Jennifer Williams

Jennifer Williams

Jennifer Williams
Lincoln Laboratory
Massachusetts Institute of Technology
Human Language Technology Group
244 Wood Street
Lexington, MA 02420-9108
email :

Jennifer Williams joined the Human Language Technology Group at MIT Lincoln Laboratory in 2012. She works on natural language processing, speech synthesis and prosodic modeling. She is interested in understanding which elements of the human speech signal contribute to the perception of meaning and fluent naturalness, as well as how speech prosody influences cognitive processing for listening and expectation.

Prior to joining the Laboratory, she was the recipient of the Singapore International Pre-Graduate Award and was also supported by the National Science Foundation as a visiting researcher in the area of machine translation for the Human Language Technology Department at the Institute for Infocomm Research (I2R) in Singapore. During her time at I2R, she developed a novel approach to discourse processing for real time speech-to-speech machine translation.

Ms. Williams began her STEM career in 2000 as an electrical engineering assistant and she was sponsored by the Apprenticeships in Science and Engineering program that was administered through the Oregon Graduate Institute of Science and Technology (now known as the Oregon Health Sciences University). Her work has spanned several of the engineering disciplines. She worked for Medford Fabrication and helped her team design blueprints for heavy steel machinery. She also worked as a museum educator in the Physics Lab and Laser Light and Holography Lab at the Oregon Museum of Science and Industry.

Ms. Williams received her BA degree, with magna cum laude honors, from Portland State University in Applied Linguistics in 2009. She received her MS degree from Georgetown University in Computational Linguistics in 2012. Her thesis demonstrated a novel method for machine reading of Twitter feeds in order to mine commonsense knowledge about events and habits for temporal reasoning in advanced text analytics. Ms. Williams has several tier-1 publications in HLT and is known for her creative problem solving abilities as well as her enthusiasm for taking technical risks in her research.


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