Currently a Professor at the Florida Institute of Technology Debasis Mitra is a Physicsist and a Computer Scientist. He was at Jackson State University before.
A central theme of Mitra's present research is to squeeze knowledge nuggets out of noisy spatio-temporal data, especially in Bio-medical images.
One of these efforts is dedicated to improving reconstruction of Nuclear Medical images of heart and brain.
A long term vision is to understand the molecular biology of deseases for improving human health.
In the past Mitra has made contributions in artificial intelligence studying the qualitative reasoning problems with spatial and temporal constraints.
Primary question my lab asks is given sequence of images acquired at different points in time and perspective, or spatio-temporal data/image, where we have some preliminaray model, how much information can we extract from it? Apart from oerall statistical model of noise, we often have extra knowledge about what we are looking for. Can we embed such knowldge in the processing pipeline to go beyond the information content in the data? In essence this is combined output of data and qualitative knowledge on the target objective.
Message for prospective students
- Haoran Chang
- Lavanya Vattikutti
- Hui Pan, Ph.D., at Microsoft Corp.
- Mahmoud Abdalah, Ph.D., a Post Doc, Moffitt Cancer Research Center, U of South Florida, Tampa
- Stephen Johnson, Ph.D., at East Florida State College, Palm Bay, Florida
- Chen Shi, M.S.
- Bo Li, M.S.
- Kimberley Day, B.S., at Google Corp.
- Daniel Eiland, M.S., at Harris Corp.
- Antall Fernandes, M.S., at Visible Measures Corp, Boston
- Richard Hoch, M.S., at General Dynamics
- Abhishek, Bannerjee, B.Tech.-IIT, Google, India
- Florent Launay, M.S., Microsoft Corp.
- Sung Park, M.S., at Microsoft Corp.
- Keith Ledig, M.S., at Harris Corp.
- Gandhali Samanth, M.S., at Progressive Insurance
- Michael Smith, M.S., (somewhere in New York!)
Alan Bundy's Page on how-to-do research
Learning about the laws of nature has always facinated me.
So I started my higher education studying physics. I did my
first Ph.D. on mathematical physics, working on Lorentz Groups.
While pursuing my Ph.D. at IIT I was hired by one of the largest
companies, Oil and Natural Gas Commission, in India, my country of origin.
I worked as an exploration geophysicist there, on areas like petroleum
geology, onshore and offshore field geophysics, and yes, a lot of data
processing. I fell in love with the art of programming and became
overwhelmed with the idea that the computers could be 'programmed'
to think. At this point in my career I decided that I have had enough
in oil business and should learn about computers and artificial
intelligence more seriously. I joined the University of Louisiana
at Lafayette as a Ph.D. student in computer science. 'Time'
became once again my area of investigation, as it was during my
past graduate study in physics. This time I looked into it from the
computational point of view - how to reason with it. After completing
my second doctorate I have decided to teach computer science while
continuing my journey of delving more and more into 'space' and 'time.'
In the end I became fatigued with working on complete abstractions,
and wanted to dip my hands into some real data, as I was doing during my
early infatuation with computers. Mathematical or scientific computing
started attracting me more. With a sabbatical at Lawrence Berkeley
National Lab I was introduced to inverse problems over noisy nuclear
imaging data. Medical image processing and management became my staple, but once again, on spatio-temporal data or dynamic images.
Thanks to a number of extraordinary collaborators and students, our learning
curve in this new area is steady.
So, here I am! But, stay tuned for more, in case you are tracking me :)
A disclaimer: I am not the Debasis Mitra, who works on distributed computing, now in Columbia University.