Šavelka
Jaromír
Affiliation
- Researcher Associate
-
Technology for Effective
and Efficient Learning Lab
School of Computer Science
Carnegie Mellon University
Contact
- Gates and Hillman Centers
Office 7225
4902 Forbes Ave
Pittsburgh, PA 15213
- jsavelka@cs.cmu.edu
Bio
I am a researcher associate at the School of Computer Science, Carnegie Mellon University (CMU) and a member of the Technology for Effective and Efficient Learning (TEEL) Lab at CMU. Before joining CMU in September 2020, I worked as a data scientist at international law firm Reed Smith (2017-2020). I obtained an undergraduate computer science and graduate law degrees from the Masaryk University in Brno, Czechia, as well as Ph.D. in Intelligent Systems mentored by prof. Kevin D. Ashley from the University of Pittsburgh. I have regularly published in and reviewed for Q1 journals, and presented at top-tier international conferences. I am a member of the editorial board of the Artificial Intelligence and Law journal. I have participated in NSF-funded research projects focused on using AI to increase fairness by improving access to justice (PI Kevin D. Ashley) and investigating the use of real-time data for augmenting teaching practice in project-based learning in STEM (PI Majd Sakr). My dissertation work was funded by the National Institute of Justice with its highly competitive Graduate Research Fellowship in Science, Technology, Engineering and Mathematics.
Research Interest
I am focused on applications of machine learning (ML) and natural language processing (NLP) in domains essential to fair and responsible society, particularly education and law. My research interests include developing and evaluating tools to support students in programming and data science classes, and instructors in designing and authoring educational materials. My recent work has explored the potential of large language models (LLMs) to assist students with their homework assignments, and to help educators with designing courses and developing assessments. I am also passionate about exploring cost-effective ways to support legal professionals in analyzing large collections of legal documents, with the aim of democratizing access to sophisticated ML tools. In my research, I am motivated by my concern for equitable access to STEM education and access to justice for all.
Featured Publications
2023
James Prather, Paul Denny, Juho Leinonen, Brett A. Becker, Ibrahim Albluwi, Michelle Craig, Hieke Keuning, Natalie Kiesler, Tobias Kohn, Andrew Luxton-Reilly, Stephen MacNeil, Andrew Peterson, Raymond Pettit, Brent N. Reeves, Jaromir Savelka (2023). The Robots are Here: Navigating the Generative AI Revolution in Computing Education. arXiv:2310.00658
2023
Liffiton, M., Sheese, B., Savelka, J., and Denny, P. (2023). Codehelp: Using large language models with guardrails for scalable support in programming classes. In Proceedings of the 23rd Koli Calling Conference on Computing Education Research (Koli Calling '23). Association for Computing Machinery, New York, NY, USA, 5-12. arXiv:2308.06921
2023
Jaromir Savelka, Arav Agarwal, Marshall An, Chris Bogart, and Majd Sakr. 2023. Thrilled by Your Progress! Large Language Models (GPT-4) No Longer Struggle to Pass Assessments in Higher Education Programming Courses. In Proceedings of the 2023 ACM Conference on International Computing Education Research V.1 (ICER '23 V1), August 07--11, 2023, Chicago, IL, USA. ACM, New York, NY, USA 15 Pages. doi.org/10.1145/3568813.3600142
2023
Savelka, J., Agarwal, A., Bogart, C., Song, Y., and Sakr, M. (2023). Can Generative Pre-trained Transformers (GPT) Pass Assessments in Higher Education Programming Courses?. In Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1 (ITiCSE 2023). Association for Computing Machinery, New York, NY, USA, pp. 117-123. doi.org/10.1145/3587102.3588792
2023
Savelka, J., Agarwal, A., Bogart, C., and Sakr, M. (2023). Large language models (gpt) struggle to answer multiple-choice questions about code. CSEDU 2023: 15th International Conference on Computer Supported Education doi.org/10.5220/0011996900003470
2023
Jaromir Savelka and Kevin D. Ashley (2023). The Unreasonable Effectiveness of Large Language Models in Zero-shot Semantic Annotation of Legal Texts. Frontiers in Artificial Intelligence, vol. 6. 10.3389/frai.2023.1279794
