CV

Mia Tanaka

CS + Data Science · UC Berkeley · Class of '26
mia.tanaka@berkeley.edu · github · linkedin

Download PDF

Junior at UC Berkeley double-majoring in Computer Science and Data Science (GPA: 3.87). Research assistant in the BAIR Lab working on efficient fine-tuning methods for large language models. Experienced in machine learning pipelines, distributed systems, and full-stack development. Looking for summer 2026 SWE or ML internships.

Experience

Software Engineering Intern

Weights & Biases · San Francisco, CA (Hybrid)

Built features for the experiment tracking platform used by ML practitioners at Tesla, OpenAI, and thousands of research labs. Owned a full-stack feature — custom metric aggregations — from design doc to production rollout.

  • Shipped custom metric aggregation UI (min/max/mean/percentile) that became one of the top-requested features
  • Reduced dashboard query p95 latency by 38% by rewriting hot-path SQL with materialized CTEs
  • Wrote 47 unit and integration tests; maintained 0 regressions across the release cycle
TypeScriptReactPythonGraphQLPostgres
May 2024 —
Aug 2024

Undergraduate Research Assistant

Berkeley AI Research (BAIR) Lab · Berkeley, CA

Working with Prof. Jitendra Malik's group on parameter-efficient fine-tuning methods for vision-language models. Implementing LoRA variants in PyTorch and running ablation studies on downstream task performance vs. compute cost.

  • Reproduced and extended results from 3 ICML 2024 papers on adapter-based fine-tuning
  • Built an automated benchmarking pipeline that cut experiment turnaround time from 6 hours to 40 minutes
  • Co-authoring a workshop paper on low-rank adaptation for few-shot classification tasks
PyTorchPythonTransformersCUDAResearch
Jan 2024 —
Present

Course Staff (uGSI)

UC Berkeley — CS 61B Data Structures · Berkeley, CA

Undergraduate student instructor for the second-largest CS course at Berkeley (~1,800 students). Led two weekly lab sections, held office hours, and contributed new exam problems on graph algorithms and tries.

  • Taught two 90-minute lab sections per week covering AVL trees, B-trees, tries, and shortest-path algorithms
  • Received a 4.7/5.0 student rating; known for drawing clear diagrams that made pointer manipulation click
JavaAlgorithmsTeachingGit
Aug 2023 —
Dec 2023

Education

University of California, Berkeley

B.S. Computer Science + B.A. Data Science (Double Major) · GPA 3.87

Relevant coursework: CS 189 (Machine Learning), CS 186 (Databases), CS 162 (OS), CS 170 (Algorithms), DATA 102 (Data, Inference & Decisions), STAT 134 (Probability), CS 164 (Compilers)

Activities: Association for Computing Machinery (ACM), Berkeley Machine Learning Club, EECS Honors Society, Hackathon organizer for CalHacks

AlgorithmsMachine LearningDatabasesStatisticsLinear Algebra
Aug 2022 —
May 2026