Honored to have received the Tong Leong Lim Pre-Doctoral Prize at UC Berkeley!

Hi, I'm Shadaj

I explore the boundaries of programming

I'm a third-year PhD student co-advised by Alvin Cheung and Joe Hellerstein in the Sky Computing Lab at UC Berkeley, where I research programming languages for distributed systems. Most recently, I've worked on optimizers for distributed dataflow languages and program synthesis algorithms for provably-correct CRDT designs. I'm grateful to be supported by an NSF Graduate Research Fellowship.

Previously, I received my BS in EE/CS at Berkeley, where I was fortunate to work with Koushik Sen on new approaches to compile-time metaprogramming. I've interned at Amazon ARG, Google Brain, Facebook, Apollo GraphQL, Khan Academy, and Coursera, leading projects ranging from Rust backends for Dafny to open-source GraphQL IDEs.

Latest News

Apr 2023
Honored to have received the Tong Leong Lim Pre-Doctoral Prize at UC Berkeley!
Nov 2022
Our vision paper that lays out a research direction for CRDT data stores based on the CALM theorem has been accepted to VLDB 2023!
Oct 2022
Katara, our system for synthesizing CRDTs, is now open-source and will appear at OOPSLA 2022!
Jun 2022
Jan 2022
Attending CIDR 2022, and learning how to pitch programming languages research to the database community
Aug 2021
Started my PhD at UC Berkeley in the RISELab, working on the Hydro project under Alvin Cheung and Joe Hellerstein
Apr 2021
Received the EECS Major Citation award!
Mar 2021



Optimizing Distributed Protocols with Query Rewrites

David Chu, Rithvik Panchapakesan, Shadaj Laddad, Lucky E. Katanahas, Chris Liu, Kaushik Shivakumar, Natacha Crooks, Joseph M. Hellerstein, Heidi Howard

Distributed protocols such as 2PC and Paxos lie at the core of many systems in the cloud, but standard implementations do not scale. This paper presents an approach for scaling any distributed protocol by applying rule-driven rewrites, borrowing from query optimization. Manual rule-driven applications of decoupling and partitioning improve the throughput of 2PC by 5x and Paxos by 3x, and match state-of-the-art throughput in recent work.

Technical Report

VLDB 2023

Keep CALM and CRDT On

Shadaj Laddad*, Conor Power*, Mae Milano, Alvin Cheung, Natacha Crooks, Joseph M. Hellerstein

CRDT guarantees extend only to data updates; observations of CRDT state are unconstrained and unsafe. We propose an agenda that embraces the simplicity of CRDTs, but provides richer, more uniform guarantees. We extend CRDTs with a query model that reasons about which queries are safe without coordination by applying monotonicity results from the CALM Theorem.


VLDB 2023 (Invited Award Paper)

Towards Auto-Generated Data Systems

Alvin Cheung, Maaz Bin Safeer Ahmad, Brandon Haynes, Chanwut Kittivorawong, Shadaj Laddad, Xiaoxuan Liu, Chenglong Wang, Cong Yan



Optimizing Stateful Dataflow with Local Rewrites

Shadaj Laddad, Conor Power, Tyler Hou, Alvin Cheung, Joseph M. Hellerstein

Optimizing a stateful dataflow language is a challenging task. There are strict correctness constraints for preserving properties expected by downstream consumers, a large space of possible optimizations, and complex analyses that must reason about the behavior of the program over time. But with e-graphs, we can dramatically simplify the process of building a correct optimizer while yielding more consistent results! In this short paper, we discuss our early work using e-graphs to develop an optimizer for the Hydroflow dataflow language.

PDF / arXiv

ApPLIED 2023 (Invited Paper)

Initial Steps Toward a Compiler for Distributed Programs

Joseph M. Hellerstein, Shadaj Laddad, Mae Milano, Conor Power, Mingwei Samuel


OSCAR 2023

Code Transpilation for Hardware Accelerators

Yuto Nishida, Sahil Bhatia, Shadaj Laddad, Hasan Genc, Yakun Sophia Shao, Alvin Cheung

arXiv / PDF


Katara: Synthesizing CRDTs with Verified Lifting

Shadaj Laddad, Conor Power, Mae Milano, Alvin Cheung, Joseph M. Hellerstein

In this paper, we present Katara, a program synthesis-based system that takes sequential data type implementations and automatically synthesizes verified CRDT designs from them. Katara is able to automatically synthesize CRDTs for a wide variety of scenarios, from reproducing classic CRDTs to synthesizing novel designs based on specifications in existing literature. Crucially, our synthesized CRDTs are fully, automatically verified, eliminating entire classes of common errors and reducing the process of producing a new CRDT from a painstaking paper proof of correctness to a lightweight specification.

ACM / PDF / GitHub

ASE 2021

VizSmith: Automated Visualization Synthesis by Mining Data-Science Notebooks

Rohan Bavishi, Shadaj Laddad, Hiroaki Yoshida, Mukul R. Prasad, Koushik Sen

We built a tool called VizSmith which enables code reuse for visualizations by mining visualization code from Kaggle notebooks and creating a database of 7176 reusable Python functions. Given a dataset, columns to visualize and a text query from the user, VizSmith searches this database for appropriate functions, runs them and displays the generated visualizations to the user.


GPCE 2020

Fluid Quotes: Metaprogramming across Abstraction Boundaries with Dependent Types

Shadaj Laddad and Koushik Sen

We introduce Fluid Quotes, a new technique that uses dependent types to let users pass quotes through abstraction boundaries in runtime code while splicing them ahead-of-time. This technique enables new metaprogramming capabilities by eliminating the traditional requirement of co-locating parameter expressions with call-sites. Fluid Quotes capture not only source code but also associated runtime context to ensure correctness.


Fellowships and Awards