# Jane Doe - Complete Portfolio & Academic Profile

> I am a PhD student at **Example University**, advised by [Prof. Ada Advisor](https://example.org/ada-advisor).

I build machine learning systems that keep working when data, hardware, or code changes underneath them. Before my PhD, I studied computer science at **Example College**.

## Contact Information
- **Website**: https://janedoe.example.com
- **Email**: jane.doe@example.com
- **GitHub**: https://github.example.com/janedoe
- **LinkedIn**: https://linkedin.example.com/in/janedoe
- **Google Scholar**: https://scholar.example.org/janedoe
- **CV / Resume**: https://academic-site-template.vercel.app/Jane_Doe_CV.pdf

## Education
### Ph.D. in Computer Science
- **Institution**: Example University
- **Timeline**: 2023 – 2028
- **Details**: Advisor: Prof. Ada Advisor

### Bachelor of Science in Computer Science
- **Institution**: Example College
- **Timeline**: 2019 – 2023
- **GPA**: 3.9
- **Details**: Senior thesis: Gradient Compression for Data-Parallel Training

## Research Experience
### PhD Researcher - Reliable ML Lab, Example University
- **Period**: Sep 2023 – Present
- **Location**: Example City, CA
- **Description**:
  - Robustness benchmarks for image classifiers under natural distribution shift.
  - Fault-tolerant distributed training on preemptible hardware.

### Undergraduate Researcher - Systems Group, Example College
- **Period**: Jan 2022 – May 2023
- **Location**: Example Town, NY
- **Description**:
  - Measured communication overhead in data-parallel training.
  - Senior thesis on gradient compression, advised by Prof. Ada Advisor.

## Publications & Pre-prints
### Stress-Testing Image Classifiers Under Realistic Distribution Shift
- **Authors**: Jane Doe, John Roe, Ada Advisor
- **Venue**: Example Conference on Learning Systems (ECLS 2025)
- **Recognition**: Spotlight
- **Abstract / Summary**: A benchmark of 40 natural distribution shifts showing that robustness rankings on synthetic corruptions do not transfer to real deployments.
- **Tags**: Machine Learning, Robustness, Benchmarking
- **Links**: [View Publication](https://example.org/papers/stress-testing) | [View Pre-Print](https://example.org/preprints/stress-testing.pdf) | [View Source](https://example.com/janedoe/shift-bench)
- **BibTeX**:
```bibtex
@inproceedings{doe2025stress,
  title={Stress-Testing Image Classifiers Under Realistic Distribution Shift},
  author={Doe, Jane and Roe, John and Advisor, Ada},
  booktitle={Example Conference on Learning Systems},
  year={2025}
}
```

### Elastic Checkpointing for Preemptible Distributed Training
- **Authors**: Sam Sample, Jane Doe, Ada Advisor
- **Venue**: Workshop on Efficient Example Systems @ ECLS 2025
- **Abstract / Summary**: Checkpoints that adapt their frequency to the preemption rate of spot instances, cutting lost GPU hours by 35% in our cluster traces.
- **Tags**: Systems, Machine Learning, Distributed Training
- **Links**: [View Publication](https://example.org/papers/elastic-checkpointing) | [Slides](https://example.org/slides/elastic-checkpointing.pdf)
- **BibTeX**:
```bibtex
@inproceedings{sample2025elastic,
  title={Elastic Checkpointing for Preemptible Distributed Training},
  author={Sample, Sam and Doe, Jane and Advisor, Ada},
  booktitle={Workshop on Efficient Example Systems},
  year={2025}
}
```

### Repairing Shape Errors in Deep Learning Code with Static Analysis
- **Authors**: J. Doe, John Roe
- **Venue**: Preprint, 2025
- **Abstract / Summary**: A static analyzer that finds tensor shape mismatches in training scripts and proposes one-line fixes, repairing 61% of bugs in a new dataset.
- **Tags**: Software Engineering, Machine Learning, Program Repair
- **Links**: [View Pre-Print](https://example.org/preprints/shape-repair.pdf)
- **BibTeX**:
```bibtex
@article{doe2025shape,
  title={Repairing Shape Errors in Deep Learning Code with Static Analysis},
  author={Doe, Jane and Roe, John},
  journal={Preprint},
  year={2025}
}
```

