Anjali Singh

singhanj@usc.edu

University of Southern California

Biography

Anjali Singh, a student hailing from Los Angeles, California, is passionate about the intricate world of machine learning, software development, and the vast domain of computers and mathematics. She approaches her academic and professional endeavors with a genuine enthusiasm for learning and contributing. Anjali’s dedication lies in exploring the intricacies of technology, and she’s always open to collaboration and growth within this dynamic field.

Interests

Machine Learning
Natural Language Processing
Software Development

Education

Masters in Computer Science, Fall'23-Present
University of Southern California
Bachelors in Information Technology, 2018-2022
Manipal Institute of Technology

Skills

Python

Java

NLP

Full-Stack Development

Machine Learning

Swift Application Development

Experience

Machine Learning Engineering Intern

SAP Ariba, Inc.

June 2024 – Present Palo Alto, California

Improved development speed by 15% through the integration of GPT-4o and Gemini 1.5 Pro for automating Figma data extraction and code generation
Built recursive LLM-based prompting systems to guide frontend development, ensuring smooth, step-by-step code creation
Automated data extraction from Figma via the REST API, streamlining frontend processes and reducing manual effort

Engineering Analyst

Goldman Sachs

August 2022 – June 2023 Bengaluru, India

Developed a high-performance volume testing framework using Gatling, Java, and React, cutting request latency by 20%
Led the front-end development of the Deal Management Website, significantly enhancing the user interface and experience
Migrated critical on-prem services to AWS, unlocking improved scalability and system efficiency

Summer Intern

Goldman Sachs

June 2021 – July 2021 Bengaluru, India

Participated in phasing out legacy applications, contributing to system modernization using Django and Appian
Designed and developed a new webpage for appraisal data entry, delivering a more intuitive and user-friendly interface

Global Remote Mentorship Intern

IBM

August 2020 – May 2021 Bengaluru, India

Explored explainability challenges in Graph Neural Networks, using the Entity Matching task to derive insights from tabular data
Suggested improvements for GNN explanations by emphasizing key features, node-level insights, and the importance of contextual edges
Research published at the ICML XAI, 2021 Workshop

Publications

Anjali Singh, Shamanth R Nayak K, Balaji Ganesan

July, 2021 In ICML XAI Workshop

Reimagining GNN Explanations with ideas from Tabular Data

Explainability techniques for Graph Neural Networks still have a long way to go compared to explanations available for both neural and decision decision tree-based models trained on tabular data. Using a task that straddles both graphs and tabular data, namely Entity Matching, we comment on key aspects of explainability that are missing in GNN model explanations.