Projects- Under Construction….

0. Machine Learning for EDA

  • LLM-aided Verilog Generation [DAC’24 WIP]

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]
  • Embedded/IoT System [DATE’22], [ICCAD’22 a], [ICCAD’22 b]

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.  Optigrid: Planning & Optimizing the Power Grid During the Low Carbon Transition in Connecticut (Sponsor: Eversource)

  • eversource-txt-logoDeveloping statistical and deep learning models to predict demand using historical weather, EV  sales, and PV installations.
  • Real-time techniques to process statistical and deep learning models.

5.  Graph Processing & Graph Neural Network – [ICCD’22] (Sponsor: SRC)

  • Exploring extreme sparsity for GNNs to achieve high energy efficiency in large core-count machines.
  • Project description: our main objective is to pioneer next-generation many cores systems forBanner the efficient execution of GNNs in the field. We will co-innovate on the algorithm and hardware to deliver strong performance scaling and energy improvements in futuristic large shared-memory
  • Personnel:  Caiwen Ding (PI), Omer Khan (Co-PI);

      6. 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).

      7. 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.
      • Personnel:  Caiwen Ding (Co-PI); Jeremy Jelliffe (Collaborator at ERS-USDA); Dongin Song, (Co-PI); Sandro Steinbach, (PI)

      8. Feasibility of Transformer-based Code Migration for HPC (Sponsor: DOE/LLNL)

      • investigating a set of large language models (LLM)-based code translation and generation techniques specially designed for meeting LLNL’s HPC application requirements.
      • Personnel:  Caiwen Ding (PI)

      9. Incorporating Large Language Models (LLMs) into Transportation Safety Analytics and Equity, DOT/NEUTC

      10. Expanding Capabilities of Artificial Intelligence (AI) Based Video Analytics for Intersection Safety and Operation for CTDOT, DOT/CCTRP

      11. Automated Wrong Way Driving Detection Using Economical Sensor Technologies, DOT/CCTRP