Find Academic Supervisors

• Pathology
• Digital Pathology
• Transplant Pathology
• +6 more

Sudhir Perincheri, MD, PhD, is an Assistant Professor of Pathology at Yale School of Medicine and serves as Director of Digital Pathology. He completed his residency in Anatomic Pathology/Clinical Pathology as well as fellowship training in hematopathology, renal and genitourinary pathology in the Departments of Pathology and Laboratory Medicine at Yale. Dr. Perincheri's research focuses on the application of emerging tools in anatomic pathology, with particular expertise in digital pathology. His work bridges clinical pathology with technological innovations, especially in the transition from traditional glass slides to digital imaging systems that may eventually incorporate artificial intelligence algorithms for more accurate disease diagnosis. His research portfolio shows a strong emphasis on renal pathology, transplant pathology, and genitourinary tumors. Analysis of his recent publications reveals a consistent focus on glomerular diseases, mechanisms of transplant rejection, and diagnostic innovations in hematopathology. His work often involves collaborative research with other Yale faculty in applying advanced imaging techniques like MultiPhoton Microscopy to renal and prostate pathology. Scientific Recognition Postdoctoral Fellowship Award from Juvenile Diabetes Research Foundation (2006) Dr. Perincheri maintains active clinical responsibilities, regularly diagnosing complex cases involving blood cancers, kidney disorders, and genitourinary malignancies. His role as Director of Digital Pathology places him at the forefront of technological transformation in the field, working on the development and implementation of digital imaging systems that could revolutionize how pathology is practiced. He serves on several professional committees including the ECOG-ACRIN Multiple Myeloma Committee, ECOG-ACRIN Laboratory Science and Pathology Committee, Medical Record and Clinical Information Committee, and the College of American Pathology Digital and Computational Pathology Committee. These roles demonstrate his leadership in advancing pathology practice standards and digital transformation in the field.

• Cytopathology
• Gynecological Pathology
• Diagnostic Biomarkers
• +3 more

Minhua Wang, MD, PhD, is an Assistant Professor of Pathology at Yale School of Medicine with dual clinical expertise in cytopathology and gynecological pathology. She specializes in microscopic analysis of fluid, tissue, and cytology specimens to guide cancer diagnosis and management. Her work emphasizes improving diagnostic accuracy through biomarker research and standardized reporting systems, particularly for pancreaticobiliary lesions and gynecological malignancies. Education: Cytopathology Fellowship: M.D. Anderson Cancer Center (2019) Surgical Pathology Fellowship: M.D. Anderson Cancer Center (2018) Residency: University at Buffalo (2017) PhD: University of Houston College of Optometry (2008) Research Focus: Biomarker validation for cancer diagnostics, prognostic cytopathology applications, and implementation of standardized diagnostic frameworks. Her studies aim to enhance clinical decision-making through precise pathological evaluation. Publications: Recent work includes validation of ACR-TI-RADS systems, prognostic analyses of endometrial cancer, and comparative immunomarker studies in serous fluids. Her research bridges cytological techniques with molecular and clinical outcomes. Professional Contributions: Board-certified in both anatomic/clinical pathology and cytopathology. Active in advancing diagnostic standards through WHO and international reporting systems.

Liliana CaldeiraView profile

Researcher

• Artificial Intelligence in Medicine
• Medical Image Analysis
• Radiology
• +4 more

