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Preprints

1. Dmytro Onishchenko, Sam Searle, Kenneth Rockwood, James A. Mastriann, Ishanu Chattopadhyay, "Rapid Universal Early Screening for Alzheimer’s Disease and Related Dementia via Pattern Discovery in Diagnostic History", preprint, 2022.   [bibtex] 
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   Bibtex

   @article{chattopADRD,
    author = {Dmytro Onishchenko and Sam Searle and Kenneth Rockwood and James A. Mastriann and Ishanu Chattopadhyay},
    eprint = {https://zed.uchicago.edu/data/pub_drafts_//PAI/zcor-ADRD.pdf},
    journal = {preprint},
    title = {Rapid Universal Early Screening for
   Alzheimer’s Disease and Related Dementia
   via Pattern Discovery in Diagnostic History},
    url = {https://zed.uchicago.edu/data/pub_drafts_//PAI/zcor-ADRD.pdf},
    year = {2022}
   }

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2. David Young, Jin Li, James Evans, Ishanu Chattopadhyay, "Its the Economy Stupid: Predictive Theory of Belief Shift Connecting Economic Stress to Societal Polarization", preprint, 2022.   [bibtex] 
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   Bibtex

   @article{chattopPOL,
    author = {David Young and Jin Li and James Evans and Ishanu Chattopadhyay},
    eprint = {https://zed.uchicago.edu/data/pub_drafts_//YFA/q_polarization.pdf},
    journal = {preprint},
    title = {Its the Economy Stupid:
   Predictive Theory of Belief Shift Connecting
   Economic Stress to Societal Polarization},
    url = {https://zed.uchicago.edu/data/pub_drafts_//YFA/q_polarization.pdf},
    year = {2022}
   }

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3. Ishanu Chattopadhyay, Yi Huang, James Evans, "Deep Learning Without Neural Networks: Fractal-nets for Rare Event Modeling", preprint, 2020.   [bibtex]  [citations: 1] 
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   Bibtex

   @article{chattopadhyay2020deep,
    author = {Ishanu Chattopadhyay and Yi Huang and James Evans},
    eprint = {https://www.researchsquare.com/article/rs-86045/v1},
    journal = {preprint},
    title = {Deep Learning Without Neural Networks: Fractal-nets for Rare Event Modeling},
    url = {https://www.researchsquare.com/article/rs-86045/v1},
    year = {2020}
   }

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4. Jin Li, Timmy Li, Ishanu Chattopadhyay, "Preparing For the Next Pandemic: Learning Wild Mutational Patterns At Scale For Analyzing Sequence Divergence In Novel Pathogens", medRxiv, 2020.   [bibtex]  [abstract] 
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   Bibtex

   @article{Li2020.07.17.20156364,
    author = {Jin Li and Timmy Li and Ishanu Chattopadhyay},
    doi = {10.1101/2020.07.17.20156364},
    eprint = {https://www.medrxiv.org/content/early/2020/07/20/2020.07.17.20156364.full.pdf},
    journal = {medRxiv},
    publisher = {Cold Spring Harbor Laboratory Press},
    title = {Preparing For the Next Pandemic: Learning Wild Mutational Patterns At Scale For Analyzing Sequence Divergence In Novel Pathogens},
    url = {https://www.medrxiv.org/content/early/2020/07/20/2020.07.17.20156364},
    year = {2020}
   }

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   ×

   Abstract

   As we begin to recover from the COVID-19 pandemic, a key question is
   if we can avert such disasters in future. Current surveillance
   protocols generally focus on qualitative impact assessments of viral
   diversity 1. These efforts are primarliy aimed at ecosystem and
   human impact monitoring, and do not help to precisely quantify
   emergence. Currently, the similarity of biological strains is
   measured by the edit distance or the number of mutations that
   separate their genomic sequences 2{\textendash}6, e.g. the number of
   mutations that make an avian flu strain human-adapted. However,
   ignoring the odds of those mutations in the wild keeps us blind to
   the true jump risk, and gives us little indication of which strains
   are more risky. In this study, we develop a more meaningful metric
   for comparison of genomic sequences. Our metric, the q-distance,
   precisely quantifies the probability of spontaneous jump by random
   chance. Learning from patterns of mutations from large sequence
   databases, the q-distance adapts to the specific organism, the
   background population, and realistic selection pressures;
   demonstrably improving inference of ancestral relationships and
   future trajectories. As important application, we show that the
   q-distance predicts future strains for seasonal Influenza,
   outperforming World Health Organization (WHO) recommended flu-shot
   composition almost consistently over two decades. Such performance
   is demonstrated separately for Northern and Southern hemisphere for
   different subtypes, and key capsidic proteins. Additionally, we
   investigate the SARS-CoV-2 origin problem, and precisely quantify
   the likelihood of different animal species that hosted an immediate
   progenitor, producing a list of related species of bats that have a
   quantifiably high likelihood of being the source. Additionally, we
   identify specific rodents with a credible likelihood of hosting a
   SARS-CoV-2 ancestor. Combining machine learning and large deviation
   theory, the analysis reported here may open the door to actionable
   predictions of future pandemics.Competing Interest StatementThe
   authors have declared no competing interest.Funding StatementThis
   work is funded in part by the Defense Advanced Research Projects
   Agency (DARPA) project $\#$FP070943-01-PR. The claims made in this
   study do not necessarily reflect the position or the policy of the
   Government, and no official endorsement should be inferred.Author
   DeclarationsI confirm all relevant ethical guidelines have been
   followed, and any necessary IRB and/or ethics committee approvals
   have been obtained.YesThe details of the IRB/oversight body that
   provided approval or exemption for the research described are given
   below:No Human Patient DataAll necessary patient/participant consent
   has been obtained and the appropriate institutional forms have been
   archived.YesI understand that all clinical trials and any other
   prospective interventional studies must be registered with an ICMJE-
   approved registry, such as ClinicalTrials.gov. I confirm that any
   such study reported in the manuscript has been registered and the
   trial registration ID is provided (note: if posting a prospective
   study registered retrospectively, please provide a statement in the
   trial ID field explaining why the study was not registered in
   advance).YesI have followed all appropriate research reporting
   guidelines and uploaded the relevant EQUATOR Network research
   reporting checklist(s) and other pertinent material as supplementary
   files, if applicable.YesModels generated in this study is included
   as supplementary material, and working software is publicly
   available at https://pypi.org/project/quasinet/. Accession numbers
   of all sequences used, and acknowledgement documentation for GISAID
   sequences in also available as supplementary information. https://gi
   thub.com/zeroknowledgediscovery/quasinet/tree/master/examples

   Close

Book Chapters

1. Ishanu Chattopadhyay, Asok Ray, "Advanced Topics in Supervisory Control", Quantitative Measure for Discrete Event Supervisory Control, pp. 95–130, 2005. [bibtex]  [citations: 0] 
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   Bibtex

   @inbook{chattopadhyay2005advanced,
    author = {Ishanu Chattopadhyay and Asok Ray},
    booktitle = {Quantitative Measure for Discrete Event Supervisory Control},
    pages = {95–130},
    publisher = {Springer},
    title = {Advanced Topics in Supervisory Control},
    year = {2005}
   }

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Journal Articles

1. Nicholas Sizemore, Kaitlyn Oliphant, Ruolin Zheng, Camilia R. Martin, Erika C. Claud, Ishanu Chattopadhyay, "A digital twin of the infant microbiome to predict neurodevelopmental deficits", Science Advances, vol. 10, no. 15, apr, 2024.   [bibtex]  [citations: 0] 
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   Bibtex

   @article{Sizemore_2024,
    author = {Nicholas Sizemore and Kaitlyn Oliphant and Ruolin Zheng and Camilia R. Martin and Erika C. Claud and Ishanu Chattopadhyay},
    doi = {10.1126/sciadv.adj0400},
    journal = {Science Advances},
    month = {apr},
    number = {15},
    publisher = {American Association for the Advancement of Science (AAAS)},
    title = {A digital twin of the infant microbiome to predict neurodevelopmental deficits},
    url = {http://dx.doi.org/10.1126/sciadv.adj0400},
    volume = {10},
    year = {2024}
   }

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2. Yi Huang, Victor Rotaru, Ishanu Chattopadhyay, "Sequence likelihood divergence for fast time series comparison", Knowledge and Information Systems, vol. 65, no. 7, pp. 3079–3098, mar, 2023.   [bibtex]  [citations: 0] 
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   Bibtex

