جست‌وجوی استاد و پژوهشگر

Corbin Jonesمشاهده پروفایل

استاد

• Genetic Adaptations
• Molecular Evolution
• Population Genetics
• +۸ مورد دیگر

Corbin Jones is a Professor in the Department of Genetics at the University of North Carolina at Chapel Hill (UNC), School of Medicine. He serves as Faculty Director of the UNC High-Throughput Sequencing Facility and is affiliated with the Carolina Center for Genome Sciences (CCGS). His academic roles include dual appointments in Biology and Genetics, and he contributes to graduate programs in Biology, Genetics and Molecular Biology, and Biomedical Engineering (BCB). Dr. Jones' research focuses on evolutionary genetics, particularly using Drosophila species to study adaptive traits. Key projects include analyzing Drosophila sechellia’s genetic resistance and attraction to Morinda citrifolia, investigating mechanisms of new gene formation through chimeric gene studies, and developing statistical tools for QTL mapping accuracy. His lab integrates computational methods, molecular genetics, and genomic data to explore how genetic changes—both regulatory and structural—drive evolutionary adaptations. Collaborations with Dr. Sally Otto and Dr. Philip Awadalla highlight his contributions to theoretical and applied genomics. His work extends to translational research, including studies on human disease genetics (e.g., schizophrenia, lupus) and microbiome assembly dynamics. Recent publications reflect diverse applications of his expertise in virology (SARS-CoV-2 treatment efficacy), cancer biology (evolutionary rescue models), and AI-driven clinical data analysis. Funding for his lab comes from the NSF and NIH, supporting projects that bridge basic and applied sciences. Grants: NSF and NIH-funded research on genomic evolution and disease mechanisms Lab Affiliations: Corbin Jones Lab in the Genome Sciences Building, UNC High-Throughput Sequencing Facility Tools Developed: QTL-based estimator method for genetic mapping, computational pipelines for genomic data analysis Notable research themes include the molecular basis of behavioral adaptations in Drosophila, the role of chimeric genes in evolutionary innovation, and statistical improvements in detecting quantitative trait loci. His studies on D. sechellia’s olfactory-driven behavior reveal insights into gene function beyond mere sensory detection, emphasizing behavioral genetics.

Rachel Patzerمشاهده پروفایل

دانشیار

• Health Services Research
• Health Disparities
• Epidemiology
• +۷ مورد دیگر

Rachel Patzer is an Associate Professor in the Department of Epidemiology at Emory University’s Rollins School of Public Health. She holds a joint appointment as an Adjunct Professor in the Department of Surgery at Emory University School of Medicine. Dr. Patzer’s research focuses on health disparities, healthcare access, and quality of care, particularly in kidney transplantation and end-stage renal disease. She leads the Health Services Research (HSR) Center, a collaborative initiative between Emory’s departments of Medicine and Surgery, aiming to improve patient outcomes through evidence-based care delivery. Education: PhD, Emory University MPH, Emory University BS, University of Wisconsin-Madison Research Interests include: Disparities in social determinants of health Community-based participatory research Predictive analytics for transplant outcomes Mobile health technology (e.g., iChoose Kidney app) Medication adherence and self-management Healthcare resource utilization Recent Publications highlight work on racial/sex disparities in kidney transplant access, predictive modeling for transplant outcomes, and equity-focused interventions. Her studies appear in high-impact journals like JAMA , Am J Kidney Dis , and Health Affairs . Leadership Roles: Director, HSR Center Chair, UNOS Data Advisory Committee Member, Scientific Registry of Transplant Recipients Co-leader of E-STAR surveillance registry Advising & Teaching: Dr. Patzer mentors students and residents in the Departments of Surgery and Medicine, and teaches in Emory’s Master’s in Clinical Research (MSCR) Program.

