Cancer Informatics Professors and Supervisors | ApplySharif

جست‌وجوی استاد و پژوهشگر

استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۷۲۱-۷۳۵ از ۵٬۴۸۰ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

Joseph C. Ayoobمشاهده پروفایل

دانشیار

University of PittsburghUnited States

Joseph C. Ayoob, PhD, is an Associate Professor in the Department of Computational and Systems Biology at the University of Pittsburgh School of Medicine. He holds roles as Associate Director of the UPMC Hillman Academy and Faculty Fellow of the University of Pittsburgh Center for Mentoring. His work focuses on innovative training programs for students at all academic levels, including the NSF-funded TECBio REU program (since 2010) and the CompBio Academy high school program (since 2011). He also directs the CPCB MetaSchool, a professional development series for graduate students. Dr. Ayoob earned his PhD in Neuroscience from Johns Hopkins University School of Medicine. His educational efforts span undergraduate, graduate, and postdoctoral trainees, emphasizing lab-based skills and professional development. He is a Principal Facilitator for the Entering Mentoring and Entering Research curricula, recognized as a National Research Mentoring Network (NRMN) Master Mentor and Mentor of the Month. Research interests include STEM education innovation, mentorship best practices, and computational biology training. He has pioneered programs like the CPCB Presentation Course and CoBB Professional Development course. Awards include recognition for mentorship excellence and contributions to mentored research training. Publications highlight his work in educational frameworks, undergraduate research effectiveness, and molecular mechanisms in neurodegenerative diseases. His lab supports interdisciplinary training through summer programs targeting underrepresented students in STEM fields.

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David Lovellمشاهده پروفایل

استاد

Queensland University of TechnologyAustralia

David Lovell is a Professor in the School of Computer Science at Queensland University of Technology (QUT). He previously served as Head of the School of Electrical Engineering and Computer Science (2014–2019) and Deputy Director of QUT’s Centre for Data Science (2020–2021). His research focuses on the intersection of humanity, science, and technology, particularly in data science and compositional data analysis (CoDA). Lovell holds a PhD in Electrical Engineering from the University of Queensland (1994) and a BEng (Honours 1) in Computer Systems (1989). Education Background: BEng(Hons 1)(Computer Systems), University of Queensland (1989) PhD, University of Queensland (1994) PGDipMgmt, Macquarie University Research Interests: David’s work emphasizes ethical and human-centric applications of data-driven decision-making systems. He advocates for CoDA in bioscience to ensure appropriate analysis of relative abundance data. His research spans bioinformatics, healthcare technology, and the societal implications of data science. Publications Overview: Lovell has contributed to over 50 peer-reviewed articles, including seminal work on proportional analysis in bioinformatics and applications of machine learning in healthcare. Recent trends include studies on Indigenous data literacy, AI in radiology training, and tumor microenvironment analysis. Awards & Recognition: While no specific awards are listed, his leadership roles and prolific publications highlight his significant contributions to the field. Advising & Grants: As Head of EECS and through interdisciplinary collaborations, Lovell fostered institutional growth. His supervision focuses on large-scale bioinformatics and information retrieval. Labs & Teams: Active in the Centre for Data Science and previously led the Statistical Bioinformatics-Agribusiness Group at CSIRO. Collaborates with organizations like EMBL Australia and Bioplatforms Australia.

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Michelle Greiverمشاهده پروفایل

استاد

University of St AndrewsUnited Kingdom

Prof Michelle Greiver is an Honorary Professor in the School of Medicine at the University of St Andrews. Her research focuses on public health outcomes in primary care settings, particularly addressing cardiovascular disease, diabetes management, and healthcare disparities. She collaborates on projects involving medication adherence patterns, mental health comorbidities, and advanced statistical modeling techniques. Her work contributes to UN Sustainable Development Goals related to good health and well-being (SDG 3). Recent studies include analyses of breast cancer screening equity among individuals with schizophrenia and the impact of adherence to medication regimens on cardiovascular outcomes in patients with diabetes and hypertension. Prof Greiver is affiliated with the University of St Andrews School of Medicine, contributing to both research and academic activities. Her methodologies often integrate epidemiological approaches with health informatics, including applications of marginal structural models and machine learning algorithms.

