Positions

Research Areas research areas

Overview

  • Jeremiah Johnson is an Assistant Professor of Data Science in the Department of Applied Engineering & Sciences.

    Dr. Johnson is a mathematician and machine learning researcher specializing in neural networks and artificial intelligence. Dr. Johnson’s recent research spans a variety of application areas, including Bayesian modeling of water contamination, algorithmic style classification of fine art, automatic nucleus segmentation in microscopy images, and generative modeling techniques for structured prediction in computer vision. Dr. Johnson developed and now co-directs the Bachelor of Science in Analytics & Data Science, an innovative new program that offered on two campuses of the University of New Hampshire.

    Dr. Johnson is an alumnus of the University of New Hampshire, earning his Ph.D in mathematics in 2010.

    • Selected Publications

    Academic Article

    Year Title
    2023 Probabilistic Forecasting of Ground Magnetic Perturbation Spikes at Mid‐Latitude Stations.  Space Weather.  21. 2023
    2022 A Diophantine Equation with an Elementary Solution.  College Mathematics Journal.  53:361-363. 2022
    2021 A Contrastive Learning Approach to Auroral Identification and Classification.  Proceedings of the 20th IEEE International Conference on Machine Learning and Applications, Dec. 2021.  772-777. 2021
    2021 Proceedings of the Future Technologies Conference (FTC) 2020, Volume 3.  Advances in Soft Computing.  3:3-120. 2021
    2021 Students from a large Australian university use Twitter to identify difficult course concepts to review during face-to-face lectorial sessions..  Advances in Physiology Education.  45:10-17. 2021
    2019 On the Diophantine Equation 1/a + 1/b = (q+1) / pq 2019
    2019 Towards the Algorithmic Detection of Artistic Style.  International Journal of Advanced Computer Science and Applications.  10:76-81. 2019
    2018 Scaling Up: Introducing Undergraduates to Data Science Early in Their College Careers.  The Journal of Computing Sciences in Colleges.  33:76-85. 2018
    2017 Data Science & Computing Across the Curriculum.  Journal of Computing Sciences in Colleges.  32:187-188. 2017
    2015 Weight ideals associated to regular and log-linear arrays.  Journal of Symbolic Computation.  67:1-15. 2015
    2013 The Number of Group Homomorphisms from Dm into Dn.  College Mathematics Journal.  44:190-192. 2013
    2012 The Number of Group Homomorphisms from $D_m$ into $D_n$.  College Mathematics Journal.  44:3. 2012
    Improved Diagnosis Of Invasive Ductal Carcinoma With Semi-Supervised Conditional GANs
    Revisiting the Ground Magnetic Field Perturbations Challenge: A Machine Learning Perspective.  FRONTIERS IN ASTRONOMY AND SPACE SCIENCES.  9.
    Teaching Neural Networks in the Deep Learning Era
    Teaching Neural Networks in the Deep Learning Era.  Journal of Computing Sciences in Colleges. 

    Chapter

    Year Title
    2021 Collaboration with College and University Undergraduate Programs 2021
    2021 Detecting Invasive Ductal Carcinoma with Semi-supervised Conditional GANs.  Advances in Soft Computing. 113-120. 2021
    2021 Generative adversarial networks in medical imaging.  271-278. 2021

    Conference Paper

    Year Title
    2021 Student Emotional Response to Oral Assessments in Computing and Mathematics.  2021 IEEE Frontiers in Engineering Education (FIE).. 1-5. 2021
    2020 Benefits and Pitfalls of Jupyter Notebooks in the Classroom.  Proceedings of the 21st Annual Conference on Information Technology Education. 32-37. 2020
    2020 Jupyter Notebooks in Education.  The Journal of Computing Sciences in Colleges. 2020
    2020 Advances in Computer Vision.  Advances in Soft Computing. 2020
    2019 Structured Prediction using cGANs with Fusion Discriminator.  Workshop on Deep Generative Models for Structured Prediction at ICLR 2019. 2019
    2019 Fusing attributes predicted via conditional GANs for improved skin lesion classification (Conference Presentation).  Medical Imaging 2019: Computer-Aided Diagnosis. 65-65. 2019
    2016 Neural Style Representations and the Large-Scale Classification of Artistic Style.  Proceedings of the Future Technologies Conference, 2017. 2016
    A Deep Learning Approach to the Forecasting of Ground Magnetic Field Perturbations at High and Mid Latitudes
    Engaging a Large Nursing Class in Small Group Twitter Discussions to Identify Difficult Course Concepts in Australia
    Predicting Ground Magnetic Field Fluctuations from Geomagnetic Storm Data Using a Novel Transformer-Based Model
    Training a Neural Network Using Geomagnetic Storm Data to Predict Ground Magnetic Field Fluctuations
    Using Machine Learning and Geomagnetic Storm Data to Determine the Risk of GIC Occurrence
    Using an LSTM and Classification Methods to Determine Risk of dB/dt Threshold Crossings as Proxy for Geomagnetically Induced Currents

