Currently a Professor at the Florida Institute of Technology Debasis Mitra is a Physicsist and a Computer Scientist.

A central theme of Mitra's present research is to squeeze knowledge nuggets from 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.

Courses

• Fall 2024: Artificial Intelligence
• Spring 2018: Topological Data Analyses
• Spring 2017 (Fall 2022): Scientific Computing
• Past Course: Formal Languages and Automata Theory
• Past Course: Analysis of Algorithms
• Past Course: Medical Imaging
• Past Course: Computational Molecular Biology
• Past Course: Constraint Reasoning
• Future Course: Topological Data Analyses

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 preliminary model, how much information can we extract from it? Apart from oerall statistical model of noise, we often have extra information 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

A talk on Medical imaging

Graduate Students

• Valerie Kobzarenko, Ph.D. Candidate

Alumni (Dissertation/Thesis/Capstone)

• Haoran Chang, Ph.D., Post-doc Univ California, Davis
• Michael Kolar, M.S., Medtronic, Boston
• Gengbo Liu, Ph.D., , Genentech Corp., San Francisco
• Haoran Chang, M.S., currently doing Ph.D. with me at FIT
• Hui Pan, Ph.D., TenCent, China, previously at Microsoft Corp.
• Mahmoud Abdalah, Ph.D., Research Associate, Moffitt Cancer Research Center, U of South Florida, Tampa
• Stephen Johnson, Ph.D., Dean, East Florida State College, Palm Bay, Florida
• Chen Shi, M.S., Stanley Black & Decker, Atlanta, GA
• Bo Li, M.S.
• Kimberley Day, B.S., Google Corp.
• Daniel Eiland, M.S., previously at Harris Corp.
• Antall Fernandes, M.S., Visible Measures Corp, Boston
• Richard Hoch, M.S., previously at General Dynamics
• Abhishek, Bannerjee, B.Tech.-IIT, Google, India
• Florent Launay, M.S., Microsoft Corp.
• Sung Park, M.S., Microsoft Corp.
• Keith Ledig, M.S., Harris Corp.
• Gandhali Samanth, M.S., Microsoft Corp.
• Michael Smith, M.S., (somewhere in New York!)

Alan Bundy's Page on how-to-do research

Collaborators

• Grant Gullberg & Group, Department of Radiotracer Development and. Imaging Technology, Lawrence Berkeley National Lab, Berkeley, CA, an old Group image
• Youngho Seo, Department of Radiology and Biomedical Imaging, UCSF
• Marcus Hohlmann, Physics and Space Sc. Department, FIT

Acknowledgements

• US National Institute of Health
• US National Science Foundation
• US Department of Homeland Security
• Peter Belhumeur, Columbia U

Some university rankings

Projects

INVERSE PROBLEMS

Radoimics with Machine Learning

We try to detect tumor in 3D medical images and correlate that with prognostic information automatically. Machine learning algorithms, including deep learning ones, are the tools and challenges are many! Medical data are not as easily available as 2D images for computer vision research. That is challenge number one! Where to look for the tumor even when we know it is on breast - how does a program know where the breast is in a whole-body image? Semantic segmentation is challenge number two? Data science is an art now. What type of network structure or parameters are better for what type of image? I am trying to understand this with respect to the images' topological invariants.

Nuclear Imaging

Our objective in this project is to transform raw camera data from SPECT (Single Photon Emmission Computer Tomography) and PET (Positron Emmission Tomography) scans into three dimensional or four dimensional view within body. We develop novel reconstruction and optization algorithms for dynamic or time-varying data for better diagnostics, and implement them on high-performance computing systems. We collaborate with imaging scientists, physicists, cardiologists, neurologists, from different labs including those in the Lawrence Berkeley National laboratory and the University of California San Francisco.

