I am an assistant professor in Halıcıoğlu Data Science Institute with affiliation to Computer Science and Engineering Department at UC San Diego.
I direct the Trustworthy Machine Learning Lab at UCSD. My research vision is to make the next generation AI systems and deep learning algorithms more robust, reliable, explainable, trustworthy and safer.
Google Scholar / Research / Teaching / Email: lweng@ucsd.edu
I’m open to collaborations with highly motivated students with strong machine learning and mathematical backgrounds!
I received my PhD (2020) in Electrical Engineering and Computer Science from MIT advised by Prof. Luca Daniel on the topic of Robust Machine Learning with focus on Neural Networks, and M.S. (2013) and B.S. (2011) degrees in Electrical Engineering from National Taiwan University advised by Prof. Tzong-Lin Wu and Prof. Peter Shiue on the topic of microwave circuits and combinatorics. Prior to UCSD, I spent 1 year in MIT-IBM Watson AI Lab and several research internship in Google DeepMind, IBM Research and Mitsubishi Electric Research Lab.
Provably Robust Conformal Prediction with Improved Efficiency
G. Yan, Y. Romano and T.-W. Weng
ICLR 2024 (accepted)
Prediction without Preclusion: Recourse Verification with Reachable Sets
A. Kothari^{1}, B. Kulynych^{1}, T.-W. Weng and B. Ustun
ICLR 2024 (accepted)
The Importance of Prompt Tuning for Automated Neuron Explanations | [code] | [project page]
J. Lee^{1}, T. Oikarinen^{1}, A. Chatha, K.-C. Chang^{3}, Y. Chen^{3}, and T.-W. Weng
NeurIPS 2023 ATTRIB workshop
CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision Networks | [code]
T. Oikarinen and T.-W. Weng
ICLR 2023, notable-top-25% (spotlight)
Label-free Concept Bottleneck Models | [code]
T. Oikarinen, Subrho Das, Lam M. Nguyen and T.-W. Weng
ICLR 2023
Min-Max Bilevel Multi-objective Optimization with Applications in Robust Machine Learning
A. Gu, S. Lu, P. Ram and T.-W. Weng
ICLR 2023
Corrupting Neuron Explanations of Deep Visual Features | [code]
D. Srivastava, T. Oikarinen, and T.-W. Weng
ICCV 2023
ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction | [code]
W. Zhang, T.-W. Weng, S. Das, A. Megretski, L. Daniel and L. Nguyen
ICML 2023
Quantifying Safety of Learning-based Self-Driving Control Using Almost-Barrier Functions
Z. Qin, T.-W. Weng and S. Gao
IROS 2022
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
ICML 2022
Adversarially Robust Conformal Prediction
A. Gendler, T.-W. Weng, L. Daniel and Y. Romano
ICLR 2022
Robust Deep Reinforcement Learning through Adversarial Loss
T. Oikarinen, W. Zhang, A. Megretski, L. Daniel and T.-W. Weng
NeurIPS 2021
On the Equivalence between Neural Network and Support Vector Machine
Y. Chen, W. Huang, L. M. Nguyen and T.-W. Weng
NeurIPS 2021
Concept-Monitor: Understanding DNN training through individual neurons
M. A. Khan^{1}, T. Oikarinen^{1}, and T.-W. Weng
Preprint 2023 (under review)
Analyzing Generalization of Neural Networks through Loss Path Kernels
Y. Chen, W. Huang, H. Wang, C. Loh, A. Srivastava, L. Nguyen, and T.-W. Weng
NeurIPS 2023 (accepted)
Attacking c-MARL More Effectively: A Data Driven Approach | [code]
N. H. Pham, L. M. Nguyen, J. Chen, H. T. Lam, S. Das, and T.-W. Weng
ICDM 2023 (accepted)
Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches
L. Liu, T. N. Hoang, L. M. Nguyen, and T.-W. Weng
ICDM 2023 (accepted)
The Importance of Prompt Tuning for Automated Neuron Explanations | [code] | [project page]
J. Lee^{1}, T. Oikarinen^{1}, A. Chatha, K.-C. Chang^{3}, Y. Chen^{3}, and T.-W. Weng
NeurIPS 2023 ATTRIB workshop
CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision Networks | [code]
T. Oikarinen and T.-W. Weng
ICLR 2023, notable-top-25% (spotlight)
Label-free Concept Bottleneck Models | [code]
T. Oikarinen, Subrho Das, Lam M. Nguyen and T.-W. Weng
ICLR 2023
Corrupting Neuron Explanations of Deep Visual Features | [code]
D. Srivastava, T. Oikarinen, and T.-W. Weng
ICCV 2023
Quantifying Safety of Learning-based Self-Driving Control Using Almost-Barrier Functions
Z. Qin, T.-W. Weng and S. Gao
IROS 2022
Adversarially Robust Conformal Prediction
A. Gendler, T.-W. Weng, L. Daniel and Y. Romano
ICLR 2022
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. Dvijotham^{2}, J. Uesato^{2}, K. Xiao^{2}, S. Gowal^{2}, R. Stanforth^{2}, Pushmeet Kohli
ICLR 2020
ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction | [code]
W. Zhang, T.-W. Weng, S. Das, A. Megretski, L. Daniel and L. Nguyen
ICML 2023
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
ICML 2022
On the Equivalence between Neural Network and Support Vector Machine
Y. Chen, W. Huang, L. M. Nguyen and T.-W. Weng
NeurIPS 2021
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. Weng^{1}, P. Zhao^{1}, 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. Chen^{2}, L. M. Nguyen^{2}, M. S. Squillante^{2}, A. Boopathy, I. Oseledets, and L. Daniel
ICML 2019
POPQORN: Quantifying Robustness of Recurrent Neural Networks
C.-Y. Ko^{1}, Z. Lyu^{1}, 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. Zhang^{1}, T.-W. Weng^{1}, P.-Y. Chen, C.-J. Hsieh and L. Daniel
NeurIPS 2018
Toward Fast Computation of Certified Robustness for ReLU Networks
T.-W. Weng^{1}, H. Zhang^{1}, H. Chen, Z. Song, C.-J. Hsieh, D. Boning, I. S. Dhillon and L. Daniel
ICML 2018
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach
T.-W. Weng^{1}, H. Zhang^{1}, 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. Weng^{1}, H. Zhang^{1}, P.-Y Chen, A. Lozano, C.-J. Hsieh and L. Daniel
IEEE GlobalSIP 2018
Robust PCA through Robust Optimization
T.-W. Weng and L. Daniel
NeurIPS 2017, Women in Machine Learning Workshop (WiML)
Computing Least Trimmed Squares Regression to Certifiable Optimality
T.-W. Weng, R. Mazumder and L. Daniel
NeurIPS 2017, Women in Machine Learning Workshop (WiML)
Uncertainty Quantification of Silicon Photonic Devices with Correlated and Non-Gaussian Random Parameters
T.-W. Weng^{1}, Z. Zhang^{1}, Z. Su, Y. Marzouk, A. Melloni and L. Daniel
Optics Express, vol. 23, Issue 4, pp.4242-4254, 2015
Stochastic Simulation and Robust Design Optimization of Integrated Photonic Filters
T.-W. Weng, D. Melati, A. Melloni and L. Daniel
Nanophotonics, vol. 6, Issue 1, pp. 299-308, July 2016
A Big-Data Approach to Handle Process Variations: Uncertainty Quantification by Tensor Recovery
Z. Zhang, T.-W. Weng, and L. Daniel
IEEE Components, Packaging and Manufacturing Technology, Dec. 2016 (Best paper award)
A Novel Miniaturized Bandstop Filter Using Defected Ground on System in Package(SiP)
T.-W. Weng and T.-L. 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
T.-W. Weng, C.-H. Tsai, C.-H. Chen, D.-H. Han and T.-L. Wu
IEEE Trans. Microw. Theory Tech., vol. 62, no.8, pp.1647-1656, Aug. 2014
Hyperbolic Expressions of Polynomial Sequences and Parametric Number Sequences Defined by Linear Recurrence Relations of Order 2
T.-X. He, P. J.-S. Shiue, T.-W. Weng
Journal of Concrete and Applicable Mathematics, vol. 12, 63-85, 2014
Sequences of Numbers Meet the Generalized Gegenbauer-Humbert Polynomials
T.-X. He, P. J.-S. Shiue, T.-W. 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)
T.-W. Weng
Mathmedia, v.34, no. 4, p.31-42, 2010