Jeff Blackadar
MA History (2019), specialization in Data Science
| Degrees: | B.A. (Carleton) |
| Email: | jeffblackadar@cmail.carleton.ca |
| Website: | Browse |
Current Program (including year of entry): MA History (2019), specialization in Data Science
Supervisor:
Academic Interests:
I was the Fall 2017 George Garth Graham Undergraduate Digital History Research Fellow and I continue to study history using digital methods. I have used OCR, image processing, data analysis, GIS, natural language processing and machine learning as elements of history projects.
Select Publications and Current Projects:
• Blackadar, J., Carter, B., & Conner, W. Object Detection Model, Image Data and Results from the “When Computers Dream of Charcoal: Using Deep Learning, Open Tools and Open Data to Identify Relict Charcoal Hearths in and Around State Game Lands in Pennsylvania” Paper. Journal of Open Archaeology Data, 9, 12 (2021). http://doi.org/10.5334/joad.81
• Conner, W., Carter, B., & Blackadar, J. Geospatial and Image Data from the “When Computers Dream of Charcoal: Using Deep Learning, Open Tools and Open Data to Identify Relict Charcoal Hearths in and Around State Game Lands in Pennsylvania” Paper. Journal of Open Archaeology Data, 9, 7 (2021). http://doi.org/10.5334/joad.80
• Carter, Benjamin P., Jeff H. Blackadar, and Weston L. A. Conner. “When Computers Dream of Charcoal: Using Deep Learning, Open Tools, and Open Data to Identify Relict Charcoal Hearths in and around State Game Lands in Pennsylvania.” Advances in Archaeological Practice 9, no. 4 (2021): 257–71. https://doi.org/10.1017/aap.2021.17.
• Graham, Shawn, Damien Huffer, and Jeff Blackadar. “Towards a Digital Sensorial Archaeology as an Experiment in Distant Viewing of the Trade in Human Remains on Instagram” Heritage 3, no. 2 (2020): 208-227. https://doi.org/10.3390/heritage3020013.
• Blackadar, Jeff. “Introduction to MySQL with R.” Programming Historian 7 (2018). https://doi.org/10.46430/phen0076.
Select Conference Contributions:
• Computer as Microscope: A Digital Microhistorical Analysis of Capt. William White’s 1917 diary. Underhill Graduate Student Colloquium, March 2021.
• Teaching a Machine to See History. Underhill Graduate Student Colloquium, February 2020.
• Creating interactive maps of historical transit routes of Ottawa. GIS day, Carleton University, November 2019.
• A Finding Aid for the Equity. Inquiry@Queen’s Undergraduate Research Conference, March 2018.
Description of Research:
My research involves historical inquiry using of Deep Learning, a type of artificial Intelligence that recognizes patterns from data it is trained on. Deep Learning has potential to aid historians as an additional research tool and my research involves using it in two ways: to recognize visible and historically significant objects in LiDAR images and use of Deep Learning based optical character recognition to automatically read historical maps.
I will document the use of Deep Learning for object and text recognition in a manner where other historians can employ the techniques. This will fulfil my goals of producing work that is of use to other historians and including methods from Data Science.