Expertise Needed for the 2024 BDSIL
Biomedical late-stage post-doc or early-stage junior faculty level investigators having research questions about the responsible use of AI in biomedicine, which would benefit from novel data science analytics, considerations, and approaches.
"Biomedical" expertise can involve but are not limited to:
Basic Neuroscience
Investigators with experience in neurobiology and neurochemistry who have research questions which might benefit from data science solutions.
Behavioral
Investigators who have research questions relating to human or animal behavior and psychology.
Bioethics
Investigators who have research questions involving ethical issues emerging from advances in biology and computational medicine or data science, including moral discernment as it relates to medical policy and practice.
Biology
Investigators who have research questions looking at the underlying biological mechanisms important to our fundamental understanding of biology.
Clinical Research
Investigators involving patient samples, comparisons between disease phenotypes, cross-sectional studies, as well as the assessment of longitudinal time courses of treatment outcomes.
Environmental Health
Investigators who have broad ranging questions on ecology, biodiversity, and the environment that have implications for the application of data science.
Epidemiology
Investigators who have research questions involving the health distribution and determinants within communities, regions, etc. to positively impact health services, access, and disease outcomes.
Medical Informatics
Investigators who have research questions relating to complex data mining and analysis relating biomedical data from health and disease. Data including but limited to medical imaging, microscopy, genomic, metabolomics, electrophysiology, electronic health records, mobile health data, wearables/sensors, and spatial location data.a.
Mental Health
Investigators who have research questions relating to the well-being of humans and their ability to cope with stresses, engage and contribute to their individual communities.
Population-Level Science
Investigators who have experience and/or training in public health, medicine, pharmacy, economics, and demography with research questions that intersect with biomedical data science. Also encouraged, are investigators interested in how policies shape these outcomes and how efforts address needed changes to harness big data initiatives to improve health outcomes.
University-Level Education
Individuals with experience in university-level education and instruction in biomedical science who possess a deep understanding of the intricate workings of the human body and its various systems. They are faculty members equipped with comprehensive knowledge in areas such as anatomy, physiology, genetics, and pharmacology, allowing them to contribute to cutting-edge research, healthcare advancements, and clinical practice. These experts play a pivotal role in undergraduate and graduate teaching, research on, diagnosing, and treating diseases, as well as seeking to push the boundaries of medical innovation through the use of data science approaches.
Data and computationally-focused late-stage post-doc or early-stage junior faculty level investigators having research questions in novel quantitative methods, data analytics, statistical modeling, machine learning, and data visualization.
"Quantitative" expertise can involve but are not limited to:
Applied Mathematics
Investigators with mathematical approaches applicable to issues in biomedical applications. For instance, the development and deployment of clinical systems in patient monitoring, the meta-analytic examination of medical records and electronic health records.
Artificial Intelligence
Those working to develop artificial intelligent systems for clinical decision making. For instance, generative models, convolutional neural networks (CNNs), clinical support systems, agent-based applications, algorithm designs, Large-Language Models (LLMs), decision trees, emergent networks, etc.
Computer Science
Investigators with computational expertise and research questions that involve developing novel software tools and workflows to harness big data capabilities, especially biomedical data types.
Data Science
Investigators with the knowledge to account for disparities, provenance and metadata annotations that are crucial to link datasets that span rural/urban communities, geography, and environments. Investigators with the knowledge and expertise to apply appropriate machine learning models and visualization of results.
Natural Language Processing
Those working in the process of converting written or spoken language to useful, digitized data and on tasks like information extraction and machine translation.
Pure Mathematics
Investigators with a solid foundation in mathematics and interest in approaching problems from a theoretically-driven point of view seeking work with biomedical investigators in creating novel mathematical models that will be tested and further developed based.
Statistics and Machine Learning
Investigators with experience in statistical methodologies, machine learning, and approaches to improve project direction and statistical significance. Investigators with experience in developing projects based at the population level are encouraged.