**Tzu-Yun Ariel Shann** ![](images/ariel.jpg width="300")
I’m an Applied AI/ML Engineer with a background in machine learning research and software engineering. I enjoy turning messy, real-world problems into practical AI-powered systems, from designing ML and data pipelines to integrating APIs and cloud infrastructure and bringing solutions into production. My experience spans startups, research labs, and customer-facing AI projects, and I’m particularly interested in using AI to automate and improve real-world workflows. I’m currently looking for opportunities to grow as an AI/ML Engineer and work on challenging problems where I can combine research-driven thinking with practical engineering. If you have an interesting opportunity and think I might be a good fit, feel free to connect with me via Email or LinkedIn. You can also find my [latest CV](https://lasirenashann.github.io/doc/tzuyun-ariel-shann-resume-sep-2026.pdf) here. Experience =================================================================== 2025/9-Present : *Delivery Engineer (Applied AI)* at *Virtual Science AI* :
_UK (Remote)_ : - Built and maintained production data and AI pipelines for pharmaceutical clients, covering data ingestion, transformation, transcription, summarization, analytics, and downstream reporting. : - Integrated client CRM and enterprise systems with internal AI/analytics platforms, including secure API-based integrations with Salesforce/Veeva environments. : - Designed and implemented analytics and monitoring tools to track platform usage and engagement. : - Worked directly with pharmaceutical clients to translate ambiguous business requirements into technical solutions. : - Delivered 110+ client projects across advisory boards, medical affairs, and social listening. 2024/2-2024/4 : *Software Engineer* at *Phazemos* :
_USA (Remote)_ : - Built Python-based automation and data-processing tools to streamline internal workflows and reduce manual operational work. : - Developed custom Salesforce Apex workflows and business logic to automate sales operations and activity tracking. : - Designed data pipelines integrating multiple internal and external data sources into client-facing analytics dashboards. : - Led data preprocessing, analysis, and visualization initiatives used by leadership to inform business strategy. : - Built workflow automations using Python, Make, Airtable, and ClickUp across internal operations. 2022/10-2023/2 : *AI Engineer* at *Onoma AI* :
_Seoul, South Korea (Remote)_ : - Led development of computer vision components for an AI-driven webtoon generation platform. : - Implemented generative and diffusion-based models to support automated visual content generation workflows. : - Built and evaluated object detection pipelines to support asset generation and scene composition. : - Worked with PyTorch and Hugging Face ecosystems to prototype and deploy experimental ML components. 2020/5-2020/11 : *Research Intern* at *Borealis AI* :
_Toronto, ON, Canada (Remote)_ : - *Supervisor*: Ruitong Huang, Pablo Hernandez Leal : - *Research topic*: exploration strategies for reinforcement learning 2017/1-2018/8 : *Research Assistant* at *National Tsing Hua University* :
_Hsinchu, Taiwan_ : - *Supervisor*: [Chun-Yi Lee](http://cymaxwelllee.wixsite.com/elsa) : - *Research topic*: reinforcement learning, multi-agent reinforcement learning 2016/7-2016/9 : *Software Engineer Intern* at *MediaTek* :
_Hsinchu, Taiwan_ Education =================================================================== 2018/9- 2022/4 : *MSc in Computer Science* at *University of British Columbia* :
_Vancouver BC, Canada_ : - *Supervisor*: Leonid Sigal, Michiel van de Panne : - *MSc Thesis*: [Reinforcement Learning in the Presence of Sensing Costs](https://open.library.ubc.ca/soa/cIRcle/collections/ubctheses/24/items/1.0413129) 2014/9-2017/6 : *BSc in Computer Science* at *National Tsing Hua University* :
_Hsinchu, Taiwan_ Publications =================================================================== + *Adversarial Exploration Strategy for Self-Supervised Imitation Learning*
Zhang-Wei Hong, Tsu-Jui Fu, *Tzu-Yun Shann*, Yi-Hsiang Chang, Chun-Yi Lee.
In Proceedings of the _Conference of Robot Learning (*CoRL*)_, Oct. 2019 (_Oral presentation_)
Paper + *Diversity-Driven Exploration Strategy for Deep Reinforcement Learning*
Zhang-Wei Hong, *Tzu-Yun Shann*, Shih-Yang Su, Yi-Hsiang Chang, Tsu-Jui Fu, Chun-Yi Lee.
In Advances in _Neural Information Processing Systems (*NeurIPS*)_, Dec. 2018.
(A [shorter version](https://openreview.net/pdf?id=BJsD7L1vz) of this paper has been accepted in ICLR 2018 Workshop)
Paper + *A Deep Policy Inference Q-Network for Multi-Agent Systems*
*Tzu-Yun Shann*♮, Shih-Yang Su♮, Zhang-Wei Hong♮, Yi-Hsiang Chang, Chun-Yi Lee.
In Proceedings of the _International Conference on Autonomous Agents and Multi-Agent Systems (*AAMAS*)_, Jul. 2018.
(♮ indicates equal contribution)
Paper Code + *Virtual-to-Real: Learning to Control in Visual Semantic Segmentation*
Zhang-Wei Hong, Yu-Min Chen, Shih-Yang Su, *Tzu-Yun Shann*, Yi-Hsiang Chang, Hsuan-Kung Yang, Brian Hsi-Lin Ho, Chih-Chieh Tu, Yueh-Chuan Chang, Tsu-Ching Hsiao, Hsin-Wei Hsiao, Sih-Pin Lai, Chun-Yi Lee.
In Proceedings of the _International Joint Conference on Artificial Intelligence (*IJCAI*)_, Jul. 2018.
Paper Video Teaching ================================================================== Winter I, 2018 : Teaching assistant for [CPSC425 Computer Vision](https://www.cs.ubc.ca/~lsigal/teaching18_Term1.html) Winter II, 2018 : Teaching assistant for [CPSC425 Computer Vision](https://www.cs.ubc.ca/~lsigal/teaching18_Term2.html) Winter I, 2019 : Teaching assistant for CPSC322 Introduction to Artificial Intelligence Dec 2019 : Teaching assistant for [IVADO/MILA/DSI Deep Learning School](https://ivado.ca/en/trainings/schools/ivado-mila-deep-learning-school-5th-edition-2/) Winter II, 2019 : Teaching assistant for [CPSC425 Computer Vision](https://www.cs.ubc.ca/~lsigal/teaching19_Term2.html) Winter II, 2020 : Teaching assistant for CPSC425 Computer Vision Winter Term I, 2021 : Teaching assistant for CPSC330 Applied Machine Learning Service ================================================================= Reviewing ----------------------------------------------------------------- - *NeurIPS* 2019, 2020 - *ICML* 2020 Volunteering ----------------------------------------------------------------- - *NeurIPS* 2018 - *ICLR* 2020 - *ICML* 2020 !!! note: Misc - I was born and raised in *Taiwan*, a beautiful island in the Pacific. - *Traveling* is something I enjoy very much. I’ve traveled across Asia, Europe, and North America, including Japan, Korea, Indonesia, the USA, the UK, Italy, Germany, the Netherlands, Belgium, Austria, Canada, Sweden, and France. - I’m a naturally curious person who loves exploring new things, learning from different cultures, and challenging myself. - When it comes to human languages ;) I’m fluent in Mandarin and English, and I also speak a little German, Spanish, Japanese, and Korean (still learning!).