Summary
Daniel Schwartz is an applied scientist and PhD candidate with a decade of hands-on experience building and deploying machine learning systems across industry and research, currently driving LLM robustness, quantization, and efficient fine-tuning at AWS. He has a strong track record of translating research into production—shipping FP8 quantization and multi-LoRA inference strategies, building hallucination-detection pipelines (FINCH-ZK), and contributing to a dozen patents from prior work at Dell Boomi. His research bridges deep learning theory and practical efficiency through convexification, kernels, sparse coding, and neuro-inspired methods, informed by applied projects from wearables to process mining. Daniel combines rigorous academic training (MS/BS with perfect GPAs and ongoing PhD) with product-minded engineering, notably optimizing adapters to deliver large gains in RL and math-reasoning benchmarks while cutting cost and latency for real-world serving. A New York–based practitioner, he pairs creative adversarial testing techniques with responsibility-focused systems to make AI both performant and safer.
10 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Drexel University
Diploma, HIGH SCHOOL/SECONDARY DIPLOMAS AND CERTIFICATES, 4.0 / 4.0, Diploma, HIGH SCHOOL/SECONDARY DIPLOMAS AND CERTIFICATES, 4.0 / 4.0 at Cherokee High School
Spanish