Mogana Darshini Ganggayahمشاهده پروفایل

مدرس

• Data Science and Analytics
• Machine Learning
• Health Informatics
• +۵ مورد دیگر

Dr. Mogana Darshini Ganggayah is a Lecturer at the Malaysia School of Business, Monash University Malaysia, specializing in Business Analytics and Data Science. She holds a PhD in Data Science (2021) and a BSc in Bioinformatics (2018) from Universiti Malaya. Her research integrates machine learning and data analytics in healthcare and business domains, with a focus on health informatics, predictive modeling, and data literacy. She collaborates with institutions like Cambridge University, University Malaya Medical Centre (UMMC), and Homi Baba Cancer Hospital. Dr. Ganggayah has received two Silver Awards in 2024 for innovative mental health and statistical assessment tools. She actively engages in public lectures, conferences, and peer review, and is an HRDF-certified trainer with over 1,000 trainees in data science and programming. Education: PhD in Data Science, Universiti Malaya (2021) BSc Bioinformatics (Distinction), Universiti Malaya (2018) Research Interests: Her work spans machine learning applications in healthcare (e.g., cancer diagnostics, mental health tracking) and business analytics (e.g., financial forecasting, consumer behavior). She emphasizes interdisciplinary collaboration and practical implementation of algorithms in real-world settings. Recent projects include developing data-driven solutions for the Habib Group and analyzing pandemic impacts on education and shopping behavior. Grants & Projects: PPRN Project (2025-2026): Building a Data-Driven Organisation with Data Warehouse and AI for the Habib Group (Chief Investigator) Labs/Teams: Engaged with UMMC’s medical data science initiatives and Monash Business School’s analytics team.

Johan Hartmanمشاهده پروفایل

استاد

• Precision Pathology
• Breast Cancer Therapy
• Digital Image Analysis
• +۴ مورد دیگر

Professor Johan Hartman is a leading academic and clinician at Karolinska Institutet, serving as a Professor and Senior Consultant in the Department of Oncology-Pathology. His primary role combines research leadership in precision pathology with clinical expertise in breast pathology at Karolinska University Hospital. Hartman leads a research group focused on personalized breast cancer therapy through advanced technologies like AI-driven histopathology analysis, molecular genomics, and patient-derived tumor models. Education & Qualifications: Doctoral Degree (2008), Karolinska Institutet Medical Degree (2008), Karolinska Institutet Docent (2015), Karolinska Institutet Research Focus: Hartman’s lab pioneers innovations in precision medicine, particularly in predicting treatment responses using digital pathology, AI, and genomic profiling. Key areas include tumor heterogeneity, AI tools for diagnostic accuracy, and translational research linking molecular profiles to clinical outcomes. His group has developed clinical-grade AI systems for breast cancer risk stratification and tumor characterization. Publications & Impact: With over 100 peer-reviewed articles in high-impact journals (e.g., Science, Nature Communications), Hartman’s work emphasizes breast cancer therapy prediction, immune profiling, and molecular diagnostics. Recent studies include AI-driven TIL analysis, spatial transcriptomics, and genomic heterogeneity in tumors. Awards & Recognition: 2023 Karolinska Institutet Innovation Award 2022 Athena Prize (Vetenskapsrådet) 2016 Clinical Investigator Award (Swedish Cancer Society) Grants & Collaborations: Leading projects funded by Radiumhemmets Forskningsfonder, Vinnova, and industry partnerships Co-founder of Stratipath, a KI spin-off advancing precision pathology tools Labs & Teams: Hartman’s group includes postdocs, PhD students, and clinical researchers. Key members focus on AI in pathology, tumor modeling, and translational oncology. The team collaborates internationally on initiatives like the International Immuno-Oncology Biomarker Working Group.

Juannan Zhouمشاهده پروفایل

استاد

• Quantitative Biology
• Evolutionary Biology
• Genotype-Phenotype Mapping
• +۳ مورد دیگر