   @article{Huang_2023,
    author = {Yi Huang and Victor Rotaru and Ishanu Chattopadhyay},
    doi = {10.1007/s10115-023-01855-0},
    journal = {Knowledge and Information Systems},
    month = {mar},
    number = {7},
    pages = {3079–3098},
    publisher = {Springer Science and Business Media LLC},
    title = {Sequence likelihood divergence for fast time series comparison},
    url = {http://dx.doi.org/10.1007/s10115-023-01855-0},
    volume = {65},
    year = {2023}
   }

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3. Dmytro Onishchenko, Robert J. Marlowe, Che G. Ngufor, Louis J. Faust, Andrew H. Limper, Gary M. Hunninghake, Fernando J. Martinez, Ishanu Chattopadhyay, "Screening for idiopathic pulmonary fibrosis using comorbidity signatures in electronic health records", Nature Medicine, sep, 2022.   [bibtex]  [citations: 11] 
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   Bibtex

   @article{Onishchenko_2022,
    author = {Dmytro Onishchenko and Robert J. Marlowe and Che G. Ngufor and Louis J. Faust and Andrew H. Limper and Gary M. Hunninghake and Fernando J. Martinez and Ishanu Chattopadhyay},
    doi = {10.1038/s41591-022-02010-y},
    journal = {Nature Medicine},
    month = {sep},
    publisher = {Springer Science and Business Media LLC},
    title = {Screening for idiopathic pulmonary fibrosis using comorbidity signatures in electronic health records},
    url = {https://www.nature.com/articles/s41591-022-02010-y},
    year = {2022}
   }

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4. Estee Y Cramer, Yuxin Huang, Yijin Wang, Evan L Ray, Matthew Cornell, Johannes Bracher, Andrea Brennen, Alvaro J Castero Rivadeneira, Aaron Gerding, Katie House, Dasuni Jayawardena, Abdul H Kanji, Ayush Khandelwal, Khoa Le, Jarad Niemi, Ariane Stark, Apurv Shah, Nutcha Wattanchit, Martha W Zorn, Nicholas G Reich, US COVID-19 Forecast Hub Consortium, "The United States COVID-19 Forecast Hub dataset", Scientific Data, vol. 9, no. 1, pp. 1–15, 2022.   [bibtex]  [citations: 85] 
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   Bibtex

   @article{Cramer2022,
    author = {Estee Y Cramer and Yuxin Huang and Yijin Wang and Evan L Ray and Matthew Cornell and Johannes Bracher and Andrea Brennen and Alvaro J Castero Rivadeneira and Aaron Gerding and Katie House and Dasuni Jayawardena and Abdul H Kanji and Ayush Khandelwal and Khoa Le and Jarad Niemi and Ariane Stark and Apurv Shah and Nutcha Wattanchit and Martha W Zorn and Nicholas G Reich and US COVID-19 Forecast Hub Consortium},
    doi = {10.1038/s41597-022-01517-w},
    journal = {Scientific Data},
    number = {1},
    pages = {1–15},
    publisher = {Nature Publishing Group},
    title = {The United States COVID-19 Forecast Hub dataset},
    url = {https://www.nature.com/articles/s41597-022-01517-w},
    volume = {9},
    year = {2022}
   }

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5. Dmytro Onishchenko, Daniel S. Rubin, James R. van Horne, R. Parker Ward, Ishanu Chattopadhyay 65;6602;1c, "Cardiac Comorbidity Risk Score: Zero Burden Machine Learning to Improve Prediction of Postoperative Major Adverse Cardiac Events in Hip and Knee Arthroplasty", Journal of the American Heart Association, vol. 11, no. 15, pp. e023745, 2022.   [bibtex] 
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   Bibtex

   @article{JAHA2022,
    author = {Dmytro Onishchenko and Daniel S. Rubin and James R. van Horne and R. Parker Ward and Ishanu Chattopadhyay 65;6602;1c},
    doi = {10.1161/JAHA.121.023745},
    journal = {Journal of the American Heart Association},
    number = {15},
    pages = {e023745},
    title = {Cardiac Comorbidity Risk Score: Zero Burden Machine Learning to Improve Prediction of Postoperative Major Adverse Cardiac Events in Hip and Knee Arthroplasty},
    url = {https://www.ahajournals.org/doi/abs/10.1161/JAHA.121.023745},
    volume = {11},
    year = {2022}
   }

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6. Robert D. Gibbons, Ishanu Chattopadhyay, Herbert Y. Meltzer, John M. Kane, Daniel Guinart, "Development of a computerized adaptive diagnostic screening tool for psychosis", Schizophrenia Research, vol. 245, pp. 116–121, jul, 2022.   [bibtex]  [citations: 11] 
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   Bibtex

   @article{Gibbons_2022,
    author = {Robert D. Gibbons and Ishanu Chattopadhyay and Herbert Y. Meltzer and John M. Kane and Daniel Guinart},
    doi = {10.1016/j.schres.2021.03.020},
    journal = {Schizophrenia Research},
    month = {jul},
    pages = {116–121},
    publisher = {Elsevier BV},
    title = {Development of a computerized adaptive diagnostic screening tool for psychosis},
    url = {https://doi.org/10.1016\%2Fj.schres.2021.03.020},
    volume = {245},
    year = {2022}
   }

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7. Victor Rotaru, Yi Huang, Timmy Li, James Evans, Ishanu Chattopadhyay, "Event-level prediction of urban crime reveals a signature of enforcement bias in US cities", Nature Human Behaviour, jun, 2022.   [bibtex]  [citations: 18] 
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   Bibtex

   @article{Rotaru_2022,
    author = {Victor Rotaru and Yi Huang and Timmy Li and James Evans and Ishanu Chattopadhyay},
    doi = {10.1038/s41562-022-01372-0},
    journal = {Nature Human Behaviour},
    month = {jun},
    publisher = {Springer Science and Business Media LLC},
    title = {Event-level prediction of urban crime reveals a signature of enforcement bias in US cities},
    url = {https://doi.org/10.1038/s41562-022-01372-0},
    year = {2022}
   }

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8. Yi Huang, Ishanu Chattopadhyay, "Universal risk phenotype of US counties for flu-like transmission to improve county-specific COVID-19 incidence forecasts", PLOS Computational Biology, 2021.   [bibtex]  [citations: 0] 
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   Bibtex

   @article{ishanu_chattopadhyay_101486635,
    author = {Yi Huang and Ishanu Chattopadhyay},
    doi = {10.1371/journal.pcbi.1009363},
    journal = {PLOS Computational Biology},
    title = {Universal risk phenotype of US counties for flu-like transmission to improve county-specific COVID-19 incidence forecasts},
    url = {http://doi.org/10.1371/journal.pcbi.1009363},
    year = {2021}
   }

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9. Dmytro Onishchenko, Yi Huang, James van Horne, Peter J. Smith, Michael E. Msall, Ishanu Chattopadhyay, "Reduced false positives in autism screening via digital biomarkers inferred from deep comorbidity patterns", Science Advances, vol. 7, no. 41, oct, 2021.   [bibtex]  [citations: 10] 
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   Bibtex

   @article{Onishchenko_2021,
    author = {Dmytro Onishchenko and Yi Huang and James van Horne and Peter J. Smith and Michael E. Msall and Ishanu Chattopadhyay},
    doi = {10.1126/sciadv.abf0354},
    journal = {Science Advances},
    month = {oct},
    number = {41},
    publisher = {American Association for the Advancement of Science (AAAS)},
    title = {Reduced false positives in autism screening via digital biomarkers inferred from deep comorbidity patterns},
    url = {https://doi.org/10.1126/sciadv.abf0354},
    volume = {7},
    year = {2021}
   }

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10. Manuel F. L\'opez-Aranda, Ishanu Chattopadhyay, Gayle M Boxx, Elizabeth R Fraley, Tawnie K Silva, Miou Zhou, Miranda Phan, Isaiah Herrera, Sunrae Taloma, Rochelle Mandanas, Karen Bach, Michael Gandal, Daniel H. Geschwind, Genhong Cheng, Andrey Rzhetsky, Stephanie A White, Alcino J Silva, "Postnatal immune activation causes social deficits in a mouse model of tuberous sclerosis: Role of microglia and clinical implications", Science Advances, vol. 7, no. 38, sep, 2021.   [bibtex]  [citations: 11] 
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    Bibtex