    Teaching Activities

  • Calculus II Taught course
  • Calculus II Taught course
  • Calculus II Taught course
  • Math for Business Applications Taught course
  • Calculus II Taught course 2024
  • Introduction to Analytics Taught course 2024
  • Linear Algebra for Application Taught course 2024
  • Introduction to Analytics Taught course 2023
  • Introduction to Analytics Taught course 2023
  • Introduction to Analytics Taught course 2022
  • Linear Algebra for Application Taught course 2022
  • Introduction to Analytics Taught course 2022
  • Predictive Analytics II Taught course 2022
  • Introduction to Analytics Taught course 2021
  • Linear Algebra for Application Taught course 2021
  • Master's Continuing Research Taught course 2021
  • Predictive Analytics I Taught course 2021
  • Calculus II Taught course 2021
  • IndStdy/Capstone Project Taught course 2021
  • Introduction to Analytics Taught course 2021
  • Master's Thesis Taught course 2021
  • Neural Networks Taught course 2021
  • Calculus II Taught course 2020
  • Introduction to Analytics Taught course 2020
  • Linear Algebra for Application Taught course 2020
  • Master's Continuing Research Taught course 2020
  • Calculus I Taught course 2020
  • Introduction to Analytics Taught course 2020
  • Master's Continuing Research Taught course 2020
  • Neural Networks Taught course 2020
  • Finite Mathematics Taught course 2019
  • IS/Applied Linear Algebra Taught course 2019
  • IS/Neural Ntwrks Med Img Anlys Taught course 2019
  • Introduction to Analytics Taught course 2019
  • Master's Continuing Research Taught course 2019
  • Predictive Analytics II Taught course 2019
  • Calculus II Taught course 2019
  • Capstone Project Taught course 2019
  • Introduction to Analytics Taught course 2019
  • Master's Continuing Research Taught course 2019
  • Predictive Analytics I Taught course 2019
  • IS/Predictive Analytics Taught course 2018
  • Introduction to Analytics Taught course 2018
  • Linear Algebra for Application Taught course 2018
  • Calculus I Taught course 2018
  • IS/Linear Alg for Applications Taught course 2018
  • Introduction to Analytics Taught course 2018
  • Neural Networks Taught course 2018
  • Neural Networks Taught course 2018
  • Introduction to Analytics Taught course 2017
  • Linear Algebra for Application Taught course 2017
  • Introduction to Analytics Taught course 2017
  • Intro Analytics Applications Taught course 2017
  • Diff Equation w/Linear Algebra Taught course 2017
  • Foundations of Data Analytics Taught course 2017
  • Diff Equation w/Linear Algebra Taught course 2017
  • Introduction to Analytics Taught course 2017
  • Predictive Analytics I Taught course 2016
  • Calculus II Taught course 2016
  • Intro Analytics Applications Taught course 2016
  • Tools and Foundations Taught course 2016
  • Calculus II Taught course 2016
  • Diff Equation w/Linear Algebra Taught course 2016
  • Foundations of Data Analytics Taught course 2016
  • Calculus I Taught course 2016
  • Introduction to Analytics Taught course 2016
  • Statistics in Comp&Engineering Taught course 2016
  • Calculus II Taught course 2015
  • Introduction to Analytics Taught course 2015
  • Statistics in Comp&Engineering Taught course 2015
  • Foundations of Data Analytics Taught course 2015
  • Intro Analytics Applications Taught course 2015
  • Tools and Foundations Taught course 2015
  • Calculus I Taught course 2015
  • Diff Equation w/Linear Algebra Taught course 2015
  • Calculus I Taught course 2015
  • Elementary Math II Taught course 2015
  • Finite Mathematics Taught course 2015
  • Analytical Methods in Eng Tech Taught course 2014
  • Calculus I Taught course 2014
  • Elementary Math II Taught course 2014
  • AdvTop/Big Data and Beyond Taught course 2014
  • Calculus II Taught course 2014
  • Diff Equation w/Linear Algebra Taught course 2014
  • Analysis & Appl of Functions Taught course 2014
  • Calculus II Taught course 2014
  • Elementary Math II Taught course 2014

    • Full Name

  • Jeremiah Johnson

    • Mailing Address

  • University of New Hampshire

    Applied Engineering & Sciences

    88 Commercial Street #105

    Manchester, NH  03101

    United States