Muon Tomography

Muons are leptons (weakly interacting elementary particles) that are highly penetrating because of low charge (same as that of electron) and high momentum (about two hundred times heavier than electron). This makes them ideal probe for detecting materials with higher atomic numbers (Z). Steady flux of muons are produced by cosmic ray at upper atmosphere. This type of natural muons can be used as non-invasive probes for charting magma chambers in volcanos, hidden chambers in pyramids, and for detecting Uranium or other high-atmoic number materials in cargo containers for border protection. Our project is related to the last area. Our group collaborates with a High Energy Physics group at FIT. We work on reconstruction algorithms for muon-tomography.

SPATIO-TEMPORAL CONSTRAINT REASONING

Detecting inconsistency in temporal information

A typical Constraint Reasoner checks for satisfiability in an input set of constraints. For a satisfiable set it may also generate a consistent scenario. However, traditionally, nothing is done with an inconsistent set of information. Any user of such a system would often wish that the system were more helpful in debugging an inconsistet situation. We harness this issue in temporal relations where constraints may be qualitative (e.g.,"before or overlaps") or quantitative, and disjunctive in nature.

Another question we have started asking recently (with a colleague) is whether a temporal reasoning approach may be helpful in timed-automata based model checking. The latter is a useful tool for verifying a designed system and a significant amount of literature exist on theoretical and practical problems.

CAPSTONE PROJECT: PREDICTING BRAIN SEIZURE WITH A MOBILE DEVICE

Detecting brain seizure on a mobile phone

This multi-disciplinary group of Senior Undergraduate students are trying to design a head mounting system that can detect brain signals, which in turn may be analyzed on a smart phone in order to predict seizure event for a potential patient. I am the advisor to the computing team of this fun project. Here is their web-site: https://jalameen2014.wixsite.com/aspis.

PAST PROJECT: COMPUTATIONAL MOLECULAR BIOLOGY

Protein Structure Comparison

A protein is a chain of amino acids that forms a complex structure in 3D. Each protein typically has the same structure and its function is often determined by this structure. Thus, proteins are classified according to their strucutures and comparing protein structures is a serious algorithmic business. Phenotypes of organisms, disease functionalities, or drug developments - all need to have understanding of protein structures. We were engaged in studying and developing such structural comparison techniques.

PAST PROJECT: KNOWLEDGE MANAGEMENT

Knowledge Base on Science/Engineering Experiments

Science or engineering researchers produce copious amounts of data in their experiments. Often management of data becomes unwildy, even when each piece of data sets is not necessarily very large. Typically researchers keep track of data with directory hierarchies and file naming conventions. This becomes extremely cumbersome and expensive in terms of time needed to find out necessary data and may result in repetition of experiments done before. A database for keeping track of metatdata (information on data) is essential. We engaged ourselves in studying this aspect of experiment-related data management. We also address the issue of organizing knowledge behind a scientific or engineering experiment and data processing activities. The objective is to help a scientist in answering queries about the experiment or data produced by the experiments. A long term goal of these activities is to understand how creativity can be computationally modeled in scientific works and how such a knowledge management system can debug an experimental procedure. Our last application focus was in bio-medical imaging experiments.

PROTO-PROJECTS

Sometimes we get involved with exploring ideas along with students or colleagues that may lead to significant contributions, or are at least very interesting. I call them proto-projects. Lack of background and students to pursue them keeps these unfinished, and me a bit frustrated. Here is a collection of some of such recent endeavours.

Temporal reasoning in real life: Can a disjunctive temporal constraint reasoning system help archeology? Or, crime detection? Can they be embedded within Timed-automata for the purpose of checking model-based systems, designs, protocols, etc.?

Hurricane path clustering: Does hurricane paths have some similarity to each other? Do the shapes of those paths carry any useful information?

Correlation in Indus Valley script (IVS): How do the symbols group together in the available texts? Can we make any semantics out of such groupings? Can we correlate such groupings with those in other similar languages? Are there any spatio-temporal between IVS and other conttemporary scripts from neighboring civilizations Sumerian or Linear-B, etc.?

