Andrew K. Mackay

A local assistant that answers programming questions from a code corpus, reviews its own answers and retries when they fall short.

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A Llama-style language model written from scratch in PyTorch, small enough to pre-train on a laptop, then fine-tuned with reinforcement learning.

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A reimplementation of the MOReL offline reinforcement learning framework with the improvements I proposed in my MSc thesis. It outperforms the original on the D4RL HalfCheetah benchmark.

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An end-to-end machine learning system, from Spark data processing to a public forecasting app on Google Cloud.

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• Met clients in health, climate, media and manufacturing to understand their problems, then designed LLM-based solutions: model compression, fine-tuning, retrieval-augmented generation and agent orchestration, plus computer vision and Whisper speech pipelines.
• Wrote the technical side of proposals for private companies and of public R&D consortium bids, including EU Horizon and Spain’s Misiones programme.
• Benchmarked models and built demos to validate proposed solutions.

• Built core components in C# and Kotlin for TrackAI, a platform that uses eye tracking to assess visual function in patients who cannot take a standard eye test, such as infants. It has been validated with more than 4,000 patients.
• Deployed deep-learning gaze-estimation models to mobile hardware (Huawei NPU) through an Android app.
• Worked with clinicians and researchers to replace manual evaluation steps with automated diagnostic tools.

• Studied how visual perception develops during infancy through eye-tracking experiments.
• Processed high-frequency gaze recordings into visualisations the research group could use for large-scale analysis.

• Built binary classifiers (XGBoost, neural networks) that predict customer conversion from real sales data, with feature-importance analysis, and presented the results to the R&D team.
• Developed blockchain web applications on Ethereum smart contracts.

Specialisation in Robotics, Perception and Interaction. Final grade: Excellent, 96/100.

Specialisation in Computing.

A. K. Mackay, L. Riazuelo, L. Montano. Sensors 22(10), 3847, 2022.

A reinforcement learning planner for service robots that navigate among people moving unpredictably. Instead of learning from raw lidar or camera data, the agent sees the scene as a dynamic object velocity space (DOVS): which velocities are safe for the robot, given its kinodynamic constraints and the obstacles’ motion. This representation makes training faster than learning from raw sensors.

@article{mackay2022rldovs,
  title   = {{RL-DOVS}: Reinforcement Learning for Autonomous Robot Navigation in Dynamic Environments},
  author  = {Mackay, Andrew K. and Riazuelo, Luis and Montano, Luis},
  journal = {Sensors},
  volume  = {22},
  number  = {10},
  pages   = {3847},
  year    = {2022},
  doi     = {10.3390/s22103847}
}

MSc thesis, University of Innsbruck. Awarded the highest grade.

Learns a model of the environment’s dynamics from offline data with neural-network ensembles, then uses it to choose actions while penalising predictions the ensemble disagrees on. The work is public as three projects: J-MOReL, an improved reimplementation of the MOReL offline RL framework; MBiCEM, a trajectory planner that runs iCEM on learned models; and the dynamics ensembles both are built on.

BEng thesis, University of Zaragoza. Extended into the RL-DOVS journal article.