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Peter Bajcsy (Fed)

Research Interests:  Advanced sensors and imaging, image processing, computer and machine vision, data mining and pattern recognition, explainable artificial intelligence.

Research Projects:  Imaging, Biosciences, Artificial Intelligence, and Big Data

Publications

Journal publications:

  • Mathias Hammer, Maximiliaan Huisman, Alessandro Rigano, Ulrike Boehm, James J. Chambers, Nathalie Gaudreault, Alison J. North, Jaime A. Pimentel, Damir Sudar, Peter Bajcsy, Claire M. Brown, Alexander D. Corbett, Orestis Faklaris, Judith Lacoste, Alex Laude, Glyn Nelson, Roland Nitschke, Farzin Farzam, Carlas Smith, David Grunwald, Caterina Strambio-De-Castillia, “Towards community-driven metadata standards for light microscopy: tiered specifications extending the OME model,” Nature Methods, December (2021); https://www.nature.com/articles/s41592-021-01327-9
  • Ulrike Boehm, Glyn Nelson, Claire M. Brown, Steve Bagley, Peter Bajcsy, Johanna Bischof, Aurelien Dauphin, Ian M. Dobbie, John E. Eriksson, Orestis Faklaris, Julia Fernandez-Rodriguez, Alexia Ferrand, Laurent Gelman, Ali Gheisari, Hella Hartmann, Christian Kukat, Alex Laude, Miso Mitkovski, Sebastian Munck, Alison J. North, Tobias M. Rasse, Ute Resch-Genger, Lucas C. Schuetz, Arne Seitz, Caterina Strambio-De-Castillia, Jason R. Swedlow & Roland Nitschke, “QUAREP-LiMi: A community-driven initiative to establish guidelines for quality assessment and reproducibility for instruments and images in light microscopy,” Nature Methods, May (2021). https://doi.org/10.1038/s41592-021-01162-y, DOI: https://doi.org/10.1038/s41592-021-01162-y
  • Peter Bajcsy, Nicholas J. Schaub, and Michael Majurski, “Designing Trojan Detectors in Neural Networks Using Interactive Simulations,” Special Issue on Machine Learning for Cybersecurity Threats, Challenges, and Opportunities, Computing and Artificial Intelligence Section, Appl. Sci. 2021, 11, 0. https://doi.org/10.3390/app11010000; URL: https://www.mdpi.com/journal/applsci/special_issues/cybersecurity_threa…
  • Peter Bajcsy, Steve Feldman, Michael Majurski, Kenneth Snyder, and Mary Brady, “Approaches to Training AI-based Multi-class Semantic Image Segmentation,” Journal of Microscopy, 2020 May 14. doi: http://dx.doi.org/10.1111/jmi.12906; URL: https://pubmed.ncbi.nlm.nih.gov/32406521/;
  • Sarala Padi, Petre Manescu, Nicholas Schaub, Nathan Hotaling, Carl Simon, Jr., Kapil Bharti, and Peter Bajcsy, “Comparison of Artificial Intelligence Based Approaches to Cell Function Prediction”, Elsevier Journal of Informatics in Medicine Unlocked, Vol. 18, Available online 23 November 2019, 100270; https://doi.org/10.1016/j.imu.2019.100270;  (Impact Factor: 2.11), https://www.sciencedirect.com/science/article/pii/S2352914819303235?via%3Dihub
  • Nicholas J. Schaub, Nathan A. Hotaling, Sarala Padi, Petre Manescu, Qin Wan, Ruchi Sharma, Joe Chalfoun, Mylene Simon, Mohamed Ouladi, Carl G. Simon, Jr., Peter Bajcsy, Kapil Bharti, “Deep learning predicts function of live retinal pigment epithelium from quantitative microscopy,” Journal of Clinical Investigation. In-press preview published online November 14, 2019. DOI: 10.1172/JCI131187 (Impact: 12.28)
  • Sowon Yoon, Peter Bajcsy, Maritoni Litorja, James Filliben, “Evaluation of Lateral Resolution of Light Field Cameras,” Journal of Optical Engineering 57(9), 093101 (8 September 2018). https://doi.org/10.1117/1.OE.57.9.093101
  • Beatriz A. Pazmino Betancourt, Stephen J. Florczyk, Mylene Simon, Derek Juba, Jack F. Douglas, Walid Keyrouz, Peter Bajcsy, Christopher Lee and Carl G. Simon, Jr., “Effect of the Scaffold Microenvironment on Cell Polarizability and Capacitance Determined by Probabilistic Computations,” Biomedical Materials, BMM-101918.R1, Vol. 13, No. 2, pages 025012, Jan. 2018; URL: http://iopscience.iop.org/article/10.1088/1748-605X/aa9650

More journal publications .... 

Books and Book Chapters:

More books and book chapters ....    