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Jytte Brenderمشاهده پروفایل

استاد مدعو

Aalborg UniversityDenmark

Jytte Brender is an Adjunct Professor in the Department of Health Science and Technology at Aalborg University, Faculty of Medicine, Denmark. Her work bridges medical informatics, health information technology, and clinical decision-making, with a strong emphasis on evaluation methodologies. She has contributed to national and international research projects focused on patient safety, laboratory information systems, and AI in healthcare. University: Aalborg University School: Faculty of Medicine Department: Department of Health Science and Technology Academic Rank: Adjunct Professor Email: jmn@hst.aau.dk Her research interests include medical and health informatics, artificial intelligence in clinical settings, clinical decision support systems, evaluation of health IT, patient safety, and theoretical frameworks such as the Mereon Matrix. She has also explored applications in human molecular genetics through theoretical modeling. The recent publications (2023–2024) highlight a growing focus on the evaluation and ethical implementation of AI in clinical decision-making, emphasizing the need for robust frameworks and avoiding redundant evaluation cycles. Her work spans journal articles, book chapters, and conference proceedings, often in collaboration with leading figures in medical informatics. Scientific Contributions: Key contributor to the PSIP project on patient safety in medication. Active in LIS and laboratory system development and assessment. Prominent voice in AI evaluation in healthcare, cited in policy and news outlets. She has advised on health IT evaluation frameworks and participated in multi-institutional research. While no formal students are listed, her collaborative work suggests mentorship and advisory roles. She is not retired or deceased, and her adjunct status suggests a continued but possibly part-time or affiliated academic role.

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A. Bozzonمشاهده پروفایل

استاد

Delft University of TechnologyNetherlands

A. Bozzon is a Full Professor at Delft University of Technology, affiliated with the Department of Web Information Systems under the Faculty of Electrical Engineering, Mathematics and Computer Science. His research focuses on Human-Centered Artificial Intelligence (HCAI), emphasizing ethical AI, human-AI interaction, and societal implications. He has authored over 240 publications and secured 8 notable awards, including Best Paper Awards at CHI 2023 and 2025. Affiliations : Delft University of Technology Research Interests : AI ethics, human-AI collaboration, algorithmic fairness, and sustainable design. His work bridges technical innovation with social impact, addressing topics like mental health diagnostic tools, automation adoption in organizations, and greenspace accessibility. Key contributions include datasets on adverse drug reactions and AI-driven urban analysis. Collaborations span global institutions, reflecting his interdisciplinary approach. Awards : Best Paper Award at CHI 2023 Best Paper Award at CHI 2025 CTwalk Map: Best Demo Award (2024) Supervised 14 academic works, fostering next-generation researchers in HCAI. Active in policy discussions around AI governance and has pioneered tools like Amalur for FAIR data integration.

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Richard M. Goldenمشاهده پروفایل

استاد

University of Texas at DallasUnited States

Richard M. Golden is a Professor of Cognitive Science and Participating Faculty in the Erik Jonsson School of Engineering and Computer Science at the University of Texas at Dallas (UTD). He directs the COgnitive INformatics and Statistics (COINS) Lab, focusing on computational psychometrics and statistical machine learning applied to educational technologies. His research integrates cognitive science, machine learning, and usability engineering to improve personalized instruction. He authored the textbook Statistical Machine Learning: A Unified Framework and hosts the Learning Machines 101 podcast. Golden leads UTD's Undergraduate Cognitive Science and Graduate Applied Cognition and Neuroscience programs. Education : Ph.D. in Experimental Psychology (Brown, 1987), M.S. in Electrical Engineering (Brown, 1986), B.S. in Electrical Engineering & Psychology (UC San Diego, 1982). Research : Prior work includes neural network models of text comprehension, model misspecification analysis, and Bayesian inference. Current projects include diagnostic assessment tools and AI-driven educational systems. Awards : Secretary-Treasurer Service Award (2017), IEEE Senior Member (1999), multiple invited speaker roles. Labs/Teams : COINS Lab develops tools like the ARCADE system for automated reading assessment. Collaborates with organizations like the Veteran's Administration and National Science Foundation.