Juannan Zhou is a Professor in the Department of Biology at the University of Florida , where they recently established their research lab. Their work focuses on genotype-phenotype mapping using machine learning , mathematical modeling , and wet lab experiments to understand evolutionary dynamics. Research Lab : Department of Biology, University of Florida Contact : juannanzhou@ufl.edu Key research themes include: Mapping genotype-phenotype relationships in diploid organisms Investigating protein fitness landscapes through unsupervised language models Developing machine-learning assisted directed evolution techniques Studying complex probability distributions over biological sequence spaces Exploring interactions between biochemical constraints and evolutionary outcomes Recent publications demonstrate technical innovations in: Gaussian process priors for sequence-function modeling Field-theoretic density estimation methods Higher-order epistasis analysis Nonparametric Bayesian approaches to biological sequence space Differentiable Smith-Waterman algorithms for sequence alignment The lab actively seeks postdocs, lab technicians, and graduate students interested in machine learning applications in population genetics and evolutionary modeling . Current projects include developing gpmap-tools for genotype-phenotype map visualization and implementing empirical variance component regression frameworks.

Angela Harrisمشاهده پروفایل

مدرس ارشد

• Remote Sensing
• Environmental Change
• Vegetation Ecology
• +۷ مورد دیگر

Dr. Angela Harris is a Senior Lecturer in Remote Sensing at the Department of Geography, School of Environment, Education and Development, University of Manchester. She holds a PhD from the University of Sheffield and has over 20 years of academic experience including roles at the University of Southampton and University of Manchester. Her research focuses on applying remote sensing technologies to monitor environmental changes in vegetation and peatland ecosystems. She is currently accepting PhD students in Earth observation and spatial analysis. Education: BSc (Marine Geography, Cardiff), MSc (Environmental Conservation, Glamorgan), PhD (Remote Sensing, Sheffield) Roles: Associate Editor of Remote Sensing in Ecology and Conservation, Chair of NERC FSF Steering Committee Her research interests include innovative uses of Earth observation data across scales—from UAVs to satellite imagery—to study ecosystem processes. She has supervised 8 PhD students, including current advisees focusing on gully erosion in India and irrigation water use modeling. Key contributions include studies on peatland hydrology, saltmarsh vegetation, and urban climate impacts. Her work aligns with UN Sustainable Development Goals related to climate action and sustainable ecosystems. Equipment and facilities include unmanned aerial systems (sUAS) and advanced remote sensing labs with GIS software and field spectrometers.

Christopher Bryantمشاهده پروفایل

استاد مهمان

• Natural Language Processing
• Grammatical Error Correction
• Codeswitching Analysis
• +۴ مورد دیگر

Christopher Bryant is a Visiting Professor in the Natural Language and Information Processing group at the University of Cambridge's Department of Computer Science and Technology. He holds a PhD from the University of Cambridge (2019), supervised by Prof. Ted Briscoe, and previously worked as a Research Assistant at the National University of Singapore under Prof. Hwee Tou Ng. His research focuses on grammatical error correction (GEC), automatic annotation, and codeswitching analysis. He developed the ERRor ANnotation Toolkit (ERRANT), widely used in GEC research, and led the BEA-2019 Shared Task. Bryant also serves as an Applied AI Research Scientist at Writer, Inc., and maintains active collaborations in NLP. Education: PhD in Computer Science, University of Cambridge (2019) MSc in Speech and Language Processing, University of Edinburgh (Year not specified) MA(Hons) in Chinese and Linguistics, University of Edinburgh (Year not specified) Research Interests: Automatic grammatical error detection/correction (GEC) for non-native English Codeswitching analysis in multilingual contexts Robust evaluation methodologies and artificial data generation Discourse parsing and linguistic annotation frameworks Applications of large language models in educational technology Key Contributions: ERRANT: Open-source tool for GEC error annotation and evaluation BEA-2019 Shared Task on GEC for educational applications Industry collaboration with Writer, Inc. on AI-driven writing tools Grants & Collaborations: Supported by the Institute for Automated Language Teaching and Assessment (ALTA) during his PhD. Active in academic-industrial partnerships through his role at Writer.