    @article{L_pez_Aranda_2021,
     author = {Manuel F. L\'opez-Aranda and Ishanu Chattopadhyay and Gayle M Boxx and Elizabeth R Fraley and Tawnie K Silva and Miou Zhou and Miranda Phan and Isaiah Herrera and Sunrae Taloma and Rochelle Mandanas and Karen Bach and Michael Gandal and Daniel H. Geschwind and Genhong Cheng and Andrey Rzhetsky and Stephanie A White and Alcino J Silva},
     doi = {10.1126/sciadv.abf2073},
     journal = {Science Advances},
     month = {sep},
     number = {38},
     publisher = {American Association for the Advancement of Science (AAAS)},
     title = {Postnatal immune activation causes social deficits in a mouse model of tuberous sclerosis: Role of microglia and clinical implications},
     url = {https://doi.org/10.1126/sciadv.abf2073},
     volume = {7},
     year = {2021}
    }

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11. Lisa A. Brenner, Lisa M. Betthauser, Molly Penzenik, Anne Germain, Jin Jun Li, Ishanu Chattopadhyay, Ellen Frank, David J. Kupfer, Robert D. Gibbons, "Development and Validation of Computerized Adaptive Assessment Tools for the Measurement of Posttraumatic Stress Disorder Among US Military Veterans", JAMA Network Open, vol. 4, no. 7, pp. e2115707, jul, 2021.   [bibtex]  [citations: 8] 
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    Bibtex

    @article{Brenner_2021,
     author = {Lisa A. Brenner and Lisa M. Betthauser and Molly Penzenik and Anne Germain and Jin Jun Li and Ishanu Chattopadhyay and Ellen Frank and David J. Kupfer and Robert D. Gibbons},
     doi = {10.1001/jamanetworkopen.2021.15707},
     journal = {JAMA Network Open},
     month = {jul},
     number = {7},
     pages = {e2115707},
     publisher = {American Medical Association (AMA)},
     title = {Development and Validation of Computerized Adaptive Assessment Tools for the Measurement of Posttraumatic Stress Disorder Among US Military Veterans},
     url = {https://doi.org/10.1001/jamanetworkopen.2021.15707},
     volume = {4},
     year = {2021}
    }

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12. Jia G, Li Y, Zhang H, Chattopadhyay I, Boeck Jensen A, Blair DR, Davis L, Robinson PN, Dahlén T, Brunak S, Benson M, Edgren G, Cox NJ, Gao X, Rzhetsky A, "Estimating heritability and genetic correlations from large health datasets in the absence of genetic data.", Nature communications, 12, 2019.   [bibtex] 
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    Bibtex

    @article{PMID:31796735,
     author = {Jia G and Li Y and Zhang H and Chattopadhyay I and Boeck Jensen A and Blair DR and Davis L and Robinson PN and Dahlén T and Brunak S and Benson M and Edgren G and Cox NJ and Gao X and Rzhetsky A},
     doi = {10.1038/s41467-019-13455-0},
     journal = {Nature communications},
     month = {12},
     title = {Estimating heritability and genetic correlations from large health datasets in the absence of genetic data.},
     url = {https://dx.doi.org/10.1038/s41467-019-13455-0},
     year = {2019}
    }

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13. Ishanu Chattopadhyay, Emre Kiciman, Joshua W Elliott, Jeffrey L Shaman, Andrey Rzhetsky, "Conjunction of factors triggering waves of seasonal influenza", eLife, vol. 7, feb, 2018.   [bibtex]  [citations: 48] 
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    Bibtex

    @article{Chattopadhyay_2018,
     author = {Ishanu Chattopadhyay and Emre Kiciman and Joshua W Elliott and Jeffrey L Shaman and Andrey Rzhetsky},
     doi = {10.7554/elife.30756},
     journal = {eLife},
     month = {feb},
     publisher = {eLife Sciences Publications, Ltd},
     title = {Conjunction of factors triggering waves of seasonal influenza},
     url = {https://doi.org/10.7554/elife.30756},
     volume = {7},
     year = {2018}
    }

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14. Ishanu Chattopadhyay, Hod Lipson, "Data smashing: uncovering lurking order in data", Journal of The Royal Society Interface, vol. 11, no. 101, pp. 20140826, dec, 2014.   [bibtex]  [citations: 32] 
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    Bibtex

    @article{Chattopadhyay_2014,
     author = {Ishanu Chattopadhyay and Hod Lipson},
     doi = {10.1098/rsif.2014.0826},
     journal = {Journal of The Royal Society Interface},
     month = {dec},
     number = {101},
     pages = {20140826},
     publisher = {The Royal Society},
     title = {Data smashing: uncovering lurking order in data},
     url = {https://doi.org/10.1098/rsif.2014.0826},
     volume = {11},
     year = {2014}
    }

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15. I. Chattopadhyay, "Scalable e-optimal decision-making and stochastic routing in large networks via distributed supervision of probabilistic automata", SIAM Journal on Control and Optimization, vol. 52, no. 4, pp. 2512–2547, 2014. [bibtex] 
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    Bibtex

    @article{chattopadhyay2014,
     author = {I. Chattopadhyay},
     journal = {SIAM Journal on Control and Optimization},
     number = {4},
     pages = {2512–2547},
     title = {Scalable e-optimal decision-making and stochastic routing in large networks via distributed supervision of probabilistic automata},
     volume = {52},
     year = {2014}
    }

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16. Chattopadhyay I, Kuchina A, Süel GM, Lipson H, "Inverse Gillespie for inferring stochastic reaction mechanisms from intermittent samples.", Proceedings of the National Academy of Sciences of the United States of America, 08, 2013.   [bibtex] 
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    Bibtex

    @article{PMID:23878234,
     author = {Chattopadhyay I and Kuchina A and Süel GM and Lipson H},
     doi = {10.1073/pnas.1214559110},
     journal = {Proceedings of the National Academy of Sciences of the United States of America},
     month = {08},
     title = {Inverse Gillespie for inferring stochastic reaction mechanisms from intermittent samples.},
     url = {https://dx.doi.org/10.1073/pnas.1214559110},
     year = {2013}
    }

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17. Chattopadhyay I, Lipson H, "Abductive learning of quantized stochastic processes with probabilistic finite automata.", Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 02, 2013.   [bibtex] 
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    Bibtex

    @article{PMID:23277601,
     author = {Chattopadhyay I and Lipson H},
     doi = {10.1098/rsta.2011.0543},
     journal = {Philosophical transactions. Series A, Mathematical, physical, and engineering sciences},
     month = {02},
     title = {Abductive learning of quantized stochastic processes with probabilistic finite automata.},
     url = {https://dx.doi.org/10.1098/rsta.2011.0543},
     year = {2013}
    }

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18. Chattopadhyay I, Ray A, "Supervised self-organization of homogeneous swarms using ergodic projections of Markov chains.", IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society, 12, 2009.   [bibtex] 
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    Bibtex

    @article{PMID:19447731,
     author = {Chattopadhyay I and Ray A},
     doi = {10.1109/tsmcb.2009.2020173},
     journal = {IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society},
     month = {12},
     title = {Supervised self-organization of homogeneous swarms using ergodic projections of Markov chains.},
     url = {https://dx.doi.org/10.1109/tsmcb.2009.2020173},
     year = {2009}
    }

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19. I. Chattopadhyay, G. Mallapragada, A. Ray, "V*: A robot path planning algorithm based on renormalised measure of probabilistic regular languages", International Journal of Control, vol. 82, no. 5, pp. 849–867, 2009. [bibtex] 
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    Bibtex

    @article{chattopadhyay2009,
     author = {I. Chattopadhyay and G. Mallapragada and A. Ray},
     journal = {International Journal of Control},
     number = {5},
     pages = {849–867},
     title = {V*: A robot path planning algorithm based on renormalised measure of probabilistic regular languages},
     volume = {82},
     year = {2009}
    }

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20. Ishanu Chattopadhyay, Asok Ray, "Supervised Self-Organization of Homogeneous Swarms Using Ergodic Projections of Markov Chains", IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 39, no. 6, pp. 1505–1515, Dec, 2009.   [bibtex]  [abstract]  [citations: 49] 
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    Bibtex

    @article{4926163,
     author = {Ishanu Chattopadhyay and Asok Ray},
     doi = {10.1109/TSMCB.2009.2020173},
     journal = {IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)},
     month = {Dec},
     number = {6},
     pages = {1505–1515},
     title = {Supervised Self-Organization of Homogeneous Swarms Using Ergodic Projections of Markov Chains},
     url = {https://dx.doi.org/10.1109/TSMCB.2009.2020173},
     volume = {39},
     year = {2009}
    }

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    ×

    Abstract

    This paper formulates a self-organization algorithm to address the
    problem of global behavior supervision in engineered swarms of
    arbitrarily large population sizes. The swarms considered in this
    paper are assumed to be homogeneous collections of independent
    identical finite-state agents, each of which is modeled by an
    irreducible finite Markov chain. The proposed algorithm computes the
    necessary perturbations in the local agents' behavior, which
    guarantees convergence to the desired observed state of the swarm.
    The ergodicity property of the swarm, which is induced as a result
    of the irreducibility of the agent models, implies that while the
    local behavior of the agents converges to the desired behavior only
    in the time average, the overall swarm behavior converges to the
    specification and stays there at all times. A simulation example
    illustrates the underlying concept.