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Mousumi Banerjeeمشاهده پروفایل

استاد پژوهشی

University of Michigan-Ann ArborUnited States

Mousumi Banerjee is the Anant M. Kshirsagar Collegiate Research Professor in the Department of Biostatistics at the University of Michigan School of Public Health. She also holds the position of Research Professor in Global Public Health and serves as Director of Biostatistics for the Pediatric Cardiac Critical Care Consortium (PC4) Analytic Center and Director of Global StatCore. Her affiliations include the Rogel Cancer Center, Institute for Healthcare Policy and Innovation, Center for Global Health Equity, and Michigan Institute for Data Science (MIDAS). Bachelor's & Master's in Statistics from Indian Statistical Institute, Kolkata PhD in Statistics from University of Wisconsin-Madison (1994) Dr. Banerjee develops advanced statistical methodologies for biomedical applications, particularly in cancer and pediatric heart disease. Her research includes predictive modeling, machine learning, causal inference, survival analysis, and correlated data techniques. She focuses on health disparities and equitable care delivery, collaborating across disciplines including neurology, surgery, and social determinants of health. Recent publications demonstrate her expertise in statistical algorithm development for clinical quality assessment, disease classification, and healthcare outcomes. Her work spans applications in bladder cancer detection, thyroid cancer follow-up imaging, HPV-related oropharyngeal carcinoma analysis, and cardiac arrest prevention in pediatric critical care settings. Scientific Awards Fellow of the American Statistical Association Elected member of the International Statistical Institute As Director of Global StatCore, Banerjee enhances biostatistical support for global public health initiatives. She leads major methodological projects for the Pediatric Cardiac Critical Care Consortium (PC4) and works with national datasets like SEER, NCDB, and Medicare claims to analyze healthcare disparities and quality improvement metrics.

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Maruf Adewoleمشاهده پروفایل

پژوهشگر ارشد

University of PennsylvaniaUnited States

Maruf Adewole, PhD, is a Postdoctoral Research Fellow and Associate Scholar at the University of Pennsylvania's Perelman School of Medicine, Department of Radiology. He is affiliated with the Center for Global Health and serves as Executive Director of the Medical Artificial Intelligence Laboratory (MAI Lab) in Lagos, Nigeria. His research focuses on leveraging artificial intelligence to improve medical imaging and cancer diagnosis in resource-constrained settings across Africa. Key areas include medical physics, radiomics, oncology, and open science. He leads impactful initiatives such as BraTS-Africa, ABreast, and SPARK Academy, which aim to build local AI capacity and promote health equity. The recent publications reflect a strong trend in applying machine learning to brain tumor segmentation and radiology in underserved populations, emphasizing inclusive dataset development and AI model validation in African patient cohorts. Scientific Awards: No scientific awards mentioned in the text. Advising and Grants: While formal students are not listed, Dr. Adewole has trained over 300 participants through the SPARK Academy, a structured AI and medical image computing program. He leads major research initiatives including BraTS-Africa and ABreast, which involve international collaboration, dataset curation, and model development for cancer care in Africa. Labs and Teams: Dr. Adewole leads the Medical Artificial Intelligence Laboratory (MAI Lab) in Lagos, Nigeria. He is also a key contributor to global health projects at the University of Pennsylvania’s Center for Global Health, working on initiatives that bridge AI, radiology, and equitable healthcare delivery.

