ICTS

The program will bring together machine learning experts, statisticians, clinicians, and public health experts to discuss how to harness modern mathematical and computational techniques to better understand health-related data across multiple domains. Basics of various machine learning techniques, including logistic regression, tree-based methods, support vector machines, Bayesian methods, and deep networks will be covered with examples of their applicability in biomedicine and health. Applications will include predicting outcomes for individual patients from clinical and lifestyle parameters, analysing patient data such as X-rays, ultrasound images and ECG measurements, genomic variant analysis, and inferring patterns in heterogeneous large-scale data.  Speakers from both computational/statistical and clinical backgrounds will be invited.

While the overarching goal is to bridge the gap between mathematical modelling and clinical problems in general, the program has these specific aims:

1. To introduce people who are trained in machine learning (both theory and practice) to data-based problems in health care. 
2. To introduce clinical practitioners with little ML background to tools that can be easily adapted to analyse their own data. 
3. To have an open discussion between clinicians and mathematical modellers about the problems faced in bridging the gap between the communities.
4. To discuss the possibility of building public health databases as resources. 
5. To generate reference material, tutorials, videos and other resources to help clinicians understand and apply ML techniques in their work. 

The event is partly supported by the IMSc Centre for Disease Modelling, The Institute of Mathematical Sciences, Chennai.

ICTS is committed to building an environment that is inclusive, non-discriminatory and welcoming of diverse individuals. We especially encourage the participation of women and other under-represented groups.

Eligibility Criteria:  PhD students in STEM fields, medical students at any level, postdoctoral fellows, faculty, professionals in any area of science, engineering, medicine.