Academic CV example: PhD candidate in Computer Science (Machine Learning)

Computer Science · PhD candidate · IEEE citations · Sidebar template

This academic CV example illustrates how a PhD candidate in computer science and machine learning typically presents their research profile. It covers conference publications in IEEE citation style, research assistantships, teaching experience, and a concise skills section — the core building blocks recruiters and PhD committees look for.

Computer science CVs at the PhD stage are publication-centred: conference papers at top venues (NeurIPS, CVPR, ICLR equivalents) carry as much weight as journal articles. Citations follow the IEEE numbered format, and the sidebar template keeps personal details and skills visible while the main column showcases research output.

Illustrative example. Yara Okonkwo is a fictional researcher and the publications below are fabricated for demonstration — any resemblance to a real person or work is coincidental.

Yara Okonkwo, PhD candidate

PhD candidate in Machine Learning

Department of Computer Science, Harwell Institute of Technology · Bristol, United Kingdom

Education

  • PhD in Computer Science (Machine Learning), Harwell Institute of Technology, Bristol, UK — expected 2026. Thesis: 'Efficient Uncertainty Quantification in Large-Scale Graph Neural Networks'. Supervisor: Prof. R. Castellano.
  • MSc in Artificial Intelligence (Distinction), Harwell Institute of Technology, Bristol, UK — 2021.
  • BSc in Mathematics and Computer Science (First Class Honours), University of Midvale, Birmingham, UK — 2020.

Research experience

  • Graduate Research Assistant, Adaptive Learning Systems Lab, Harwell Institute of Technology, 2021–present. Developing scalable Bayesian inference methods for graph-structured data; maintaining the lab's open-source benchmarking toolkit.
  • Research Intern, DeepSense AI (industry), London, UK, Summer 2023. Investigated test-time adaptation strategies for distribution-shifted medical imaging; work incorporated into internal product pipeline.
  • Undergraduate Research Assistant, Optimisation and Learning Group, University of Midvale, 2019–2020. Implemented gradient-free optimisation baselines for hyperparameter search; contributed to codebase used in two subsequent lab publications.

Publications

  • [1] Y. Okonkwo, R. Castellano, and P. Ferreira, 'Calibrated Posterior Estimates for Node Classification via Stochastic Depth Sampling,' in Proc. Int. Conf. Neural Information Processing Systems (NIPS-X), Montreal, Canada, Dec. 2023, pp. 4812–4825.
  • [2] Y. Okonkwo and R. Castellano, 'Laplacian Dropout Regularisation for Graph Convolutional Networks,' in Proc. Int. Conf. Learning Representations (ICLR-W), Workshop on Geometrical and Topological Representation Learning, Vienna, Austria, May 2023.
  • [3] Y. Okonkwo, S. Ndiaye, and L. Hartman, 'BenchGNN: A Reproducible Evaluation Suite for Uncertainty in Graph Neural Networks,' arXiv preprint arXiv:2311.09182, 2023. [Preprint]
  • [4] S. Ndiaye, Y. Okonkwo, and R. Castellano, 'Scalable Variational Inference for Deep Graph Models,' IEEE Trans. Neural Netw. Learn. Syst., vol. 35, no. 4, pp. 1923–1936, Apr. 2024. doi:10.0000/tnnls.2024.00412
  • [5] Y. Okonkwo, T. Bergstrom, and R. Castellano, 'Test-Time Uncertainty Adaptation under Covariate Shift for Medical Image Segmentation,' in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, USA, Jun. 2024, pp. 301–308.

Teaching

  • Teaching Assistant, CS3420 — Deep Learning (undergraduate), Harwell Institute of Technology, Spring 2024. Delivered weekly lab sessions (60 students); designed three assessed practicals in PyTorch.
  • Teaching Assistant, CS2210 — Algorithms and Data Structures (undergraduate), Harwell Institute of Technology, Autumn 2022 and Autumn 2023. Led problem-solving tutorials; held weekly office hours.
  • Guest Lecturer, MSc AI Research Methods module, Harwell Institute of Technology, Feb. 2024. One-hour lecture on 'Reproducibility and Open Science in Machine Learning'.

Conference presentations

  • Oral presentation: 'Calibrated Posterior Estimates for Node Classification via Stochastic Depth Sampling,' NIPS-X, Montreal, Canada, Dec. 2023.
  • Poster: 'Laplacian Dropout Regularisation for Graph Convolutional Networks,' ICLR-W Workshop on Geometrical and Topological Representation Learning, Vienna, Austria, May 2023.
  • Poster: 'Test-Time Uncertainty Adaptation under Covariate Shift,' CVPRW, Seattle, USA, Jun. 2024.
  • Talk: 'Open Benchmarking Practices for Graph Neural Networks,' UK Machine Learning PhD Symposium, Edinburgh, UK, Sep. 2023.

Awards & honours

  • EPSRC Doctoral Training Partnership Studentship (full fees + stipend), 2021–2025.
  • Best Student Paper Runner-Up, UK Machine Learning PhD Symposium, 2023.
  • Faculty Prize for Outstanding MSc Dissertation, Harwell Institute of Technology, 2021.
  • Dean's List, University of Midvale, 2018–2020.

Skills

  • Programming languages: Python (expert), C++ (proficient), Julia (familiar), Bash.
  • ML frameworks: PyTorch, PyTorch Geometric, JAX, scikit-learn, Hugging Face Transformers.
  • Tools & infrastructure: Git, Docker, Weights & Biases, SLURM (HPC), LaTeX.
  • Research methods: Bayesian deep learning, graph neural networks, uncertainty quantification, distribution shift, reproducibility.
  • Languages: English (native), French (intermediate).

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