Laura Kieras
About Laura Kieras
Laura Kieras is a Senior Software Engineer with extensive experience in natural language processing and machine learning. She has worked at several companies, including M*Modal, Amazon, and Edge Case Research, and holds a Master's Degree from Carnegie Mellon University.
Current Role at Edge Case Research
Laura Kieras serves as a Senior Software Engineer at Edge Case Research, a position she has held since 2019. In this role, she contributes her expertise in software development and engineering to advance the company's projects. Her responsibilities include designing and implementing software solutions that meet the specific needs of clients and stakeholders.
Previous Experience at M*Modal
Before joining Edge Case Research, Laura Kieras worked at M*Modal as a Senior NLP Engineer from 2010 to 2014. During her four years at M*Modal, she focused on natural language processing technologies, contributing to the development of innovative solutions in the healthcare sector.
Experience at Amazon as Machine Learning Scientist
Laura Kieras was employed at Amazon as a Machine Learning Scientist from 2015 to 2016. In this role, she applied her knowledge of machine learning algorithms and data analysis to enhance Amazon's technology offerings. Her work involved developing models and systems that improved the efficiency and effectiveness of various applications.
Educational Background in Language Technologies
Laura Kieras earned her Master’s Degree from Carnegie Mellon University, specializing in the Language Technologies Institute. This advanced education provided her with a strong foundation in language processing and computational linguistics, which she has applied throughout her career.
Undergraduate Studies in Computer Science and Linguistics
Laura Kieras completed her Bachelor’s Degree with a double major in Computer Science and Linguistics at the University of Michigan College of Literature, Science, and the Arts. This interdisciplinary background has equipped her with a unique skill set that combines technical programming knowledge with linguistic insights.