    Close
21. G. Mallapragada, I. Chattopadhyay, A. Ray, "Autonomous robot navigation using optimal control of probabilistic regular languages", International Journal of Control, vol. 82, no. 1, pp. 13–26, 2009. [bibtex]  [citations: 7] 
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    Bibtex

    @article{chattopadhyay2009,
     author = {G. Mallapragada and I. Chattopadhyay and A. Ray},
     journal = {International Journal of Control},
     number = {1},
     pages = {13–26},
     title = {Autonomous robot navigation using optimal control of probabilistic regular languages},
     volume = {82},
     year = {2009}
    }

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22. I. Chattopadhyay, A. Ray, "Generalised projections in finite state automata and decidability of state determinacy", International Journal of Control, vol. 81, no. 10, pp. 1626–1644, 2008. [bibtex]  [citations: 2] 
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    Bibtex

    @article{chattopadhyay2008,
     author = {I. Chattopadhyay and A. Ray},
     journal = {International Journal of Control},
     number = {10},
     pages = {1626–1644},
     title = {Generalised projections in finite state automata and decidability of state determinacy},
     volume = {81},
     year = {2008}
    }

    Close
23. I. Chattopadhyay, A. Ray, "Structural transformations of probabilistic finite state machines", International Journal of Control, vol. 81, no. 5, pp. 820–835, 2008. [bibtex]  [citations: 52] 
    ×

    Bibtex

    @article{chattopadhyay2008,
     author = {I. Chattopadhyay and A. Ray},
     journal = {International Journal of Control},
     number = {5},
     pages = {820–835},
     title = {Structural transformations of probabilistic finite state machines},
     volume = {81},
     year = {2008}
    }

    Close
24. G. Mallapragada, I. Chattopadhyay, A. Ray, "Automated behaviour recognition in mobile robots using symbolic dynamic filtering", Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering, vol. 222, no. 6, pp. 409–424, 2008. [bibtex]  [citations: 19] 
    ×

    Bibtex

    @article{chattopadhyay2008,
     author = {G. Mallapragada and I. Chattopadhyay and A. Ray},
     journal = {Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering},
     number = {6},
     pages = {409–424},
     title = {Automated behaviour recognition in mobile robots using symbolic dynamic filtering},
     volume = {222},
     year = {2008}
    }

    Close
25. I. Chattopadhyay, A. Ray, "Generalized language measure families of probabilistic finite state systems", International Journal of Control, vol. 80, no. 5, pp. 789–799, 2007. [bibtex]  [citations: 2] 
    ×

    Bibtex

    @article{chattopadhyay2007,
     author = {I. Chattopadhyay and A. Ray},
     journal = {International Journal of Control},
     number = {5},
     pages = {789–799},
     title = {Generalized language measure families of probabilistic finite state systems},
     volume = {80},
     year = {2007}
    }

    Close
26. I. Chattopadhyay, A. Ray, "A language measure for partially observed discrete event systems", International Journal of Control, vol. 79, no. 9, pp. 1074–1086, 2006. [bibtex]  [citations: 8] 
    ×

    Bibtex

    @article{chattopadhyay2006,
     author = {I. Chattopadhyay and A. Ray},
     journal = {International Journal of Control},
     number = {9},
     pages = {1074–1086},
     title = {A language measure for partially observed discrete event systems},
     volume = {79},
     year = {2006}
    }

    Close
27. I. Chattopadhyay, A. Ray, "Generalized language measure of finite state logical systems", Proceedings - Eighth International Workshop on Discrete Event Systems, WODES 2006, pp. 366–371, 2006. [bibtex]  [citations: 0] 
    ×

    Bibtex

    @article{chattopadhyay2006,
     author = {I. Chattopadhyay and A. Ray},
     journal = {Proceedings - Eighth International Workshop on Discrete Event Systems, WODES 2006},
     pages = {366–371},
     title = {Generalized language measure of finite state logical systems},
     year = {2006}
    }

    Close
28. I. Chattopadhyay, A. Ray, "Renormalized measure of regular languages", Proceedings - Eighth International Workshop on Discrete Event Systems, WODES 2006, pp. 45–50, 2006. [bibtex]  [citations: 37] 
    ×

    Bibtex

    @article{chattopadhyay2006,
     author = {I. Chattopadhyay and A. Ray},
     journal = {Proceedings - Eighth International Workshop on Discrete Event Systems, WODES 2006},
     pages = {45–50},
     title = {Renormalized measure of regular languages},
     year = {2006}
    }

    Close
29. I. Chattopadhyay, A. Ray, "A complex measure for linear grammars", Demonstratio Mathematica, vol. 38, no. 3, pp. 761–775, 2005. [bibtex]  [citations: 4] 
    ×

    Bibtex

    @article{chattopadhyay2005,
     author = {I. Chattopadhyay and A. Ray},
     journal = {Demonstratio Mathematica},
     number = {3},
     pages = {761–775},
     title = {A complex measure for linear grammars},
     volume = {38},
     year = {2005}
    }

    Close
30. I. Chattopadhyay, A. Ray, "Advanced topics in supervisory control", Quantitative Measure for Discrete Event Supervisory Control, pp. 95–130, 2005. [bibtex]  [citations: 0] 
    ×

    Bibtex

    @article{chattopadhyay2005,
     author = {I. Chattopadhyay and A. Ray},
     journal = {Quantitative Measure for Discrete Event Supervisory Control},
     pages = {95–130},
     title = {Advanced topics in supervisory control},
     year = {2005}
    }

    Close

Conference Articles

1. Boyuan Chen, Harvey Wu, Warren Mo, Ishanu Chattopadhyay, Hod Lipson, "Autostacker: A compositional evolutionary learning system", Proceedings of the genetic and evolutionary computation conference, pp. 402–409, 2018.   [bibtex]  [citations: 108] 
   ×

   Bibtex

   @inproceedings{chen2018autostacker,
    author = {Boyuan Chen and Harvey Wu and Warren Mo and Ishanu Chattopadhyay and Hod Lipson},
    booktitle = {Proceedings of the genetic and evolutionary computation conference},
    doi = {10.1145/3205455.3205586},
    pages = {402–409},
    title = {Autostacker: A compositional evolutionary learning system},
    url = {https://dx.doi.org/10.1145/3205455.3205586},
    year = {2018}
   }

   Close
2. Shahab Asoodeh, Yi Huang, Ishanu Chattopadhyay, "A Tamper-Free Semi-Universal Communication System for Deletion Channels", 2018 IEEE Conference on Decision and Control (CDC), pp. 4181–4186, Dec, 2018.   [bibtex]  [abstract]  [citations: 0] 
   ×

   Bibtex

   @inproceedings{8619215,
    author = {Shahab Asoodeh and Yi Huang and Ishanu Chattopadhyay},
    booktitle = {2018 IEEE Conference on Decision and Control (CDC)},
    doi = {10.1109/CDC.2018.8619215},
    month = {Dec},
    pages = {4181–4186},
    title = {A Tamper-Free Semi-Universal Communication System for Deletion Channels},
    url = {https://dx.doi.org/10.1109/CDC.2018.8619215},
    year = {2018}
   }

   Close
   ×

   Abstract

   We investigate the problem of reliable communication between two
   legitimate parties over deletion channels under an active
   eavesdropping (aka, jamming) adversarial model. To this goal, we
   develop a theoretical framework based on probabilistic finite-state
   automata to define novel encoding and decoding schemes that ensure
   small error probability in both message decoding as well as tamper
   detecting. We then experimentally verify the reliability and tamper-
   detection property of our scheme.