• Cybersecurity
• Data Protection
• Artificial Intelligence
• +۴ مورد دیگر

Dr. Chaminda Hewage serves as Interim Associate Dean for Research and Reader (Associate Professor) in the Cardiff School of Technologies at Cardiff Metropolitan University. He leads the Computer Security Programme and holds a Ph.D. in Multimedia Communications from the University of Surrey (2009) and a B.Sc. in Electrical and Information Engineering from the University of Ruhuna (Sri Lanka). His research focuses on cybersecurity, data protection in AI/ML systems, blockchain applications, and multimedia communication security. He has authored over 50 peer-reviewed publications and edited major books on emerging technologies' privacy challenges. Education Background: B.Sc. (Hons) Electrical & Information Engineering, University of Ruhuna (2004) Ph.D. in Multimedia Communications, University of Surrey (2009) Postgraduate Certificate in HE Teaching & Learning, Kingston University (2014) Research Interests: Data protection mechanisms in AI/ML Cybersecurity frameworks for Industry 4.0 Blockchain for public and private sector applications Metaverse privacy and security Edge AI and embedded system security Regulatory compliance in digital transformation Recent Contributions: Co-edited the 2024 Springer volume Data Protection: The Wake of AI and Machine Learning , published influential works on Kubernetes security, V2X communication cryptography, and metaverse privacy. Active in international conferences including C3AI and ICCS. Awards & Recognition: Gold Medal for Academic Excellence (2004), University of Ruhuna ORS Scholarship (2005), HEFCE Fellow of the Higher Education Academy Grants & Collaborations: Leads research projects on cybersecurity for SMEs, smart city infrastructure, and digital transformation. Engages with industry partners through Cardiff Met's innovation networks. Advises on UK government cyber policy initiatives. Labs/Teams: Oversees the Cybersecurity Research Group and collaborates with the ZERO2FIVE Food Industry Centre for IoT applications. Active in multidisciplinary teams addressing Industry 5.0 challenges.

• Neural Engineering
• Biomedical Engineering
• Human-Computer Interaction
• +۳ مورد دیگر

Alexandre Armengol Urpi is a Postdoctoral Associate at the Massachusetts Institute of Technology (MIT), affiliated with the Department of Mechanical Engineering within the School of Engineering. His research focuses on neural engineering, biomedical engineering, human-computer interaction, IoT systems, and signal processing. He explores applications such as brain-computer interfaces (BCI), EEG-based diagnostics, and augmented reality (AR) integration with IoT devices. His work emphasizes translating tacit expert knowledge into actionable data, improving medical diagnostics through machine learning, and enhancing cybersecurity in BCI systems. Projects include developing wireless health-monitoring systems and hands-free AR navigation tools using SSVEP signals. Recent studies address covert spatial attention decoding, industrial IoT-AR systems, and RFID innovations beyond traditional identification. While no formal student advisees are listed, his research collaborations involve interdisciplinary teams across MIT. His contributions span theoretical frameworks and practical implementations, with a focus on closing the gap between human expertise and technological systems. Key research trends include integrating biosignals with smart devices, enhancing neural interface accuracy, and applying deep learning to object pose estimation in industrial contexts. His publications highlight a balance between foundational neuroscience and applied engineering solutions. No scientific awards or grants are explicitly mentioned in the provided texts.

Simon Harperمشاهده پروفایل

استاد

• Human Computer Interaction
• Web Accessibility
• Digital Health
• +۴ مورد دیگر

Simon Harper is a Professor of Human Computer Systems at the University of Manchester, leading the Web Ergonomics Lab. His research focuses on web accessibility, human-computer interaction, and healthcare technology, with a particular emphasis on disabilities like visual impairment. He has pioneered initiatives such as the Web4All conference series and serves on steering committees for major conferences like ACM HT and ASSETS. Harper has secured over £1.45M in grants since 2007 and holds an h-index of 20. His work spans medical technology innovations (e.g., HYPO-CHEAT hypoglycemia reduction system), accessibility evaluation methods (OPTIMAL-EM tool), and environmental monitoring via citizen science. He has received prestigious awards including the ACM Doug Engelbart Prize (2000) and Best Paper awards at W4A. Harper's academic contributions include over 140 publications, with key areas in diabetes management systems, Parkinson’s disease monitoring via smartphone data, and web accessibility evaluation methodologies. His teaching integrates cutting-edge research into professional computing education, emphasizing real-world industry practices. He is actively involved in standards bodies like the W3C Web Accessibility Initiative and serves on editorial boards for journals such as ACM Transactions on Accessible Computing. Research Groups: Information Management, Data Science Institute, Web Ergonomics Lab Professional Roles: Chair of ACM SIG Hypertext, RCUK EPSRC Inclusion Cluster Expert Lab Focus: Developing computational models for human behavior in digital environments

