Connor Watts AI Researcher

CV

Connor Watts

PhD Researcher/Engineer

Phone: +447502352035
Email: connorwatts@hotmail.com
Location: London
LinkedIn: linkedin.com/in/connor-watts
GitHub: github.com/ConnorWatts

EXPERIENCE

Research Engineer - Hybrid Vision (2024-Present)

  • Researching solutions for control and assurance of autonomous multi-agent systems.
  • Leading the experiment designs and building the system architecture.
  • Designed a graph-based MOE architecture to scale the system efficiently to 100+ agents.

ML Engineer - Midas AI (2022-2024)

  • Founding Engineer. Led LLM operations and backend development.
  • Took the product from proof of concept to first client deployment.
  • Designed the pipeline for real-time streaming, processing, and analysis of financial data.
  • Designed a custom temporal graph-based RAG system for dynamic prompting.

ML Engineer - Freelance (2021-2022)

  • Developed RL algorithms for poker-playing software, boosting prediction accuracy by 20%.
  • Designed a custom environment loader and multiprocessing framework, resulting in a speedup in model training.

Software Engineer - Xenomorph Software (2018-2022)

  • Implemented large-scale IPV and MCC workflows for several leading commercial banks.
  • Developed an ML model for market data anomaly detection & gap filling, outperforming rule-based models by 23%.

EDUCATION

Ph.D. in Artificial Intelligence - Queen Mary University of London (2023-Present)

  • Thesis: Automated Multi-Agent Systems (Supervisor: Paulo Rauber)
  • Skills: Reinforcement Learning · Multi-Agent Systems · LLMs · Evolutionary Algorithms · Safe AI

M.Sc. in Computational Statistics & Machine Learning - University College London (2021-2022)

  • Thesis: Generalized Energy-Based Time-Series Models (Supervisor: Brooks Paige). Top 10%.
  • Outperformed SOTA Time-Series GANs by 9%.
  • Skills: PyTorch · NLP · Transformers · Generative Modeling · Deep Learning Theory

B.Sc. in Mathematics - University of Bristol (2015-2018)

  • Thesis: An Introduction to Random Matrices: With Application to Data Science (Supervisor: Tamara Grava)
  • Skills: Probability Theory · Financial Math · Nonlinear Dynamics · Bayesian Statistics · Linear Algebra

SKILLS

Programming Languages:

  • Python (Advanced) · Jax (Advanced) · C++ (Intermediate) · R (Intermediate) · Rust (Basic)

Machine Learning:

  • PyTorch (Advanced) · Scikit-Learn (Advanced) · Pandas (Advanced) · TensorFlow (Intermediate)

LLMs:

  • Fine Tuning (Advanced) · Prompting (Advanced) · Optimizing (Intermediate) · Assurance (Intermediate)

MLOps:

  • Deployment (Advanced) · Monitoring (Prometheus, Grafana) · Versioning (MLflow) · AWS SageMaker
  • Data Platforms (Hadoop, Spark) · Streaming (Apache Kafka) · Serialization (Apache Avro) · SQL · GraphSQL

DevOps/Back-End:

  • CI/CD Pipelines (Jenkins, GitLab CI) · APIs (Flask, FastAPI) · Git · Kubernetes · Docker

Misc:

  • Leadership · Mentorship · Knows the best place in London for lunch (Advanced)