   Close
3. Ishanu Chattopadhyay, "Discrimination at the Edge of Noise: A Hilbert Space of Stationary Ergodic Processes", 2017 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 942–948, Nov, 2017.   [bibtex]  [abstract]  [citations: 0] 
   ×

   Bibtex

   @inproceedings{8215763,
    author = {Ishanu Chattopadhyay},
    booktitle = {2017 IEEE International Conference on Data Mining Workshops (ICDMW)},
    doi = {10.1109/ICDMW.2017.129},
    month = {Nov},
    pages = {942–948},
    title = {Discrimination at the Edge of Noise: A Hilbert Space of Stationary Ergodic Processes},
    url = {https://dx.doi.org/10.1109/ICDMW.2017.129},
    year = {2017}
   }

   Close
   ×

   Abstract

   Identifying meaningful signal buried in noise is a problem of
   interest arising in diverse scenarios of data-driven modeling. We
   present here a theoretical framework for exploiting intrinsic
   geometry in data that resists noise corruption, and might be
   identifiable under severe obfuscation. Our approach is based on
   uncovering a valid complete inner product on the space of ergodic
   stationary finite valued processes, providing the latter with the
   structure of a Hilbert space on the real field. This rigorous
   construction, based on non-standard generalizations of the notions
   of sum and scalar multiplication of finite dimensional probability
   vectors, allows us to meaningfully talk about "angles" between data
   streams and data sources, and, make precise the notion of orthogonal
   stochastic processes. In particular, the relative angles appear to
   be preserved, and identifiable, under severe noise, and will be
   developed in future as the underlying principle for robust
   classification, clustering and unsupervised featurization
   algorithms.

   Close
4. Ishanu Chattopadhyay, Hod Lipson, "Computing entropy rate of symbol sources & a distribution-free limit theorem", 2014 48th Annual Conference on Information Sciences and Systems (CISS), pp. 1–6, March, 2014.   [bibtex]  [abstract]  [citations: 8] 
   ×

   Bibtex

   @inproceedings{6814175,
    author = {Ishanu Chattopadhyay and Hod Lipson},
    booktitle = {2014 48th Annual Conference on Information Sciences and Systems (CISS)},
    doi = {10.1109/CISS.2014.6814175},
    month = {March},
    pages = {1–6},
    title = {Computing entropy rate of symbol sources & a distribution-free limit theorem},
    url = {https://dx.doi.org/10.1109/CISS.2014.6814175},
    year = {2014}
   }

   Close
   ×

   Abstract

   Entropy rate of sequential data-streams naturally quantifies the
   complexity of the generative process. Thus entropy rate fluctuations
   could be used as a tool to recognize dynamical perturbations in
   signal sources, and could potentially be carried out without
   explicit background noise characterization. However, state of the
   art algorithms to estimate the entropy rate have markedly slow
   convergence; making such entropic approaches non-viable in practice.
   We present here a fundamentally new approach to estimate entropy
   rates, which is demonstrated to converge significantly faster in
   terms of input data lengths, and is shown to be effective in diverse
   applications ranging from the estimation of the entropy rate of
   English texts to the estimation of complexity of chaotic dynamical
   systems. Additionally, the convergence rate of entropy estimates do
   not follow from any standard limit theorem, and reported algorithms
   fail to provide any confidence bounds on the computed values.
   Exploiting a connection to the theory of probabilistic automata, we
   establish a convergence rate of O(log|s|/3√|s|) as a function of the
   input length |s|, which then yields explicit uncertainty estimates,
   as well as required data lengths to satisfy pre-specified confidence
   bounds.

   Close
5. Ishanu Chattopadhyay, "Scalable ∊-optimal self-organization in communicating swarms using implicit probabilistic automata", 2012 American Control Conference (ACC), pp. 6047–6052, June, 2012.   [bibtex]  [abstract] 
   ×

   Bibtex

   @inproceedings{6315413,
    author = {Ishanu Chattopadhyay},
    booktitle = {2012 American Control Conference (ACC)},
    doi = {10.1109/ACC.2012.6315413},
    month = {June},
    pages = {6047–6052},
    title = {Scalable ∊-optimal self-organization in communicating swarms using implicit probabilistic automata},
    url = {https://dx.doi.org/10.1109/ACC.2012.6315413},
    year = {2012}
   }

   Close
   ×

   Abstract

   The problem of scalable near-optimal distributed control of swarms
   is investigated with emphasis on decentralized ε-optimal path
   planning to locally sensed targets. The proposed algorithm only uses
   information that can be locally queried, and rigorous theoretical
   results on convergence, robustness, scalability are established, and
   effect of system parameters such as the agent-level communication
   radius and agent velocities on global performance is analyzed. The
   fundamental philosophy of the proposed approach is to percolate
   local information across the swarm, enabling agents to indirectly
   access the global context A gradient emerges, reflecting the
   performance of agents, computed in a distributed manner via local
   information exchange between neighboring agents. It is shown that to
   follow near-optimal routes to a target which can be only sensed
   locally, and whose location is not known a priori, the agents need
   to simply move towards its “best” neighbor, where the notion of
   “best” is obtained by computing the state-specific language measure
   of an underlying probabilistic finite state automata. The
   theoretical results are validated in high-fidelity simulation
   experiments, with a minimum of 104 agents. Several controlled
   behaviors of interest are demonstrated in simulation which go beyond
   rendezvous-type scenarios, thus potentially laying the framework to
   complex collaborative task execution by large scale engineered
   swarms in near future.

   Close
6. Ishanu Chattopadhyay, Yicheng Wen, Asok Ray, Shashi Phoha, "Unsupervised inductive learning in symbolic sequences via Recursive Identification of Self-Similar Semantics", Proceedings of the 2011 American Control Conference, pp. 125–130, June, 2011.   [bibtex]  [abstract]  [citations: 15] 
   ×

   Bibtex

   @inproceedings{5991453,
    author = {Ishanu Chattopadhyay and Yicheng Wen and Asok Ray and Shashi Phoha},
    booktitle = {Proceedings of the 2011 American Control Conference},
    doi = {10.1109/ACC.2011.5991453},
    month = {June},
    pages = {125–130},
    title = {Unsupervised inductive learning in symbolic sequences via Recursive Identification of Self-Similar Semantics},
    url = {https://dx.doi.org/10.1109/ACC.2011.5991453},
    year = {2011}
   }

   Close
   ×

   Abstract

   This paper presents a new pattern discovery algorithm for
   constructing probabilistic finite state automata (PFSA) from
   symbolic sequences. The new algorithm, described as Compression via
   Recursive Identification of Self-Similar Semantics (CRISSiS), makes
   use of synchronizing strings for PFSA to localize particular states
   and then recursively identifies the rest of the states by computing
   the n-step derived frequencies. We compare our algorithm to other
   existing algorithms, such as D-Markov and Casual-State Splitting
   Reconstruction (CSSR) and show both theoretically and experimentally
   that our algorithm captures a larger class of models.

   Close
7. Ishanu Chattopadhyay, Asok Ray, "GODDeS: Globally ∈-Optimal Routing Via Distributed Decision-theoretic Self-organization", Proceedings of the 2011 American Control Conference, pp. 3215–3220, June, 2011.   [bibtex]  [abstract] 
   ×

   Bibtex

   @inproceedings{5991451,
    author = {Ishanu Chattopadhyay and Asok Ray},
    booktitle = {Proceedings of the 2011 American Control Conference},
    doi = {10.1109/ACC.2011.5991451},
    month = {June},
    pages = {3215–3220},
    title = {GODDeS: Globally ∈-Optimal Routing Via Distributed Decision-theoretic Self-organization},
    url = {https://dx.doi.org/10.1109/ACC.2011.5991451},
    year = {2011}
   }

   Close
   ×

   Abstract

   This paper introduces GODDeS: a fully distributed self-organizing
   decision-theoretic routing algorithm designed to effectively exploit
   high quality paths in lossy ad-hoc wireless environments, typically
   with a large number of nodes. The routing problem is modeled as an
   optimal control problem for a decentralized Markov Decision Process,
   with links characterized by locally known packet drop probabilities
   that either remain constant on average or change slowly. The
   equivalence of this optimization problem to that of performance
   maximization of an explicitly constructed probabilistic automata
   allows us to effectively apply the theory of quantitative measures
   of probabilistic regular languages, and design a distributed highly
   efficient solution approach that attempts to minimize source to-sink
   drop probabilities across the network. Theoretical results provide
   rigorous guarantees on global performance, showing that the
   algorithm achieves near-global optimality, in polynomial time. It is
   also argued that GODDeS is significantly congestion-aware, and
   exploits multi-path routes optimally. Theoretical development is
   supported by high-fidelity network simulations.

