About Me

I am Andrew Gambardella (Japanese name: Akira Takanami), postdoctoral researcher at the University of Tokyo, under the supervision of Professor Yutaka Matsuo.

Prior to joining UTokyo I spent a year as a postdoctoral researcher at KAIST.

I obtained my DPhil (PhD) in Engineering Science at Oxford, advised by Professor Phil Torr.

During my undergrad years I studied EECS at UC Berkeley, where my undergraduate research was advised by Professor Satish Rao.

I have lived in the USA, Japan, Korea, and the UK. I speak fluent English and Japanese and less-fluent Korean and Chinese.

My full CV can be found here.

A bio for media coverage and similar purposes can be found here.

Research

My research interests include:

• Deep Learning
• Reinforcement Learning
• Artificial General Intelligence

Publications

Journal Articles

• gOd, mOther and sOldier: A Story of Oppression, Told Through the Lens of AI. Andrew Gambardella, Meeyung Chung, Doyo Choi, Jinjoon Lee. To Appear in Leonardo, 2023. [MIT Press Direct]

Conference Proceedings

• Multitask Soft Option Learning. Maximilian Igl, Andrew Gambardella, Nantas Nardelli, N. Siddharth, Wendelin Böhmer, Shimon Whiteson. In Uncertainty in Artificial Intelligence, 2020. [UAI Proceedings] [arXiv]

Workshops

• Detecting and Quantifying Malicious Activity with Simulation-based Inference. Andrew Gambardella, Bogdan State, Naeemullah Khan, Leo Tsourides, Philip H. S. Torr, Atılım Güneş Baydin. In International Conference on Machine Learning: Workshop on Socially Responsible Machine Learning, 2021. [arXiv]
• Hierarchical Semantic Sonification for the Visually Impaired. Andrew Gambardella, Jinjoon Lee. In IJCAI: Workshop on Artificial Intelligence for Social Good, 2021. [AI4SG-21]
• Simulation-Based Inference for Global Health Decisions. Christian Schroeder de Witt, Bradley Gram-Hansen, Nantas Nardelli, Andrew Gambardella, Rob Zinkov, Puneet Dokania, N. Siddharth, Ana Belen Espinosa-Gonzalez, Ara Darzi, Philip Torr, Atılım Güneş Baydin. In International Conference on Machine Learning Workshop: ML for Global Health, 2020. [arXiv]

Preprints

• Language Models Do Hard Arithmetic Tasks Easily and Hardly Do Easy Arithmetic Tasks. Andrew Gambardella, Yusuke Iwasawa, Yutaka Matsuo. In submission, 2024.
• Real-World Robot Applications of Foundation Models: A Review. Kento Kawaharazuka, Tatsuya Matsushima, Andrew Gambardella, Jiaxian Guo, Chris Paxton, Andy Zeng. In submission, 2024. [arXiv]
• Efficient Data Mosaicing with Simulation-based Inference. Andrew Gambardella, Youngjun Choi, Doyo Choi, Jinjoon Lee. In submission, 2023. [arXiv]
• Transflow Learning: Few-shot Adaptation for Normalizing Flow Models. Andrew Gambardella, Atılım Güneş Baydin, Philip H. S. Torr. In submission, 2019. [arXiv]

Thesis

• Deep Transfer Learning with Bayesian Inference. Andrew Gambardella. DPhil Thesis, 2021. [Oxford University Research Archive]

Contact

You can reach me at my UTokyo email address and GitHub.

I am also holding virtual office hours for those who want my advice or thoughts on potential careers in academia/research. Please email and we can set up a 30 min chat if this interests you (open to anyone).