Welcome to Tsui-Wei (Lily) Weng’s Homepage!

I am an assistant professor in Halıcıoğlu Data Science Institute with affiliation to Computer Science and Engineering Department at UC San Diego. My research vision is to make the next generation AI systems and deep learning algorithms more robust, reliable, trustworthy and safer.

I’m open to collaborations with highly motivated students and post-doc researchers with strong machine learning and mathematical backgrounds! Please contact me with your CV and research experience once you have completed the form:

• For UCSD students: If you are a current/admitted UCSD student, please fill out this form
• For Visiting scholars: If you are interested in visiting opportunities, please fill out this form

Education

• Ph.D in Massachusetts Institute of Technology, Cambridge, MA, 2020
  Electrical Engineering and Computer Science
  Advisor: Prof. Luca Daniel
  Thesis: Evaluating robustness of neural networks
  

• M.S. in National Taiwan University, Taiwan, 2013
  Graduate Institue of Communication Engineering
  Advisor: Prof. Tzong-Lin Wu
  Thesis: Design of Broadband Common-Mode Filters
  

• B.S. in National Taiwan University, Taiwan, 2011
  Major: Electrical Engineering
  Graduated at class rank in the top 5%
  

Work experience

• MIT-IBM Watson AI Lab, Cambridge, MA
  Research Staff Member, 2020-2021

• Google DeepMind, London, UK
  Research Scientist Intern, Summer 2019
  Project: Evaluating robustness of deep reinforcement learning agents with continuous control tasks
  Results published in ICLR 2020

• IBM Research, Yorktown Heights, NY
  Research Intern, Summer 2018
  Project: Evaluating robustness of neural networks

• IBM Research, Yorktown Heights, NY
  Research Intern, Summer 2017
  Project: Trajectory data mining and anomaly detection

• Mitsubishi Electric Research Lab, Cambridge, MA
  Research Intern, Summer 2015
  Project: Irregular bin-packing on 2D objects

• Institute of Mathematics, Academia Sinica, Taipei, Taiwan
  Research Intern, Summer 2009
  Project: Combinatorics

Research on Enhancing robustness of deep neural networks & AI safety

• Robust Deep Reinforcement Learning through Adversarial Loss
  T. Oikarinen, W. Zhang, A. Megretski, L. Daniel and T.-W. Weng
  NeurIPS 2021

• On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta Learning
  R. Wang, K. Xu, S. Liu, P.-Y. Chen, T.-W. Weng, G. Chuang and M. Wang
  ICLR 2021

• Fast Training of Provably Robust Neural Networks by SingleProp
  A. Boopathy, T.-W. Weng, S. Liu, P.-Y. Chen, G. Zhang and L. Daniel
  AAAI 2021

• Neural Network Control Policy Verification with Persistent Adversarial Perturbations
  Y.-S. Wang, T.-W. Weng, and L. Daniel,
  ICML 2020

• Robust Deep Reinforcement Learning through Adversarial Loss
  T. Oikarinen, T.-W. Weng and L. Daniel
  ICML 2020, Uncertainty and Robustness in Deep Learning workshop

• Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control
  T.-W. Weng, K. Dvijotham², J. Uesato², K. Xiao², S. Gowal², R. Stanforth², Pushmeet Kohli
  ICLR 2020

Research on Neural network robustness, Deep learning theory, and Foundation models

• Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework
  C.-Y. Ko, J. Mohapatra, S. Liu, P.-Y. Chen, L. Daniel and T.-W. Weng
  Preprint

• On the Equivalence between Neural Network and Support Vector Machine
  Y. Chen, W. Huang, L. M. Nguyen and T.-W. Weng
  NeurIPS 2021

Research on Neural network robustness verification and certification (with provable guarantees)

• Hidden Cost of randomized smoothing
  J. Mohapatra, C.-Y. Ko, T.-W. Weng, P.-Y. Chen, S. Liu and L. Daniel
  AIstats 2021

• Higher order certification for randomized smoothing
  J. Mohapatra, C.-Y. Ko, T.-W. Weng, P.-Y. Chen, S. Liu and L. Daniel
  NeurIPS 2020

• Certified Interpretability Robustness for Class Activation Mapping
  A. Gu, T.-W. Weng, P.-Y. Chen, S. Liu and L. Daniel
  NeurIPS 2020, ML4AD workshop [Video]

• Towards Verifying Robustness of Neural Networks Against a Family of Semantic Perturbations
  J. Mohapatra, T.-W. Weng, P.-Y. Chen, S. Liu and L. Daniel
  CVPR 2020 [Video]

• Towards Certificated Model Robustness Against Weight Perturbations
  T.-W. Weng¹, P. Zhao¹, S. Liu, P.-Y. Chen and X. Lin, L. Daniel
  AAAI 2020