Neural Network learning a formal language: Can a neural network (NN) be trained to learn a regular or higher level language? If so, what does it mean? Does the NN become a representation of the accepting/generating machine/grammar? What type of NN architecture is needed? How do the inner layers look like after training? Are NN good at discovering hidden topologies in a string-space?
In a similar line, can an NN be trained to detect a sequence of numbers, e.g., Fibonacci number? Will it be able to learn a generating function with pole or singularity? Can a generative NN do Fourier transform?

Neural Network learning over a maze: Path-finding puzzles over mazes may be a great way to explore artificial neural learning. Can a neural network be trained to learn existence of a path in a maze? Or, finding an actual path? How does the learning efficiency relates to the texture or other topological properties of mazes?

Publications

Nuclear Imaging

• ‚àö¬®Reconstruction of 4-D Dynamic SPECT Images From Inconsistent Projections Using a Spline Initialized FADS Algorithm (SIFADS).‚àö√Ü Mahmoud Abdalah, Rostyslav Boutchko, Debasis Mitra, Grant T. Gullberg. IEEE Transactions in Medical Imaging, 34(1): 216-228, 2015.
• ‚àö¬®Clustering Initiated Factor Analysis (CIFA) Application for Tissue Classification in Dynamic Brain PET.‚àö√Ü Rostyslav Boutchko, Debasis Mitra, Suzane Baker, William Jagust, and Grant T. Gullberg. Journal of Cerebral Blood Flow & Metabolism ‚àö¬± Nature, doi:10.1038/jcbfm.2015.69, 2015.
• ‚àö¬®High Performance Fully 3D and 4D Image Reconstruction in SPECT Using a Big Data Analytic Tool Running on a Supercomputer.‚àö√Ü Huang S-Y., Lee J.H., Pan H., Boutchko R., Shrestha U., Gullberg G.T., Mitra D., Yao Y., and Seo Y. The 13th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully-3D), Newport, Rhode Island, USA, 2015.
• ‚àö¬®Parallelization of Iterative Reconstruction Algorithms in Multiple Modalities,‚àö√Ü D. Mitra, H.Pan, Fares Alhassen, and Youngho Seo. Proc. IEEE Nuclear Science Symposium and Medical Imaging Conference, Seattle, WA, 2014.
• "SinoCor: Sinogram Level Motion Correction in SPECT", Debasis Mitra, Daniel Eiland, Rostyslav Boutchko, Grant T. Gullberg. Lawrence Berkeley National Laboratory Software Licensee CR-3016, 2013

Muon Tomography

• "A Volume Clearing Algorithm for Muon Tomography‚àö√Ü D. Mitra, K. Day, and M. Hohlmann. Proceedings of IEEE Nuclear Science Symposium and Medical Imaging Conference, Seattle, WA, 2014.
• "Imaging of high-Z material for nuclear contraband detection with a minimal prototype of a muon tomography station based on GEM detectors." Nuclear Instruments and Methods in Physics Research A, , Kondo Gnanvo, Leonard V. Grosso III, Marcus Hohlman, Judson B. Locke, Amilkar Quintero, and Debasis Mitra. 652 (2011) 16‚àö¬±20
• “GEANT4 Simulation of a Cosmic Ray Muon Tomography System with Micro-Pattern Gas Detectors for the Detection of High-Z Materials,” "Transactions on Nuclear Science", Vol. 56, No. 3, June 2009, Marcus Hohlmann, Patrick Ford, Kondo Gnanvo, Jennifer Helsby, David Pena, Richard Hoch, and Debasis Mitra. [PDF]
• “Muon Tomography Algorithms for Nuclear Threat Detection,” "Lecture Notes in Artificial Intelligence" series, Springer Verlag, 2009, Richard Hoch, Debasis Mitra, Marcus Hohlman, and Kondo Gnanvo. [PDF]
• “Performance Expectations for a Tomography System Using Cosmic Ray Muons and Micro Pattern Gas Detectors for the Detection of Nuclear Contraband,” in Proc. of the IEEE Nucl. Sci. Symp. 2008, Dresden, Germany, pp. 1278-1284, IEEE Cat. CFP08NSS-CDR, ISBN 978-1-4244-2715-4, ISSN 1082-3654, e-Print: arXiv:0812.1007, Kondo Gnanvo, Patrick Ford, Jennifer Helsby, Richard Hoch, Debasis Mitra, and Marcus Hohlman. [PDF]