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Shareef Syedمشاهده پروفایل

استادیار

University of California, San FranciscoUnited States

Dr. Shareef Syed is an Assistant Professor in the Department of Surgery at the University of California, San Francisco (UCSF) School of Medicine. He serves as the educational lead for general surgery residents and actively participates in enhancing surgical education curricula. His clinical expertise spans abdominal organ transplantation, pediatric transplant surgery, and advanced laparoscopic procedures for living donor nephrectomy. Syed's research focuses on transplant patient outcomes, novel noninvasive rejection detection methods, and global health collaborations with Costa Rican transplant centers. University of Leicester - MBChB (2008) Royal College of Surgeons - MRCS (2011) University of Southampton - Internal Medicine & Surgery (2010) University of Bristol - Cardiothoracic Surgery (2011) Central Michigan University - General Surgery Residency (2014) UCSF - General Surgery Residency (2016) & Abdominal Transplant Fellowship (2018) His research explores critical areas in transplantation medicine including liver preservation , renal allograft rejection , and Hepatocellular carcinoma outcomes in transplant patients. Recent publications investigate robotic-assisted surgical techniques, machine perfusion applications, and educational methodologies in surgical training. Syed is board-certified in Surgery (2016) and has received teaching awards from UCSF residents. Scientific Awards: Louis E. Zeile Outstanding Resident in Surgery (2014) Lawrence W. Way Critical Thinking Award (2016) Faculty Teaching Award (2019) Professional Memberships: American Society of Transplant Surgeons Royal College of Surgeons The Transplantation Society Americas Hepato-Pancreato-Biliary Association American College of Surgeons American Association for the Study of Liver Diseases American Society of Transplantation As an active clinician-scientist, Dr. Syed contributes to both clinical practice and research methodology development in transplant surgery. His work addresses critical challenges in organ procurement, surgical education, and patient outcome optimization.

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Alexander Gottschalkمشاهده پروفایل

استاد

University of California, San FranciscoUnited States

Dr. Alexander Gottschalk is a Professor in the Department of Radiation Oncology at the University of California San Francisco (UCSF). He serves as Director of the CyberKnife Radiosurgery Program and Vice Chair of Clinical Services, overseeing clinical operations at multiple UCSF sites including Mount Zion, Parnassus Mission Bay, and Washington Hospital. He actively participates in clinical care for Urologic, Thoracic, Orthopedic, and Gastrointestinal Oncology programs, while also mentoring radiation oncology residents and medical students. MD, University of Chicago (1996) Dr. Gottschalk's research spans both neuroscience (focusing on optogenetics, synaptic transmission, and C. elegans models) and radiation oncology (specializing in stereotactic body radiotherapy, brachytherapy, and cancer treatment optimization). His recent publications highlight innovations in light-activated ion channels, optogenetic tools for neuronal control, and advanced radiation therapy frameworks. Notable clinical research includes trials on prostate cancer treatment combining radiation therapy with pharmacological agents, and systematic analyses of radiation therapy for adrenal metastases. His work also encompasses neurogenetic engineering and biomedical imaging applications. Commitment to Patient Care Award, UCSF (2012-2013) Lifetime Teaching Award, UCSF (2012-2013) Educator of the Year, UCSF (2009-2010) As Vice Chair, Dr. Gottschalk oversees clinical operations and proctors faculty in CyberKnife Radiosurgery. He collaborates extensively with multidisciplinary teams across oncology programs and contributes to the development of optogenetic technologies through his laboratory research.