Dr. Liliana Caldeira serves as a Core Scientist and team leader at the Center for Data and Simulation Science (CDS) within the University Hospital of Cologne, University of Cologne. She directs innovative data science research in the Department of Diagnostic and Interventional Radiology, specializing in AI algorithm development for 3D medical imaging analysis including Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Research Focus: Her work centers on clinical AI implementation in radiology, with expertise in deep learning for medical image segmentation (brain tumors, lymph nodes, organs), image denoising, and disease classification. Key applications include brain metastasis detection in melanoma, lymph node staging for cancer, kidney segmentation in polycystic disease, and pulmonary hypertension assessment—emphasizing clinical validation and multicenter study designs to bridge AI research with practical medical workflows. Publication Trends: Analysis of her 2020-2023 publications reveals consistent focus on clinically validated AI tools, particularly using 3D convolutional neural networks for segmentation tasks. Her research demonstrates strong collaboration across radiology subspecialties, with shared last authorship indicating principal investigator leadership in translating algorithms to clinical practice. Scientific Awards: No awards were documented in the provided text. Advising and Grants: Student mentorship and grant funding details were not specified in the source material. Labs and Teams: Dr. Caldeira leads a dedicated data science team within the radiology department, collaborating with CDS under Scientific Domain 2 (Computational Biology and Medicine). The team focuses on high-quality dataset creation, AI evaluation frameworks, and algorithm implementation for medical imaging diagnostics, operating from the University Hospital campus at Kerpener Str. 62.

Arron LaceyView profile

Senior Lecturer

• Health Data Science
• Machine Learning
• Natural Language Processing
• +4 more

Dr Arron Lacey is a Senior Lecturer in Health Data Science and Natural Language Processing at Swansea University Medical School. He has been based at the SAIL Databank since 2011. His academic background includes a BSc in Physics, MSc in Computer Science, and a PhD in Healthcare Studies from Swansea University. Education: BSc Physics, Swansea University MSc Computer Science, Swansea University PhD Healthcare Studies, Swansea University Research Interests: Focuses on applying health data science and natural language processing (NLP) to healthcare challenges. Key areas include clinical data extraction from unstructured records, cardiovascular epidemiology, epilepsy outcomes analysis, and genomic data integration. His work emphasizes population-level data linkage and translational research. Recent Research Trends: Recent publications highlight NLP applications in extracting structured data from clinical letters, cardiovascular disease management post-intervention, and epilepsy mortality during the pandemic. His work bridges computational methods with real-world healthcare data to improve diagnosis, treatment monitoring, and public health policy. Grants & Collaborations: Led grants including HDR UK-funded NLP implementation projects (2017). Collaborates with multidisciplinary teams on initiatives like MedGATE for scalable NLP healthcare frameworks. Labs/Teams: Core member of the SAIL Databank, part of Swansea University's health data science ecosystem. Involved in the Center for Doctoral Training in Computation.

Joshua L. WarrenView profile

Associate Professor

• Spatial Statistics
• Spatiotemporal Modeling
• Bayesian Statistics
• +7 more

Joshua L. Warren is an Associate Professor in the Department of Biostatistics at the Yale School of Public Health. His academic appointments include primary affiliation with Biostatistics and secondary affiliations with Climate Change and Health, Public Health Modeling, Yale Superfund Research Center, and Yale-BI Biomedical Data Science Fellowship. Dr. Warren received his Ph.D. in Statistics from North Carolina State University in 2011 and completed his postdoctoral training at the University of North Carolina at Chapel Hill. He has been a faculty member at Yale University since July 2014. Dr. Warren's research focuses on statistical methods in public health with an emphasis on environmental health problems. His work primarily involves: Introducing spatial and spatiotemporal models in Bayesian frameworks Studying associations between environmental exposures (particularly air pollution) and health outcomes Developing methods for analyzing data related to preterm birth, low birth weight, and congenital anomalies Applying statistical models in collaborative settings including epidemiology, geography, nutrition, and glaucoma research Theoretical work in spatial/spatiotemporal modeling and Bayesian nonparametrics His recent publications demonstrate expertise across diverse areas including environmental health, infectious disease modeling (particularly tuberculosis), reproductive health, and glaucoma research. His work frequently employs sophisticated spatial statistical methods to analyze complex public health data and identify critical windows of exposure to environmental hazards. Scientific Awards Coauthor, Kenneth Rothman Epidemiology Prize Paper (2018) First Author, Best Paper in Biometrics (2012) Dr. Warren serves as an Associate Editor for Statistics in Biosciences (2015-present) and previously for the Journal of the American Statistical Association - Theory and Methods (2018-2020). He has also served as a reviewer for organizations including the Health Effects Institute, Swiss National Science Foundation, and NIH. His research collaborations include frequent work with Nicole Deziel, Daniel Weinberger, Ted Cohen, Xiaomei Ma, Virginia Pitzer, and Yu Lan.

