Curriculum Vitae

Currently a grad student at McGill University and Mila working, working on applied machine learning for climate change, deep learning and computervision.

Even the best résumés leave a lot to be desired: they lack context and narrative. If you’d like a better idea of how I work, I think you’ll find the rest of this page a lot more helpful.

I’m not looking to change jobs at this point — but you’re still welcome to say hello!

About Me

I am a Software Engineer by trade, over the past few years, my focus has been on trying to understand how we can use computational methods from Machine Learning and Artificial Intelligence for Societal good.


Msc. Computer Science - McGill University, Mila - Quebec AI Inst. Advisor: David Rolnick. Interests; Climate Change, Computer Vision, Deep Learning.

I graduated with a **Bachelor of Science in Software Engineering from Makerere University in Uganda in January 2019. My final thesis  —  Using machine learning to improve in field diagnosis of Cassava Crop Diseases.

My Work

Current: McGill University | Mila |

I am currently a grad student at McGill University, I spend time between Mila and Sunbird AI working on AI for Good projects

Earlier Work

Sama (previously Samasource)

Worked with the R&D team on tools and techniques to improve data labelling with Machine Learning

Mila Quebec AI Institute

From January - December 2020 Research Intern under the Supervision of Prof. Yoshua Bengio, working on machine learning for climate change and healthcare; - Machine Learning for Glacier Monitoring in the Hindu Kush Himalayas

Makerere University AI Lab

Throughout my undergrad at Makerere University, I was involved with the Machine Learning group, we worked on Machine Learning for the developing world, AI for Good, Machine Learning for Healthcare.

Sunbird AI

Building Language models for Ugandan Languages based on BERT

My Projects

Papers, open source, and hobby project

Open Source Software

Machine Learning

  • Glacier Mapping Pipeline:
  • Machine Learning for Crop disease surveillance


  • DSA 2020: Intro to Deep Learning and computer vision
  • Deep learning indaba: Nairobi, Kenya: Tiny Machine Learning for Agriculture Pest surveillance
  • Data Science Africa: Addis Ababa: Intro to Data Science, Python, Pandas, Numpy and Matplotlib
  • GDG: Machine Learning in the cloud
  • DSA 2018: Nyeri, Kenya: Intro to Deep Learning



  1. Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya  —  S. Baraka, B. Akera, B. Aryal, T. Sherpa, F. Shresta , A. Ortiz, K. Sankaran, J. Lavista, M. Matin, Y. Bengio (2020) Spotlight paper, Tackling Climate Change with Machine Learning, NeurIPS 2020 pdf

  2. A dataset of necrotized cassava root cross-section images  —  J. Nabende, B. Akera, J. Tusubira, S . Nsumba, E .Mwebaze (2020) Published in Data in Brief Journal . pdf

  3. Scoring Root Necrosis in Cassava Using Semantic Segmentation  —  Tusubira, J. F., Akera, B., Nsumba, S., Nakatumba-Nabende, J., & Mwebaze, E. (2020). Accepted at CVPR 2020 Vision for Agriculture Workshop proceeedings . pdf

  4. A new approach for microscopic diagnosis of malaria parasites in thick blood smears using pre-trained deep learning models  —  R. Nakasi, E. Mwebaze,J Tusubira, B Akera, G Maiga (2020). Published in Springer SN Applied Sciences . pdf

  5. Improving In-field Cassava Whitefly Pest Surveillance with Machine Learning  —  B Akera, J Tusubira, S Nsumba, F Ninsiima, G Acellam, J Nakatumba, E Mwebaze, J Quinn, T Oyana. (2020) Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. pdf


  1. Keyword Spotter Model for Crop Pest and Disease Monitoring from Community Radio Data  —  Akera, B., Nakatumba-Nabende, J., Mukiibi, J., Hussein, A., Baleeta, N., Ssendiwala, D., & Nalwooga, S. (2019). Presented at NeurIPS 2019 Workshop on Machine Learning For the Developing Worlds. arxiv