Ting-Wu Rudy Chin (金廷武)

Ph.D., Electrical and Computer Engineering, Carnegie Mellon University E-mail: tingwuc [AT] alumni [DOT] cmu [DOT] edu Affiliation: [EnyAC] [OPAL] Google Scholar

About me

I am a quantitative researcher at Citadel Securities in Chicago.

I received my Ph.D. from CMU ECE co-advised by Prof. Diana Marculescu and Prof. Gauri Joshi. My research interests include Model Compression, Neural Architecture Search and Trasnsfer Learning for Deep ConvNets.

Before joining CMU, I received the B.S. and M.S. degrees in computer science from National Chiao Tung University (NCTU), in 2015 and 2017, respectively.

Industrial Experiences

Citadel Securities 2021.07 - present Quantitative Researcher Applying machine learning and statistical techniques to model markets

Facebook AI Research 2020.05 - 2020.12 Research Intern Mentors: Ari Morcos Project: On the Transferability of Channel Optimization

Facebook Reality Labs 2019.05 - 2019.08 Research Intern Mentors: Pierce Chuang and Vikas Chandra Project: One Weight Bitwidth to Rule Them All

Microsoft Research 2018.05 - 2018.08 Research Intern Mentors: Cha Zhang Project: Efficient Model Compression with Learned Global Ranking

First-authored Peer-reviewed Publications

• Joslim: Joint Widths and Weights Optimization for Slimmable Neural Networks [Code]
  Ting-Wu Chin, Ari S. Morcos, Diana Marculescu
  European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD’21) (21% Acceptance Rate)
  Abridged (4 pages) versions were accepted at ICML 2020 Workshops DMMLSys and RealML

• Width Transfer: On the (in)variance of Width Optimization
  Ting-Wu Chin, Diana Marculescu, Ari S. Morcos
  The IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW’21: Efficient Deep Learning for Computer Vision)
  Abridged (4 pages) versions were accepted at ICLR 2021 Workshops NAS 2021

• Renofeation: A Simple Transfer Learning Method for Improved Adversarial Robustness [Code]
  Ting-Wu Chin, Cha Zhang, Diana Marculescu
  The IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW’21: Fair, Data Efficient and Trusted Computer Vision) (Best Paper Award)

• One Weight Bitwidth to Rule Them All
  Ting-Wu Chin, Pierce Chuang, Vikas Chandra, Diana Marculescu
  European Conference on Computer Vision Workshops (ECCVW’20: Embedded Vision Workshop) (Best Paper Award)

• Towards Efficient Model Compression via Learned Global Ranking [Code]
  Ting-Wu Chin, Ruizhou Ding, Cha Zhang, Diana Marculescu
  The IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20) (Oral, 5% Acceptance Rate)
  An abridged (4 pages) version was accepted at NeurIPS 2018 Workshop MLPCD 2 (Oral)

• AdaScale: Towards Real-time Video Object Detection using Adaptive Scaling [Code]
  Ting-Wu Chin, Ruizhou Ding, Diana Marculescu
  Conference on Machine Learning and Systems (MLSys’19) (Oral, 17% Acceptance Rate)

• Domain-Specific Approximation for Object Detection
  Ting-Wu Chin, Chia-Lin Yu, Matthew Halpern, Hasan Genc, Shiao-Li Tsao, Vijay Janapa Reddi
  IEEE Micro, SI: Autonomous Computing

Co-authored Peer-reviewed Publications

• Regularizing Activation Distribution for Training Binarized Deep Networks
  Ruizhou Ding, Ting-Wu Chin, Diana Marculescu, Zeye Liu
  The IEEE Conference on Computer Vision and Pattern Recognition (CVPR’19)

• FLightNNs: Lightweight Quantized Deep Neural Networks for Fast and Accurate Inference
  Ruizhou Ding, Zeye Liu, Ting-Wu Chin, Diana Marculescu, R. D. (Shawn) Blanton
  ACM/IEEE Design Automation Conference (DAC’19)

• Understanding the Impact of Label Granularity on CNN-based Image Classification
  Zhuo Chen, Ruizhou Ding, Ting-Wu Chin, Diana Marculescu
  ICDM 2018 Workshop on Data Science and Big Data Analytics

• Designing Adaptive Neural Networks for Energy-Constrained Image Classification
  Dimitrios Stamoulis, Ting-Wu Chin, Anand Krishnan Prakash, Haocheng Fang, Sribhuvan Sajja, Mitchell Bognar, Diana Marculescu
  Proceedings of the 37th International Conference on Computer-Aided Design (ICCAD’18)

Honors and Awards

• Best paper award at CVPR 2021 Workshop on Fair, Data Efficient, and Trusted Computer Vision

• Outstanding reviewers for CVPR 2021

• Best paper award at ECCV 2020 Embedded Vision Workshop

• Top 10% reviewers for NeurIPS 2020

• Top 33% reviewers for ICML 2020

• Qualcomm Innovation Fellowship 2020 Finalist

• David Barakat and LaVerne Owen-Barakat Fellowship, Carnegie Institute of Technology

• 3rd Place for Siemens FutureMakers Challenge at CMU

Services

Reviewer/PC member for

Machine Learning

• Conference on Neural Information Processing Systems (NeurIPS): 2020, 2021

• International Conference on Machine Learning (ICML): 2020, 2021

• International Conference on Learning Representations (ICLR): 2021

• The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD): 2021

• Conference on Machine Learning and Systems (MLSys): 2020

• Neural Compression Workshop (ICLR’21 Workshop): 2021

• Compact Deep Neural Network Representation with Industrial Applications (NeurIPS’18 Workshop CDNNRIA): 2018

Computer Vision

• Conference on Computer Vision and Pattern Recognition (CVPR): 2021

• International Conference on Computer Vision (ICCV): 2021

• IEEE Transactions on Image Processing (TIP): 2021

• International Journal of Computer Vision (IJCV): 2020

• IEEE Journal of Selected Topics in Signal Processing (JSTSP): 202