• Verification of Neural Network Control Policy Under Persistent Adversarial Perturbation
  Y.-S. Wang, T.-W. Weng and L. Daniel
  NeurIPS 2019, Safety and Robustness in Decision Making Workshop

• PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach
  T.-W. Weng, P.-Y. Chen², L. M. Nguyen², M. S. Squillante², A. Boopathy, I. Oseledets, and L. Daniel
  ICML 2019

• POPQORN: Quantifying Robustness of Recurrent Neural Networks
  C.-Y. Ko¹, Z. Lyu¹, T.-W. Weng, L. Daniel, N. Wong, and D. Lin
  ICML 2019

• CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks
  A. Boopathy, T.-W. Weng, P.-Y. Chen, S. Liu and L. Daniel
  AAAI 2019

• Efficient Neural Network Robustness Certification with General Activation Functions
  H. Zhang¹, T.-W. Weng¹, P.-Y. Chen, C.-J. Hsieh and L. Daniel
  NeurIPS 2018

• Toward Fast Computation of Certified Robustness for ReLU Networks
  T.-W. Weng¹, H. Zhang¹, H. Chen, Z. Song, C.-J. Hsieh, D. Boning, I. S. Dhillon and L. Daniel
  ICML 2018

Research on Neural network robustness evaluation and estimation

• Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach
  T.-W. Weng¹, H. Zhang¹, P.-Y Chen, J. Yi, D. Su, Y. Gao, C.-J. Hsieh and L. Daniel
  ICLR 2018

• On extensions of clever: A neural network robustness evaluation algorithm
  T.-W. Weng¹, H. Zhang¹, P.-Y Chen, A. Lozano, C.-J. Hsieh and L. Daniel
  IEEE GlobalSIP 2018

Research on Robust Regression and Robust PCA

• Robust PCA through Robust Optimization
  Tsui-Wei Weng and Luca Daniel
  NeurIPS 2017, Women in Machine Learning Workshop (WiML)

• Computing Least Trimmed Squares Regression to Certifiable Optimality
  Tsui-Wei Weng, Rahul Mazumder and Luca Daniel
  NeurIPS 2017, Women in Machine Learning Workshop (WiML)

Research on Uncertainty Quantification in silicon photonics

• Uncertainty Quantification of Silicon Photonic Devices with Correlated and Non-Gaussian Random Parameters
  Tsui-Wei Weng¹, Zheng Zhang¹, Zhan Su, Youssef Marzouk, Andrea Melloni and Luca Daniel
  Optics Express, vol. 23, Issue 4, pp.4242-4254, 2015

• Stochastic Simulation and Robust Design Optimization of Integrated Photonic Filters
  Tsui-Wei Weng, Daniele Melati, Andrea Melloni and Luca Daniel
  Nanophotonics, vol. 6, Issue 1, pp. 299-308, July 2016

• A Big-Data Approach to Handle Process Variations: Uncertainty Quantification by Tensor Recovery
  Zheng Zhang, Tsui-Wei Weng, and Luca Daniel
  IEEE Components, Packaging and Manufacturing Technology, Dec. 2016 (Best paper award)

Research on Microwave circuits and Signal Integrity

• A Novel Miniaturized Bandstop Filter Using Defected Ground on System in Package(SiP)
  Tsui-Wei Weng and Tzong-Lin Wu
  IEEE EPEPS, Tempe, Arizona, USA, Oct. 2012

• Synthesis Model and Design of a Common-Mode Bandstop Filter (CM-BSF) with An All-Pass Characteristic for High-Speed Differential Signals
  Tsui-Wei Weng, Chung-Hao Tsai, Chung-Hao Chen, Don-Ho Han and Tzong-Lin Wu
  IEEE Trans. Microw. Theory Tech., vol. 62, no.8, pp.1647-1656, Aug. 2014

Research on Combinatorics

• Hyperbolic Expressions of Polynomial Sequences and Parametric Number Sequences Defined by Linear Recurrence Relations of Order 2
  Tian-Xiao He, Peter J.-S. Shiue, Tsui-Wei Weng
  Journal of Concrete and Applicable Mathematics, vol. 12, 63-85, 2014

• Sequences of Numbers Meet the Generalized Gegenbauer-Humbert Polynomials
  Tian-Xiao He, Peter J.-S. Shiue, Tsui-Wei Weng
  ISRN Discrete Mathematics, vol. 2011, Article ID 674167, 16 pages, 2011

• On relations of Chebyshev polynomial, Morgan-Voyce polynomial, Fibonacci number, Pell number and Lucas number (in Chinese)
  Tsui-Wei Weng
  Mathmedia, v.34, no. 4, p.31-42, 2010