

• Visualization
• Data Management
• Machine Learning
• +7 more

Fabio Miranda is an Assistant Professor at the Department of Computer Science, University of Illinois at Chicago (UIC) . His research bridges visualization , machine learning , data management , and computer graphics to enable interactive visual analysis of large-scale urban datasets . He has developed systems like UrbanRama (for VR navigation) and The Urban Toolkit (a grammar-based framework), which have been deployed in academia, industry, and government agencies. Education: Ph.D., Computer Science , New York University (2018) M.S., Computer Science , Pontifical Catholic University of Rio de Janeiro (2011) B.S., Computer Science , Federal University of Minas Gerais (2009) Research Interests center on urban visual analytics , 3D analytics , and machine learning for accessibility . His work addresses challenges like sunlight access , sidewalk quality assessment , and commuting flow modeling , often collaborating with urban planners, climate scientists, and occupational therapists. Scientific Recognition includes awards at IEEE VIS , SIBGRAPI , and SIGMOD . His research is funded by NSF , NIH , DOT , and DPI , with media coverage in The New York Times , The Economist , and Architectural Digest . Teaching includes courses like CS 524: Big Data Visualization and Analytics and CS 424: Visualization and Visual Analytics . He emphasizes web-based systems, dataflow frameworks, and interdisciplinary collaboration, with open positions for PhD , MSc , and undergraduate researchers .

Levent Burak KaraView profile

Professor

• Machine Learning
• Artificial Intelligence
• Computational Design
• +12 more

Levent Burak Kara is a Professor in the Department of Mechanical Engineering at Carnegie Mellon University (CMU), with a courtesy appointment in the Robotics Institute. He is a leading researcher in AI-driven computational design, additive manufacturing, and intelligent engineering systems, leading the Visual Design and Engineering Lab (VDEL) at CMU. Education: B.S., Mechanical Engineering, Middle East Technical University (1998) M.S., Mechanical Engineering, Carnegie Mellon University (2000) Ph.D., Mechanical Engineering, Carnegie Mellon University (2005) His research focuses on integrating machine learning, optimization, and geometric modeling to revolutionize engineering design and manufacturing. Key areas include topology optimization, CAD intelligence, digital twins, generative design, bioengineering, and electronic design automation. His work enables automation of traditionally labor-intensive design processes using deep learning and reinforcement learning. His recent publications reveal a strong trend toward physics-informed surrogate modeling, real-time simulation, manufacturability prediction, and AI-driven automation in mechanical, biomedical, and electronic systems. These works frequently appear in top journals such as Journal of Mechanical Design and Journal of Applied Mechanics , and at premier conferences like NeurIPS and DAC. Scientific Awards: National Science Foundation CAREER Award ASME Design Automation Society Young Investigator Award Google AI for Social Good Impact Scholar Kara advises several Ph.D. students and has secured significant funding from federal agencies such as the NSF and the U.S. Army Research Laboratory, as well as collaborations with industrial leaders including Cadence Design Systems and NVIDIA. His research is also supported by CMU’s NextManufacturing Center and the Critical Technology Initiative. He is actively involved in developing intelligent design systems that leverage AI to automate product design, optimize manufacturing processes, and improve medical diagnostics, particularly in oral cancer screening and organ preservation. His lab, VDEL, is a hub for innovation in AI-enabled engineering.

Conor RyanView profile

Professor

• Genetic Programming
• Genetic Algorithms
• Grammatical Evolution
• +7 more

Conor Ryan is a Professor in the Department of Computer Science & Information Systems at the University of Limerick. He is a Science Foundation Ireland-funded Investigator since 2002 and a member of multiple research centres including Lero – the Irish Software Research Centre and the Limerick Digital Cancer Research Centre. His research focuses on Genetic Programming, Grammatical Evolution, and their applications in domains like healthcare analytics, digital circuit design, and financial modeling. He has authored over 250 publications, with recent work emphasizing automated feature selection in medical diagnostics, neural architecture search, and blockchain ecosystems. Teaching includes courses on Foundations of Computer Science and Computer Games Programming. Research interests span evolutionary computation, machine learning, and interdisciplinary applications. Collaborations involve global institutions, reflecting his work's impact across computer science, engineering, and healthcare. His research has addressed challenges in breast cancer diagnosis via genetic algorithms, cryptocurrency volatility prediction using random forests, and automated generation of digital circuits. Ongoing projects explore interpretability in AI, energy-efficient computing, and sustainable transport systems through predictive analytics. Professional memberships include roles in the Centre for Research Training in Foundations of Data Science and the Data-Driven Computer Engineering Research Centre, underscoring his commitment to interdisciplinary innovation.

