3 citations · 17 across the 23 of their papers we have counts for
27 papers
Bolt-on, Verifiable Provenance for LLM-Powered Data Processing
Yiming Lin, Sepanta Zeighami, Aditya G. Parameswaran
Large Language Models (LLMs) are powerful tools for processing data. However, LLMs are also complex black-boxes, returning answers to queries on data, without any indication for wh…
Who's Keeping Score? Interactive Steering of LLM-Powered Scoring with Attune
Bhavya Chopra, Meng Chen, Rebecca Dang +5
Large language models (LLMs) are increasingly used to score text records at scale (e.g., rating candidate resumes on a 1-5 scale). However, existing LLM-powered approaches do not a…
Semantic Data Processing with Holistic Data Understanding
Youran Sun, Sepanta Zeighami, Bhavya Chopra +2
Semantic operators have increasingly become integrated within data systems to enable processing data using Large Language Models (LLMs). Despite significant recent effort in improv…
Can AI Agents Answer Your Data Questions? A Benchmark for Data Agents
Ruiying Ma, Shreya Shankar, Ruiqi Chen +7
However, building reliable data agents remains difficult because real enterprise data is fragmented across many heterogeneous database systems, with duplicated and inconsistent dat…
Arming Data Agents with Tribal Knowledge
Shubham Agarwal, Asim Biswal, Sepanta Zeighami +3
Natural language to SQL (NL2SQL) translation enables non-expert users to query relational databases through natural language. Recently, NL2SQL agents, powered by the reasoning capa…
Task Cascades for Efficient Unstructured Data Processing
Shreya Shankar, Sepanta Zeighami, Aditya Parameswaran
Modern database systems allow users to query or process unstructured text or document columns using LLM-powered functions. Users can express an operation in natural language (e.g.,…
Featurized-Decomposition Join: Low-Cost Semantic Joins with Guarantees
Sepanta Zeighami, Shreya Shankar, Aditya Parameswaran
Large Language Models (LLMs) are being increasingly used within data systems to process large datasets with text fields. A broad class of such tasks involves a semantic join-joinin…
Multi-Objective Agentic Rewrites for Unstructured Data Processing
Lindsey Linxi Wei, Shreya Shankar, Sepanta Zeighami +3
One year ago, we open-sourced DocETL, a declarative system for LLM-powered data processing that, as of March 2026, has 3.7K GitHub stars and users across domains (e.g., journalism,…
Cut Costs, Not Accuracy: LLM-Powered Data Processing with Guarantees
Sepanta Zeighami, Shreya Shankar, Aditya Parameswaran
Large Language Models (LLMs) are being increasingly used as a building block in data systems to process large text datasets. To do so, LLM model providers offer multiple LLMs with…
Supporting Our AI Overlords: Redesigning Data Systems to be Agent-First
Shu Liu, Soujanya Ponnapalli, Shreya Shankar +12
Large Language Model (LLM) agents, acting on their users' behalf to manipulate and analyze data, are likely to become the dominant workload for data systems in the future. When wor…
RAG Without the Lag: Interactive Debugging for Retrieval-Augmented Generation Pipelines
Quentin Romero Lauro, Shreya Shankar, Sepanta Zeighami +1
Retrieval-augmented generation (RAG) pipelines have become the de-facto approach for building AI assistants with access to external, domain-specific knowledge. Given a user query,…
LLM-Powered Proactive Data Systems
Sepanta Zeighami, Yiming Lin, Shreya Shankar +1
With the power of LLMs, we now have the ability to query data that was previously impossible to query, including text, images, and video. However, despite this enormous potential,…
Towards Establishing Guaranteed Error for Learned Database Operations
Sepanta Zeighami, Cyrus Shahabi
Machine learning models have demonstrated substantial performance enhancements over non-learned alternatives in various fundamental data management operations, including indexing (…
Theoretical Analysis of Learned Database Operations under Distribution Shift through Distribution Learnability
Sepanta Zeighami, Cyrus Shahahbi
Use of machine learning to perform database operations, such as indexing, cardinality estimation, and sorting, is shown to provide substantial performance benefits. However, when d…
NUDGE: Lightweight Non-Parametric Fine-Tuning of Embeddings for Retrieval
Sepanta Zeighami, Zac Wellmer, Aditya Parameswaran
-Nearest Neighbor search on dense vector embeddings (-NN retrieval) from pre-trained embedding models is the predominant retrieval method for text and images, as well as Retr…
Towards Accurate and Efficient Document Analytics with Large Language Models
Yiming Lin, Madelon Hulsebos, Ruiying Ma +4
Unstructured data formats account for over 80% of the data currently stored, and extracting value from such formats remains a considerable challenge. In particular, current approac…
BiasBuster: a Neural Approach for Accurate Estimation of Population Statistics using Biased Location Data
Sepanta Zeighami, Cyrus Shahabi
While extremely useful (e.g., for COVID-19 forecasting and policy-making, urban mobility analysis and marketing, and obtaining business insights), location data collected from mobi…
On Distribution Dependent Sub-Logarithmic Query Time of Learned Indexing
Sepanta Zeighami, Cyrus Shahabi
A fundamental problem in data management is to find the elements in an array that match a query. Recently, learned indexes are being extensively used to solve this problem, where t…
NeuroSketch: Fast and Approximate Evaluation of Range Aggregate Queries with Neural Networks
Sepanta Zeighami, Cyrus Shahabi, Vatsal Sharan
Range aggregate queries (RAQs) are an integral part of many real-world applications, where, often, fast and approximate answers for the queries are desired. Recent work has studied…
A Neural Approach to Spatio-Temporal Data Release with User-Level Differential Privacy
Ritesh Ahuja, Sepanta Zeighami, Gabriel Ghinita +1
Several companies (e.g., Meta, Google) have initiated "data-for-good" projects where aggregate location data are first sanitized and released publicly, which is useful to many appl…
A Neural Database for Differentially Private Spatial Range Queries
Sepanta Zeighami, Ritesh Ahuja, Gabriel Ghinita +1
Mobile apps and location-based services generate large amounts of location data that can benefit research on traffic optimization, context-aware notifications and public health (e.…
NeuroDB: A Neural Network Framework for Answering Range Aggregate Queries and Beyond
Sepanta Zeighami, Cyrus Shahabi
Range aggregate queries (RAQs) are an integral part of many real-world applications, where, often, fast and approximate answers for the queries are desired. Recent work has studied…
Towards Accurate Spatiotemporal COVID-19 Risk Scores using High Resolution Real-World Mobility Data
Sirisha Rambhatla, Sepanta Zeighami, Kameron Shahabi +2
As countries look towards re-opening of economic activities amidst the ongoing COVID-19 pandemic, ensuring public health has been challenging. While contact tracing only aims to tr…
Estimating Spread of Contact-Based Contagions in a Population Through Sub-Sampling
Sepanta Zeighami, Cyrus Shahabi, John Krumm
Physical contacts result in the spread of various phenomena such as viruses, gossips, ideas, packages and marketing pamphlets across a population. The spread depends on how people…