Our undergraduate students can start working with a faculty member in a field they are interested in, starting from their second year. These studies give fruitful results particularly in the junior and senior years. Through these studies, our students participate in research and gain experience in writing articles during their undergraduate education. Students can also perform these studies within the scope of elective courses (CS390 Individual Research Study and CS490 Senior Research Project) that are part of the curriculum.
In the Fall 2024 semester, a total of 26 students took CS390 and CS490. The final presentations were held on January 2-3, 2025. We congratulate all our students for their successful completion of these research projects.
Projects:
- Causality on diffusion models
- Subproblem generation in 2D pick-place puzzles
- Assessment of pain intensity through a transformer architecture
- Encoder-decoder transformer model to generate RNA sequences for compounds with high bidding affinity
- Sequential manipulation through subgoal generation
- Improving scheme-linking for text-to-SQL task through generating intermediate inferences
- BugScope: Automated visual bug report analysis
- Identity preserving 3D head stylization with multiview score distillation
- Image-guided style transfer in video domain
- Comparison of autoregressive and non-autoregressive models in biological sequence generation
- Cross-modality sequence generation for compound-RNA and protein-RNA with multilingual T5
- Using locally consistent parsing for variation graphs: A method for Representing Variations
- Automated analysis of refactor oriented pull request with large language models
- Hades-FL: Hybrid approach for encrypted federated learning
- Personality expression in LLM-based personas
- Vision transformers for whole slide breast histopathology image classification
- BugCraft: End-to-end crash bug reproduction using LLMs in Minecraft
- Halo-based graph partitioning
- A transformer-based framework for RNA-protein interactions: Integrating GenerRNA and ProtBERT for sequence generation
- BAIS: Background-informed adaptive CNN for semi-supervised stance detection
- Detecting software requirement smells through retrieval-augmented generation
- Evaluation and benchmarking of string partitioning and sketching techniques