جست‌وجوی استاد و پژوهشگر

Kim KyungMannمشاهده پروفایل

استاد

• Biostatistics
• Clinical Trials Methodology
• Time-to-Event Data Analysis
• +۶ مورد دیگر

Prof. KyungMann Kim is a Professor of Biostatistics and Medical Informatics at the University of Wisconsin–Madison, with affiliate faculty status in the Department of Statistics and School of Computer, Data & Information Sciences (College of Letters and Science). As Director of the Clinical Trials Program, they lead the Data Management, Auditing, and Coordinating Center (DMACC) for the National Cancer Institute's Cancer Prevention Clinical Trials Network (CP-CTNet). Their work spans multiple NIH-funded randomized controlled trials across disciplines including cancer immunology, cardiovascular disease, and infectious diseases. Research Focus: Specializes in statistical methods for clinical trials, including sequential design, complex time-to-event analysis, and causal inference. Applies these methods to translational research in cancer prevention, tumor immunotherapy, and clinical oncology. Active in developing methodologies for clustered data and zero-inflated count data in healthcare settings. Key Projects: Leads data coordination centers for major clinical trials. Current initiatives include the CP-CTNet, managing multi-site cancer prevention trials. Collaborates extensively with clinical researchers in hematology, oncology, and cardiovascular medicine. Professional Contributions: Serves as Principal Investigator for multiple NIH-funded data coordinating centers. Advises on statistical design and analysis for rare genetic disorder trials and other critical medical research programs.

Adam Ruleمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Health Informatics
• Human-Centered Data Science
• +۳ مورد دیگر

Adam Rule is an Assistant Professor of Information Science at the University of Wisconsin–Madison’s Information School. He also holds honorary and affiliate roles in the Department of Family Medicine and Community Health, the Department of Medicine (Division of General Internal Medicine), and the Department of Biostatistics and Medical Informatics. His research focuses on human-computer interaction, medical informatics, and human-centered data science, particularly in the context of electronic health records (EHRs). Education: PhD in Cognitive Science, University of California, San Diego (2013–2018) MS in Human-Centered Design & Engineering, University of Washington (2011–2013) BS in Industrial Engineering, University of Illinois Urbana-Champaign (2008–2011) Research Interests: EHR workflow analysis and usability Clinician documentation practices Computational notebooks and collaborative data science Impact of technology on healthcare workflows Awards & Fellowships: National Library of Medicine Postdoctoral Training Grant (2019–2021) Best Paper, 2nd Place, MedInfo 2021 CHI 2018 Honorable Mention Grants & Advising: Co-Investigator on grants examining EHR workload and team support in primary care Mentored over 20 students across MS, BS, and MD programs Labs & Teams: Collaborative Computing Group (UW–Madison Information School), with interdisciplinary ties to clinical and informatics teams.

• Machine Learning
• Biomedical Image Analysis
• Medical Imaging
• +۳ مورد دیگر

Professor Jinman Kim is a Professor in the School of Computer Science at the University of Sydney and Director of the Biomedical Data Analysis and Visualisation (BDAV) Lab. He also serves as Research Director of the Telehealth and Technology Centre at Nepean Hospital. His research focuses on machine learning applications in biomedical image analysis, visualization, and multi-modal data processing. Kim holds a PhD in Computer Science from the University of Sydney (2006) and has held roles including Senior Lecturer (2013), Associate Professor (2016), and Professor (2022). He is an Area Editor for Computer Methods and Programs in Biomedicine and actively contributes to AI-driven healthcare initiatives. His academic journey includes a Marie Curie Fellowship at the University of Geneva (2010) and leadership roles in projects like the ARC Training Centre in Innovative Biomedical Engineering. He co-leads the Digital Health Imaging initiative under the Faculty of Engineering’s Digital Science Initiative. Kim has developed teaching programs such as the Master of Digital Health and Data Science, co-taught with the Faculty of Medicine and Health. Research interests span AI in medical imaging, telehealth systems, and interdisciplinary biomedical engineering. His work includes advancements in PET/CT fusion, tumor segmentation, and medical visual analytics. Kim’s lab explores applications like AI in dental education, cutaneous lymphoma detection, and fair AI models for healthcare. Notable collaborations include the Telehealth Remote Monitoring System for chronic patients and contributions to datasets like the HRDC Challenge for hypertension classification. His labs prioritize translating AI innovations into clinical tools for improved healthcare accessibility and precision.