   Close
8. Yicheng Wen, Asok Ray, Ishanu Chattopadhyay, Shashi Phoha, "Modeling of symbolic systems: Part II - Hilbert space construction for model identification and order reduction", Proceedings of the 2011 American Control Conference, pp. 5139–5144, June, 2011.   [bibtex]  [abstract] 
   ×

   Bibtex

   @inproceedings{5990620,
    author = {Yicheng Wen and Asok Ray and Ishanu Chattopadhyay and Shashi Phoha},
    booktitle = {Proceedings of the 2011 American Control Conference},
    doi = {10.1109/ACC.2011.5990620},
    month = {June},
    pages = {5139–5144},
    title = {Modeling of symbolic systems: Part II - Hilbert space construction for model identification and order reduction},
    url = {https://dx.doi.org/10.1109/ACC.2011.5990620},
    year = {2011}
   }

   Close
   ×

   Abstract

   This paper, which is the second of two parts, is built upon the
   vector space of symbolic systems represented by probabilistic finite
   State automata (PFSA) reported in the first part. This second part
   addresses the Hilbert space construction for model identification,
   where order reduction is achieved via orthogonal projection. To this
   end, a family of inner products is constructed and the norm induced
   by an inner product is interpreted as a measure of information
   contained in the PFSA, which also quantifies the error due to model
   order reduction. A numerical example elucidates the process of model
   order reduction by orthogonal projection from the space of PFSA onto
   a subspace that belongs to the class of shifts of finite type.

   Close
9. Yicheng Wen, Asok Ray, Ishanu Chattopadhyay, Shashi Phoha, "Modeling of symbolic systems: Part I - Vector space representation of probabilistic finite state automata", Proceedings of the 2011 American Control Conference, pp. 5133–5138, June, 2011.   [bibtex]  [abstract] 
   ×

   Bibtex

   @inproceedings{5990763,
    author = {Yicheng Wen and Asok Ray and Ishanu Chattopadhyay and Shashi Phoha},
    booktitle = {Proceedings of the 2011 American Control Conference},
    doi = {10.1109/ACC.2011.5990763},
    month = {June},
    pages = {5133–5138},
    title = {Modeling of symbolic systems: Part I - Vector space representation of probabilistic finite state automata},
    url = {https://dx.doi.org/10.1109/ACC.2011.5990763},
    year = {2011}
   }

   Close
   ×

   Abstract

   This paper, which is the first of two parts, brings in the notions
   of vector addition and the associated scalar multiplication
   operations on probabilistic finite state automata (PFSA). A class of
   PFSA is shown to constitute a vector space over the real field R,
   where the zero element is semantically equivalent to a subclass of
   PFSA, referred to as symbolic white noise. A norm is introduced on
   the vector space of PFSA and it quantifies the non-probabilistic
   behavior of a PFSA. The second part constructs a family of inner
   products on this vector space and presents numerical examples and
   applications.

   Close
10. Abhishek Srivastav, Yicheng Wen, Evan Hendrick, Ishanu Chattopadhyay, Asok Ray, Shashi Phoha, "Information fusion for object & situation assessment in sensor networks", Proceedings of the 2011 American Control Conference, pp. 1274–1279, June, 2011.   [bibtex]  [abstract]  [citations: 18] 
    ×

    Bibtex

    @inproceedings{5991171,
     author = {Abhishek Srivastav and Yicheng Wen and Evan Hendrick and Ishanu Chattopadhyay and Asok Ray and Shashi Phoha},
     booktitle = {Proceedings of the 2011 American Control Conference},
     doi = {10.1109/ACC.2011.5991171},
     month = {June},
     pages = {1274–1279},
     title = {Information fusion for object & situation assessment in sensor networks},
     url = {https://dx.doi.org/10.1109/ACC.2011.5991171},
     year = {2011}
    }

    Close
    ×

    Abstract

    A semantic framework for information fusion in sensor networks for
    object and situation assessment is proposed. The overall vision is
    to construct machine representations that would enable human-like
    perceptual understanding of observed scenes via fusion of
    heterogeneous sensor data. In this regard, a hierarchical framework
    is proposed that is based on the Data Fusion Information Group
    (DFIG) model. Unlike a simple set-theoretic information fusion
    methodology that leads to loss of information, relational
    dependencies are modeled as cross-machines called relational
    Probabilistic Finite State Automata using the xD-Markov machine
    construction. This leads to a tractable approach for modeling
    composite patterns as structured sets for both object and scene
    representation. An illustrative example demonstrates the superior
    capability of the proposed methodology for pattern classification in
    urban scenarios.

    Close
11. Ishanu Chattopadhyay, Sonia Mohinta, Asok Ray, "A decision-theoretic model of selection modulated intra-host antigenic variation for multi-strain pathogens", Proceedings of the 2010 American Control Conference, pp. 454–459, June, 2010.   [bibtex]  [abstract]  [citations: 0] 
    ×

    Bibtex

    @inproceedings{5530990,
     author = {Ishanu Chattopadhyay and Sonia Mohinta and Asok Ray},
     booktitle = {Proceedings of the 2010 American Control Conference},
     doi = {10.1109/ACC.2010.5530990},
     month = {June},
     pages = {454–459},
     title = {A decision-theoretic model of selection modulated intra-host antigenic variation for multi-strain pathogens},
     url = {https://dx.doi.org/10.1109/ACC.2010.5530990},
     year = {2010}
    }

    Close
    ×

    Abstract

    A decision-theoretic approach to modeling the response of multi-
    strain pathogens to humoral defense is investigated in this paper.
    The reported theory of language-measure theoretic optimal control is
    used to compute the optimal response of the invading pathogen. The
    model is validated by comparison of simulation results with in-vivo
    observations and contrasted with prominent existing modeling
    approaches.

    Close
12. Ishanu Chattopadhyay, Yicheng Wen, Shashi Phoha, Asok Ray, "Mathematical Foundations of Sensor Network Design Based On Linguistic Informatics", Proceedings of the 2010 American Control Conference, pp. 1073–1078, June, 2010.   [bibtex]  [abstract]  [citations: 2] 
    ×

    Bibtex

    @inproceedings{5530774,
     author = {Ishanu Chattopadhyay and Yicheng Wen and Shashi Phoha and Asok Ray},
     booktitle = {Proceedings of the 2010 American Control Conference},
     doi = {10.1109/ACC.2010.5530774},
     month = {June},
     pages = {1073–1078},
     title = {Mathematical Foundations of Sensor Network Design Based On Linguistic Informatics},
     url = {https://dx.doi.org/10.1109/ACC.2010.5530774},
     year = {2010}
    }

    Close
    ×

    Abstract

    We propose a design approach for sensor networks based on formal
    linguistic representations of information. The approach exploits the
    concepts of space-time neighborhoods for dynamic sensor grid
    formation in the vicinity of an event, and symbolization and
    nonlinear filtering to formulate rigorous mathematical methods that
    capture the causal dynamics of distributed fusion processes. We
    formulate the Fundamental Equation of Linguistic Sensing relating
    physical design parameters to those in the Information Space, and
    lays the framework for design and operation of sensor networks that
    dynamically cluster sensing, processing and communications resources
    in space-time neighborhoods of emergent hotspots for efficient event
    tracking.

    Close
13. Ishanu Chattopadhyay, Yicheng Wen, Asok Ray, "Pattern classification in symbolic streams via semantic annihilation of information", Proceedings of the 2010 American Control Conference, pp. 492–497, June, 2010.   [bibtex]  [abstract]  [citations: 2] 
    ×

    Bibtex

    @inproceedings{5530857,
     author = {Ishanu Chattopadhyay and Yicheng Wen and Asok Ray},
     booktitle = {Proceedings of the 2010 American Control Conference},
     doi = {10.1109/ACC.2010.5530857},
     month = {June},
     pages = {492–497},
     title = {Pattern classification in symbolic streams via semantic annihilation of information},
     url = {https://dx.doi.org/10.1109/ACC.2010.5530857},
     year = {2010}
    }

    Close
    ×

    Abstract

    We propose a technique for pattern identification in symbolic
    streams via selective erasure of observed symbols, in cases where
    the patterns of interest are represented as Probabilistic Finite
    State Automata (PFSA). We define an additive abelian group for a
    slightly restricted set of probabilistic machines, and the group sum
    is used to formulate pattern-specific semantic annihilators. The
    annihilators attempt to identify pre-specified patterns via removal
    of inter-symbol correlations from observed sequences, thereby
    turning them into symbolic white noise. Thus a perfect annihilation
    corresponds to a pattern match. This approach of classification via
    information annihilation is shown to be strictly advantageous, with
    theoretical guarantees, for a large class of PFSA models. The
    results are supported by simulation experiments.