• Translational Hematology
• Cancer Immunology
• Somatic Mutations
• +6 more

Dr. Satu Mustjoki is a Professor of Translational Hematology at the Faculty of Medicine, University of Helsinki (UH), Finland. She serves as Director of the Translational Immunology Research Program (2019-2025), Scientific Director of the iCAN Digital Precision Cancer Medicine Flagship (2022-2026), and leads multiple international research consortia including EraPerMed EU and Heal-Art projects. Adjunct Professor (2011), Experimental Hematology, UH Specialist in Clinical Chemistry (2008), UH PhD (2001), UH MD (1997), UH Her research focuses on somatic mutations in hematological malignancies and autoimmune diseases, cancer immunology, and personalized medicine. The work integrates immunogenomics, functional genomics, and clinical translation to identify novel therapy targets and biomarkers. Recent publications highlight somatic mutation analysis in T cells (chronic GVHD, RA), NK cell interactions with blood cancers, and single-cell approaches to melanoma immunotherapy. Key themes include mutation-driven immune dysregulation and precision targeting of apoptotic pathways in leukemia. Helsinki Institute of Life Science (HiLife) Fellow (2017-2025) ERC Consolidator grant awardee (2015-2020) Biomedicum Helsinki award (2018) City of Helsinki Science award (2020) Academy of Finland Senior Research Fellowship (2010-2015) She supervises 9 completed + 9 ongoing post-doctoral researchers, 18 completed + 13 ongoing PhD students, and 22 completed MSc/MD theses. Funding includes major grants from the Erkko Foundation, Leukemia & Lymphoma Society, and Academy of Finland. She participates in European and American hematology scientific committees and leads international clinical studies as principal investigator.

Markku Hauta-KasariView profile

Professor

• Spectral Imaging
• Pattern Recognition
• Computer Vision
• +3 more

Markku Hauta-Kasari is the 1st Vice Dean and a Professor of Computer Science at the University of Eastern Finland (UEF), affiliated with the School of Computing under the Faculty of Science, Forestry and Technology. He holds a MSc (1994) and PhD (1999) in computer science and information processing from the University of Kuopio and Lappeenranta University of Technology, respectively. His career includes roles as Director of InFotonics Center Joensuu (2003–2010) and Head of the School of Computing (2014–2021). As a researcher, he focuses on spectral color science, pattern recognition, and computer vision with applications in biomedical imaging, material analysis, and clinical diagnostics. His work bridges academia and industry through projects like the BioColour Library and the ODSI-DB dental imaging database. Key research areas include hyperspectral imaging for tumor detection, spectral analysis of biological tissues, and optimizing light sources for medical visualization. He has led over 50 peer-reviewed publications and contributed to international collaborations, including visiting research roles in Japan. He chairs the Pattern Recognition Society of Finland and the CIE TC8-07 Technical Committee on Multispectral Imaging. Hauta-Kasari’s innovations span clinical tools like real-time spectral imaging systems for neurosurgery and dental diagnostics, leveraging machine learning for improved diagnostic accuracy. His laboratories emphasize interdisciplinary approaches to solve challenges in medicine, forestry, and material science.

• Medical Imaging
• Computer Vision
• Deep Learning
• +4 more

Dr Abdullah Nazib is a Research Fellow at Queensland University of Technology (QUT) in the Faculty of Engineering, School of Electrical Engineering & Robotics. His research focuses on applying advanced computational techniques to medical imaging challenges, particularly in cancer detection and treatment planning. His research interests include: Medical image registration and segmentation Deep learning architectures for healthcare applications 3D medical image analysis Prostate cancer detection and grading Organ segmentation for radiation therapy Radiomics-based diagnostic systems Analysis of Dr Nazib's publication history reveals a clear evolution from general computer vision research toward specialized medical applications. His recent work demonstrates significant contributions to uncertainty quantification in medical image segmentation and multimodal learning approaches for diagnostic imaging. The 2024 publications particularly highlight his focus on clinically relevant applications with immediate potential impact on cancer diagnosis and treatment planning. His collaborative research network includes strong partnerships with Dr Fookes, Dr Perrin, and other members of QUT's biomedical imaging group, reflecting an interdisciplinary approach that bridges computer science, engineering, and clinical medicine to develop practical AI solutions for healthcare challenges.

