Kyle Williams

Kyle Williams

Senior Scientist

Microsoft


Biography

I’m a Senior Scientist at Microsoft Healthcare working on AI + Healthcare. I research and develop novel machine learning systems for healthcare and support them from conception to production. I love using my technical skills to have an impact in the healthcare space.

I have experience conducting research and developing production quality models in the fields of natural language processing, conversational AI, knowledge graphs, information retrieval, evaluation, metrics, and experimentation.

I received my PhD in 2016 from The Pennsylvania State University and have co-authored over 40 peer reviewed research papers related to my work.

Interests
  • Information Retrieval
  • Machine Learning
  • Experimentation
  • Artificial Intelligence
  • Natural Language Processing
Education

  • PhD in Information Sciencies and Technology, 2016

    The Pennsylvania State University

  • MS in Computer Science, 2012

    The University of Cape Town

  • BS in Management Studies with a specialization in Computer Science, 2009

    The University of Cape Town

Featured News

I have joined the EmpowerMD in Microsoft Healthcare to work on NLP + Healthcare!
I have joined the EmpowerMD within Microsoft Healthcare to work on problems related to NLP + Healthcare. EmpowerMD is a tool that produces clinical notes from doctor-patient conversations.
Last updated on 9 December 2020
Paper on Zero Shot Intent Classication using LSTMs accepted for publication at INTERSPEECH 2019!
Our paper on Zero Shot Intern Classification using LSTMs has been accepted for publication at INTERSPEECH 2019. In this paper we propose an LSTM-based encoder that can be used to map user utterances to dialog intents. We show how the LSTM-based method outperforms a DSSM on this task, both for zero shot and few shot intent classification.
Last updated on 14 July 2019
Paper on Automatic Task Completion Flows from Web APIs accepted at SIGIR!
Our paper on Automatic Tasks Completion Flows from Web APIs was accepted as a short paper at SIGIR 2019. This paper, co-authored with Seyyed Hadi Hashemi and Imed Zitouni proposed a method for automatically creating task completion flows by chaining together Web APIs. Given a user query, it automatically constructs a flow by chaining together APIs and allowing data to flow from one API to the next. The order in which the APIs are chained is learned by a machine learning model, which generates a set of plans and then scores them by the likelihood of them leading to user satisfaction.
Last updated on 14 July 2019

Contact