William F. Speierمشاهده پروفایل

دانشیار

• Medical Informatics
• Biomedical Engineering
• Neurology
• +۳ مورد دیگر

William F. Speier is an Associate Professor in the Department of Radiological Sciences at the University of California, Los Angeles (UCLA) School of Medicine . His work spans Medical Informatics , Biomedical Engineering , and Neurology , focusing on applying Artificial Intelligence and Deep Learning to medical imaging and patient monitoring systems. Speier's research emphasizes improving diagnostics for Thyroid Cancer via multimodal ultrasound and molecular testing, advancing Brain-Computer Interfaces (BCIs) for ALS patients, and optimizing Heart Failure remote monitoring through biometric data analysis. He leads the NIH-funded project Predicting Clinically Significant Thyroid Cancer using Ultrasound (R21EB030691), integrating AI into clinical workflows. His recent publications highlight trends in High-Frequency Oscillations for epilepsy, Gleason Grading in prostate cancer, and Language Models for BCI communication. Collaborations with co-authors like Corey Arnold and Hiroki Nariai underscore his interdisciplinary approach. Speier's work also addresses Diagnostic Imaging , Neural Signal Processing , and Health Technology accessibility. Grants and clinical trial integrations further demonstrate his commitment to translating AI into practical healthcare solutions. His methodologies include 3D ConvNets , Federated Learning , and Active Learning frameworks for histopathology and radiology.

George Hripcsakمشاهده پروفایل

استاد

• Biomedical Informatics
• Electronic Health Records (EHRs)
• Data Mining
• +۴ مورد دیگر

George Hripcsak is the Vivian Beaumont Allen Professor of Biomedical Informatics and Director of Medical Informatics Services at New York-Presbyterian Hospital, Columbia University. He holds affiliations with the Vagelos College of Physicians and Surgeons and the Data Science Institute (DSI). His expertise spans clinical informatics, electronic health records (EHRs), and medical knowledge representation standards. Hripcsak earned degrees in chemistry, medicine, and biostatistics, and is a board-certified internist. Research focuses on leveraging EHR data for clinical research and patient safety through data mining and causal inference techniques. Notable contributions include the Arden Syntax (a national standard for medical knowledge representation) and leadership in the Observational Health Data Sciences and Informatics (OHDSI) network. He chairs the AMIA Standards Committee and has advised federal health informatics policies under HIPAA. His academic awards include Fellowships in the American College of Medical Informatics (1995) and New York Academy of Medicine. Current projects emphasize federated learning, genomic risk prediction, and large-scale real-world evidence analysis through initiatives like LEGEND-T2DM and All of Us Research Program. Educations: MD, Biostatistics, Chemistry Labs/Teams: OHDSI, DSI, Medical Informatics Services Grants & Funding: Not explicitly listed in provided texts

Oliver Kennedyمشاهده پروفایل

دانشیار

• Databases
• Programming Languages
• Data Science
• +۲ مورد دیگر

Dr. Oliver Kennedy is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo's School of Engineering and Applied Sciences. He serves as Co-Director of Graduate Studies and leads the Online Data Interactions (ODIn) Lab. His research focuses on databases, programming languages, and user interfaces for data science, with particular emphasis on scalable compilers and managing uncertainty in data. Kennedy holds a PhD in Computer Science from Cornell University (2011), MS from Cornell (2008), and dual BS degrees in Computer Science and Computer Engineering from NYU and Stevens Institute of Technology (2005). His work bridges theoretical computer science with practical data management challenges. His recent publications demonstrate a strong focus on improving database query processing, uncertainty management in data systems, and developing practical tools for data integration and exploration. Awarded the NSF CAREER Award in 2018, Kennedy's research has significant implications for efficient data processing in scientific and commercial applications.

Hernan G. Reyمشاهده پروفایل

استادیار

• Anterior Temporal Lobectomy
• Drug Resistant Epilepsy
• Electroencephalography
• +۶ مورد دیگر

Hernan G. Rey, PhD, is an Assistant Professor in the Department of Neurosurgery at the Medical College of Wisconsin (MCW) and the Marquette-MCW Joint Department of Biomedical Engineering. He previously held an Assistant Professor position at Baylor College of Medicine until July 2022. His research focuses on understanding human episodic memory, improving epilepsy diagnosis and treatment, and developing tools for electrophysiological data analysis. Rey's lab records single-neuron activity and intracranial EEG from epilepsy patients to investigate brain mechanisms underlying memory and neurophysiological processes. Education: PhD in Engineering, University of Buenos Aires (2009) Postdoctoral Fellowship in Biomedical Informatics, University of Leicester (2012–2015) Bachelor's in Electronics Engineering, University of Buenos Aires (2002) Research Interests: Dr. Rey explores anterior temporal lobectomy, drug-resistant epilepsy, electrophysiology, hippocampal function, machine learning applications in neuroscience, and signal processing. His work bridges clinical neurosurgery, biomedical engineering, and cognitive neuroscience to advance both fundamental understanding and clinical interventions. Publications: His recent work highlights studies on parietal cortex function in action monitoring, single-neuron responses in memory encoding, and neurophysiological correlates of depression. These reflect a focus on translational neuroscience and interdisciplinary collaboration. Awards: EPSRC Rising Star Award (2014) Labs/Teams: The ReyLab drives innovation in electrophysiological data acquisition and analysis, emphasizing clinical application for epilepsy and memory disorders.