• Medical Image Analysis
• Computer Vision
• Machine Learning
• +7 more

Tolga Tasdizen is a Professor in the Department of Electrical & Computer Engineering and an Adjunct Professor at the School of Computing, University of Utah. His research bridges computer vision, machine learning, and interdisciplinary applications in medical imaging, nuclear forensics, and urban health. Primary affiliation: Department of Electrical & Computer Engineering, University of Utah Secondary affiliation: School of Computing, University of Utah Research Interests Developing novel deep learning frameworks for histopathological image analysis and medical signal processing Applying computer vision to characterize built environments and examine public health outcomes Advancing nuclear forensic techniques through material morphology and machine learning Exploring biases in AI models for clinical and epidemiological applications Creating explainable AI workflows for medical diagnostics Analyzing urban infrastructure impacts on traffic safety and chronic disease prevalence Article Trends demonstrate expertise in: Medical imaging (histopathology, ECG analysis, chest X-rays) with applications in Alzheimer's disease and cancer diagnostics Urban health studies using Google Street View data to assess built environments' impacts on obesity, diabetes, and traffic injuries Nuclear forensics through SEM image analysis of uranium oxides and actinide materials Robust AI training techniques (contrastive learning, domain adaptation) for clinical and environmental datasets Collaborative Networks span radiology, cardiology, epidemiology, and nuclear engineering disciplines. His work often involves multi-institutional teams and emphasizes scalable data collection methods like eye-tracking and computer vision.

Ruchi GabaView profile

Associate Professor

• Endocrinology
• Diabetes
• Thyroid Cancer
• +7 more

Ruchi Gaba is an Associate Professor in the Department of Medicine – Endocrinology at Baylor College of Medicine, Houston, TX. She is a board-certified endocrinologist with clinical expertise in endocrine neoplasia, particularly advanced thyroid cancer, and complex thyroid disorders including hyperthyroidism, hypothyroidism, thyroid nodules, fine-needle aspiration (FNA), and thyroid disease in pregnancy. As Co-Director of the Thyroid and Parathyroid Program at the Dan L Duncan Comprehensive Cancer Center, she collaborates with multidisciplinary teams including surgery, ENT, radiology, nuclear medicine, and pathology. Her professional interests include: Thyroid Cancer Thyroid Nodules and FNA Thyroid Disorders in Pregnancy Ketosis-Prone Diabetes General Endocrine Disorders Atypical Diabetes Dr. Gaba’s research focuses on Ketosis-Prone Diabetes, where she investigates metabolic and immunological factors influencing beta-cell function and insulin dependence. Her publications reveal a consistent focus on arginine metabolism, autoantibodies (particularly against GAD65-DPD), and relapse predictors in A-β+ KPD. These studies bridge clinical endocrinology with molecular metabolism, highlighting her translational research approach. She has been recognized with the Women in Excellence Award at Baylor College of Medicine for her contributions to women’s health and equity in medicine. Her work emphasizes transformative leadership in medical education and healthcare access. Dr. Gaba mentors junior researchers and collaborates with experts like Ashok Balasubramanyam and Paras Mehta. Though no formal advisees are listed, her involvement in multiple research projects indicates active supervision and collaboration. She is affiliated with major professional societies including the American College of Clinical Endocrinology (AACE), Endocrine Society, American Thyroid Association (ATA), and Texas Endocrine Association. She maintains a clinical practice at the O’Quinn Medical Tower at Baylor St. Luke’s Medical Center and is actively engaged in both patient care and research.

