Kasthuri Kannan

I am an Associate Professor and directing the spatial and systems biology programs at The University of Texas MD Anderson Cancer Center. I am a data scientist (with 15 years experience) with expertise in genomics (especially cancer), biomedical informatics and image processing. My several years of data-driven training include machine learning and statistics. Expert in analyzing large-scale data sets and mathematical modeling. Of late, my interest has converged on systems biology of gliomas and point pattern analysis with aplication to pathology slides.

My science interest is diverse and includes cancer biology, formal logic, physical theories, computing, and mathematics. My interest in biology and in particular cancer genomics has resulted in numerous publications. Also, I am very passionate about teaching and have actively designed, developed and directed data science, machine learning and biomedical informatics courses.

Professionally, I have a primary appointment in the Department of Translational Molecular Pathology with a joint appointment in the Department of Neurosurgery at MD Anderson Cancer Center, Houston. Moreover, I hold a visiting faculty appointment (Visiting Associate Professor), an honorary position, at the National University of Singapore (NUS) at their Cancer Science Institute. I hold the following formal trainings:

I have obtained several directed insights in biology through data science applied to genomics data. A majority of my contributions in science are in omics and numerical algorithms applied to physics problems aka computational physics.

However, my research has now converged on two exciting and emerging areas (1) Spatial Pathology (2) Systems Biology. In the area of spatial pathology, I am building a program that applies point process algorithms to multiplexed and H&E images. On the systems biology front, I am developing an integrated clinical, imaging and multiomics database for brain tumors. The concept paper for this effort can be viewed here.

Contributions in omics highlights my key work during the years 2009-2020, primarily at New York University where I was a faculty and at Memorial Sloan-Kettering Cancer Center where I was a post-doc. Contributions in computational physics marks my work as a graduate student at Texas A&M University between the years 2005-2008.

Over the course of years and during my tenure at New York University School of Medicine, I developed and taught several topics in biomedical informatics.

Programming for Data Analysis is about the fundementals of data science using the R programming language. This course is primarily based on tidyverse and ggplot packages. We also cover a bit of mathematical modeling (such as optimization) towards the end of the course, but the main focus of the course is for non-programmers to get some expereince in doing data analysis. As a case study for analysis we use clinical databases (diabetes and critical care databases).

Machine Learning and AI consists of several important topics in this area such as classification, ensemble methods, feature selection and regularization. The focus is on the depth of these topics from a statistical perspective. For example, the lecture on ensemble methods would tell us the statistical basis for bootstrapping and random forests. The idea is to make students realize that machine learning is not just programming or data exploration but it is actually statistics. In contrast, this lecture series also contains an "hands-on" tutorial on AI based image classification.

Methods in Quantitative Biology is a set of four disparate lectures I developed in Fall 2017 as a part of biomedical informatics program at NYU. I believe these topics are a part of core subjects in informatics that data science students need to gain a good understanding. For example, algorithms are the core engines of any computing task and it is important to understand the analysis of their complexities. Similarly, linear algebra is a very important topic and plays a crucial role, be it quantum computing or deep learning.

A consolidated version of Programming for Data Analysis and Methods in Quantitative Biology courses can be found here.

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