Lesia Semenovaمشاهده پروفایل

استادیار

• Machine Learning
• Responsible and Trustworthy AI
• Interpretability
• +۴ مورد دیگر

Lesia Semenova is an incoming Assistant Professor in the Department of Computer Science at Rutgers University, set to join in Fall 2025. She currently serves as a postdoctoral researcher at Microsoft Research, NYC. Her research focuses on machine learning, responsible AI, interpretability, and healthcare applications. She holds a PhD from Duke University's Computer Science Department and prior degrees from Taras Shevchenko National University of Kyiv. Education Background: Ph.D., Computer Science, Duke University M.S. and B.S., Applied Mathematics, Taras Shevchenko National University of Kyiv Research Interests: Machine Learning Interpretability Responsible AI Frameworks Healthcare Applications of AI Reinforcement Learning Human-Centered Design Selected Awards and Recognition: 2024 Rising Stars in Computational and Data Sciences PhD Dissertation Award from Duke University Advising and Professional Activities: Semenova advises students such as Harry Chen and Dennis Tang. She has organized workshops at NeurIPS and presented at conferences like the International Joint Conference CFE-CMStatistics. Her work bridges theoretical machine learning with practical healthcare and policy applications. Professional Engagement: She is affiliated with the Artificial Intelligence and Machine Learning research groups at Rutgers, and has collaborated with institutions like the Johns Hopkins Applied Physics Lab.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۵ مورد دیگر

Ling Zhao is a distinguished Professor at the School of Management, Huazhong University of Science and Technology, China, with extensive research contributions spanning artificial intelligence, machine learning, information systems, and biomedical applications. With over 150 publications since 2008, Dr. Zhao has established herself as a leading researcher in multiple interdisciplinary domains, particularly in applying computational methods to solve complex real-world problems. Dr. Zhao's research interests encompass a broad spectrum of cutting-edge topics including artificial intelligence, machine learning, data mining, control systems, and information systems. Her work demonstrates exceptional versatility, bridging theoretical computer science with practical applications in healthcare, transportation, cybersecurity, and business management. Notably, she has made significant contributions to sentiment analysis, medical image processing, algorithmic management, and privacy-preserving data analysis. Her research methodology often combines deep learning approaches with domain-specific knowledge to develop innovative solutions. Analysis of Dr. Zhao's recent publications (2023-2025) reveals a strong focus on interdisciplinary applications of AI, with particular emphasis on healthcare informatics (medical image analysis, disease diagnosis), human-computer interaction (algorithmic management effects), and advanced machine learning techniques (graph neural networks, multimodal learning). Her work shows a consistent trend toward increasingly complex and integrated systems that address real-world challenges across multiple domains. Dr. Zhao has made substantial contributions to academic advising and research mentorship, though specific student names aren't detailed in the available publications. Her research has been supported by various grants enabling work in AI applications, biomedical engineering, and information systems. Dr. Zhao maintains active collaborations with researchers across China and internationally, as evidenced by her co-authorship patterns. While specific laboratory information isn't explicitly mentioned in the publication records, Dr. Zhao appears to lead or be significantly involved in research groups focusing on AI applications in management and healthcare. Her work on medical imaging, sentiment analysis, and control systems suggests involvement in multiple specialized research teams addressing different application domains through computational approaches.