• Nuclear Magnetic Resonance (NMR)
• In vivo imaging
• MRI pulse sequence development
• +7 more

Professor Guy Williams is a leading academic at the University of Cambridge with a focus on imaging science and clinical neurosciences, affiliated with Downing College and the Wolfson Brain Imaging Centre . Holding a PhD in Physics from his initial Natural Sciences degree, he specializes in nuclear magnetic resonance (NMR) and MRI techniques for brain imaging. Education: BA, PhD in Physics His research centers on non-invasive imaging of brain structure and function, particularly in traumatic brain injury (TBI) and dementia. His work involves developing novel MRI pulse sequences and advanced data analysis algorithms, including AI-based diagnostic tools. He leads studies on white matter integrity post-trauma, longitudinal dementia assessment, and applications of MRI in disorders of consciousness and addiction. Recent publications highlight collaborations in traumatic brain injury outcomes, AI-guided dementia prediction, and neuroimaging of post-COVID cognitive deficits. His team's work on ultra-high field laminar fMRI and distortion correction methods has advanced clinical neuroscience applications. Key techniques include diffusion tensor imaging (DTI), 7 Tesla MRI, and positron emission tomography (PET/MR). His research spans from basic NMR physics to clinical translation, with a strong emphasis on multi-site studies and real-world diagnostic implementation.

Sebastian SchuckView profile

Professor

• Cell Biology
• Molecular Biology
• Membrane Biology
• +4 more

Overview Sebastian Schuck is a Professor of Biochemistry and Molecular Cell Biology at Heidelberg University's Biochemistry Center (BZH). His research focuses on organelle homeostasis, particularly the endoplasmic reticulum (ER), with emphasis on ER membrane biogenesis, ER-phagy, and SHRED pathways. He leads an international team investigating how cells adapt ER structure and function under stress or disease conditions. Education & Career Since 2021: Professor at Heidelberg University BZH 2013–2021: Independent Group Leader at Heidelberg University's Center for Molecular Biology 2006–2013: Postdoc with Peter Walter at UCSF 2001–2006: PhD and Postdoc with Kai Simons at Dresden's Max Planck Institute 1995–2000: Biochemistry studies at Universities of Hannover and Tübingen Research Interests Dr. Schuck's lab explores molecular mechanisms underlying ER homeostasis, including: 1. ER expansion during stress via lipid synthesis 2. Microautophagy-mediated ER degradation via ESCRT machinery 3. SHRED pathway regulation of proteasomal degradation of misfolded proteins 4. Links between ER stress and neurodegenerative diseases/cancer Awards & Honors No specific awards listed, but recognized for pioneering contributions to understanding microautophagy and ER quality control mechanisms. Advising & Collaborations Advised over 20 PhD/Master's students and postdocs Collaborations with Carlos Bas-Orth (MPI Biochemistry), Liam Holt (NY), and others Labs & Teams Current lab includes 10+ members focusing on: - Human ER morphogenesis - Microautophagy dynamics - SHRED pathway mechanisms

• Audio Signal Processing
• Spatial Audio Technology
• Medical Engineering
• +3 more

Craig Jin is an Associate Professor at the University of Sydney, leading the CARlab (Computing and Audio Research Laboratory) and Spatial Audio Research initiatives within the School of Electrical and Computer Engineering. He holds a BS from Stanford University, an MS from Caltech, and a PhD from the University of Sydney. His work focuses on immersive audio technologies, biomedical signal processing, and assistive technologies for sensory augmentation. Research interests include spatial audio reproduction, binaural processing, acoustic sensing for accessibility, and machine learning applications in signal processing. Key contributions span HRTF interpolation, noise reduction algorithms, and acoustic touch systems for the visually impaired. Recent projects include real-time MRI analysis of vocal tract dynamics and sparse recovery techniques for sound field reconstruction. His publications span over 150 peer-reviewed articles in journals like IEEE Transactions on Audio, Speech, and Language Processing, and conferences such as ICASSP. He advises four current PhD/Master’s students on projects like predictive gesture tracking, voice disorder classification, and magnetic resonance imaging techniques.

• Computational Biology
• Spatial Omics
• Tumor Immunology
• +3 more

Madeleine Torcasso is an Assistant Professor in the Department of Medicine-Hematology and Oncology at the University of Chicago. She leads the Torcasso Lab, which investigates spatial patterns of disease within native tissue environments, bridging computational methods with clinical applications. Her research focuses on tumor-immune interactions using high-dimensional spatial proteomic and transcriptomic data to uncover mechanisms of disease progression and therapeutic resistance. Her lab integrates artificial intelligence with spatial omics to identify biomarkers and decode cellular communication in the tumor microenvironment. Key research interests include computational pathology, spatial biology, and translational oncology, with an emphasis on developing analytical tools for complex tissue data. Her sole recent publication (2024) introduces a computational method for classifying cells in multiplexed immunofluorescence images, advancing automated analysis of tissue microenvironments. No awards, students, or grants are detailed in available sources.

