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We are considering volunteer interns for an ongoing project for the current semester. Please submit materials to jkrichel@syr.edu by Sep. 24, 2025 for full consideration. If a suitable volunteer is not identified by the applications provided at this time, we will consider applicants who apply by Sep. 30, 2025. Applicants who apply after this date will not receive any response.

• This is an unpaid, volunteer position
• This opportunity is only for Syracuse MS and BS students
• The project will involve formulating a formal research goal, training LLMs to evaluate the hypotheses, and describing the findings in peer-reviewed conference and journal papers
• We mostly interview graduate students, while excellent undergrads may be considered
• The intern(s) will be official affiliate(s) of the lab and will cooperate with Amazon, providing potential future opportunities
• Intern(s) may be able to get course credit for their research in the future
• Those we are interested are welcome to apply should send the following to jkrichel@syr.edu by Sep. 24, 2025 for full consideration:
  • CV, including experience with Pytorch, summary of programming experience (with a focus on industry and research experience in machine learning) and links to any authored papers and/or coding projects (e.g., GitHub sites, Kaggle contests participated in, etc.)
  • Full transcripts (unofficial transcripts only). Students must include transcripts that have the current term. All students (including graduate students) must include undergraduate transcripts.
  • Students should include the contact information for one Syracuse faculty member for reference. We will only contact references if you advance to the final rounds of selection.

Happy to announce that I have joined the faculty advisory board for Cuse AI – a Syracuse student group focused on AI. If you are a Syracuse student interested in AI, please check out their site: https://www.cuseai.org/

Two new papers were accepted to ICLP and will be presented this fall. See the below video for a quick preview.

PyReason-ML integration for process automation: https://arxiv.org/abs/2506.17776 Reasoning about medical triage optimization: https://arxiv.org/abs/2507.10781

We are pleased to announce a new course, CIS 700: Hybrid Artificial Intelligence that will be offered this fall at Syracuse University. The course description can be found below along with a “test lecture” on YouTube.

Today’s state-of-the-art machine learning systems such as deep neural networks have shown impressive capabilities for perceiving and generating information by learning models from data but often fail at reasoning tasks. Symbolic systems, on the other hand, excel in various forms of inference but often struggle with learning. Recent advances in systems that combine machine learning with symbolic artificial intelligence present promising new directions toward the combination of reasoning and learning. In this course, we examine fundamental concepts related to hybrid artificial intelligence as well as several major lines of research in this area. The course will start with initial lectures on dual process theory, symbol grounding, and metacognition. The remainder of the course will include the following topics: neurosymbolic AI (e.g., logic tensor networks, logical neural networks), abductive learning (to include general discussions on abduction), inductive logic programming, cognitive inspired systems (to include hyperdimensional computing), symbolic regression, and related topics.