Zhaolin Chenمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning
• Medical Imaging
• +۵ مورد دیگر

Zhaolin Chen is an Associate Professor in the Department of Data Science & AI at Monash University's Faculty of Information Technology. He holds a PhD in Biomedical Imaging from Monash University and has held roles at the University of Melbourne, Florey Neuroscience Institutes, and the medical imaging industry in Europe. He is an Australian Research Council MCR Industry Fellow and leads Australia's first Point-of-Care MRI network at the National Imaging Facility. His research focuses on AI-driven medical imaging, MRI/PET methods, and multimodal data analysis. He has secured over $8M in research funding, including leadership roles in major projects like the National Mobile MRI Network. Education: PhD in Biomedical Imaging, Monash University Research Fellowships at University of Melbourne and Florey Neuroscience Institutes Research Interests: Deep learning and machine learning in medical imaging MRI/PET acquisition/reconstruction methods Multimodal imaging (e.g., simultaneous MR-PET) Translational research with 10+ patents (5 commercialized) Awards & Grants: ARC Discovery Project (Primary Chief Investigator) 5 highly cited papers (top 10% worldwide in 2021) 2021 SciVal: 90% publications in top 10% journals Recipient of Douglas Lampard Research Prize, ISMRM Magna Cum Laude Leadership & Service: President-Elect, ANZ Chapter of ISMRM (2024) Associate Editor for IEEE ISBI (2022-2023) Program Committee Member for ISMRM (2018-2021) Labs & Teams: Monash Biomedical Imaging leadership National Mobile MRI Network project leadership Collaborations across global institutions (e.g., Hyperfine Inc., University of Queensland)

Marla Kellerمشاهده پروفایل

استاد

• HIV Prevention in Women
• Vaginal Microbicides
• Pre-Exposure Prophylaxis (PrEP)
• +۴ مورد دیگر

Dr. Marla Keller is a tenured Professor of Medicine and Obstetrics & Gynecology at Albert Einstein College of Medicine, currently serving as Executive Dean and holder of the Jacob and Jeanne Barkey Chair in Medicine. Director, NIH-funded Harold and Muriel Block Institute for Clinical and Translational Research Interim Senior Associate Dean for Clinical and Translational Research Vice Chair of Research for Department of Medicine Dr. Keller's research focuses on: Biomedical HIV prevention strategies Vaginal microbicide development PrEP implementation in women Cervical cancer screening in HIV+ women Immune responses to SARS-CoV-2 COVID-19 clinical trials Her recent publications emphasize: HPV infection and cervical precancer Vaginal microbiome interactions PrEP delivery systems HIV/HSV coinfection dynamics Convalescent plasma therapy Metronidazole treatment response Key scientific awards include: Infectious Disease Mentoring Award (2013) COVID-19 Distinguished Service Award (2021) Spirit of Achievement Changemaker Award (2023) New York Super Doctor (2011-2015)

Amany Faragمشاهده پروفایل

دانشیار

• Patient Safety
• Medication Safety
• Nurse Fatigue
• +۴ مورد دیگر

Amany Farag is a tenured Associate Professor at the University of Iowa College of Nursing and Co-Director of the VA Quality Scholars Program (Iowa City site). Her work bridges nursing science, human factors engineering, and data science to address critical patient safety challenges, with a specific focus on medication administration practices across healthcare settings. Education: Postdoctoral Scholar, Case Western Reserve University, Frances Payne Bolton School of Nursing PhD, Case Western Reserve University, Frances Payne Bolton School of Nursing MSN, University of Alexandria, Alexandria Egypt BSN, University of Alexandria, Alexandria Egypt Dr. Farag's research centers on reactive and proactive approaches to patient safety , with dual emphasis on medication error reporting systems and nurse fatigue prevention. Her work integrates human factors engineering and machine learning to develop novel interventions. Key themes include understanding how social and system factors influence nurses' error reporting behaviors, examining fatigue as a precursor to errors, and developing self-management strategies for nurse wellness. Recent projects explore intershift recovery, sleep hygiene using consumer technology, and the impact of shift work on cognitive performance. Publication trends reveal a strong focus on interdisciplinary safety science , with consistent output in nursing, human factors, and healthcare quality journals. Her work increasingly incorporates AI methodologies while maintaining clinical relevance to frontline nursing practice. Scientific Recognition: Mary Hanna Memorial Journalism Award (Journal of Peri-Anesthesia Nursing, 2016) Author of the Year (Journal of Emergency Medicine, 2018) Junior Investigator Award (Midwest Nursing Research Society, 2018) Rogers Endowed Lectureship Award (Mississippi Medical Center, 2018) Dr. Farag secures significant funding from national agencies including the National Council of State Boards of Nursing (NCSBN), NIOSH-funded Healthier Workforce Center of the Midwest, CDC-funded Injury Prevention Research Center, and University of Iowa Institute for Clinical and Translational Science. Her collaborative approach spans nursing, data science, ergonomics, and public health teams. As Co-Director of the VA Quality Scholars Program, she mentors future healthcare quality leaders while advancing her research on medication safety systems and nurse fatigue mitigation strategies through interdisciplinary partnerships.

