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Berkeley · CA · —

Weishu Zhang

Currently Engineering backend for an AGI Health Coach @ BalanX-BIO · LLM optimization for clinical extraction @ UCSF · Incoming SWE intern @ ASML (Summer 2026).
★ Latest guide
CS170 Final · Problem-Driven Review →
6 topics · 3 passes each · final May 15
v3 · 2026 press ? for shortcuts

Experience

UC Berkeley · BS EECS · 4.0/4.0 · May 2027
Software Engineering in Test
ASML
San Jose, CA · May 2026 – Aug 2026
Incoming
CS Intern — Backend
BalanX-BIO
Remote · Jan 2026 – Present
  • Architecting high-performance Express.js backend for an AGI Health Coach with server-authoritative synchronization engine for real-time state consistency
  • Engineered AI Orchestrator Proxy interfacing with LLM APIs, managing dynamic context injection (personality vectors, health summaries) for personalized health insights
  • Designed offline-first conflict resolution logic for seamless data sync across HealthKit and Google Fit devices
  • Built automated data pipelines using AWS EventBridge and Lambda for daily health metric aggregation, secured via Cognito and JWT
Express.js AWS LangChain
Undergraduate Research Assistant
UCSF
San Francisco, CA · Aug 2025 – Present
  • Engineered high-throughput clinical extraction pipeline by decomposing monolithic tasks into modular sub-tasks; used in-context label propagation to reduce token consumption by 90%
  • Constructed 4,000+ entry domain-specific dataset and fine-tuned Qwen LLMs via QLoRA; implemented Self-Consistency checks ensuring 100% schema fidelity for medical compliance
  • Optimized inference infrastructure using vLLM and PagedAttention — 3× throughput increase for long-context medical records, ~80% VRAM reduction via quantization
PyTorch QLoRA vLLM MedSpacy
Lead Systems Architect
UC Berkeley — GradeView / GradeSync
Berkeley, CA · Aug 2025 – Present
  • Engineered distributed course management ecosystem, migrating fragmented legacy data into high-concurrency PostgreSQL cluster with normalized schema
  • Optimized system-wide read performance with Redis-backed caching layer and Python pre-rendering service — 95% query latency reduction
  • Hardened infrastructure with Row-Level Security and granular RBAC for 100+ concurrent students and staff
  • Resolved 200+ N+1 query patterns; deployed Nginx reverse proxy for 70% reduction in deployment overhead
React Node.js PostgreSQL Redis
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Selected Work

three projects · click title for live links
GradeView live ↗
Mastery learning dashboard under Prof. Garcia. Aggregates and normalizes scores from Gradescope, PrairieLearn, and iClicker into a unified Student View.
React Node.js PostgreSQL Redis
DA Chat source ↗
AI educational assistant for De Anza students. RAG over college policies + gamified transfer planning + intelligent course counselor agent backed by OpenAI function calling.
Spring Boot Vue.js OpenAI MySQL
NLP Pipeline · Medical Data Structuring · UCSF
Clinical Note Structure Extraction private (privacy policy)
High-precision NLP pipeline converting unstructured clinical notes into standardized structured outputs. LoRA fine-tuning + MedSpacy entity recognition + vLLM-served inference for long-context records.
Python PyTorch MedSpacy LoRA vLLM
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Learn

深入领域的问题驱动学习笔记 · self-imposed curricula
Below are the guides I'm building while I learn. Each follows the same loop — drive question → predict → fill the gap → read real code → self-check.
★ Active
Infra Learning Path
Going deep into AI infrastructure · 6-month roadmap
用 vLLM 当锚点，把 OS 当母语。问题驱动 → 先猜后看 → 代码锚点 → 自检。含 6 个月月度路线、PR 节奏、10 篇必读论文、原创 mini-vLLM 项目设计。
9
chapters
6
months
10
papers
3+
vLLM PRs
vLLM PagedAttention CUDA OS Scheduler
read the guide →
★ Active · Final May 15
CS170 Final · Problem-Driven Review
Berkeley CS170 Spring 2026 · multi-pass exam prep
Self-imposed curriculum focused on post-MT2 material. 每个 topic 三轮递进：直觉 → 套路 → 难题。优先 Q4 (zero-sum games) · Q5 (NP-completeness) · Q6 (hashing/streaming)，cheat sheet 排版同步给出。
6
topics
3
passes / topic
12h
plan
Q4·5·6
priority
Zero-Sum Games MWU NP-Complete Streaming LP Duality
read the guide →
more guides coming soon
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About

Who

EECS at UC Berkeley, graduating May 2027. I build systems at the intersection of infrastructure engineering and applied AI — clinical NLP pipelines, distributed course management platforms, and backend systems for health tech. Currently researching LLM optimization for clinical data extraction at UCSF and engineering backend infrastructure for an AGI health platform at BalanX-BIO. Incoming SWE intern at ASML (Summer 2026). Biased toward things that ship, scale, and matter.

Skills
Languages
Python · Java · C/C++ · SQL · TypeScript · JavaScript
Frameworks
FastAPI · React · Node.js · Spring Boot · Next.js · LangChain
Cloud & DevOps
AWS · Kubernetes · Docker · Terraform · CI/CD
AI & Data
PyTorch · vLLM · LoRA/QLoRA · MedSpacy · HuggingFace
Links
weszhang@berkeley.edu LinkedIn GitHub
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