Moseli Motsoehli

DeepsMoseli.github.io

moselim@hawaii.edu

DeepsMoseli

moselim@hawaii.edu

1680 East-West Rd, Post 314-6

Honolulu, Hawaii 96822 USA

808-476-2232

Interests

Deep learning (Deep Active Learning, NLP, Computer vision), Software engineering (react.js, Meteor, Flask), Start ups

Skills

Languages and Frameworks: Python, Pytorch, Tensorflow, React.js, C++, Meteor

Toolsets/OS: Windows, Linux, Git/GitHub

Education

University of Hawaii, Honolulu, HI

Ph.D., Computer Science

  • Deep Active Learning
  • Computer Vision

2021 - Present

University of Hawaii, Honolulu, HI

M.S, Computer Science

  • Computer Vision
  • Deep Learning
  • Natural Language Processing
  • Evolutionary Computation
  • Computer Networks
  • Software Engineering II
  • Order of Magnitude Physics

2019 - 2021

University of Pretoria, South Africa

M.S., Data science

2017 - 2019

Work

Data Science Lead, The Shard - South Africa

www.theshard.co.za/

Tecnical client engagements, Project Scoping, Research, Architecture Design, Overseeing Implementation

  • African bank
  • Capitec bank
  • National treasury

2022 - 2022

Graduate Research Fellow, Hawaii Data Science Institute - University of Hawaii

datascience.hawaii.edu/students/fellows/

Using Computer vision and Deep learning methods to help improve marine life preservation efforts.

2020 - 2022

Summer Research Assistant, UH Manoa ICS

Using Computer vision and Deep learning methods to help improve marine life preservation efforts.

2020 - 2020

Teaching Assistant, UH Manoa

I assist students during my office hours, set up assignments, recitations, grade assignments and projects

  • ICS483: Computer Vision
  • ICS434: Intro to Data Science
  • ICS435: Machine Learning Fundamentals
  • ICS311: Algorithms

2019 - Curr

Credit risk data analyst, Vodacom South Africa

www.vodacom.co.za/

  • Building and maintaining predictive models
  • Automation of credit tracking processes using R and SAS

2015 - 2017

Publications

GCI-ViTAL: Gradual Confidence Improvement with Vision Transformers for Active Learning on Label Noise, Mots'oehli .M, Baek. K, TBS (To be submitted), 2024

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FishNet: Deep Neural Networks for Low-cost Fish Stock Estimation, Mots'oehli .M, Nikolaev .A, IGede .W.B, Lynham .J, Mous .P.J, Sadowski .P, ACM International Conference on Multimedia and Image Processing (ICMIP 2024)(Submitted), 2024

https://www.icmip.org/index.html

Comparision of Adversarial and Non-adversarial LSTM Music Generative Models, Mots'oehli .M, Bosman. A, De Villiers .JP, Science and Information (SAI) Conference 2023 (Springer), page 428., 2023

https://link.springer.com/chapter/10.1007/978-3-031-37717-4_28

PuoBERTa: Training and evaluation of a curated language model for Setswana, Marivate .V, Mots’Oehli .M, Wagnerinst .V, Lastrucci .R, Dzingirai .I,Southern African Conference for Artificial Intelligence Research (SACAIR 2023), 2023

https://arxiv.org/abs/2310.09141

Deep Active Learning in the Presence of Label Noise: A Survey, Mots'oehli .M, Baek. K, Ph.D. Literature Review, 2023

https://arxiv.org/pdf/2302.11075.pdf

Public Parking Spot Detection and Geo-localization Using Transfer Learning, Mots'oehli .M, Yang .Y, Proceedings of the 3rd Southern African Conference for Artificial Intelligence Research (SACAIR 2022), page 109, 2022

https://arxiv.org/abs/2209.00213

References

Available upon request