Satya Phanindra Kalaga

I work on making language model systems reliable and accountable in high-stakes settings, including education, workforce policy, and regulated finance. I am applying to PhD programs in the current admissions cycle.

AI policy in higher education. At the Open Syllabus Project, I lead an analysis of how AI-use policies in academic syllabi have changed over time, working toward a public dashboard for students, universities, and journalists.

Traceable skill extraction. I am the first author of LAiSER, an open-source framework, under review at the Journal of Open Source Software, that links skills in job postings and syllabi to national taxonomies and records the source and similarity score of every match. I scaled its extraction pipeline to more than 1M job descriptions, reducing processing time from 2 months to 8 days on high-performance computing clusters. The framework is integrated into the MIT Digital Credentials Consortium’s Credential Co-writer, and analysis built on it supported recommendations on two local training policies for the Texas Workforce Commission.

Accountable autonomous agents. I am a co-author of a manuscript submitted to Software: Practice and Experience that systematizes threats and controls for autonomous agents in regulated financial systems, and I built its open-source reference implementation.

Beyond research, I have taught workshops on multi-agent systems to 100+ students and faculty, served as a graduate assistant for Natural Language Processing, and co-organize Google Developer Groups DC. I hold an MS in Data Science from George Washington University (3.90 GPA).