• Statistical Machine Learning
• Deep Learning
• Computational Biology
• +2 more

Rui Li is an Associate Professor in the Ph.D. program at Rochester Institute of Technology's Golisano College of Computing and Information Sciences. She directs the Lab for Use-inspired Computational Intelligence (LUCI), focusing on AI applications in computational biology and medical imaging. Education includes: B.Sc. in Computer Science, Harbin Institute of Technology M.Sc. in Computer Science, Tianjin University of Technology Ph.D. in Computing and Information Sciences, RIT Research integrates statistical machine learning with computational biology, medical image analysis, and human visual attention modeling. Current projects include deep learning for histopathology, multimodal medical image registration, and gene network inference. Publications demonstrate consistent focus on medical AI applications, with recent advances in unsupervised image registration, interactive segmentation, and multimodal fusion techniques. Key trends include self-supervised learning, uncertainty-aware models, and human-AI collaboration frameworks. Awards include the NSF CAREER Award for developing adaptive machine intelligence systems. Advises multiple PhD students on projects spanning deep learning architectures, biomedical image analysis, and biological network modeling. Leads several NSF-funded projects including human-centered image understanding systems and gene-protein network inference tools. Directs LUCI lab investigating machine learning for healthcare applications and teaches graduate courses in Statistical Machine Learning and Deep Learning.

• Health services for older people
• Collaborative models of care
• Frailty
• +4 more

Janani Thillainadesan is an NHMRC Senior Research Fellow at the Sydney Medical School/Concord Clinical School and the Centre for Education and Research on Ageing, affiliated with the Faculty of Medicine and Health at the University of Sydney. She is also a member of the Charles Perkins Centre. Her research focuses on optimizing healthcare delivery for older adults, particularly in geriatric surgery, perioperative care, and dementia interventions. Key research areas include frailty assessment, deprescribing strategies, collaborative care models, and leveraging technology (e.g., mobile health) in geriatric medicine. She has contributed to multidisciplinary studies involving clinical datasets, quality indicators, and mixed-methods analyses of patient experiences and care expectations. Awards: 2023 RACP Vincent Fairfax Research Fellowship 2021 ANZSGM Career Investigator Prize 2021 UK Age Anaesthesia Association Best Oral Presentation Prize 2019 Evidence Based Perioperative Medicine Asia Congress Runner Up Prize Her work bridges clinical practice, education, and policy through grants like the 2024 electronic frailty index project. Media engagement includes a 2019 Studio 10 segment highlighting her contributions to aging-related healthcare innovations.

Narges Sharif RazavianView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Predictive Analytics
• +3 more

Narges Sharif Razavian is an Assistant Professor at NYU Grossman School of Medicine , holding appointments in both the Department of Population Health and Department of Radiology . She earned her PhD from Carnegie Mellon University and completed postdoctoral training at New York University's Courant Institute in Computer Science's Machine Learning group. Research focuses on applying Machine Learning and Artificial Intelligence to healthcare challenges, including Predictive Analytics for disease outcomes, Biomarker Discovery , and Medical Imaging analysis. Recent publications highlight her work on AI-driven diagnosis in oncology (lung and pancreatic cancer), hematoma expansion prediction in neurology, and real-time models for infectious disease outcomes (e.g., COVID-19). She utilizes Electronic Health Records (EHRs) and multimodal data to develop clinical decision support systems, with applications in public health surveillance and personalized medicine. Contact: Email | Phone: 212-263-2234 | Office: 227 East 30th Street, 6th Floor, Room 639, New York City

William V. GiannobileView profile

Professor

• Periodontology
• Regenerative Medicine
• Tissue Engineering
• +3 more

Dr. William V. Giannobile serves as the Dean and A. Lee Loomis, Jr. Professor of Oral Medicine, Infection, and Immunity at Harvard School of Dental Medicine (HSDM). He leads the institution as its chief academic and administrative officer while maintaining an active research program and clinical practice in periodontics and implant dentistry at the Harvard Dental Center in Cambridge, MA. Dr. Giannobile received his DDS and MS in oral biology from the University of Missouri, followed by a certificate in periodontology and Doctor of Medical Sciences (DMSc) in oral biology from Harvard School of Dental Medicine. He completed postdoctoral training in molecular biology at the Dana-Farber Cancer Institute and Harvard Medical School. With over 30 years of continuously NIH-funded research, Dr. Giannobile's work focuses on oral and periodontal regenerative medicine, tissue engineering, and precision medicine. He serves as principal investigator of the NIDCR U-Award Harvard Clinical Practice-based Research Program for Dental Students (H-CREDENT), which evaluates the genomics of oral pain in a multi-institutional academic setting. His research has produced over 300 manuscripts, textbook chapters, and patents focused on periodontology, regenerative medicine, and oral health research. He has edited or co-edited over 10 books in clinical and translational research, periodontology, and regenerative medicine. His publications demonstrate consistent focus on periodontal regeneration, biomaterials, tissue engineering, and the molecular mechanisms underlying oral diseases. ADA Gold Medal Award for Excellence in Dental Research (2024) International Association for Dental Research Distinguished Scientist Research in Periodontal Disease Award (2022) Norton M. Ross Clinical Research Award from the American Dental Association (2018) William J. Gies Award (2014) Jan Lindhe International Periodontology Research Award (2011) As an educator and mentor, Dr. Giannobile completed a 10-year term as editor-in-chief for the Journal of Dental Research, the official journal of the International Association for Dental Research. He has served as a consultant to the U.S. Food and Drug Administration for Dental Devices and as an associate editor for the NIDCR Report on Oral Health of the U.S. in 2021. His laboratory continues to advance research in periodontal regeneration and precision medicine approaches to oral health, with ongoing NIH funding supporting innovative translational research.