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Stefan Ćirkovićمشاهده پروفایل

مدرس

University of KragujevacSerbia

Stefan Ćirković is a Teaching Associate in the Department of Information Technologies at the Faculty of Technical Sciences in Čačak, University of Kragujevac. His office is located at Saint Sava 65, 32102 Čačak, Serbia. He is currently pursuing doctoral studies in Information Technology (2023–present) at the same institution. Education: Doctoral Academic Studies – Information Technology, University of Kragujevac (2023–present) Master of Academic Studies – Information Technology, University of Kragujevac (2022–2023) Basic Academic Studies – Information Technology, University of Kragujevac (2018–2022) Specialized Program – Cybersecurity, University of Kragujevac (2024) Research Focus: Ćirković specializes in applied artificial intelligence with significant work in cybersecurity, medical imaging, and network systems. His research demonstrates strong emphasis on practical AI implementations including real-time object detection (YOLO algorithm), anomaly detection in networks, federated learning for healthcare applications, and cybersecurity techniques using large language models. His work frequently intersects with medical informatics, particularly in diagnostic applications. Publication Trends: Recent publications (2024–2025) show predominant focus on AI/ML applications across multiple domains. Cybersecurity remains a core theme with novel approaches to web application security and network anomaly detection. Medical applications feature prominently, especially in diagnostic imaging (kidney stones, skin cancer) and hemodialysis optimization. Methodologically, there's consistent exploration of deep learning architectures and real-time systems. Laboratory Affiliations: Associated with the Computer Science Laboratory at the Faculty of Technical Sciences in Čačak. Utilizes institutional resources including the Moodle e-learning platform and Microsoft 365/Teams infrastructure for academic activities.

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Kathrin Cresswellمشاهده پروفایل

استاد

University of EdinburghUnited Kingdom

Kathrin Cresswell is a Professor of Digital Innovations in Health and Care at the University of Edinburgh's Usher Institute , within the College of Medicine and Veterinary Medicine. She is a social scientist with expertise in formative evaluations of digital health technologies, focusing on implementation science, patient safety, and sociotechnical systems. She has consulted for the World Health Organization, Harvard Medical School, and NHS England, and served as co-chair of the International Medical Informatics Association (IMIA) Evaluation Working Groups. Education : BSc in Psychology, MSc in Health Psychology, and PhD in Medical Informatics. Her research explores health information technology (e.g., electronic health records, AI, robotics), antimicrobial stewardship, cloud computing, and equitable digital transformation. She has led evaluations of the NHS Digital Academy, Global Digital Exemplar Programme, and electronic prescribing systems, emphasizing stakeholder alignment and organizational change. Her work has secured £56 million in grants and over 140 peer-reviewed publications. Recent research trends include AI-driven decision support systems, digital maturity assessments, and cloud technology in healthcare. She advocates for theory-informed evaluations to mitigate bias and improve resilience in health IT implementations. Scientific Awards : Yvonne Carter Award for Outstanding New Researcher; Fellow of the International Academy of Health Sciences Informatics; Member of the Royal Society of Edinburgh Young Academy of Scotland. She contributes to national and international policy through written evidence submissions to parliamentary committees and leadership in digital health curricula like Scotland's digital health leadership program. Her methodology combines qualitative, mixed-methods, and systematic reviews to address real-world healthcare challenges.

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Sarah L Cutronaمشاهده پروفایل

استاد

University of Massachusetts Chan Medical SchoolUnited States

Sarah L Cutrona is a Professor at UMass Chan Medical School, affiliated with the T.H. Chan School of Medicine and Morningside Graduate School of Biomedical Sciences. She specializes in the Department of Population and Quantitative Health Sciences and the Division of Health Informatics and Implementation Science. Education: BA in English from Yale University MPH in Clinical Effectiveness from Harvard University School of Public Health MD from Columbia University Research Interests: Cutrona focuses on health informatics, implementation science, and medical informatics. Her work examines electronic health record (EHR) transitions, digital health interventions, tobacco cessation support tools, and health services research within the Veterans Health Administration (VHA). She investigates clinician-patient communication, implementation fidelity, and health equity in technology adoption. Scientific Contributions: Her 15 most recent publications (2023-2025) cover EHR implementation challenges, digital divide risk scores, cancer survivorship programs, and tobacco cessation monitoring. Key themes include health informatics, internal medicine, and implementation science with subfields like EHR usability, clinician burnout, patient engagement, and health equity. Collaborations: Cutrona works extensively with institutions including the Veterans Health Administration, Harvard, and Columbia. Her research spans clinical trials, mixed methods studies, and qualitative analyses of healthcare technology adoption.

