Erik Wijmans
Erik Wijmans is an AI researcher whose work spans embodied navigation, reinforcement learning, and large language model training. He earned bachelor's degrees in engineering physics and computer engineering in 2017 and completed a computer science PhD at Georgia Tech in 2022, when he shared the AAAI/ACM SIGAI Doctoral Dissertation Award. He coauthored Habitat and Habitat 2.0 for embodied agents, alongside DD-PPO and VER for scalable navigation and mobile-manipulation learning. He shared the 2023 ICLR Outstanding Paper Award for a study of maps learned by blind navigation agents, and his 2025 Cut Cross-Entropy paper reduced memory use in large-vocabulary language-model training. He works in AI research at Thinking Machines, where he scales Tinker and improves fine-tuning models.
- Now at
- Thinking Machines Lab
- Badges
Timeline
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2017
Two bachelor's degrees
In 2017, he earns a BS in engineering physics at Juniata College, graduating magna cum laude, and a BS in computer engineering at Washington University in St. Louis, graduating summa cum laude.
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2019
Habitat for embodied AI
In 2019, he coauthors Habitat, a platform for research in embodied AI that trains virtual agents in photorealistic 3D simulations. Habitat-Sim provides the simulator, and Habitat-API supports task definition, training, and benchmarking.
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2020
Scaling PointGoal navigation
In 2020, he coauthors DD-PPO, a distributed reinforcement-learning method for virtual robots navigating Habitat-Sim. It scales training to 2.5 billion experience steps and reaches near-perfect PointGoal navigation in unseen environments.
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2021
Habitat 2.0 for home tasks
In 2021, he coauthors Habitat 2.0, a physics-enabled simulator for virtual robots in interactive 3D environments, and the Home Assistant Benchmark for mobile-manipulation tasks such as tidying rooms, stocking groceries, and setting a table.
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2022
A PhD and dissertation award
He completes a computer-science PhD at Georgia Tech in 2022 under Irfan Essa and Dhruv Batra and shares the AAAI/ACM SIGAI Doctoral Dissertation Award for his thesis. His 2022 paper VER scales on-policy reinforcement learning across many GPUs to train embodied-navigation and mobile-manipulation policies.
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2023
An ICLR Outstanding Paper Award
In 2023, he shares the ICLR Outstanding Paper Award for Emergence of Maps in the Memories of Blind Navigation Agents. The study examines representations learned by artificial blind navigating agents.
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2025
Cut Cross-Entropy for LLMs
In 2025, he coauthors Cut Your Losses in Large-Vocabulary Language Models, which introduces Cut Cross-Entropy to compute cross-entropy loss without materializing logits for all tokens into global memory.
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As of October 2026
AI research at Thinking Machines
In 2026, he joins Thinking Machines from Apple, where he works with Vladlen Koltun. At Thinking Machines, he works on scaling Tinker, the lab's fine-tuning service, and making fine-tuning models such as Inkling easier and more effective.
Career
- Since 2026Thinking Machines Lab
Education
- 2022Georgia Institute of TechnologyPhD in computer science
- 2017Juniata CollegeBS in engineering physics
- 2017Washington University in St. LouisBS in computer engineering
Sources and credits
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- Erik Wijmans
- Habitat: A Platform for Embodied AI Research
- DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
- Habitat 2.0: Training Home Assistants to Rearrange their Habitat
- AAAI/ACM SIGAI Doctoral Dissertation Award
- VER: Scaling On-Policy RL Leads to the Emergence of Navigation in Embodied Rearrangement
- Announcing the ICLR 2023 Outstanding Paper Award Recipients
- Cut Your Losses in Large-Vocabulary Language Models
- Meta’s loss is Thinking Machines’ gain




