
Linh Tran (she/her), Ph.D.
Email | CV | Google Scholar | Twitter
I am a Senior Research Scientist at Autodesk AI lab. I also serve as a Board Member for the non-profit organization Women in Machine Learning (WiML). I completed my PhD at the Imperial College London under the supervision of Maja Pantic. Before joining Autodesk, I was a research intern at Google Brain and Samsung AI.
I am generally interested in learning with limited supervision, deep generative modeling, representation learning, and its application to 2D and 3D vision. My research focuses on learning interpretable and human-controllable representations using deep probabilistic modeling. In particular, I am interested in learning representations with limited supervision that generalizes to unseen environments and across tasks. I am excited by applying these methods to 2D and 3D computer vision, fairness, and robotics (vision-based, Sim-to-Real transfer) in which large-scale data is available, however, annotations are scarce. I am also interested in sequential data modeling, approximate inference, and model distillation.
Please contact me if you are interested to collaborate. The Autodesk AI lab has internship positions open every summer.
Outside of work, I enjoy travelling the world with family & friends, gardening on my balcony and trying numerous balancing yoga poses.
News
Publications
Pre-prints

S. Asgari, A. Gholami, F. Khani, K. Choi, L. Tran, R. Zhang and A. Khani
arXiv preprint arXiv:2207.01548 2022
[ arxiv ]
Conference articles

P. J. Bentley, S. L. Lim, A. Gaier and L. Tran
17th International Conference on Parallel Problem Solving from Nature (PPSN XVII), 2022
[ code ]

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 ]

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 ]

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 ]

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 ]

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 ]
Journal articles

L. Tran, M. Pantic and M.P. Deisenroth
Journal of Machine Learning Research, 2022
[ arxiv | jmlr proceedings | code ]

L. Tran, J. Kossaifi, Y. Panagakis and M. Pantic
International Journal of Computer Vision, pp.1-21, 2019.
[ ijcv open access ]

L. Tran, T. Hamp and B. Rost
Plos ONE, 2018
[ bioarxiv | plos one open access | project page ]
Peer-reviewed workshop articles

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 ]

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 ]

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 ]