    Close
14. Ishanu Chattopadhyay, Asok Ray, "Supervised self-organization of large homogeneous Swarms using Ergodic Projections of Markov Chains", 2009 American Control Conference, pp. 2922–2927, June, 2009.   [bibtex]  [abstract]  [citations: 4] 
    ×

    Bibtex

    @inproceedings{5160372,
     author = {Ishanu Chattopadhyay and Asok Ray},
     booktitle = {2009 American Control Conference},
     doi = {10.1109/ACC.2009.5160372},
     month = {June},
     pages = {2922–2927},
     title = {Supervised self-organization of large homogeneous Swarms using Ergodic Projections of Markov Chains},
     url = {https://dx.doi.org/10.1109/ACC.2009.5160372},
     year = {2009}
    }

    Close
    ×

    Abstract

    This paper formulates a self-organization algorithm to addresses the
    problem of emergent behavior supervision in engineered swarms of
    arbitrary population size. Based on collections of independent
    identical finite-state agents, the algorithm is derived to compute
    necessary perturbations in the local agents' behavior, which
    guarantees convergence to the desired observed state of the swarm. A
    simulation example illustrates the underlying concept.

    Close
15. Ishanu Chattopadhyay, Asok Ray, "Optimal path-planning under finite memory obstacle dynamics based on probabilistic finite state automata models", 2009 American Control Conference, pp. 2403–2408, June, 2009.   [bibtex]  [abstract]  [citations: 3] 
    ×

    Bibtex

    @inproceedings{5160369,
     author = {Ishanu Chattopadhyay and Asok Ray},
     booktitle = {2009 American Control Conference},
     doi = {10.1109/ACC.2009.5160369},
     month = {June},
     pages = {2403–2408},
     title = {Optimal path-planning under finite memory obstacle dynamics based on probabilistic finite state automata models},
     url = {https://dx.doi.org/10.1109/ACC.2009.5160369},
     year = {2009}
    }

    Close
    ×

    Abstract

    The v*-planning algorithm is generalized to handle finite memory
    obstacle dynamics. A sufficiently long observation sequence of
    obstacle dynamics is algorithmically compressed via symbolic dynamic
    filtering to obtain a probabilistic finite state model which is
    subsequently integrated with the navigation automaton to generate an
    overall model reflecting both navigation constraints and obstacle
    dynamics. A v*-based solution then yields a deterministic plan that
    maximizes the difference of the probabilities of reaching the goal
    and of hitting an obstacle. The approach is validated by simulated
    solution of dynamic mazes.

    Close
16. Ishanu Chattopadhyay, Asok Ray, "Generalization of v⋆ path planning for accommodation of amortized dynamic uncertainties in plan execution", 2009 American Control Conference, pp. 2415–2420, June, 2009.   [bibtex]  [abstract] 
    ×

    Bibtex

    @inproceedings{5160367,
     author = {Ishanu Chattopadhyay and Asok Ray},
     booktitle = {2009 American Control Conference},
     doi = {10.1109/ACC.2009.5160367},
     month = {June},
     pages = {2415–2420},
     title = {Generalization of v⋆ path planning for accommodation of amortized dynamic uncertainties in plan execution},
     url = {https://dx.doi.org/10.1109/ACC.2009.5160367},
     year = {2009}
    }

    Close
    ×

    Abstract

    A significant generalization to the language-measure-theoretic path
    planning algorithm v* is presented that accounts for average dynamic
    uncertainties in plan execution. The planning problem thus can be
    solved with parametric input from the dynamics of the robotic
    platform under consideration. Applicability of the algorithm is
    demonstrated in a simulated maze solution and by experimental
    validation on a mobile robotic platform in the laboratory
    environment.

    Close
17. Kushal Mukherjee, Asok Ray, Thomas Wettergren, Ishanu Chattopadhyay, Shashi Phoha, "Signal threshold estimation in a sensor field for undersea target tracking", 2009 American Control Conference, pp. 3591–3596, June, 2009.   [bibtex]  [abstract]  [citations: 1] 
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    Bibtex

    @inproceedings{5159844,
     author = {Kushal Mukherjee and Asok Ray and Thomas Wettergren and Ishanu Chattopadhyay and Shashi Phoha},
     booktitle = {2009 American Control Conference},
     doi = {10.1109/ACC.2009.5159844},
     month = {June},
     pages = {3591–3596},
     title = {Signal threshold estimation in a sensor field for undersea target tracking},
     url = {https://dx.doi.org/10.1109/ACC.2009.5159844},
     year = {2009}
    }

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    ×

    Abstract

    This paper addresses the problem of online surveillance of undersea
    targets moving over a deployed sensor field. A real-time algorithm
    has been formulated to estimate the detection threshold based on the
    ensemble of sensor time series data collected from the track of a
    moving target. The probabilistic-state-machine-based algorithm is
    optimal in the sense of weighted linear least squares. The algorithm
    has been tested with sensor data from several tracks on a simulation
    test bed.

    Close
18. Wei Lu, Ishanu Chattopadhyay, Goutham Mallapragada, Asok Ray, "A real time implementable All-Pair Dynamic Planning Algorithm for robot navigation based on the renormalized measure of probabilistic regular languages", 2009 American Control Conference, pp. 5174–5179, June, 2009.   [bibtex]  [abstract]  [citations: 0] 
    ×

    Bibtex

    @inproceedings{5160373,
     author = {Wei Lu and Ishanu Chattopadhyay and Goutham Mallapragada and Asok Ray},
     booktitle = {2009 American Control Conference},
     doi = {10.1109/ACC.2009.5160373},
     month = {June},
     pages = {5174–5179},
     title = {A real time implementable All-Pair Dynamic Planning Algorithm for robot navigation based on the renormalized measure of probabilistic regular languages},
     url = {https://dx.doi.org/10.1109/ACC.2009.5160373},
     year = {2009}
    }

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    ×

    Abstract

    The recently reported v planning algorithm is modified to handle on-
    the-fly dynamic updates to the obstacle map. The modified algorithm
    called All-Pair-Dynamic-Planning(APDP), models the problem of robot
    path planning in the framework of finite state probabilistic
    automata and solves the all-pair planning problem in one setting. We
    use the concept of renormalized measure of regular languages to plan
    paths with automated trade-off between path length and robustness
    under dynamic uncertainties, from any starting location to any goal
    in the given map. The dynamic updating feature of APDP efficiently
    updates path plans to incorporate newly learnt information about the
    working environment.

    Close
19. Ishanu Chattopadhyay, Goutham Mallapragada, Asok Ray, "L*: An intelligent path planning algorithm based on renormalized measure of probabilistic regular languages", 2008 American Control Conference, pp. 1249–1254, June, 2008.   [bibtex]  [abstract]  [citations: 2] 
    ×

    Bibtex

    @inproceedings{4586664,
     author = {Ishanu Chattopadhyay and Goutham Mallapragada and Asok Ray},
     booktitle = {2008 American Control Conference},
     doi = {10.1109/ACC.2008.4586664},
     month = {June},
     pages = {1249–1254},
     title = {L*: An intelligent path planning algorithm based on renormalized measure of probabilistic regular languages},
     url = {https://dx.doi.org/10.1109/ACC.2008.4586664},
     year = {2008}
    }

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    ×

    Abstract

    A novel path planning algorithm L* is introduced that reduces the
    problem to optimization of a probabilistic finite state machine and
    applies the rigorous theory of language-measure-theoretic optimal
    control to compute v-optimal paths to the specified goal. It is
    shown that although the underlying navigation model is
    probabilistic, the proposed algorithm computes plans that can be
    executed in a deterministic sense with automated optimal trade-off
    between path length and robustness under dynamic uncertainty. The
    algorithm has been validated on mobile robotic platforms in a
    laboratory environment.

    Close
20. Ishanu Chattopadhyay, Asok Ray, "Language-measure-theoretic optimal control of probabilistic finite-state systems", 2007 46th IEEE Conference on Decision and Control, pp. 5930–5935, Dec, 2007.   [bibtex]  [abstract]  [citations: 47] 
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    Bibtex

    @inproceedings{4434495,
     author = {Ishanu Chattopadhyay and Asok Ray},
     booktitle = {2007 46th IEEE Conference on Decision and Control},
     doi = {10.1109/CDC.2007.4434495},
     month = {Dec},
     pages = {5930–5935},
     title = {Language-measure-theoretic optimal control of probabilistic finite-state systems},
     url = {https://dx.doi.org/10.1109/CDC.2007.4434495},
     year = {2007}
    }

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    ×

    Abstract

    Supervisory control theory for discrete event systems, introduced by
    Ramadge and Wonham, is based on a non-probabilistic formal language
    framework. Building on the concept of signed real measure of regular
    languages, this paper formulates a comprehensive theory for optimal
    control of finite-state probabilistic processes. It is shown that
    the resulting discrete-event supervisor is optimal in the sense of
    elementwise maximizing the renormalized language measure vector for
    the controlled plant behavior and is efficiently computable.

