We are pleased to announce BMI graduate student Thevaa Chandereng, working with Professor Rick Chappell, has been awarded a MBSW Student Financial Grant for their project on Imbalanced Randomization in Clinical Trials.
The current explosion of biomedical data, including Electronic medical records (EHR), biomedical imaging, and genomics/proteomics/metabolomics, provide an awesome opportunity to improve understanding of the mechanisms of disease and ultimately to improve human health care. The Doctoral Degree Program in Biomedical Data Science will provide you with a unique blend of skills including programming, data management, data analysis, and machine learning. Successful graduates will be prepared to fully harness the power of high-dimensional, heterogeneous data. Potential students include both students with bachelor’s degrees in an area of data-science (e.g., computer science, statistics), as well as health professionals, clinicians, and others with degrees related to biomedicine (e.g., biology, biochemistry, genetics). Consider applying today!
Research to improve the analysis of big biomedical data is active at the interface of computer sciences, statistics, and various biomedical disciplines, including genomics, molecular biology, neuroscience, cancer research, and population health. The mission of the Bio-Data Science (BDS) training program is to provide predoctoral research training at this interface, preparing graduate students for key roles in academia, industry, or government.
Read More
Interested in quantitative biology? The Quantitative Biology Initiative (QBI) is a university-wide initiative that brings together students, staff, and faculty conducting research in the quantitative biological sciences. We aim to be a hub for computational, experimental, statistical, and theoretical biology on campus.
Click here to signup up for the QBI mailing list!
We are pleased to announce BMI graduate student Thevaa Chandereng, working with Professor Rick Chappell, has been awarded a MBSW Student Financial Grant for their project on Imbalanced Randomization in Clinical Trials.
Long-range regulatory interactions occur between a gene and a regulatory sequence element hundreds of kilobases away.
We are pleased to announce BMI graduate student Muxuan Liang, working with Professor Menggang Yu, has been awarded a 2018 ASA Biometrics Section JSM Travel Award for scientific paper on model effect modification.
Annual Lecture Series In Health and Quantitative Investigation
Biostatistics & Medical Informatics announces the third annual lecture series on Nov. 9th and 10th with Robert Tibshirani as the featured speaker.
Read more about the 2017 lectures
SDAC - the Clinical Trials Statistical Data Analysis Center
is a Leader in promoting statistical practice, applications, and research in the design and analysis of clinical trials especially in the preparation of Data Monitoring Committee reports necessary to evaluate the accumulating evidence for safety and efficacy.
Read More
The Center for Predictive Computational Phenotyping is developing innovative computational and statistical methods and software for a broad range of problems that can be cast as computational phenotyping. The Center has a particular focus on health screening and is investigating how to exploit a wide array of data types for these tasks, including molecular profiles, medical images, electronic health records, and population-level data.
The Department of Biostatistics and Medical Informatics is home to internationally-recognized faculty engaged in both collaborative and methodological research who have flourished in the university's deep culture of collaborative interdisciplinary science. Faculty areas of expertise include biostatistics applied to pre-clinical, clinical and population health research; biomedical and clinical informatics; and statistical genetics and genomics.