Shawn Wen
Data AnalystAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
CTO at PolyAI
Tsung-Hsien (Shawn) Wen is a co-founder and CTO of PolyAI, a London-based startup looking to use the latest developments in NLP and ML to create a general platform for deploying spoken dialogue systems. He holds a PhD from the Dialogue Systems group, University of Cambridge, where he worked under the supervision of Professor Steve Young. His research focuses on language generation and end-to-end dialogue modelling, specifically in learning to generate responses for task-oriented dialogue systems. He was the tutor of the "Deep Learning and NLG" tutorial at INLG 2016 and has given invited seminars to research groups at Google, Apple, Xerox, and Baidu China. Before PolyAI, He was the invited lecturer for Samsung's corporate training course in Warsaw, a research consultant at IPSoft Amelia team, and a research intern at Google Brain. He received best paper awards at EMNLP 2015 and SigDial 2015.
University of Cambridge
Doctor of Philosophy (Ph.D.), Engineering
January 1, 2014 – January 1, 2017
National Taiwan University
Master's degree, Electrical and Electronics Engineering
January 1, 2011 – January 1, 2013
National Taiwan University
Bachelor's degree, Electrical and Electronics Engineering
January 1, 2007 – January 1, 2011
PolyAI
CTO and Co-Founder
November 1, 2017 – Present
London Area, United Kingdom · Hybrid
Entrepreneur First
Cohort Member
November 1, 2017 – March 1, 2018
London Area, United Kingdom
Researcher
June 1, 2017 – October 1, 2017
Mountain View, CA, USA
IPsoft
Research Consultant
September 1, 2016 – June 1, 2017
Greater Cambridge Area
StorySense Computing
Algorithm Developer Intern
September 1, 2012 – August 1, 2013
Taipei City, Taiwan
National Taiwan University
Teaching Asistant
September 1, 2011 – June 1, 2013
Taipei City, Taiwan
StorySense Computing
Mobile Developer Intern
June 1, 2011 – September 1, 2012
Taipei City, Taiwan
NNDIAL
March 1, 2016 – Present
NNDial is an open source toolkit for building end-to-end trainable task-oriented dialogue models. It is released by Tsung-Hsien (Shawn) Wen from Cambridge Dialogue Systems Group under Apache License 2.0.
RNNLG
October 1, 2014 – February 1, 2016
RNNLG is an open source benchmark toolkit for Natural Language Generation (NLG) in spoken dialogue system application domains. It is released by Tsung-Hsien (Shawn) Wen from Cambridge Dialogue Systems Group under Apache License 2.0.
Search by Sketch!
April 1, 2013 – June 1, 2013
Developed an interactive sketch-based image retrieval system with a user-friendly webpage interface. Sketch as query to search from 7,000 world landmark photos.
Cultural Fit Analysis
The candidate's background is heavily skewed towards research, machine learning, and natural language processing, with significant entrepreneurial experience. While these are valuable, the target role is 'Data Analyst'. There is a potential mismatch in the primary focus; the candidate's profile suggests a more advanced research or data science role rather than a pure data analyst position. The projects are highly specialized in AI/ML, which might indicate a preference for deep technical innovation over routine data analysis tasks. The lack of explicit data analysis projects or skills (e.g., SQL, BI tools, A/B testing) in the resume makes it difficult to assess direct cultural fit for a typical data analyst team.
Soft Skills & Operational Fit
The candidate's entrepreneurial background as a CTO and co-founder, coupled with research roles, suggests strong problem-solving, innovation, and leadership skills. The academic and research focus indicates a methodical and analytical approach. However, the provided data does not offer direct insights into specific soft skills like teamwork or stress handling beyond general inferences from leadership roles.