![avatar](/images/avatar.png)
Linh Tran (she/her), Ph.D.
Email | CV | Google Scholar | Twitter
I am a Research Scientist at Meta working on deep generative models, in particular Text-to-Image Diffusion Models. Before joining Meta, I was a Senior Research Scientist at Autodesk AI lab and interned at Google Brain and Samsung AI. Before my time in industry, I spent several years at Imperial College London completing my PhD under the supervision of Maja Pantic.
I am generally interested in deep probablisitc modeling, learning with limited supervision, and representation learning. I am excited to apply these methods to 2D and 3D computer vision and robotics (vision-based, Sim-to-Real transfer). Further, I am also interested in developing interpretable and trustworthy machine learning models. My PhD research focused on learning interpretable and human-controllable representations using deep generative modeling.
I have been volunteering for several non-profit organizations. I was fortunate to be one of the organizers of the Women in Machine Learning (WiML) workshop (co-located at NeurIPS) in 2021. I served as a Board Member for the non-profit organization Women in Machine Learning (WiML) from April 2022 until July 2023. In collaboration with Autodesk Foundation and Bridges to Prosperity,, I led a multi-disciplinary team over six months, analyzing geospatial data and developing deep learning models for remote bridge site identification in 2022.
Outside of work, I am mum to a little toddler called Milo. I enjoy being creative, organising events for friends & family, travelling the world, gardening and yoga.
I am always happy to chat about research and possibilities of collaborations. Feel free to drop me an email.
News
Publications
Selected publications
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L. Tran, M. Pantic and M.P. Deisenroth
Journal of Machine Learning Research, 2022
[ arxiv | jmlr proceedings | code ]
![pubavatar](/images/hydra_workshop.png)
L. Tran, B.S. Veeling, K. Roth, J. Swiatkowski, J.V. Dillon, J. Snoek, S. Mandt, T. Salimans, S. Nowozin and R. Jenatton
Uncertainty & Robustness in Deep Learning (UDL), ICML 2020
[ arxiv (long version) | workshop page | workshop paper ]
![pubavatar](/images/regularised_gagan.jpg)
L. Tran, J. Kossaifi, Y. Panagakis and M. Pantic
International Journal of Computer Vision, pp.1-21, 2019.
[ ijcv open access ]
![pubavatar](/images/gagan.jpg)
J. Kossaifi, L. Tran, Y. Panagakis and M. Pantic
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
[ arxiv | cvpr proceedings ]
![pubavatar](/images/deepcoder.png)
D. L. Tran*, R. Walecki*, Ognjen (Oggi) Rudovic*, S. Eleftheriadis, B. Schüller and M. Pantic (* denotes equal contribution)
Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017
[ arxiv | iccv proceedings | poster ]
Conference articles
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S. Asgari, A. Khani, F. Khani, A. Gholami, L. Tran, Ali Mahdavi Amiri and Ghassan Hamarneh
Advances in Neural Information Processing Systems (NeurIPS), 2023
[ NeurIPS openreview | arxiv | code ]
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H. Chu, A. H. Khasahmadi, K. D. D. Willis, F. Anderson, Y. Mao, L. Tran, J. Matejka, J. Vermeulen
International Conference on Machine Learning and Applications (ICMLA), 2023
[ ICMLA proceedings | arxiv ]
![pubavatar](/images/coil_conference.png)
P. J. Bentley, S. L. Lim, A. Gaier and L. Tran
17th International Conference on Parallel Problem Solving from Nature (PPSN XVII), 2022
[ PPSN proceedings | code ]
![pubavatar](/images/joinable.png)
K. D. D. Willis, P. K. Jayaraman, H. Chu, Y. Tian, Y. Li, D. Grandi, A. Sanghi, L. Tran, J. G. Lambourne, A. Solar-Lezama and W. Matusik
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
[ arxiv | cvpr proceedings | poster | code | dataset ]
![pubavatar](/images/cold_posterior.png)
F. Wenzel*, K. Roth*, B.S. Veeling*, J. Swiatkowski, Tran, L., S. Mandt, J. Snoek, T. Salimans, R. Jenatton and S. Nowozin (* denotes equal contribution)
37th International Conference on Machine Learning (ICML), 2020
[ arxiv | icml proceedings | github ]
![pubavatar](/images/ktied_bnn_icml.png)
J. Swiatkowski, K. Roth, B.S. Veeling, L. Tran, J.V. Dillon, S. Mandt, J. Snoek, T. Salimans, R. Jenatton and S. Nowozin
37th International Conference on Machine Learning (ICML), 2020
[ arxiv | icml proceedings ]
Journal articles
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L. Tran, T. Hamp and B. Rost
Plos ONE, 2018
[ bioarxiv | plos one open access | project page ]
Peer-reviewed workshop articles
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P. J. Bentley, S. L. Lim, A. Gaier and L. Tran
Workshop on Enhancing Generative Machine Learning with Evolutionary Computation (EGML), GECCO 2022
[ arxiv (long version) | workshop page | workshop paper | code ]
![pubavatar](/images/groupvae.png)
L. Tran, S.A. Taghanaki, A.H. Khasahmadi and A. Sanghi
Workshop on Weakly Supervised Learning (WeaSuL), ICLR 2021
[ arxiv (long version) | workshop page | workshop paper ]
Pre-prints
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M. F. Alam, Y. Wang, L. Tran, C.-Y. Cheng and J. Luo
arXiv preprint arXiv:2309.02583 2022
[ arxiv ]
![pubavatar](/images/ct.png)
S. Asgari, A. Gholami, F. Khani, K. Choi, L. Tran, R. Zhang and A. Khani
arXiv preprint arXiv:2207.01548 2022
[ arxiv ]
Other
L. Tran, M. Nadaf, R. Bushinsky, P. S. Alluru, D. Goswami, A. Noriega and J. Shirley
[ pdf | code ]
Interdisciplinary, machine learning pro bono project for remote bridge site identification with Autodesk Foundation and Bridges to Prosperity.