Kale-ab Tessera

PhD Candidate

Autonomous Agents Group, University of Edinburgh

Biography

Kale-ab is a PhD student working on Multi-Agent Reinforcement Learning (MARL) at the Autonomous Agents research group at the University of Edinburgh, under the supervision of Stefano Albrecht. He has 7.5 years of experience (three years in software development and four and a half years in machine learning). Prior to his PhD, he was a research engineer at InstaDeep, working on Multi-Agent Reinforcement Learning (MARL) research.

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.

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

Omayma Mahjoub, Ruan John de Kock, Siddarth Singh, Wiem Khlifi, Abidine Vall, Kale-ab Tessera, Rihab Gorsane, Arnu Pretorius (2024). Efficiently Quantifying Individual Agent Importance in Cooperative MARL. (Oral) eXplainable AI approaches for deep reinforcement learning (XAI4DRL) Workshop @ AAAI, 2024.

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Siddarth Singh, Omayma Mahjoub, Ruan John de Kock, Wiem Khlifi, Abidine Vall, Kale-ab Tessera, Arnu Pretorius (2024). How much can change in a year? Revisiting Evaluation in Multi-Agent Reinforcement Learning. eXplainable AI approaches for deep reinforcement learning (XAI4DRL) Workshop @ AAAI, 2024.

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Kale-ab Tessera, Callum Tilbury, Sasha Abramowitz, Ruan de Kock, Omayma Mahjoub, Benjamin Rosman, Sara Hooker, Arnu Pretorius (2023). Generalisable Agents for Neural Network Optimisation. WANT@ NeurIPS 2023 and OPT NeurIPS 2023 workshops.

PDF Poster

Andries Smit, Paul Duckworth, Nathan Grinsztajn, Kale-ab Tessera, Thomas Barrett, Arnu Pretorius (2023). Are we going MAD? Benchmarking Multi-Agent Debate between Language Models for Medical Q&A. Deep Generative Models for Health Workshop NeurIPS 2023.

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Claude Formanek, Callum Rhys Tilbury, Jonathan Shock, Kale-ab Tessera, Arnu Pretorius (2023). Reduce, Reuse, Recycle: Selective Reincarnation in Multi-Agent Reinforcement Learning. (Oral) Workshop on Reincarnating Reinforcement Learning at ICLR 2023.

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Kale-ab Tessera, Chiratidzo Matowe, Arnu Pretorius, Benjamin Rosman, Sara Hooker (2022). Just-in-Time Sparsity: Learning Dynamic Sparsity Schedules. Dynamic Neural Networks ICML Workshop 2022.

PDF Poster Slides

Ibrahim Salihu Yusuf, Kale-ab Tessera, Thomas Tumiel, Zohra Slim, Amine Kerkeni, Sella Nevo, Arnu Pretorius (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

Arnu Pretorius, Kale-ab Tessera, Andries P. Smit, Claude Formanek, St John Grimbly, Kevin Eloff, Siphelele Danisa, Lawrence Francis, Jonathan Shock, Herman Kamper, Willie Brink, Herman Engelbrecht, Alexandre Laterre, Karim Beguir (2021). Mava: a research framework for distributed multi-agent reinforcement learning.

PDF Cite Code Blog

Kale-ab Tessera, Sara Hooker, Benjamin Rosman (2021). Keep the Gradients Flowing: Using Gradient Flow to Study Sparse Network Optimization. Sparsity in Neural Networks Workshop 2021.

PDF Cite Poster Slides

Kale-ab Tessera (2021). On Sparsity In Deep Learning: The Benefits And Pitfalls Of Sparse Neural Networks And How To Learn Their Architectures. Faculty of Science, University of the Witwatersrand.

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Recent & Upcoming Talks

Introduction to Deep Reinforcement Learning - University of Pretoria

May 26, 2023 5:00 PM

Introduction to Deep Reinforcement Learning - Indaba X Ghana

Sep 23, 2022 5:00 PM

“Introduction to ML using JAX” and “Introduction to Reinforcement Learning” tutorials

Aug 25, 2022 8:00 AM

Spotlight talk - Indaba 2022

Aug 25, 2022 8:00 AM

Playing Starcraft and Saving the World Using Multi-agent Reinforcement Learning

Oct 29, 2021 5:00 PM

See all events

Open Source

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

Mava: a research framework for distributed multi-agent reinforcement learning

A framework for MARL, written in JAX.

Deep Learning Indaba Practicals 2022

A collection of high-quality practicals covering a variety of modern machine learning techniques.

Pseudo Absence Generation and Locust Prediction

Locust breeding ground prediction using pseudo-absence generation and machine learning.

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.