I've been trained as an Applied Physicist and Large Scale Computer Simulationist at IIT Kharagpur, India and Yale University, USA. I'm deeply interested in applying principles of Physics, computer simulation, algorithm, big data and systems modeling to real world problems like medical imaging, neuro-feedback technology, digital health etc. With that mission, I've worked on several multi-disciplinary problems like blood-flow in cardiac chambers, cardiac infarction modeling, developing mobile apps for mental training etc. Some of my research areas in this space are phase unwrapping problem in higher dimenions, evaluation of cardio-vascular mechanical properties through flow velocity imaging coupled with traditional image processing, segmentation, feature detection, ROI quantification techniques.
Digital health and mental wellbing has been a key part of my research. In collaboration with Dr. Brewer I built the mobile app platform for Mindscience that is now part of Sharecare digital intervention portfolio. Interventions like Craving to Quit are serving throusands of people across the globe to help people take control of addiction and behavioral change using mindfulness and moble app tools.
Me and my collaborators also translated the findings through high density sensor arrays into a small subset of leading sensors to using data driven Monte-carlo simulations.
Noise has been a big part of all the data I've been dealing with. Out of frustration with noisy data I invented a novel technology named SOCKS to exclusively handle the wildest kinds of noisy signal and outliers while revealing the underlying unperturbed features. SOCKS project revealed that, most real world data can be decomposed into equilibrium and out of equilibrium components. Equilibrium component of the data is what represents the underlying system while out of equilibrium component represents the perturbations.
By the law of large number, good evidence comes through lots of large convergent data sets. To handle this issue I developed a novel tool Giant Signal File Random Sampler (GSFRS) to provide random access to exa scale (in principle) data sets.
Prospective and existing applications of these technologies are grounded on the hypothesis that mind and body work as one integrated system. It is only the cognitive qualities of human mind and brain that are responsible for the exponential growth of the human civilization over other primates. However, cognition is hardly quantifiable other than crude measures like scoring well in exams. Utilizing the theory of probability, random sampling, computational geometry and principles of perception I've developed a cognition training and enhancement tool, named MindScope to quantify attention, curiosity and other cognitive faculties. For past several years I've been deeply fascinated by a single question: Can we see the mind of ours that that makes us see the world? Everything else of my current research follows from this single question. I've created MindView technology to visualize the human mind on a display screen by stitching together incomplete pieces of information obtained from medical imaging sensors like EEG. One of the broad goal of my research is to amalgamate the mechanical computing aparatus with boundless possibilities of human potential. I'm deeply interested in mental and physical wellbeing of our race in conjuction with healthy evolution while keeping up with the massive exposure of uncontrolled volume of information. Mental state characterization as well as image processing and data science problems are key parts of my research. The ideas around my work are being applied to Alzheimer and schizophrenia diagnosis and intervention problems.
Some of my upcoming research interests involve science of quantifiable human perception, mental resilience training through neuro-feedback and mindfulness tools.