Vaibhavi Singh

I am a graduate student in Computer Science at NYU Courant, specializing in agentic reasoning and representation learning. I research complex reasoning and autonomous planning in language models to enable robust, general-purpose intelligence.

Prior to NYU, I engineered large-scale software systems at Adobe and Salesforce, building the core libraries that power Creative Cloud and Einstein AI for millions of users. Most recently, I developed production NLP systems and clinical risk prediction models in healthcare.

Email  /  LinkedIn  /  GitHub  /  Twitter  /  Writings

Research

Engineered the evaluation harness to audit the faithfulness of reasoning traces in Vision-Language Models. Developed a granular failure taxonomy to disentangle perception hallucinations from deductive logic errors, analyzing how models hallucinate reasoning paths even on correct answers.

Conducted an empirical analysis on the limits of self-supervised learning in low-resource regimes. Analyzed the trade-off between tokenization density and dataset scale, finding that domain-aligned tokenization serves as a stronger supervision signal than sheer data volume for specialized distributions.

Academic Service

Ethics Reviewer — Datasets & Benchmarks Track
Technical Reviewer — Workshops - (UniReps) Unifying Representations in Neural Models, (ML4PS) Machine Learning and the Physical Sciences

Experience

Built clinical risk prediction models (XGBoost, TCN) achieving 0.87 F1-score through feature engineering, SMOTE for class imbalance, & hyperparameter optimization. Processed sparse EMR data for early-stage healthcare applications.

Engineered petabyte-scale data ingestion pipelines, reducing latency by 30% for Einstein AI & real-time analytics. Scaled multi-tenant Kubernetes infrastructure on AWS for 200+ microservices.

Optimized heterogeneous compute (CPU/GPU) architectures for on-device neural inference, reducing latency for 20M+ users. Extended core C++ text-processing engines to handle complex document analysis and font parsing, ensuring high-throughput performance under strict SLAs.

Scaled distributed data serving infrastructure, optimizing high-throughput request handling for 10M+ daily users. Reduced compute overhead by 12% through system-level performance profiling.

Education

Research focus: agentic reasoning & representation learning

Coursework: Deep Learning (Yann LeCun), Natural Language Processing (Eunsol Choi), Computer Vision (Saining Xie)

Graduated in the top 10% of the department
Recipient of EPFL-Swiss Government scholarship (Scala Days 2019)
Google Summer of Code Mentor, Anita Borg Institute

Last updated: February 1, 2026
Website template from Jon Barron