Badihi, S., Tegegn, M., Riddell, E., Czarnecki, K., & Rubin, J. (2026). Hey, ChatGPT, Look at My Work: Using Conversational AI in Requirements Engineering Education Presented at the International Conference on Software Engineering (ICSE) conference. Rio de Janeiro, Brazil: IEEE. (Original work published 2026)
References
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2026
Riddell, E., Riddell, J., Sun, G., Antkiewicz, M., & Czarnecki, K. (2026). Stalled, Biased, and Confused: Uncovering Reasoning Failures in LLMs for Cloud-Based Root Cause Analysis Presented at the International Conference on AI Foundation Models and Software Engineering (FORGE) conference. https://doi.org/10.1145/3793655.3793732 (Original work published 2026)
Czarnecki, K. (2026). Systematizing The Unusual: A Guideword- And Ontology-Based Method For Road Hazard Identification And Analysis In Driving Automation Safety Assurance (Gobi) And A Hazard Catalog Presented at the International Technical Conference on the Enhanced Safety of Vehicles (ESV) conference. Toronto, Canada: NHTSA. Retrieved from https://static.nhtsa.gov/esv/pdf/ESV/Proceedings/28/ESV26-241.pdf (Original work published 2026)
Wang, Y., Riddell, E., Chow, A., Sedwards, S., & Czarnecki, K. (2026). Mitigating the modality gap: Few-shot out-of-distribution detection with multi-modal prototypes and image bias estimation Presented at the IEEE/CVF/Winter/Conference/on/Applications/of/Computer/Vision conference. Tucson, Arizona, USA. Retrieved from https://openreview.net/pdf?id=tgnTVmRybs
Czarnecki, K. (2026). Public Crash Video Evidence For Adapting Euro Ncap Intersection Scenarios To North America Presented at the International Technical Conference on the Enhanced Safety of Vehicles (ESV) conference. Toronto, Canada: NHTSA. Retrieved from https://static.nhtsa.gov/esv/pdf/ESV/Proceedings/28/ESV26-279.pdf (Original work published 2026)
Choi, Y. M., Ali, A. B., Czarnecki, K., & Shi, F. (2026). The Illusion of Choice: Assessing Impact of Manipulated LLMs Presented at the Second Workshop on Technical AI Governance Research (TAIGR), ICML conference. Seoul, South Korea. Retrieved from https://openreview.net/forum?id=cOru6rrCT3 (Original work published 2026)
Azadani, M. N., Riddell, J., Sedwards, S., & Czarnecki, K. (2026). Rethinking the Mixture of Vision Encoders Paradigm for Enhanced Visual Understanding in Multimodal LLMs Transactions on Machine Learning Research. Retrieved from https://openreview.net/pdf?id=tgnTVmRybs (Original work published 2026)
2025
Wang, Y., & Czarnecki, K. (2025). AiDe: Improving 3D Open-Vocabulary Semantic Segmentation by Aligned Vision-Language Learning Presented at the IEEE/CVF/Winter/Conference/on/Applications/of/Computer/Vision/(WACV) conference. Tucson, AZ, USA: IEEE. https://doi.org/10.1109/WACV61041.2025.00265 (Original work published 2025)
Zhang, R., Sun, C., Ning, M., Valiollahimehrizi, R., Lu, Y., Czarnecki, K., & Khajepour, A. (2025). Quantifying Learning Algorithm Uncertainties in Autonomous Driving Systems: Enhancing Safety through Polynomial Chaos Expansion and High-Definition Maps Accident Analysis & Prevention, 211, 11. https://doi.org/10.1016/j.aap.2024.107903 (Original work published 2025)
Hu, B. C., Di Sandro, A., Marsso, L., Czarnecki, K., & Chechik, M. (2025). Arguing Reliability of Machine Learning-based Components Presented at the IEEE Engineering Reliable Autonomous Systems (ERAS) conference. Worcester, MA, USA: IEEE. https://doi.org/10.1109/ERAS63351.2025.11135634 (Original work published 2026)