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.
Graduated, McGill Class of 2023!
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About Me — A summary of what I do and my academic background Msc. Computer Science
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My Work — Where I’ve worked and the tech I used :
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My Projects — including talks I’ve given, and open-source software I’ve developed or contributed to:
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.
Education
M.Sc. Computer Science - McGill University & Mila - Quebec AI Institute (2023)
Advisor: Dr. David Rolnick
Research Interests: Climate Change, Biodiversity, Computer Vision, Deep Learning
B.Sc. Software Engineering - Makerere University, Uganda (January 2019)
Thesis: Using Machine Learning to Improve In-Field Diagnosis of Cassava Crop Diseases
My Work
Current: Mila | Sunbird.ai
- Research Fellow, Mila - Quebec AI Institute
- Applied ML Research, Sunbird AI - Building AI solutions for African challenges
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- 👨🍳 Cooking up something
Earlier Work
IBM Research Africa - Nairobi
June-Sept, 2022. Spent summer of 2022 in Nairobi, with the Climate and Sustainability team in IBM. Worked on streamlining data pipelines for remote sensing and incoporating machine learning methods for Accelerated Discovery in Climate research.
Sama (previously Samasource)
Dec 2020 - Aug 2021 Worked with the R&D team on tools and techniques to improve data labelling with Machine Learning
Mila Quebec AI Institute
Jan - Dec 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
Sunbird AI
Jan 2020 - Now Building Language models for Ugandan Languages based on BERT
Makerere University AI Lab
2014-2018 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.
My Projects
Papers, open source, and hobby project
Papers
2023
Bird Distribution Modelling using Remote Sensing and Citizen Science data Teng, M., Elmustafa, A., Akera, B., Larochelle, H., & Rolnick, D., (2023)pdf
Multilingual Model and Data Resources for Text-To-Speech in Ugandan Languages –Owomugisha, I., Akera, B., Mwebaze, E., and Quinn, J., (Climate Change AI workshop - ICLR 2023)pdf
2022
Machine translation for african languages: Community creation of datasets and models in uganda –Akera, B., Mukiibi, J., Sanyu Naggayi, L., Babirye, C., Owomugisha, I., Nsumba, S., Nakatumba-Nabende, J., Bainomugisha, E., Mwebaze, E. and Quinn, J., (2022)pdf
2021
hBert+ BiasCorp–Fighting Racism on the Web — Onabola, O., Akera, B., Ma, Z., Xie, Y., Ibraheem, A., Xue, J., Liu, D. and Bengio, Y (2021) arXiv preprint. arxiv
2020
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
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
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
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
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
2019
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
Open Source Software
Machine Learning
- Glacier Mapping Pipeline:
- Machine Learning for Crop disease surveillance
Talks
2020
- DSA 2020: Intro to Deep Learning and computer vision
2019
- 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
2018
- DSA 2018: Nyeri, Kenya: Intro to Deep Learning