Ivan Violaمشاهده پروفایل

دانشیار

• Visualization
• Computer Graphics
• Biological Visualization
• +۳ مورد دیگر

Ivan Viola is an Associate Professor at the Institute of Computer Graphics and Algorithms, part of the Faculty of Informatics at TU Wien, Austria. He holds a leave of absence until December 2024 while also being affiliated with King Abdullah University of Science and Technology (KAUST) as an Associate Professor funded by the Vienna Research Groups program. His research focuses on visualization techniques in medicine, biological sciences, and earth sciences, with a specialty in illustrative visualization and DNA-nanotechnology applications. Viola has contributed over 100 scientific works and serves as a reviewer and panelist for major conferences in computer graphics and visualization. Education: M.Sc. (2002) and Ph.D. (2005) in Computer Graphics from TU Wien. Postdoctoral research at the University of Bergen (2006-2011), where he became Full Professor before returning to TU Wien. Research Interests: Whole-cell visualization Molecular modeling Interactive 3D environments Biomedical visualization Data-driven colormap techniques Awards: IEEE VIS 2017 Best Paper Honorable Mention, 'Best Overall Concept' for CellView, and multiple visualization awards. Active in EuroVis and IEEE VIS organizing roles. Grants & Supervision: Leads the Visualization Group at TU Wien, supervising student projects and master’s theses. Involved in grants like the Vienna Research Groups program. Labs/Teams: Visualization Group at TU Wien, collaborating on projects like CellView and Molecumentary.

• Rheumatology
• Liver Disease
• Alcoholic Hepatitis
• +۳ مورد دیگر

Krishnasai Abhishek Madathanapalli is a Clinical Fellow in the Section of Rheumatology, Allergy, and Immunology at Yale School of Medicine since July 2023. He holds the rank of Research Fellow and works under mentorship of Monique Hinchcliff, MD, MS. His academic journey spans diverse environments: born in Bahrain, medical training in India (MBBS 2018), residency at University of Connecticut (2023), and prior research experience at Indiana University’s Department of Gastroenterology and Hepatology. Education: MBBS in Medicine: Jawaharlal Institute of Post-Graduate Medical Education and Research, Pondicherry, India (2018) Residency in Internal Medicine: University of Connecticut School of Medicine (2023) Visiting Research Scholar: Indiana University Department of Gastroenterology and Hepatology Research Interests: Krishnasai’s work bridges rheumatology and immunology with liver disease research. He focuses on health-related quality of life assessment in chronic conditions, biomarker dynamics (e.g., myostatin), and translational studies for alcoholic hepatitis. His research emphasizes patient-centered outcomes and improving clinical tools for liver disease evaluation. Awards: Yale International Physician-Scientist Resident and Fellow Research Award (2023) Rheumatology Research Foundation Resident Research Preceptorship Award (2022, International) Advising & Grants: Supported by grants from the Rheumatology Research Foundation and Yale School of Medicine, he collaborates with the Clinical and Translational Research Accelerator directed by Dr. Hinchcliff. No formal advisees/students are listed, but his mentorship and grant activities reflect active participation in physician-scientist development programs. Labs & Teams: Affiliated with the Section of Rheumatology, Allergy, & Immunology at Yale and engaged in interdisciplinary research through the Janeway Society and Office of Physician-Scientist Development initiatives.

• Machine Learning
• Artificial Intelligence
• Virtual Reality
• +۳ مورد دیگر

Simon Colreavy Donnelly is an Associate Professor in the Department of Computer Science & Information Systems at the University of Limerick. He is a member of the Interaction Design Centre and focuses on interdisciplinary research at the intersection of artificial intelligence, educational technology, and healthcare informatics. His work spans machine learning applications in medical data analysis, virtual reality (VR) and extended reality (XR) for inclusive education, and deep learning techniques in chemical analysis and spectroscopy. Research Interests: His primary areas of investigation include generative AI for education equity, semisupervised learning algorithms, virtual learning environments design, and the ethical deployment of immersive technologies in healthcare and palliative care. He also explores NMR spectroscopy analysis using deep learning and develops tools for nutritional content estimation through image processing. Collaborations: His recent collaborations span international teams addressing challenges in toxicity-free online discourse (PAN 2024), semisupervised learning distribution mismatches, and VR applications for post-pandemic blended learning. His work integrates computational methods with real-world applications in education, healthcare, and chemical analysis. Labs/Teams: Active within the Interaction Design Centre at UL, his research group develops practical solutions for accessibility in digital education and healthcare systems, emphasizing user-centered design principles for extended reality applications.