2023
Savelka, J. (2023). Unlocking practical applications in legal domain: Evaluation of gpt for zero-shot semantic annotation of legal texts. In Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law (pp. 101-110). doi.org/10.1145/3594536.3595161
2023
Gray, M., Savelka, J., Oliver, W., and Ashley, K. (2023, June). Automatic Identification and Empirical Analysis of Legally Relevant Factors. In Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law (pp. 101-110). doi.org/10.1145/3594536.3595157
2022
Savelka, J., and Ashley, K. D. (2022). Legal Information Retrieval for Understanding Statutory Terms. Artificial Intelligence and Law. Springer. doi.org/10.1007/s10506-021-09293-5
2022
2021
An, M., Zhang, H., Savelka, J., Zhu, S., Bogart, C., and Sakr, M. (2021, June). Are Working Habits Different Between Well-Performing and at-Risk Students in Online Project-Based Courses?. In Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 1 (pp. 324-330). 10.1145/3430665.3456320
2021
Jaromir Savelka, Hannes Westermann, Karim Benyekhlef, Charlotte S. Alexander, Jayla C. Grant, David Restrepo Amariles, Rajaa El Hamdani, Sébastien Meeùs, Aurore Troussel, Michał Araszkiewicz, Kevin D. Ashley, Alexandra Ashley, Karl Branting, Mattia Falduti, Matthias Grabmair, Jakub Harašta, Tereza Novotná, Elizabeth Tippett, and Shiwanni Johnson. 2021. Lex Rosetta: transfer of predictive models across languages, jurisdictions, and legal domains. In Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law (ICAIL '21). Association for Computing Machinery, New York, NY, USA, 129-138. 10.1145/3462757.3466149
2021
Savelka, J., and Ashley, K. D. (2021). Discovering Explanatory Sentences in Legal Case Decisions Using Pre-trained Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2021 (pp. 4273-4283). 10.18653/v1/2021.findings-emnlp.361
2021
2020
2020
2019
2019
2018
2018
2017
2017
2016
2015
Jaromir Savelka, Gaurav Trivedi, and Kevin D. Ashley. Applying an Interactive Machine Learning Approach to Statutory Analysis. In Antonino Rotolo. Legal Knowledge and Information Systems (JURIX 2015). Amsterdam: IOS Press, 2015.
2015
Jaromir Savelka and Jakub Harašta. Open Texture in Law, Legal Certainty and Logical Analysis of Natural Language. Logic in the Theory and Practice of Lawmaking, Springer, 2015.
2015
Jaromir Savelka and Kevin D. Ashley. Transfer of Predictive Models for Classification of Statutory Texts in Multi-jurisdictional Settings. In Katie Atkinson. Fifteenth International Conference on Artificial Intelligence and Law (ICAIL 2015). New York: ACM, 2015, pp. 216-220.
2014
Jaromir Savelka, Kevin D. Ashley and Matthias Grabmair. Mining Information from Statutory Texts in Multi-jurisdictional Settings. In Rinke Hoekstra. Legal Knowledge and Information Systems (JURIX 2014). Amsterdam: IOS Press, 2014, pp. 133-142.
2013
Jaromir Savelka. Coherence as Constraint Satisfaction: Judicial Reasoning Support Mechanism. In Michał Araszkiewicz and Jaromir Savelka (eds.). Coherence: Insights from Philosophy, Jurisprudence and Artificial Intelligence, Law and Philosophy Library, vol. 107, 2013, pp. 203-216.
2013
Matěj Myška and Jaromir Savelka. A Model Framework for publishing Grey Literature in Open Access. Journal of Intellectual Property, Information Technology and E-Commerce Law, Digital Peer Publishing, 2013, vol. 4, issue 2, pp. 104-115.
2013
Michal Koščík and Jaromir Savelka. Dangers of Over-Enthusiasm in Licensing under Creative Commons. Masaryk University Journal of Law and Technology, Brno: Masarykova univerzita, 2013, vol. 7, issue 2, pp. 201-228.
2012
Matěj Myška, Terezie Smejkalová, Jaromir Savelka and Martin Škop. Creative Commons and Grand Challenge to Make Legal Language Simple. In Monica Palmirani, Ugo Pagallo, Pompeu Casanovas, Giovanni Sartor. AI Approaches to the Complexity of Legal Systems. Models and Ethical Challenges for Legal Systems, Legal Language and Legal Ontologies, Argumentation and Software Agents. Berlin,Heidelberg, New York: Springer, 2012, pp. 271-285.
2012
Michał Araszkiewicz and Jaromir Savelka. Refined Coherence as Constraint Satisfaction Framework for Representing Judicial Reasoning. In Burkhard Schäfer. Legal Knowledge and Information Systems (JURIX 2012). Amsterdam: IOS Press, 2012, pp. 1-10.