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publications
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.
talks
Talk 1 on Relevant Topic in Your Field
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Tutorial 1 on Relevant Topic in Your Field
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Talk 2 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
CS 0007 INTRODUCTION TO COMPUTER PROGRAMMING (FALL ‘22)
Undergraduate course, University of Pittsburgh, School of Computing and Information, 2022
Role: Undergraduate Teaching Assistant
- Introduced course projects during lab time
- Assisted students with conceptual understanding of course projects
- Held office hours to assist students with issues with course content
Course Description: This is a first course in computer science programming. It is recommended for those students intending to major in computer science who do not have the required background for the School of Computing and Information’s intermediate programming class. It may also be of interest to students majoring in one of the social sciences or humanities. The focus of the course is on problem analysis and the development of algorithms and computer programs in a modern high-level language.
CMPINF 0401 INTERMEDIATE PROGRAMMING (Spring ‘23)
Undergraduate course, University of Pittsburgh, School of Computing and Information, 2023
Role: Undergraduate Teaching Assistant
- I led recitations to review course material and answer questions
- Held office hours to assist students with issues with course content
Course Description: This is an intermediate programming course that focuses on programming via an object-oriented paradigm. Students entering CMPINF 0401 are expected to have some previous concepts and then focus on object-oriented programming, including classes, encapsulation and abstraction, inheritance, polymorphism and interfaces. Some introductory data structures and algorithms will also be covered in this course.This class is a programming-intensive course, and students will be expected to complete several non-trivial programming projects throughout the term.
CS 0011 INTRODUCTION TO COMPUTING FOR SCIENTISTS (FALL ‘23)
Undergraduate course, University of Pittsburgh, School of Computing and Information, 2023
Role: Undergraduate Teaching Assistant
- Introduced course projects during lab time
- Assisted students with conceptual understanding of course projects
- Held office hours to assist students with issues with course content
Course Description: All of the CS 001X courses will introduce students to the concepts of computing and computer programming. Students in these courses will learn how a computer works and how to write programs in order to use the computer as a problem solving tool. A major focus of the class will be on developing problem-solving skills (e.g., how to decompose a problem into more manageable parts and how to combine those parts into an overall solution). CS 0011 in particular will focus on problems related to the natural sciences with an emphasis on computational biology. Domain-specific projects and labs will be assigned throughout the course to encourage students in the natural sciences to apply computing to their field of study.