Summary
Ishani Mondal is a research-focused NLP and ML scientist with nine years of experience bridging academic rigor and industry research across Microsoft, IBM, Adobe, and Google DeepMind. Currently a PhD candidate and Graduate Research Assistant at University of Maryland, she develops BioNLP and knowledge-graph augmentation methods for drug repurposing, large-scale literature/patent mining, and QA evaluation, with several arXiv publications on human-AI collaboration and answer equivalence. Her work spans end-to-end KG construction, KG-embedding link prediction, and automated feature extraction using deep learning, informed by practical healthcare and multilingual conversational AI deployments at Microsoft. She combines strong software engineering roots from early industry roles with hands-on annotation schema design, ASR/TTS pipeline work for low-resource languages, and a track record of internships and fellowships that translate research into applied systems. Notably, she has contributed to projects that probe how LLMs change adversarial QA generation and to interactive IE studies assessing human-AI synergy in information extraction.
9 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, 4/4, Doctor of Philosophy - PhD, Computer Science, 4/4 at University of Maryland
English, Hindi, Bengali