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.
Ph.D., University of New Hampshire
M.S., University of New Hampshire
B.S., University of New Hampshire
Electronic Neural Networks
557: Introduction to Analytics
645: Linear Algebra for Application
674: Predictive Analytics I
675: Predictive Analytics II
900: Master's Continuing Research
COMP 750/850: Neural Networks
COMP/DATA 750/850/750: Neural Networks
COMP/GRAD 899/900: Master's Thesis
DATA 557: Introduction to Analytics
DATA 674: Predictive Analytics I
DATA 675: Predictive Analytics II
DATA 750: Neural Networks
DATA 790: Capstone Project
GRAD 900: Master's Continuing Research
MATH 420: Finite Mathematics
MATH 425: Calculus I
MATH 426: Calculus II
MATH 645: Linear Algebra for Application
MATH 696: IS/Linear Alg for Applications
UMIS 599: IndStdy/Capstone Project
Johnson, J. W. (n.d.). A Diophantine Equation with an Elementary Solution. The College Mathematics Journal, 1-3. doi:10.1080/07468342.2022.2118993
Johnson, J. W., Hari, S., Hampton, D., Connor, H. K., & Keesee, A. (2021). A Contrastive Learning Approach to Auroral Identification and Classification. 20TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2021), 772-777. doi:10.1109/ICMLA52953.2021.00128
Halpin, P. A., Johnson, J., & Badoer, E. (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(1), 10-17. doi:10.1152/advan.00147.2020
Johnson, J. W. (2020). Benefits and Pitfalls of Jupyter Notebooks in the Classroom. In Proceedings of the 21st Annual Conference on Information Technology Education. ACM. doi:10.1145/3368308.3415397
Johnson, J., & Jin, K. (2020). Jupyter Notebooks in Education. In B. Lu (Ed.), The Journal of computing Sciences in Colleges Vol. 35. virtual: Association for Computing Machinery.
Mahmood, F., Johnson, J., Yang, Z., & Durr, N. J. (2019). Fusing attributes predicted via conditional GANs for improved skin lesion classification (Conference Presentation). In Unknown Conference (pp. 65). doi:10.1117/12.2513139
Johnson, J., Coughlan, M., Keesee, A., Pinto, V., & Connor, H. (2020, July 20). Training a Neural Network Using Geomagnetic Storm Data to Predict Ground Magnetic Field Fluctuations. In Geospace Environment Modeling Workshop. Virtual.
Mahmood, F., Xu, W., Durr, N. J., Johnson, J. W., & Yuille, A. (n.d.). Structured Prediction using cGANs with Fusion Discriminator. In Workshop on Deep Generative Models for Structured Prediction at ICLR 2019. Retrieved from http://arxiv.org/abs/1904.13358v1
Johnson, J. (n.d.). Teaching Neural Networks in the Deep Learning Era.
Johnson, J., Coughlan, M., Keesee, A., Pinto, V., & Connor, H. (2020, December 1). Using Machine Learning and Geomagnetic Storm Data to Determine the Risk of GIC Occurrence. In American Geophysical Union Fall Meeting 2020.
Johnson, J. W. (n.d.). Adapting Mask-RCNN for Automatic Nucleus Segmentation. In Proceedings of the 2019 Computer Vision Conference, Vol. 2. doi:10.1007/978-3-030-17798-0
Johnson, J., Pinto, V., Keesee, A., Coughlan, M., Gadbois, M., & Connor, H. (2020, December 1). A Deep Learning Approach to the Forecasting of Ground Magnetic Field Perturbations at High and Mid Latitudes. In American Geophysical Union Fall Meeting 2020. Virtual.
Johnson, J., Hari, S., Pinto, V., Coughlan, M., Keesee, A., & Connor, H. (2020, December 1). Predicting Ground Magnetic Field Fluctuations from Geomagnetic Storm Data Using a Novel Transformer-Based Model. In American Geophysical Union Fall Meeting. Virtual.