Machine Learning for Biological Research
Led by Dr. Fei Sha, the Center of Data, Algorithms, and Systems for Health (DASH) focuses primarily on two areas of research:
- Theoretical and Data Sciences (TEDS), which advances fundamental research in theory, methods and large-scale computing for statistical machine learning
- Artificial Intelligence for Medical Science (AIMS), which applies the cutting-edge findings of statistical machine learning (including advances from TEDS), specifically to medical science
“Until now, my research has been Statistical Machine Learning, with a broad application to Artificial Intelligence. … I am excited by the chance to apply algorithmic, computational and statistical models to unravel mysteries in biological organisms and to generate life-saving biomedical research outcomes.” – Dr. Fei Sha
Dr. Sha explained that as a technologist, interdisciplinary research and collaboration almost surely leads to convergence in its best form.
“I am interested in working with anyone who believes that machine learning and artificial intelligence can and should make an impact to her or his research field, as well as society at large,” Dr. Sha said.
Bringing the leading edge in Statistical Machine Learning and Artificial Intelligence to scientific discovery will help meet the enormous demand to make sense of data from varying fields including biology, biomedical and clinical science.
“Data is central to my research. Biological data is wickedly complex, highly intra- and inter-connected, extremely noisy and in many cases it is still expensive or impossible to acquire. This presents a whole new set of challenges to the current statistical methodologies, computing and algorithmic frameworks,” Dr. Sha said.