Marcella Montagneseمشاهده پروفایل

پژوهشگر ارشد

• Neuroinformatics
• Cognitive Neuroscience
• Psychiatry
• +۵ مورد دیگر

Marcella Montagnese is a Junior Research Fellow in Biological and Medical Sciences at Christ’s College, Cambridge, and a Research Associate in Neuroinformatics within the Department of Psychology. She is also an affiliate of the Department of Clinical Neurosciences. Her work bridges computational methods, clinical neuroscience, and mental health research. Research Focus : Dr. Montagnese investigates dementia prediction using AI-driven tools, neuropsychiatric symptoms in neurodegenerative disorders, and metacognition in psychosis. She employs machine learning, neuroimaging, and behavioral neuroscience approaches to address clinical challenges such as early diagnosis of Alzheimer’s disease and understanding hallucination mechanisms. Collaborations : She collaborates with Prof. Zoe Kourtzi (Department of Psychology) and Dr. Richard Bethlehem (Research Associate), focusing on interdisciplinary projects at the intersection of neuroinformatics and clinical practice. Her research emphasizes translating computational models into actionable clinical insights. Labs & Teams : Affiliated with neuroinformatics and clinical neuroscience groups, her work contributes to advancing technologies for early disease detection and improving mental healthcare strategies.

• Astrophysics
• Optical Interferometry
• Meteoritics
• +۳ مورد دیگر

Dr. John Young is a Researcher at the University of Cambridge's Cavendish Laboratory, affiliated with the Battcock Centre for Astrophysics. His work focuses on advancing observational astrophysics through cutting-edge instrumentation and interdisciplinary research. Key areas include the development and operation of the Magdalena Ridge Observatory Interferometer (MROI), meteorite studies via the UK Fireball Network, and innovative optical interferometry techniques. His research emphasizes the design of advanced instruments like the FOURIER beam combiner and automated alignment systems, addressing challenges in thermal stability, data reduction pipelines, and cryogenic performance. Contributions to meteoritics include analyses of the Winchcombe meteorite, offering insights into solar system origins. He actively participates in international collaborations, such as the Planet Formation Imager project and OIFITS standardization efforts. Recent publications highlight advancements in fringe tracking, automated alignment systems, and machine learning applications for mitigating atmospheric turbulence. Dr. Young's work bridges engineering, data science, and astrophysics, contributing to both technical infrastructure and fundamental scientific discoveries.

Nicole Seiberlichمشاهده پروفایل

استاد

• Medical Imaging
• Magnetic Resonance Imaging (MRI)
• Quantitative Imaging
• +۳ مورد دیگر

Nicole Seiberlich, Ph.D., is a Professor in the Department of Radiology and Biomedical Engineering at the University of Michigan. She is affiliated with the University of Michigan Medical School and leads the Seiberlich Lab, focusing on developing advanced MRI techniques for rapid and quantitative imaging. Her research emphasizes improving cardiac, abdominal, and neurological MRI, particularly at low-field strengths (e.g., 0.55 T), to enhance clinical accessibility and safety. Key areas include MR fingerprinting, image reconstruction algorithms, and low-field MRI optimization. Education details are not explicitly listed, but her work reflects expertise in biomedical engineering and radiology. Research interests span quantitative imaging, MRI safety, and translational applications in cardiology and neurology. Notably, her lab has pioneered techniques like Rosette MRI trajectories and deep learning-driven image reconstruction. Recent work includes optimizing low-field MRI for clinical use, evaluating RF heating in neurostimulation devices, and advancing cardiac MRI fingerprinting for disease discrimination. Her projects bridge engineering and clinical medicine, addressing unmet needs in imaging speed, safety, and diagnostic accuracy. The lab collaborates across disciplines to translate innovations into clinical practice. Advising details are not provided here, but her research outputs suggest active mentorship of graduate students and postdocs in biomedical imaging. Grants and funding sources are implied through her high-impact publications but not explicitly stated. The lab also explores applications in prostate biopsies, liver perfusion, and synthetic late gadolinium enhancement imaging. Publications highlight contributions to MRI physics, algorithm development, and clinical validation. Her work underscores the potential of low-field MRI to expand imaging access while maintaining diagnostic quality, challenging traditional assumptions about field strength requirements.

