Curriculum Vitae

Personal Information

Name: Dr. Stephen Mander
Position: AI/ML Engineer
Phone: 07856 651445
Email: st7ma784@gmail.com
Address: 84 Grange Road, Birmingham, UK, B14 7RJ
GitHub: Your GitHub Profile

Profile

A problem-solver and AI enthusiast with a specialization in machine learning, recently completed PhD thesis titled “Evaluating understanding in cross-modal multi-encoder systems”. Enjoys contributing to cutting-edge research projects, proposing innovative solutions, and successfully deploying machine learning models in real-world scenarios. Eager to leverage expertise in AI and management skills to tackle complex challenges in the field.

Education

Ph.D. in Machine Learning

Institution: Lancaster University
Year: 2020 - 2025
Thesis: “Evaluating understanding in cross-modal multi-encoder systems”

B.Sc. in Computer Science (2:1 Hons)

Institution: Lancaster University
Year: 2017 - 2020
Specialization: Advanced Programming, Software Design, and Artificial Intelligence

A-Levels

Institution: King Edward VI Camp Hill School for Boys
Year: 2010 - 2017
Subjects: Physics, Computer Science and Mathematics (ABB)

Professional Experience

Research Software Engineer

Lancaster University | 2024 - 2025

  • Consulting on a range of ML/HPC projects

  • Programming in C++, Python, JavaScript, and SLURM

  • Optimizing machine learning model deployment on High-Performance Computing systems

PhD Researcher in Machine Learning

Lancaster University | 2020 - 2024

  • Conducted extensive research on various machine learning tasks, with contributions published in prestigious ICLR and presented at NLDB conference

  • Proposed novel training techniques for multi-modal learning

  • Improved tooling for High-Performance Computing (HPC) systems, optimizing the efficiency of ML model deployment

Machine Learning Researcher

GCHQ | Summer 2023

  • Collaborated with a team of researchers to deploy machine learning and natural language processing (NLP) techniques for complex cross-modal knowledge extraction challenges

  • Named primary academic partner of research programme and sole representative of Lancaster University

Image Recognition Enhancement Project

Lancaster University | 2017 - 2020

  • Collaborated with senior lecturers to propose a novel method to enhance common image recognition approaches using ideas from natural language processing (NLP)

Research Interests

  • Cross-modal Learning: Multi-dimensional contrastive learning and vision-language model optimization

  • Adversarial Machine Learning: Attack visualization, robustness evaluation, and defense mechanisms

  • High-Performance Computing: ML framework optimization for SLURM clusters and distributed systems

  • Computational Physics: Hybrid plasma simulation and atmospheric modeling

  • Edge AI: Efficient algorithms for resource-constrained environments

  • Scientific Computing: Large-scale simulation frameworks and numerical optimization

Key Research Contributions

See my complete publications list for peer-reviewed articles, conference proceedings, and preprints.

Major Research Achievements:

  • 6DIMCOCO Framework: Novel multi-dimensional CLIP training with advanced loss functions (ICLR submission)

  • Adversarial Robustness: PGD attack visualization and security assessment frameworks

  • HPC Optimization: ML-SLURM template for efficient cluster deployment and hyperparameter optimization

  • Plasma Physics: JERICHO hybrid simulation framework for magnetospheric research

  • Edge Computing: 8-bit LSA and tiny contrastive learning for efficient deployment

Selected Publications:

  • “PICTAR: Probe Informed Contrastive Training for Adversarial Robustness” (ICLR)

  • “Improving PGN attacks with unsupervised training” (Security Research)

  • “8Bit LSA: Efficient Latent Semantic Analysis” (Edge AI)

  • Multiple works on tiny contrastive learning and BERTScore optimization

Teaching Experience

Computer Science - TA

Institution | 2020-2022 Role: Instructor/Teaching Assistant

  • Delivering online courses at BJTU

Another Course

Institution | 2023-2024 Role: Instructor/Teaching Assistant

  • In person TA support delivering SCC-411 advanced distributed systems

Technical Skills

Programming Languages

  • Python: PyTorch, PyTorch Lightning, NumPy, SciPy, scikit-learn, transformers

  • C++: High-performance computing, MPI, Eigen, scientific computing

  • JavaScript: Web interfaces, Flask APIs, interactive visualizations

  • SLURM: Workload management, distributed computing, HPC optimization

Machine Learning Specializations

  • Deep Learning: CLIP models, contrastive learning, multi-modal systems

  • Computer Vision: Image-text models, adversarial attacks, robustness evaluation

  • Natural Language Processing: BERT, tokenization, semantic analysis

  • Adversarial ML: PGD attacks, defense mechanisms, security assessment

  • Edge AI: Model compression, 8-bit quantization, efficient inference

Research Software Development

  • Framework Design: 6DIMCOCO, PGDVisualisation, ML-SLURM-Template, JERICHO

  • HPC Computing: Distributed training, GPU acceleration, cluster optimization

  • Scientific Computing: Plasma physics simulation, atmospheric modeling

  • API Development: REST APIs, web interfaces, job management systems

Additional Skills and Activities

Leadership and Organizational Experience

  • Active participation in a Living History Society with responsibility for shows and tournaments nationwide

  • Church council trustee, demonstrating experience in organizational governance

  • Full and clean driving licence

Professional Development

  • Experience in project management and team collaboration

  • Strong problem-solving and analytical skills

  • Effective communication and presentation abilities

Languages

  • English: Native/Fluent

References

Available upon request.

Last updated: June 2025