• Signal Processing
• Machine Learning
• Artificial Intelligence
• +12 more

Arash Mohammadi is an Assistant Professor in the Department of Electrical and Computer Engineering at Concordia University, Montreal, Canada. He holds a PhD from the University of Toronto (2015) and was formerly affiliated with Amirkabir University of Technology, Iran. His research bridges signal processing, artificial intelligence, and biomedical applications. Research Interests: Signal and image processing for healthcare (e.g., lung cancer detection, ECG analysis) Machine learning for smart grids and cyber-physical systems AI in mobile edge computing and 6G networks Transformer and diffusion models for medical and motion data Federated and efficient deep learning for edge devices His recent publications (2021–2025) in top venues like IEEE TSP, ICASSP, and AAAI demonstrate a strong focus on applying cutting-edge AI—especially vision transformers, Mamba architectures, and diffusion models—to critical domains such as medical diagnostics, gesture recognition, and network security. Trends include multimodal fusion, uncertainty quantification, and efficient model design. Scientific Contributions: Developed novel frameworks like NYCTALE and MIXCAPS for lung nodule malignancy prediction Introduced CacheMamba and TEDGE-Caching for edge network optimization Advanced EMG-based gesture recognition using hybrid and transformer models Contributed to cybersecurity in smart grids via attack detection models He actively advises students and collaborates with researchers such as Konstantinos N. Plataniotis and Jamshid Abouei. He has contributed to special issues on neurorehabilitation and AI for COVID-19 diagnosis. His work often involves interdisciplinary teams and real-world applications in healthcare and smart infrastructure.

• Epidemiology of Obstructive Pulmonary Diseases
• Lung Function
• Lung Cancer
• +3 more

Arnulf Langhammer is a Professor at the Norwegian University of Science and Technology (NTNU), affiliated with the HUNT Research Center within the Department of Community Medicine and Nursing under the Faculty of Medicine and Health Sciences. He has been active at the HUNT Research Center since 1994 and concurrently practices as a general practitioner in Steinkjer. His research focuses on epidemiology of pulmonary diseases, osteoporosis, lung cancer, and primary healthcare quality assurance. Key roles include leading projects in the HUNT Study series (HUNT2-4), such as the Osteoporosis and Lung Projects. He co-led the Osteoporosis Project in HUNT2 and managed the HUNT data bank until 2023. His work integrates population health studies with clinical practice. Research interests span obstructive lung disease epidemiology, drug effects on bone health, and respiratory outcomes. He has contributed to over 150 publications, emphasizing longitudinal cohort analyses in Norway and Europe. Notable collaborations include the Nordic EpiLung Study and international consortia on lung cancer and osteoporosis. His articles address topics such as fracture risk prediction, occupational lung function decline, and socioeconomic disparities in lung cancer. While no explicit awards are listed, his extensive contributions to public health research and clinical practice underscore his impact in epidemiology and respiratory medicine.

Gültekin IşıkView profile

Associate Professor

• Deep Learning
• Machine Learning
• Computer Vision
• +4 more

Assoc. Prof. Gültekin Işık is affiliated with the Department of Computer Hardware at Iğdır University's Faculty of Engineering. He holds a PhD in Computer Engineering from Hacettepe University (2011–2019), focusing on Turkish dialect recognition using deep learning. His academic roles include serving as Head of the Department from 2019 to 2021. His research interests span deep learning applications in computer vision (e.g., YOLO-based crowd detection), optimization algorithms (e.g., Slime Mould Algorithm), environmental modeling (solar power efficiency prediction), and healthcare (breast cancer detection). He has authored numerous peer-reviewed articles and books, including works on convolutional neural networks for plant disease identification and hybrid optimization techniques for data clustering. Prof. Işık teaches advanced courses such as İleri Derin Öğrenme (Advanced Deep Learning) and Sinir Ağları (Neural Networks). His recent work emphasizes real-time video analysis for public health (social distancing) and energy systems optimization. Despite no listed awards, his contributions to interdisciplinary fields like bioacoustics and renewable energy are notable. He has advised two master’s students: Mehmet Şirin Gündüz (2023) on YOLO-based crowd detection and Seda Bayat (2021) on bird species recognition using deep learning. His research integrates theoretical models with practical applications in domains ranging from agriculture to medical imaging.

