109 lines
4.7 KiB
Typst
109 lines
4.7 KiB
Typst
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// Page setup
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#set page(margin: (x: 0.7in, y: 0.55in))
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#set text(font: "New Computer Modern", size: 10.5pt)
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#set par(justify: true, leading: 0.42em, spacing: 0.42em)
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#show link: set text(fill: rgb("#333333"))
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#set list(indent: 0pt, body-indent: 0.4em, marker: [•], spacing: 5pt)
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// Section header
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#let section(title) = {
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v(7pt)
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text(size: 11pt, weight: "bold", smallcaps(title))
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v(-2pt)
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line(length: 100%, stroke: 0.4pt)
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v(2pt)
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}
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// Unified entry: primary header + date, secondary header, then body
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#let entry(primary, date, secondary, body) = {
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grid(
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columns: (1fr, auto),
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text(weight: "bold", primary),
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align(end, text(date)),
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)
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v(-1pt)
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text(style: "italic", secondary)
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linebreak()
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body
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}
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// === HEADER ===
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#align(center)[
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#text(size: 14pt, weight: "bold")[Sicheng Pan] #h(6pt)
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#text(size: 9.5pt)[
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#link("mailto:sicheng0129@gmail.com")[sicheng0129\@gmail.com] #h(4pt) | #h(4pt)
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+1 (510) 301-0622 #h(4pt) | #h(4pt)
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#link("https://www.linkedin.com/in/sicheng-pan-0129/")[LinkedIn]
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]
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]
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// === EDUCATION ===
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#section("Education")
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#entry(
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[University of California, Berkeley], [2023 -- 2024],
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[M.S. in Electrical Engineering and Computer Sciences, GPA: 3.94/4.0],
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[Thesis: _Extensible Rule Language for Query Optimizer_, advised by Alvin Cheung],
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)
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#v(5pt)
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#entry(
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[University of California, Berkeley], [2019 -- 2023],
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[B.A. in Computer Science and Statistics, GPA: 4.0/4.0],
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[Highest Distinction in General Scholarship · Honors in Computer Science],
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)
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// === EXPERIENCE ===
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#section("Experience")
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#entry(
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[Chroma], [Aug 2024 -- Present],
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[Member of Technical Staff, Data Plane],
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)[
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- Integrated 4-bit RaBitQ quantization into the SPANN index, reducing compaction time from 20 min to 2 min per 1M vectors (1536-dim) with 5× lower memory usage, enabling collections to scale from 5M to 50M with ~40ms query latency and >90% recall\@10.
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- Designed and implemented a hybrid search API supporting composable KNN expressions (e.g., reciprocal rank fusion), sparse vector indexing (BM25, SPLADE) via Block-Max WAND, achieving sub-100ms latency at 1M scale. Shipped end-to-end across engine, API, Python/JS/Rust clients, and docs.
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- Co-led rewrite of the distributed frontend from Python to Rust (tokio/axum), owning the read path (query plan serialization, executors). Throughput increased from 800 to 6,000+ RPS on 16 cores with latency spikes eliminated. Also shipped as the new Rust-based local client.
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- Designed a serializable query plan and pushed query orchestration from the frontend to the query server, reducing network round trips from 3 to 1 per query and eliminating large intermediate data transfers.
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- Implemented instant collection forking with copy-on-write semantics, enabling users to checkpoint datasets and share sample collections without incurring storage copy.
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- Built regex query support via two-stage approach: extracting required literals from regex patterns to narrow candidates via the trigram index, then brute-force matching survivors, achieving sub-100ms latency at 1M scale.
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- Implemented efficient limit/offset pagination, negation filters using roaring bitmaps, CMEK encryption (GCP), and group-by deduplication for chunked-document search results.
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]
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#v(5pt)
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#entry(
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[Duolingo], [Summer 2022],
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[Software Engineer Intern, Data Infrastructure and Experimentation Team],
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)[
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- Implemented approximate query pipeline on BigQuery for the analytics dashboard, saving >50% query time at \<1% uncertainty.
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- Implemented caching mechanism for common queries based on historical frequencies (AWS, Jenkins), saving >80% time for analysts.
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]
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#v(5pt)
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#entry(
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[R-Polars Project], [Summer 2023],
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[Contributor, Google Summer of Code],
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)[
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- Exported Polars features to R including streaming I/O in Apache Parquet and Arrow formats.
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- Refactored error handling with recoverable errors from Rust and implemented background query pipeline via multi-threading, saving >50% user wait time.
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]
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// === RESEARCH ===
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#section("Research")
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#entry(
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[QED: A Powerful Query Equivalence Decider for SQL], [2021 -- 2024],
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[UC Berkeley EECS, advised by Alvin Cheung · Published at VLDB 2024],
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)[
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- Co-developed QED, a SQL query equivalence prover in Rust using a novel formalism (Q-expressions) under bag semantics with a complete checking algorithm for a general query fragment parameterized by first-order theories.
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- Verified 299/444 query rewrite pairs from Apache Calcite and 979/1287 from CockroachDB, more than 2× the coverage of prior state-of-the-art.
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]
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// === SKILLS ===
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#v(4pt)
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#line(length: 100%, stroke: 0.4pt)
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*Languages:* Rust, Python, SQL, R, Nix \
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*Tools & Infrastructure:* Linux, Git, gRPC/Protobuf, tokio, axum, AWS, GCP, Kubernetes, Docker
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