Aditya Parameswaran

schoolUniversity of Illinoisbar_chart#1278 Global Rank

Total Papers

HCI Rank

#1278

Research Areas

Years Active

Recent Publications

RAG Without the Lag: Enabling "What-If" Analysis for Retrieval-Augmented Generation Pipelines

Retrieval-augmented generation (RAG) pipelines have become the de-facto approach for building AI assistants with external knowledge. Given a user query, RAG pipelines retrieve (R) information from external sources, before invoking a Large Language Model (LLM), augmented (A) with this information, to generate (G) respo…

UIST '255th+ AuthorHonorable MentionHuman-LLM Collaboration +2

Steering Semantic Data Processing With DocWrangler

Unstructured text has long been difficult to analyze automatically at scale. Large language models (LLMs) now make it possible to perform “semantic data processing,” where common data operators like map, reduce, and filter are powered by LLMs instead of traditional code. However, building these pipelines is challengin…

UIST '25CorrespondingHuman-LLM Collaboration +1

Rethinking Dataset Discovery with DataScout

Dataset Search---the process of finding appropriate datasets for a given task---remains a critical yet under-explored challenge in data science workflows. Assessing dataset suitability for a task (e.g., training a classification model) is a multi-pronged affair that involves understanding: data characteristics (e.g. g…

CSCW '24CorrespondingSession 3e: Trusting the Machine: Fake News. AI Decision-Making, and Auditing

"We Have No Idea How Models will Behave in Production until Production": How Engineers Operationalize Machine Learning

Organizations rely on machine learning engineers (MLEs) to deploy models and maintain ML pipelines in production. Due to models' extensive reliance on fresh data, the operationalization of machine learning, or MLOps, requires MLEs to have proficiency in data science and engineering. When considered holistically, the j…

UIST '244th AuthorHuman-LLM Collaboration

Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences

Due to the cumbersome nature of human evaluation and limitations of code-based evaluation, Large Language Models (LLMs) are increasingly being used to assist humans in evaluating LLM outputs. Yet LLM-generated evaluators simply inherit all the problems of the LLMs they evaluate, requiring further human validation. We…

CHI '21CorrespondingAI-Assisted Decision-Making & Automation +1

Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows

Efforts to make machine learning more widely accessible have led to a rapid increase in Auto-ML tools that aim to automate the process of training and deploying machine learning. To understand how Auto-ML tools are used in practice today, we performed a qualitative study with participants ranging from novice hobbyists…

IUI '19CorrespondingInteractive Data Visualization +1

Avoiding Drill-down Fallacies with VisPilot: Assisted Exploration of Data Subsets

As datasets continue to grow in size and complexity, exploring multi-dimensional datasets remain challenging for analysts. A common operation during this exploration is drill-down---understanding the behavior of data subsets by progressively adding filters. While widely used, in the absence of careful attention toward…

CHI '18CorrespondingUser Research Methods (Interviews, Surveys, Observation) +1

Characterizing Scalability Issues in Spreadsheet Software using Online Forums

In traditional usability studies, researchers talk to users of tools to understand their needs and challenges. Insights gained via such interviews offer context, detail, and background. Due to costs in time and money, we are beginning to see a new form of tool interrogation that prioritizes scale, cost, and breadth by…

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