Lasanthi Watagodaمشاهده پروفایل

استادیار

• Statistical Learning
• Linear Regression
• Variable Selection
• +۳ مورد دیگر

Dr. Lasanthi Watagoda is an Assistant Professor in the Department of Mathematical Sciences at Appalachian State University. She joined the university in 2017 as a Visiting Assistant Professor and transitioned to her current role. Her research focuses on statistical learning, particularly in linear regression, variable selection, prediction intervals, and bootstrap methods, with additional interests in actuarial science. She holds a Ph.D. in Mathematics from Southern Illinois University Carbondale. Dr. Watagoda teaches a variety of courses, including Statistical Data Analysis, Linear Regression Models, and Probability. Education: Ph.D. in Mathematics, Southern Illinois University Carbondale Research Interests: Dr. Watagoda’s work emphasizes developing methods for inference after variable selection, with applications in statistical modeling and prediction. Her research integrates bootstrap techniques to enhance the accuracy of prediction intervals and explores shrinkage estimators for improved model performance. She also applies machine learning to wearable sensor data for sprint assessment and examines theoretical aspects of regression models, such as consistency and asymptotic properties. Teaching: She has taught numerous courses, including STT 3851 (Statistical Data Analysis II), STT 4830 (Linear Regression Models), and introductory statistics courses. Her teaching emphasizes reproducible research workflows and the use of tools like R and RStudio. Publications: Her recent work includes studies on distribution-free bootstrap prediction intervals, shrinkage estimators, and the theoretical underpinnings of variable selection in regression. These contributions highlight her focus on advancing statistical methodology and its practical applications. Labs/Teams: No specific lab or team affiliations are mentioned in the provided texts.

William M. Jones Jr.مشاهده پروفایل

استاد

• High Performance Computing (HPC)
• Fault Tolerance
• Resilience Engineering
• +۵ مورد دیگر

Dr. William M. Jones Jr. is a Professor in the Department of Computing Sciences at Coastal Carolina University (CCU) since 2017. He concurrently serves as a Guest Scientist at Los Alamos National Laboratory (LANL) and is the founder of the LANL-CCU Collaboration initiative, established in 2007. His roles include HPC Visiting Scholar/Sabbatical at LANL (2025–present) and Guest Scientist at LANL (2023–present). Former positions include Department Chair of Computing Sciences at CCU (2012–2018) and Assistant Professor at the U.S. Naval Academy (2006–2008). Education: Ph.D. & M.S. in Computer Engineering (2005, 2000, Clemson University); B.S. in Computer Engineering (1999, Clemson University); B.A. in Spanish (2017, Coastal Carolina University). Research focuses on resilience and fault tolerance in HPC systems, including soft error mitigation, parallel computing optimization, and applied machine learning. He leads collaborative projects with LANL, emphasizing high-performance computing and student research engagement. Key contributions include the LANL-CCU partnership, yielding numerous publications and external funding since 2007. Articles highlight advancements in HPC resilience, machine learning applications, and fault tolerance, with recent trends emphasizing spatio-temporal modeling and statistical frameworks for system reliability. His work also extends to language pedagogy, with contributions to Spanish as a foreign language teaching methodologies. Advising and grants: Directs student researchers through the LANL-CCU initiative and has secured external funding for collaborative projects. Grants and partnerships focus on advancing HPC resilience and educational outreach in STEM. Labs/Teams: Co-leads the LANL-CCU Collaboration, fostering interdisciplinary research between academia and national labs. Active in the Ultrascale Systems Research Center (since 2014) and previously in the National Security Education Center at LANL (2019–2023).