Professor, Department of Electrical Engineering and Computer Science
Luca Daniel is a professor in MIT’s Department of Electrical Engineering and Computer Science, and a principal investigator in the Research Laboratory of Electronics. His research interests include development of numerical techniques related to uncertainty quantification, inverse problems, robust optimization, parameterized model order reduction and integral equation solvers. His current applications of interest include evaluating and improving robustness of deep neural networks as well as of magnetic resonance imaging scanners, silicon photonics integrated systems, and electrical power distribution networks.
Daniel has received best-paper awards from several journals of the Institute of Electrical and Electronics Engineers. Other honors include an IBM Corporation Faculty Award, an IEEE Early Career Award in Electronic Design Automation, and the MIT School of Engineering’s Spira Award for Excellence in Teaching. He earned a PhD in electrical engineering and computer sciences at the University of California, Berkeley.
- Mohapatra, J., Weng, T. W., Chen, P. Y., Liu, S., Daniel, L. (2020). Towards Verifying Robustness of Neural Networks Against a Family of Semantic Perturbations. Conference on Computer Vision and Pattern Recognition (CVPR).
- Weng, T. W., Zhao, P., Liu, S., Chen, P. Y., Lin, X., Daniel, L. (2020). Towards Certificated Model Robustness Against Weight Perturbations. Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI).
- Ko, C.Y, Lyu, Z., Weng, T.W., Daniel, L., Wong, N., Lin, D. (2019). POPQORN: Quantifying robustness of recurrent neural networks. International Conference on Machine Learning (ICML).
- Boopathy, A., Weng, T. W., Chen, P. Y., Liu, S., Daniel, L. (2019).CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks. AAAI Conference on Artificial Intelligence (AAAI).
- Weng, T. W., Chen, P. Y., Nguye, L. M., Squillante, M. S., Oseledets, I., Daniel, L. (2019). PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach. International Conference on Machine Learning (ICML).
- Weng, T. W., Zhang, H., Chen, P. Y., Yi, J., Su, D., Gao, Y., Hsieh, C. J., Daniel, L. (2018). Evaluating the robustness of neural networks: An extreme value theory approach. International Conference on Learning Representations (ICLR).
- September 30, 2019: MIT News, MIT.nano awards inaugural NCSOFT seed grants for gaming technologies.
- March 7, 2019: MIT News, Combining artificial intelligence with their passions.
- February 20, 2019: Techtalks.com, Robust AI: Protecting neural networks against adversarial attacks.
- January 30, 2019: EE Times, AI Tradeoff: Accuracy or Robustness?
- August 13, 2018: Medium, Getting CLEVER(er): Expanding the Scope of a Robustness Metric for Neural Networks.