    Close
21. Ishanu Chattopadhyay, Asok Ray, "Generalized Projections in Finite State Automata & Decidability of State Determinacy", 2007 American Control Conference, pp. 5664–5669, July, 2007.   [bibtex]  [abstract] 
    ×

    Bibtex

    @inproceedings{4282235,
     author = {Ishanu Chattopadhyay and Asok Ray},
     booktitle = {2007 American Control Conference},
     doi = {10.1109/ACC.2007.4282235},
     month = {July},
     pages = {5664–5669},
     title = {Generalized Projections in Finite State Automata & Decidability of State Determinacy},
     url = {https://dx.doi.org/10.1109/ACC.2007.4282235},
     year = {2007}
    }

    Close
    ×

    Abstract

    Under loss of sensors and communication links in discrete event
    systems (DES), an event may conceivably be observable at one state
    and unobservable at another state and the observability may become
    dependent on the history of event occurrences. This paper presents a
    framework for analysis of generalized projection maps in DES,
    including the maps that introduce possibly unbounded memory.

    Close
22. Ishanu Chattopadhyay, Asok Ray, "Generalized Language Measure of Finite State Logical Systems", 2006 8th International Workshop on Discrete Event Systems, pp. 366–371, July, 2006.   [bibtex]  [abstract]  [citations: 0] 
    ×

    Bibtex

    @inproceedings{4267646,
     author = {Ishanu Chattopadhyay and Asok Ray},
     booktitle = {2006 8th International Workshop on Discrete Event Systems},
     doi = {10.1109/WODES.2006.382395},
     month = {July},
     pages = {366–371},
     title = {Generalized Language Measure of Finite State Logical Systems},
     url = {https://dx.doi.org/10.1109/WODES.2006.382395},
     year = {2006}
    }

    Close
    ×

    Abstract

    The signed real measure of regular languages has been introduced and
    validated in recent literature for quantitative analysis and
    synthesis of discrete-event supervisory (DES) control systems. This
    paper reports several generalizations of the language measure, which
    are applicable to synthesis of optimal discrete event supervisory
    control. These generalizations eliminate a user-selectable parameter
    in the original concept of language measure. The concepts are
    illustrated with simple examples

    Close
23. I. Chattopadhyay, A. Ray, "Renormalized measure of regular languages", 2006 8th International Workshop on Discrete Event Systems, pp. 45–50, 2006.   [bibtex]  [citations: 37] 
    ×

    Bibtex

    @inproceedings{1678406,
     author = {I. Chattopadhyay and A. Ray},
     booktitle = {2006 8th International Workshop on Discrete Event Systems},
     doi = {10.1109/WODES.2006.1678406},
     pages = {45–50},
     title = {Renormalized measure of regular languages},
     url = {https://dx.doi.org/10.1109/WODES.2006.1678406},
     year = {2006}
    }

    Close
24. Goutham Mallapragada, Ishanu Chattopadhyay, Asok Ray, "Autonomous Navigation of Mobile Robots Using Optimal Control of Finite State Automata", Proceedings of the 45th IEEE Conference on Decision and Control, pp. 2400–2405, Dec, 2006.   [bibtex]  [abstract]  [citations: 7] 
    ×

    Bibtex

    @inproceedings{4177434,
     author = {Goutham Mallapragada and Ishanu Chattopadhyay and Asok Ray},
     booktitle = {Proceedings of the 45th IEEE Conference on Decision and Control},
     doi = {10.1109/CDC.2006.377302},
     month = {Dec},
     pages = {2400–2405},
     title = {Autonomous Navigation of Mobile Robots Using Optimal Control of Finite State Automata},
     url = {https://dx.doi.org/10.1109/CDC.2006.377302},
     year = {2006}
    }

    Close
    ×

    Abstract

    This paper presents a novel approach to autonomous intelligent
    navigation of mobile robotic platforms, which is based on the
    concept of language-theoretic discrete-event supervisory control.
    The proposed algorithm combines real-time sensor data and model-
    based information on motion dynamics into a probabilistic finite
    state automaton model to dynamically compute a time-varying
    supervisory algorithm. The performance and robustness of the
    autonomous intelligent navigation algorithm have been experimentally
    validated on Segway RMP robotic platforms

    Close
25. Ishanu Chattopadhyay, Subhadeep Chakraborty, Asok Ray, "Intelligent Navigation in Space Under Supervisory Control", Infotech@Aerospace, 2005.   [bibtex]  [citations: 0] 
    ×

    Bibtex

    @inproceedings{intnav05,
     author = {Ishanu Chattopadhyay and Subhadeep Chakraborty and Asok Ray},
     booktitle = {Infotech@Aerospace},
     doi = {10.2514/6.2005-6943},
     title = {Intelligent Navigation in Space Under Supervisory Control},
     url = {https://dx.doi.org/10.2514/6.2005-6943},
     year = {2005}
    }

    Close
26. I. Chattopadhyay, Asok Ray, Xi Wang, "A complex measure of non-regular languages for discrete-event supervisory control", Proceedings of the 2004 American Control Conference, vol. 6, no. , pp. 5120–5125 vol.6, 2004.   [bibtex]  [citations: 6] 
    ×

    Bibtex

    @inproceedings{1384664,
     author = {I. Chattopadhyay and Asok Ray and Xi Wang},
     booktitle = {Proceedings of the 2004 American Control Conference},
     doi = {10.23919/ACC.2004.1384664},
     number = {},
     pages = {5120–5125 vol.6},
     title = {A complex measure of non-regular languages for discrete-event supervisory control},
     url = {https://dx.doi.org/10.23919/ACC.2004.1384664},
     volume = {6},
     year = {2004}
    }

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27. I. Chattopadhyay, A. Ray, "A language measure for partially observed discrete event supervisory control systems", 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601), vol. 1, pp. 45–50 Vol.1, 2004.   [bibtex]  [citations: 2] 
    ×

    Bibtex

    @inproceedings{1428604,
     author = {I. Chattopadhyay and A. Ray},
     booktitle = {2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601)},
     doi = {10.1109/CDC.2004.1428604},
     pages = {45–50 Vol.1},
     title = {A language measure for partially observed discrete event supervisory control systems},
     url = {https://dx.doi.org/10.1109/CDC.2004.1428604},
     volume = {1},
     year = {2004}
    }

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28. Xi Wang, I. Chattopadhyay, A. Ray, "Probabilistic fault diagnosis in discrete event systems", 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601), vol. 5, no. , pp. 4794–4799 Vol.5, 2004.   [bibtex]  [citations: 11] 
    ×

    Bibtex

    @inproceedings{1429548,
     author = {Xi Wang and I. Chattopadhyay and A. Ray},
     booktitle = {2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601)},
     doi = {10.1109/CDC.2004.1429548},
     number = {},
     pages = {4794–4799 Vol.5},
     title = {Probabilistic fault diagnosis in discrete event systems},
     url = {https://dx.doi.org/10.1109/CDC.2004.1429548},
     volume = {5},
     year = {2004}
    }

    Close
29. D. Friedlander, I. Chattopadhyay, A. Ray, S. Phoha, N. Jacobson, "Anomaly prediction in mechanical systems using symbolic dynamics", Proceedings of the 2003 American Control Conference, 2003., vol. 5, no. , pp. 4275–4280 vol.5, 2003.   [bibtex]  [citations: 11] 
    ×

    Bibtex

    @inproceedings{1240508,
     author = {D. Friedlander and I. Chattopadhyay and A. Ray and S. Phoha and N. Jacobson},
     booktitle = {Proceedings of the 2003 American Control Conference, 2003.},
     doi = {10.1109/ACC.2003.1240508},
     number = {},
     pages = {4275–4280 vol.5},
     title = {Anomaly prediction in mechanical systems using symbolic dynamics},
     url = {https://dx.doi.org/10.1109/ACC.2003.1240508},
     volume = {5},
     year = {2003}
    }

    Close

Miscellaneous

1. Carolyn Begeman, Marian Anghel, Ishanu Chattopadhyay, "Interpretable Deep Learning for the Earth System with Fractal Nets", , 2021.   [bibtex]  [citations: 1] 
   ×

   Bibtex

   @misc{begeman2021interpretable,
    author = {Carolyn Begeman and Marian Anghel and Ishanu Chattopadhyay},
    institution = {Artificial Intelligence for Earth System Predictability},
    title = {Interpretable Deep Learning for the Earth System with Fractal Nets},
    url = {https://www.osti.gov/biblio/1769730},
    year = {2021}
   }

   Close