Data Science, Machine Learning & Research Engineering

Services

Focused data analysis and engineering.

Short-term, fixed-scope consulting sprints designed for labs and data-driven teams that need clear deliverables across research engineering, data science, machine learning (ML), and exploratory analysis without long-term hiring.

Discuss a project See the process

Engagement packages

Fixed-scope sprints with clear deliverables.

Analysis Sprint (Decision-Ready Results)

Best for: New messy datasets, exploratory analysis, "what's actually going on?"

Duration: 1-2 weeks

You get

  • Exploratory analysis and sanity checks.
  • Publication-grade plots or dashboards (as appropriate).
  • Hypothesis tests and modeling baselines.
  • Interpretable "what we know now" summary.

Deliverables

  • Report with clean figures.
  • Notebooks/scripts (handoff-ready).
  • "Next experiments / next analyses" roadmap.

Reproducible Pipeline Sprint (From Chaos -> System)

Best for: Research teams who have code that "works on one laptop."

Duration: 2-4 weeks

You get

  • Refactor into a maintainable pipeline.
  • Reproducibility and versioning.
  • HPC/cloud-friendly execution (optional).

Deliverables

  • Repo with standard structure and docs.
  • Docker/conda and environment lock.
  • Runbook ("how to run / debug / extend").
  • Automated checks and basic tests.

Scientific ML Modeling Sprint (Preliminary Modeling)

Best for: "We need a credible first model and evaluation—fast."

Duration: 2-4 weeks

You get

  • Problem formulation + target definition (what to predict, what success means).
  • Model selection (baselines → classical ML → deep learning if justified).
  • Training design (splits, augmentation, regularization, tuning strategy).
  • Evaluation + validation plan (leakage checks, robustness, uncertainty).

Deliverables

  • Reproducible training and evaluation pipeline.
  • Benchmark report with clear model comparisons + metrics.
  • Validated preliminary model ready for iteration or handoff.
  • Recommendations for next steps (data, labels, experiments, architecture).

Model Evaluation & Risk Review (Red Team / Audit)

Best for: We have a model, but we don’t trust it in production.

Duration: 1-3 weeks

You get

  • Evaluation redesign (correct splits, leakage checks, realistic validation).
  • Robustness testing under shift (edge cases, subgroup performance, drift).
  • Error analysis to identify failure modes and root causes.
  • Actionable recommendations: what to fix now vs later.

Deliverables

  • Model risk & reliability report (decision-maker friendly).
  • reproducible evaluation harness (scripts/notebooks + metrics).
  • Prioritized solution plan (quick wins + longer-term improvements).

Don't see what you're looking for? Send a short project brief.

Service areas

Each engagement is scoped to a specific outcome. If your need is broader, we will help narrow it to a workable sprint.

Exploratory analysis

Rapid, structured analysis to understand datasets and inform next steps. Analysis tailored to your experiments and data types.

Data science / Machine Learning (ML)

Applied machine learning, feature engineering, and evaluation for questions that benefit from statistical or computational approaches.

Pipeline design

Build or redesign data pipelines for imaging, experimental, and survey data with attention to traceability and repeatability.

Improve reproducibility

Package workflows and environments so results can be rerun by your team or collaborators.

Performance optimization

Identify bottlenecks and apply targeted improvements using profiling, allocation analysis, algorithm optimization, parallelization (CUDA, multi-threading, HPC).

Real-time instrumentation

Low-latency instrumentation software (GUI) to acquire (NIDAQ, camera, etc.), record, and process for closed-loop experiments. This is only available for local, on-site clients.

Custom tooling

Create internal tools for data management, lab workflows, or analytics operations.

Documentation & handoff

Leave your team with clear documentation and a stable foundation for further development.

Customization

Not seeing what you're looking for?

We tailor scopes to your data types, constraints, and timelines. Share a short brief and we'll shape a focused sprint around your needs.

Contact

Selected projects

Outcome-focused engagements spanning data engineering, experimental systems, and applied ML for research teams.

Deep learning-based image processing

Problem

We have terabytes of imaging/video data and analysis is slow, fragile, and not reproducible.

Approach

  • End-to-end computer vision tools including deep learning models for feature extraction, classification, segmentation, tracking, etc.

Deliverables

  • Reproducible pipeline deployable to HPC and cloud.

Impact

  • Reduced time-to-analysis (weeks to hours).
  • Production-grade data engineering beyond modeling.

High-dimensional datasets + time series datasets

Problem

We have multi-omic data and need to uncover drivers and temporal patterns.

Approach

  • Normalization, feature engineering, and dimensionality reduction.
  • Time series modeling suitable for your underlying data.

Deliverables

  • Reports detailed with methods, preliminary findings, and suggested next steps.
  • Reproducible notebooks + data for reproducibility and downstream work.

Impact

  • Clear hypotheses and measurable next steps for experiments or modeling.

Performance optimization

Problem

Our code is too slow and hindering our work.

Approach

  • Profiling and code optimization
  • Algorithm optimization, allocation optimization, parallelization (including multi-threading, CUDA, and HPC)

Deliverables

  • Optimized code.
  • Benchmark statistics comparing before and after.

Impact

  • Clients often get multiple orders of magnitude improvements on key components in their projects.
  • A clear diagnosis of what's slowing things down helps your team write better code in future.

Custom interactive web app for data visualization

Problem

We need an interactive web app to visualize and share our datasets and findings.

Approach

  • Tailored interactive plots for time series, graphs, images, etc.
  • Cloud (GCP, AWS, etc.) for reproducible, reliable, and scalable deployment.

Deliverables

  • Web app code and documentation.
  • Deployment to cloud or server for either public or private access.

Impact

  • Standardized web tool to visualize complex datasets for everyone in the team.
  • Share research datasets and findings to increase impact.

Technical focus

Julia Python PyTorch TensorFlow CUDA Docker AppTainer

Selected methods and domains we work with—not exhaustive.

Modeling & inference

Machine learning Deep learning Probabilistic models Latent variable models Time series Bayesian inference Optimization Graph models Computer vision

Scientific data analysis

Time series High-dimensional data Neural data Behavioral data Experimental datasets Image data Representation analysis

Research systems & pipelines

End-to-end pipelines Data QC & diagnostics Cloud/HPC Reproducible research

Tools & frameworks

Python Julia PyTorch TensorFlow GPU/CUDA