• Proteomics
• Systems Biology
• Protein Methylation
• +5 more

Marc Wilkins is a Professor of Systems Biology at the University of New South Wales (UNSW) School of Biotechnology and Biomolecular Sciences. He currently serves as Deputy Dean (Research and Enterprise) for the Faculty of Science and leads the UNSW node of the $35M ARC Centre of Excellence in Mathematical and Statistical Frontiers (MACSYS). Previously, he directed the Ramaciotti Centre for Genomics (2011-2022) and the NSW Systems Biology Initiative. His research focuses on: Protein methylation networks and their regulatory functions in eukaryotic cells Large-scale analysis of protein-protein interactions using crosslinking mass spectrometry (XL-MS) Genomics and transcriptomics with expertise in next-generation sequencing Systems biology approaches combining proteomics, bioinformatics, and molecular biology He has published over 260 peer-reviewed papers and pioneered the concept of the 'proteome' in 1994. His publications demonstrate consistent focus on proteomics technology development, post-translational modifications, and multi-omics integration across diverse biological systems. Recent work emphasizes structural proteomics, methylation networks, and conservation genomics. Major Awards: Doctor of Science (D.Sc.) from UNSW (2017) ASBMB Beckman Coulter Discovery Science Award (2012) Elected Council Member of Human Proteome Organisation (2009-2024) He has supervised 38 honours students (6 University Medalists), 16 PhD students, and 5 Master's students. His ARC-funded laboratory focuses on proteomics and systems biology. Major grants include leadership in the $35M MACSYS Centre of Excellence and previous direction of genomics facilities funded by NCRIS, ARC LIEF, and NSW Government schemes. He co-founded two biotechnology companies: Tyrian Diagnostics (proteomic diagnostics) and Regeneus (regenerative medicine), both listed on the ASX. His team maintains expertise in cutting-edge proteomic and genomic technologies with strong industry collaborations.

Matiss OzolsView profile

Lecturer

• Bioinformatics
• Data Science
• Proteomics
• +6 more

Dr. Matiss Ozols is a Honorary Lecturer (Teaching & Research) in the Division of Cell Matrix Biology & Regenerative Medicine at the University of Manchester and holds a Senior Bioinformatics Data Scientist position at the Wellcome Sanger Institute. His work integrates bioinformatics, software engineering, and data science to address complex biological questions, particularly focusing on single-cell genomic/proteomic data analysis for human genetics insights. He has expertise in programming languages (Python, R, etc.), machine learning, and web development, alongside wet lab techniques like mass spectrometry and cell culture. Education: Bachelor of Engineering in Medical Engineering (University of Bradford, 2014). Research Interests: Ozols focuses on proteomics, genomics, and regenerative medicine with an emphasis on ageing and extracellular matrix remodelling. His lab develops computational tools like the Manchester Peptide Location Fingerprinter (MPLF) to analyze protein modifications. Key areas include tetrapeptide matrikines for skin rejuvenation, vascular ageing mechanisms, and AI applications in healthcare diagnostics. Publications: Recent work spans AI-driven medical imaging (e.g., lung compromise in COVID-19), novel peptide therapeutics for skin health, and systems biology approaches to inflammatory bowel disease. These studies highlight interdisciplinary methods combining computational modeling with experimental biology. Awards: Received the Best Oral Presentation award in 2019. Grants & Projects: Co-Investigator in the Manchester Bioelectronics Network (2018), focusing on bioelectronics applications in healthcare. Active in collaborative research networks including Manchester Precision Medicine Institute and Institute for Data Science and AI. Labs/Teams: Leads bioinformatics initiatives at the Sanger Institute and collaborates with multidisciplinary teams in dermatology, cardiovascular biology, and AI ethics.