Spatio-temporal Reasoning

• “Explanation Generation over Temporal Interval Algebra,” in Qualitative Spatio-Temporal Representation and Reasoning: Trends and Future Directions. Shyamanta M. Hazarika (Editor), Information Science Publishing, ISBN: 1616928689, August 1, 2010.
  Debasis Mitra and Florent Launay. [PDF]
• “Spatial-reasoning for Agents in Multiple Dimensions,” Journal of Universal Computer Science, vol. 8, no. 8, pp. 774-791, August, 2002
  Debasis Mitra, and Gerard Ligozat. [PDF]
• “Spatial and Temporal Reasoning: Beyond Allen's Calculus,” Gerard Ligozat, Debasis Mitra and Jean-Francois Condotta, AI Communications (European journal on Artificial Intelligence), Vol. 17, no. 4, pp 223‚àö‚â•233, 2004.
• “Modeling and Reasoning with Star Calculus: An Extended Abstract,” Debasis Mitra, Proc. of Eighth International Symposium on AI and Math, January 2004, Ft. Lauderdale, Florida. http:/www.cs.fit.edu/~dmitra/Pub/MitraAiMath04CamRdy.pdf
• “Qualitative Direction Calculi with Arbitrary Granularity,” Jochen Renz and Debasis Mitra, Pacific Rim Conference on AI (PRICAI), 2004.
• "Characterization of Temporal Sequences in Geophysical Databases," Arie Shoshani, Preston Holland, Janet Jacobsen and Debasis Mitra, Proc. of the Statistical and Scientific Database Management (SSDBM) conference, Sweden, 1996.

Mathematical Physics

• “The Lorentz group in oscillator realization III - the group SO(3,1),” D. Basu and D. Mitra, Journal of Mathematical Physics, Vol 22, p 946, Am. Inst. Phys., 1981.
• “The Lorentz group in oscillator realization II - integral transform and matrix elements of SO(2,1),” D. Basu and D. Mitra, Journal of Mathematical Physics, Vol 21, p 636, Am. Inst. Phys., 1980.

Data & Knowledge Management

• “ReMI: An Object-relational Image Database for Nuclear Medicine Research,” Boutchko, Rostyslav; Fernandes, Antall; Pan, Hui; Abdalah, Mahmoud; Giannakidis, Archontis; Boswell, Martin; Mitra, Debasis; Gullberg, Grant T. Annual Conference of Society of Nuclear Medicine, Vancouver, BC, Canada, 2013.
• “A Data Management System with Web Interface for Pre-clinical Multi-modality Imaging: ReMI,” Mitra, Debasis ; Pan, Hui; Abdalah, Mahmoud; Boutchko, Rostyslav; Boswell, Martin; and Gullberg, Grant T. The Sixth World Molecular Imaging Congress, Savannah GA., 2013.
• “Three generations of research in computational creativity and beyond,” in Association for Advancement of Artificial Intelligence Spring Symposium on Creative Intelligent Systems (AAAI Tech Report). (Eds.) Ventura, D., Maher, M. L., and Colton, S.; Stanford, California, March 2008. Debasis Mitra.
• “Data cleaning and enriched representations for anomaly detection in system calls,” G. Tandon, P. Chan, and D. Mitra. Book Chapter in Machine Learning and Data Mining for Computer Security: Methods and Applications, M. Maloof (editor), Springer, 2005.

Computatinal Molecular Biology

• “Correlogram-based method for comparing biological sequences,” Debasis Mitra, Gandhali Samant and Kuntal Sengupta. Springer Verlag Lecture Notes on AI, 2006.