### Flaky Tests in Distributed ML Pipelines
- **Authors**: Jane Doe
- **Venue**: Example University Research Symposium 2024
- **Abstract / Summary**: An empirical look at why tests of distributed training code fail intermittently, and which fixes developers apply.
- **Tags**: Software Engineering, Systems, Testing
- **Links**: [View Poster](https://example.org/posters/flaky-tests.pdf)

## Projects
### ShiftBench: Benchmarking Robustness to Real Distribution Shift
- **Timeline**: Jan 2024 – Present
- **Technologies**: Python, PyTorch, Benchmarking
- **Summary**: Collected 40 natural distribution shifts from public image datasets.
Built a leaderboard that re-evaluates models nightly.
Released the benchmark as an open-source Python package.
- **Collaborators**: [John Roe](https://example.org/john-roe)
- **Links**: [GitHub](https://example.com/janedoe/shift-bench) | [Website](https://example.org/shift-bench)

### ShapeCheck: Static Shape Checking for PyTorch
- **Timeline**: Jun 2024 – Dec 2024
- **Technologies**: Python, Static Analysis
- **Summary**: Wrote an abstract interpreter that tracks tensor shapes through PyTorch programs.
Suggests one-line fixes for common shape bugs.
- **Links**: [GitHub](https://example.com/janedoe/shapecheck) | [Paper](https://example.org/preprints/shape-repair.pdf)

### Course Scheduler
- **Timeline**: Sep 2021 – Dec 2021
- **Technologies**: JavaScript, React
- **Summary**: Class project: a web app that builds conflict-free course schedules from a list of wanted classes.
- **Links**: [Live Demo](https://example.com/course-scheduler)

## Work Experience
### Graduate Student Researcher - Reliable ML Lab, Example University
- **Period**: Sep 2023 – Present
- **Location**: Example City, CA
- **Description**:
  - Lead the [ShiftBench](https://example.com/janedoe/shift-bench) robustness benchmark.
  - Study failure modes of distributed training on preemptible hardware.
  - Mentor two undergraduate researchers.

### Machine Learning Engineering Intern - Example Corp
- **Period**: Jun 2024 – Sep 2024
- **Location**: Remote
- **Description**:
  - Added data-drift monitoring to a production recommendation pipeline.
  - Reduced false alerts by 40% with per-feature thresholds.

### Undergraduate Research Assistant - Systems Group, Example College
- **Period**: Jan 2022 – May 2023
- **Location**: Example Town, NY
- **Description**:
  - Profiled communication overhead in data-parallel training.
  - Wrote a senior thesis on gradient compression.

## Talks & Presentations
- **[Oct 2025]** **Spotlight Talk**, [ECLS 2025](https://example.org/ecls-2025), Example City, CA.
- **[Apr 2025]** **Invited Talk**, Machine Learning Reading Group, [Example College](https://college.example.org).

## Teaching Experience
- **Teaching Assistant, Introduction to Machine Learning**, Example University (Jan 2025 – Mar 2025)
  Ran weekly discussion sections for 60 students.
Wrote two programming assignments on model evaluation.
- **Peer Tutor, Data Structures**, Example College (Sep 2021 – May 2022)
  Held drop-in tutoring hours twice a week.

## Awards & Honors
- **Spotlight Paper** - Example Conference on Learning Systems (2025)
- **Graduate Fellowship** - Example University (2023-2025)
  Two-year fellowship for incoming PhD students.

## Recent News
- **[Sep 2025]** Our paper on [realistic distribution shift](https://example.org/papers/stress-testing) was accepted as a **Spotlight** at ECLS 2025.
- **[Jun 2025]** Presented elastic checkpointing at the [Workshop on Efficient Example Systems](https://example.org/eeds-2025).
- **[Sep 2023]** Started my PhD at **Example University** with Prof. Ada Advisor.

## Service & Outreach
- **Reviewer**:
  - Example Conference on Learning Systems (ECLS) (2024, 2025) [Link](https://example.org/ecls)
  - Workshop on Efficient Example Systems (2025)
- **Artifact Evaluation Committee**:
  - Example Systems Symposium (2025) [Link](https://example.org/ess-2025/artifacts)

## Technical & Language Skills
- **Languages**: Python, C++, JavaScript
- **Machine Learning**: PyTorch, JAX, scikit-learn
- **Systems**: Slurm, Ray, Kubernetes
- **Tools**: Git, Docker, LaTeX