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Leveraging LLMs for Data Coding
Published in Carnegie Mellon University, Software Engineering Institute Digital Library, 2024
In this paper, we investigate the usefulness and design methodology of applying large language models (LLMs) to improve and automate the process of coding case data. We introduce tools to guide LLMs to assist in this coding process.
Recommended citation: D. Kutscher, A. Whisnant, "Leveraging LLMs for Data Coding," Carnegie Mellon University, Software Engineering Institute Digital Library. Software Engineering Institute, White Paper, 04-Nov-2024
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Physics-Guided Fair Graph Sampling for Water Temperature Prediction in River Networks
Published in AAAI Conference on Artificial Intelligence, 2025
This work introduces a graph neural network method to predict stream water temperature and reduce model bias across locations with different socioeconomic attributes.
Recommended citation: Erhu He, Declan Kutscher, Yiqun Xie, Jacob Zwart, Zhe Jiang, Huaxiu Yao, Xiaowei Jia. (2025). "Physics-Guided Fair Graph Sampling for Water Temperature Prediction in River Networks." AAAI Conference on Artificial Intelligence.
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REOrdering Patches Improves Vision Models
Published in Neural Information Processing Systems (NeurIPS) 2025, 2025
We proposed a reinforcement learning technique using a Plackett-Luce policy to find the optimal ordering of patches for long-sequence vision transformers.
Recommended citation: Declan Kutscher, David M. Chan, Yutong Bai, Trevor Darrell, Ritwik Gupta. (2025). "REOrdering Patches Improves Vision Models." Neural Information Processing Systems (NeurIPS) 2025.
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On the Effectiveness of Pretrained Models for Remote Sensing
Published in M.S. Thesis, Department of Computer Science, University of Pittsburgh, 2025
M.S. thesis analyzing when ImageNet or satellite pretraining helps remote sensing, and how representation quality drives transfer.
Recommended citation: Declan Kutscher (2025). On the Effectiveness of Pretrained Models for Remote Sensing. M.S. thesis, Department of Computer Science, University of Pittsburgh.
Tinted Frames: Question Framing Blinds Vision-Language Models
Published in EMNLP 2026, 2026
We show that vision-language models are selectively blind: linguistic framing alters visual attention, degrading grounding and accuracy. We introduce a prompt-tuning method that realigns attention and restores performance.
Recommended citation: Wan-Cyuan Fan, Jiayun Luo, Declan Kutscher, Leonid Sigal, Ritwik Gupta. (2026). "Tinted Frames: Question Framing Blinds Vision-Language Models." EMNLP 2026.