Bruce (Zhi) Wen

Applied research scientist at Mila - Quebec AI Institute

I work in the applied research team at Mila, where we solve real-world problems with deep learning. As an example, we worked with Dialogue to develop automatic symptom detection systems and published our work at NeurIPS 2022, in main track and in dataset track.

I earned my master degree at McGill University, and I did my undergrad at Wuhan University, China. My research interests include machine learning and NLP for healthcare, and NLP with knowledge. In the past, I worked on COVID-19 media news surveillance for public health measures, constructing a large medical NLP pre-training dataset, among others.

I listen to jazz in the morning with coffee, post-rock or Pink Floyd when I use my brain (e.g. reading papers or coding), and shoegazing whenever I feel the need. In my free time I (used to) play soccer and (more recently) learn guitar.

selected publications

  1. NeurIPS
    Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors' Reasoning with Deep Reinforcement Learning
    Fansi Tchango, Arsene, Goel, Rishab, Martel, Julien, Wen, Zhi, Caron, Gaetan, and Ghosn, Joumana
    Thirty-sixth Conference on Neural Information Processing Systems 2022
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  2. Patterns
    Inferring global-scale temporal latent topics from news reports to predict public health interventions for COVID-19
    Wen, Zhi, Powell, Guido, Chafi, Imane, Buckeridge, David L, and Li, Yue
    Patterns 2022
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  3. EMNLP Workshop
    MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining
    Wen, Zhi, Lu, Xing Han, and Reddy, Siva
    In Proceedings of the 3rd Clinical Natural Language Processing Workshop 2020
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  4. Nat. Commun.
    Inferring multimodal latent topics from electronic health records
    Li, Yue, Nair, Pratheeksha, Lu, Xing Han, Wen, Zhi, Wang, Yuening, Dehaghi, Amir Ardalan Kalantari, Miao, Yan, Liu, Weiqi, Ordog, Tamas, Biernacka, Joanna M, and others,
    Nature communications 2020
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