Projects- Under Construction….

1. Efficient Machine Learning Systems, Natural Language Processing, Computing Vision

  • Sparse Training – [ICCD’22]
  • FPGA Acceleration and Architecture – [MICRO’17], [FPGA’18], [FPGA’19], [DAC’20], [DAC’22]
  • GPU Acceleration – [SC’21]
  • Efficient DNN Pruning – [EMNLP’20], [DAC’21], [IJCAI’21 a], [ACL’22]
  • Efficient Training –  [IJCAI’21]
  • Run-time Reconfigurable Inference – [DAC’21], [DATE’22]

2. Privacy-Preserving Machine Learning

  • Gradient Attack – [EMNLP’21 a]
  • Membership Inference Attack – [IJCAI’21 b]
  • Secure Federated Learning – [EMNLP’21 b(Oral)]

3. Non-von Neuman computing & Emerging Tech.

  • ReRAM Based ML – [ISCA’21], [ISLPED’19], [DATE’21 (best paper nomination)], [ISCAS’22]
  • Quantum ML – [ICCAD’21 c], [MLSys’22].

4. ML on Drug Discovery – [ICCAD’21 a] (similarity search), [ICCAD’21 b] (federated molecular generation))

      5. Change and Damage Detection from Aerial Images. (Sponsor: Travelers)

        • Project Description: This project will perform data preprocessing and data understanding of the satellite image from Travelers, and propose change detention via Siamese Networks on pre and post-event images. We will also develop unsupervised anomaly (change) detection on aerial images and Hail damage detection.
        • Personnel: Jinbo Bi (PI), Caiwen Ding (Co-PI), Dongjin Song (Co-PI); Ph.D. student: Shaoyi Huang, Yueying Liang, Binghao Lu, Fei Dou, Qianying Ren

      6. Evaluating the Impact of Preferential Trade Agreements on Agricultural and Food Trade: New Insights from Natural Language Processing and Machine Learning: This work is supported by the Agriculture and Food Research Initiative (Award Number 2022-67023-36399) from the National Institute of Food and Agriculture.

      • Project Description: This project generates new knowledge regarding the formation of preferential trade agreements (PTAs), their impact on global trade, and the consequences for U.S. agricultural and food businesses and employment. To accomplish this goal, we will rely on modern statistical modeling techniques to thoroughly investigate the factors that influence the formation of PTAs. This analysis builds on newly collected PTA data captured with the help of Neural Machine Translation and Natural Language Processing systems. To determine factors that influence the formation of PTAs, we will adopt the Random Forest algorithm. This statistical analysis will provide new insights regarding the role of economic, social, and political factors in forming PTAs with agricultural and food provisions. We will use the newly created dataset to investigate the impact of PTA provisions on agricultural and food trade in the sectoral three-way gravity model context relying on an adaptation of the Prior least absolute shrinkage and selection operator to the Poisson pseudo-maximum likelihood estimator. This innovative machine learning approach will enable us to incorporate prior information, reduce over-fitting, and facilitate feature selection in a high-dimensional context. We will also assess the impact of PTA provisions on the structure and conduct of the U.S. agricultural and food sector and evaluate employment effects. A better understanding of these trade policy consequences will shed light on a critical driver of structural change. Such knowledge is essential for the functioning of global supply chains.
      • Personnel:  Caiwen Ding (Co-PI); Jeremy Jelliffe (Collaborator at ERS-USDA); Dongin Song, (Co-PI); Sandro Steinbach, (PI) Principal Investigator University of Connecticut