Conference Papers:

  • Victoria H. DiStefano, Jacob M. LaManna, David L. Jacobson, Paul A. Kienzle, Daniel S. Hussey, and Peter Bajcsy, “Assessment of Dose Reduction Strategies in Wavelength-Selective Neutron Tomography,” The 2nd International Conference on Image Processing and Vision Engineering (IMPROVE 2022), 22-24 April 2022; On-line streaming URL: https://improve.scitevents.org/ (selected for the best paper award)
  • Mylene Simon, Nicholas Schaub, Sunny Yu, Jayapriya Nagarajan, Mohamed Ouladi, Sudharsan Prativadi Bayankaram, Peter Bajcsy and Nathan Hotaling, “Quantifying Variability in Microscopy Image Analyses for COVID-19 Drug Discovery,” 6th IEEE Workshop on Computer Vision for Microscopy Image Analysis (CVMI) at CVPR 2021, June 25, 2021
  • Michael Majurski, Derek Juba, Tim Blattner, Peter Bajcsy, Alden Dima, Walid Keyrouz, “Trojan Detection Evaluation: Finding Hidden Behavior in AI Models,” NVIDIA GTC conference, Cybersecurity session, October 2020, URL: https://www.nvidia.com/en-us/on-demand/session/gtcfall20-a21714/
  • Peter Bajcsy and Michael Majurski, “Baseline Pruning-Based Approach to Trojan Detection in Neural Networks, “ Arxiv, eprint 2101.12016, January 2021; URL: https://arxiv.org/abs/2101.12016
  • Peter Bajcsy and Michael Majurski, “Baseline Pruning-Based Approach to Trojan Detection in Neural Networks, “ Security and Safety in Machine Learning Systems Workshop at ICLR 2021, URL: https://aisecure-workshop.github.io/aml-iclr2021/cfp (oral presentation), May 7th, 2021
  • Peter Bajcsy, Nicholas Schaub, and Michael Majurski, “Scientific Calculator for Designing Trojan Detectors in Neural Networks, “Association for the Advancement of Artificial Intelligence (AAAI), Fall Symposium Series (FSS), AI in Government and Public Sector Applications, Washington, DC, November 11–-14, 2020 (accepted oral) arXiv: arXiv:2006.03707v2 [cs.CR]; URL: https://arxiv.org/pdf/2006.03707.pdf
  • Peter Bajcsy and Nathan Hotaling, “Interoperability of Web Computational Plugins for Large Microscopy Image Analyses,” BioImage Informatics 2019 conference, Allen Institute, Seattle, WA, October 2-4, 2019 (oral)
  • Michael Majurski, Petru Manescu, Sarala Padi, Nicholas Schaub, Nathan Hotaling, Carl Simon Jr. and Peter Bajcsy, “Cell Image Segmentation using Generative Adversarial Networks, Transfer Learning, and Augmentations,” oral presentation, CVMI workshop at IEEE Computer Vision and Pattern Recognition conference, Long Beach, CA, June 17, 2019
  • Soweon Yoon, Peter Bajcsy, Litorja Maritoni, and James Filliben , “Evaluation of Lateral and Depth Resolutions of Light Field Camera,” M&M conference 2018, August 5-9, 2018, Baltimore, MD, (oral presentation)
  • E. Kwee, A. Peterson, J. Stinson, M. Halter, L. Yu, M. Majurski, J. Chalfoun, P. Bajcsy, and J. Elliott, “Large Field of View Quantitative Phase Imaging of Induced Pluripotent Stem Cells and Optical Pathlength Reference Materials,” Quantitative Phase Imaging IV, SPIE 2018, 28 - 30 January 2018.
  • N. Hotaling, M. Simon, M. Ouladi, N. Schaub, T. Uddin, P. Manickam, D. Ortolan, H. Zhao, J. Chalfoun, P. Bajcsy, and K. Bharti, “REShAPE – A Cloud-Computing Based Cell Morphometry Analyzer for Development of a Release Criteria of Clinical Grade Retinal Pigment Epithelium,” Invest. Ophthalmol. Vis. Sci. 2017;58(8):396; The 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.

  • M. Simon, J. Chalfoun, M. Brady, and P. Bajcsy, “Do We Trust Image Measurements? Variability, Accuracy and Traceability of Image Features,” IEEE International Conference on Big Data, Washington DC, December 5-8, 2016.
  • P. Bajcsy, A. Vandecreme, and M. Brady, “Interactive Web-based Spatio-Statistical Image Modeling from Gigapixel Images to Improve Discovery and Traceability of Published Statistical Models,” Microscopy and Microanalyses, Columbus, Ohio. July 24-28, 2016.
  • M. Majurski, J. Chalfoun, S. Lund, P. Bajcsy, and M. Brady, "Methodology for Increasing Image Feature Measurement Accuracy", CVPR Workshop entitled “Computer Vision for Microscopy Image Analysis”, July 1, 2016, Las Vegas, Nevada.

More conference papers ....  

 

Recent Presentations

2022

2021

2020

2010 - 2019 Presentations ....  


Quotes About My Work


Teaching Experience


Community Services

Awards

NIST Material Measurement Laboratory (MML) Accolade 2015, Cross-division/Cross-OU Teamwork; June 17, 2015.

Department of Commerce Bronze Medal Award, December 13, 2017.

Publications

Towards community-driven metadata standards for light microscopy: tiered guidelines extending the OME model

Author(s)
Peter Bajcsy, Mathias Hammer, Maximiliaan Huisman, Alex Rigano, Ulrike Boehm, James J. Chambers, Nathalie Gaudreault, Jaime A. Pimentel, Damir Sudar, Claire M. Brown, Alexander D. Corbett, Orestis Faklaris, Judith Lacoste, Alex Laude, Glyn Nelson, Roland Nitschke, Alison J. North, Renu Gopinathan, Farzin Farzam, Carlas Smith, David Grunwald, Caterina Strambio-De-Castillia
While the power of modern microscopy techniques is undeniable, rigorous record-keeping and quality control are required to ensure that imaging data may be
Created June 19, 2018, Updated September 7, 2022