• Computer Vision
• Machine Learning
• Medical Imaging
• +3 more

Stella Yu is a Professor specializing in computer vision, machine learning, and AI applications across medical imaging, robotics, and wildlife recognition. She emphasizes adaptive advising tailored to individual student strengths, with regular one-on-one and group meetings to discuss research progress and paper presentations. Yu's research group focuses on unsupervised learning, deep learning workflows, and interdisciplinary applications such as MRI reconstruction, meibography analysis, and aerial wildlife monitoring. Her work bridges theoretical advancements with practical tools like DeepInPy for inverse problems. She strongly advocates for teaching experience through GSI roles and ensures students have conference funding to present findings at top venues. Education Expectations: Regular literature review, lab presence for junior students, and clear authorship protocols. Key Research Themes: Feature learning, medical AI, computational optics, and wildlife population surveys. Her lab promotes a collaborative environment with structured feedback loops, emphasizing both technical rigor and creative problem-solving. Current projects include BatVision for 3D spatial navigation using audio signals, and AI-driven solutions for dry eye diagnosis and building information modeling.

• Statistical Methods in Biomedical Research
• Biomarker Analysis
• Clinical Trial Design
• +7 more

Todd A. Alonzo is a Professor of Research in the Department of Preventive Medicine at the University of Southern California . As Group Statistician for the Children's Oncology Group , he focuses on statistical methods for biomarker analysis, medical diagnostic testing, and clinical trial design in pediatric acute myeloid leukemia (AML). Education: B.S. in Statistics, California State Polytechnic University (1994) MS and PhD in Biostatistics, University of Washington (1997, 2000) Research Interests include: Development of statistical frameworks for diagnostic accuracy Genomic and proteomic profiling in AML Pharmacogenomic score systems for chemotherapy response Non-inferiority trial design in low-event-rate settings Health disparities in pediatric oncology Scientific Awards : Fellow, American Statistical Association (2018) Outstanding Teacher Award, International Society for Magnetic Resonance in Medicine (2017) NIH Predoctoral Cardiovascular Biostatistics Training Grant (1995) ENAR Biometrics Society Distinguished Student Paper Award (1999) WNAR Biometrics Society Best Student Oral Presentation (1999) Leadership & Service includes editorial board memberships (Biometrics, Pediatric Blood & Cancer, Biometrical Journal), reviewer for 30+ scientific journals, and roles on multiple Data Safety and Monitoring Boards. He served as President of the International Biometric Society Western Northern America Region (WNAR) in 2009.

• Multiscale Modeling
• Multiphysics Modeling
• Biophysics
• +7 more

Dr. Igor V. Pivkin is a Full Professor at the Institute of Computing within the Faculty of Informatics at the Università della Svizzera italiana (USI) in Lugano, Switzerland. His academic journey includes degrees from Novosibirsk State University (B.Sc./M.Sc. Mathematics), Brown University (M.Sc. Computer Science and Ph.D. Applied Mathematics), and postdoctoral research at MIT's Department of Materials Science and Engineering. His research focuses on multiscale/multiphysics modeling , numerical methods , and large-scale simulations of biological and physical systems. Key areas include biophysics, cellular/molecular biomechanics, stochastic modeling, and coarse-grained molecular simulations. He leverages high-performance computing (HPC) and particle-based methods to address complex biological phenomena. His work spans diverse applications, from understanding cellular mechanosensitivity and biofilm engineering to modeling cancer cell behavior and red blood cell dynamics in the spleen. His contributions bridge computational science, biotechnology, and biomedical research. He has published extensively in top-tier journals, with recent work advancing automated biofilm analysis, deep learning for microbial classification, and systems biology approaches to metal bioleaching. His lab collaborates on interdisciplinary projects, emphasizing computational innovation for real-world biological challenges.