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Mohsen Khademمشاهده پروفایل

استاد

University of EdinburghUnited Kingdom

Mohsen Khadem serves as a Reader in Robotics at the School of Informatics, University of Edinburgh, where he directs the Surgical and Interventional Robotics Group. He holds dual affiliations with the Institute for Perception, Action and Behaviour and the Center for Inflammation Research and the Institute of Regeneration and Repair. As a UKRI Future Leaders Fellow, his work bridges engineering and clinical medicine through the development of advanced robotic systems for surgical interventions. His research focuses on continuum robotics with primary applications in surgical and interventional robotics. Key areas include surgical robotics and image-guided therapies, computer vision and medical image processing, mechanics-based modeling and simulation, and applications of control theory in robotics. Khadem's approach emphasizes solving clinical problems first, working closely with surgeons to develop appropriate robotic solutions, whether new hardware designs or novel algorithms. His recent publications demonstrate a strong trajectory toward medical robotics applications, particularly in bronchoscopy navigation and surgical skill assessment. The work combines computer vision techniques with robotic control systems to address clinical challenges in minimally invasive procedures, showing increasing sophistication in both image processing and robotic manipulation capabilities. UKRI Future Leaders Fellow Khadem actively supervises multiple PhD students including Balint Thamo (focusing on concentric tube robots), Emile Makute (specializing in machine learning for continuum robots), Alex Adams (working on spectroscopy for cancer diagnosis), and Zhaoxing Deng (researching surgical skill assessment). His major projects include leading the Cyber-Physical System for Unified Diagnosis and Treatment of Lung Diseases and participating in the EPSRC-funded MicroTex research hub. Based at the Bayes Centre and IRR South facilities, Khadem's Surgical and Interventional Robotics Group operates as a cross-institutional research team with strong connections to clinical partners. The lab focuses on developing therapeutic and diagnostic robotic instruments through close collaboration with surgeons and clinicians to identify optimal solutions for specific medical challenges.

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Michael F. Bergerمشاهده پروفایل

استادیار

Cornell UniversityUnited States

Cancer Biology
Omics
Bioinformatics
+۵ مورد دیگر

Michael F. Berger is an Assistant Professor at Weill Cornell Medicine's Graduate School of Medical Sciences and Co-Director of the Marie-Josée and Henry R. Kravis Center for Molecular Oncology at Memorial Sloan Kettering Cancer Center (MSKCC). He serves as the inaugural Chief of the Clinical Computational Diagnostics Service in the Department of Pathology and Laboratory Medicine at MSKCC. Education: PhD in Biophysics from Harvard University; Bachelor’s in Physics from Princeton University Research Interests: Berger specializes in genomic and computational techniques to identify molecular drivers of cancer and develop targeted therapies. His work focuses on biomarker discovery, tumor evolution, and clinical implementation of next-generation sequencing assays like MSK-IMPACT (FDA-authorized tumor profiling) and MSK-ACCESS (liquid biopsy). Recent Articles: His publications highlight population-scale clinical genomic datasets, integrating mutational signatures, clonality, and ancestry in cancer progression. Key trends include applications of machine learning, liquid biopsies, and genome-derived tumor typing for personalized medicine. Grants & Collaborations: Berger collaborates with clinical leaders at MSKCC, leveraging large-scale sequencing data for translational research. His lab explores inherited variants, clonal hematopoiesis, and drug resistance mechanisms through clinical partnerships. Labs & Teams: He leads the Michael Berger Lab at MSKCC, part of the Marie-Josée and Henry R. Kravis Center for Molecular Oncology and the Gerstner Sloan Kettering Graduate School of Biomedical Sciences.

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