Kale-ab Tessera

Kale-ab Tessera

AI Research Engineer

InstaDeep

Biography

Kale-ab is a Research Engineer with experience working on challenging theoretical and applied problems in deep learning and reinforcement learning. He considers an excellent work ethic, deep technical knowledge, and his ability to think innovatively as his greatest strengths.

His research interests include Multi-Agent Reinforcement Learning (MARL) (namely scalable coordination or value decomposition) and Deep Learning (specifically Pruning and Generalization).

He is also passionate about using technology to help the African continent. To this end, he has worked on projects that aim to have a high impact in Africa and has also worked on increasing diversity and representation in machine learning by serving on the steering committee of the Deep Learning Indaba.

For more information, you can view my resumé .

Interests
  • Multi-Agent Reinforcement Learning (MARL)
  • Reinforcement Learning
  • Deep Learning
Education
  • MSc in Computer Science, Focusing on Deep Learning (Distinction), 2018 - March, 2021

    University of Witwatersrand

  • Honours in Computer Science (Distinction), 2016

    University of Pretoria

  • BSc. in Computer Science, 2013 - 2015

    University of Pretoria

Recent Publications

(2022). Just-in-Time Sparsity: Learning Dynamic Sparsity Schedules. Dynamic Neural Networks ICML Workshop 2022.

PDF Poster Slides

(2021). On pseudo-absence generation and machine learning for locust breeding ground prediction in Africa. AI + HADR 2021 and ML4D 2021 NeurIPS Workshops.

PDF Cite Code Blog

(2021). Mava: a research framework for distributed multi-agent reinforcement learning.

PDF Cite Code Blog

(2021). Keep the Gradients Flowing: Using Gradient Flow to Study Sparse Network Optimization. Sparsity in Neural Networks Workshop 2021.

PDF Cite Poster Slides

Open Source

You can see a full list of my open source projects on github - https://github.com/KaleabTessera .

Deep Learning Indaba Practicals 2022
A collection of high-quality practicals covering a variety of modern machine learning techniques.
Deep Learning Indaba Practicals 2022
Pseudo Absence Generation and Locust Prediction
Locust breeding ground prediction using pseudo-absence generation and machine learning.
Pseudo Absence Generation and Locust Prediction
Baobab
Baobab is an open source multi-tenant web application designed to facilitate the application and selection process for large scale meetings within the machine learning and artificial intelligence communities globally.
Baobab
DQN Agent playing Pong
A DQN agent playing pong.
DQN Agent playing Pong
Robotics Navigation
A project that compared the navigation ability of two robots (a Turtlebot and a Kuri robot) in challenging dynamic and static environments.
Robotics Navigation
PRM Path Planning
Implementation of Probabilistic Roadmaps – a path planning algorithm used in Robotics.
PRM Path Planning