Resume

resume/resume.typ

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+// Page setup
+#set page(margin: (x: 0.7in, y: 0.55in))
+#set text(font: "New Computer Modern", size: 10.5pt)
+#set par(justify: true, leading: 0.42em, spacing: 0.42em)
+#show link: set text(fill: rgb("#333333"))
+#set list(indent: 0pt, body-indent: 0.4em, marker: [•], spacing: 5pt)
+
+// Section header
+#let section(title) = {
+  v(7pt)
+  text(size: 11pt, weight: "bold", smallcaps(title))
+  v(-2pt)
+  line(length: 100%, stroke: 0.4pt)
+  v(2pt)
+}
+
+// Unified entry: primary header + date, secondary header, then body
+#let entry(primary, date, secondary, body) = {
+  grid(
+    columns: (1fr, auto),
+    text(weight: "bold", primary),
+    align(end, text(date)),
+  )
+  v(-1pt)
+  text(style: "italic", secondary)
+  linebreak()
+  body
+}
+
+// === HEADER ===
+#align(center)[
+  #text(size: 14pt, weight: "bold")[Sicheng Pan] #h(6pt)
+  #text(size: 9.5pt)[
+    #link("mailto:sicheng0129@gmail.com")[sicheng0129\@gmail.com] #h(4pt) | #h(4pt)
+    +1 (510) 301-0622 #h(4pt) | #h(4pt)
+    #link("https://www.linkedin.com/in/sicheng-pan-0129/")[LinkedIn]
+  ]
+]
+
+// === EDUCATION ===
+#section("Education")
+
+#entry(
+  [University of California, Berkeley], [2023 -- 2024],
+  [M.S. in Electrical Engineering and Computer Sciences, GPA: 3.94/4.0],
+  [Thesis: _Extensible Rule Language for Query Optimizer_, advised by Alvin Cheung],
+)
+
+#v(5pt)
+
+#entry(
+  [University of California, Berkeley], [2019 -- 2023],
+  [B.A. in Computer Science and Statistics, GPA: 4.0/4.0],
+  [Highest Distinction in General Scholarship · Honors in Computer Science],
+)
+
+// === EXPERIENCE ===
+#section("Experience")
+
+#entry(
+  [Chroma], [Aug 2024 -- Present],
+  [Member of Technical Staff, Data Plane],
+)[
+  - 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.
+  - 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.
+  - 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.
+  - 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.
+  - Implemented instant collection forking with copy-on-write semantics, enabling users to checkpoint datasets and share sample collections without incurring storage copy.
+  - 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.
+  - Implemented efficient limit/offset pagination, negation filters using roaring bitmaps, CMEK encryption (GCP), and group-by deduplication for chunked-document search results.
+]
+
+#v(5pt)
+
+#entry(
+  [Duolingo], [Summer 2022],
+  [Software Engineer Intern, Data Infrastructure and Experimentation Team],
+)[
+  - Implemented approximate query pipeline on BigQuery for the analytics dashboard, saving >50% query time at \<1% uncertainty.
+  - Implemented caching mechanism for common queries based on historical frequencies (AWS, Jenkins), saving >80% time for analysts.
+]
+
+#v(5pt)
+
+#entry(
+  [R-Polars Project], [Summer 2023],
+  [Contributor, Google Summer of Code],
+)[
+  - Exported Polars features to R including streaming I/O in Apache Parquet and Arrow formats.
+  - Refactored error handling with recoverable errors from Rust and implemented background query pipeline via multi-threading, saving >50% user wait time.
+]
+
+// === RESEARCH ===
+#section("Research")
+
+#entry(
+  [QED: A Powerful Query Equivalence Decider for SQL], [2021 -- 2024],
+  [UC Berkeley EECS, advised by Alvin Cheung · Published at VLDB 2024],
+)[
+  - 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.
+  - 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.
+]
+
+// === SKILLS ===
+#v(4pt)
+#line(length: 100%, stroke: 0.4pt)
+*Languages:* Rust, Python, SQL, R, Nix \
+*Tools & Infrastructure:* Linux, Git, gRPC/Protobuf, tokio, axum, AWS, GCP, Kubernetes, Docker