University of Arizona Systems & Industrial Engineering

Our faculty are making an impact far beyond the classroom!

Professor Furfaro's work in orbital mechanics, machine learning, and AI is helping build the tools needed to track and monitor the growing number of objects in space preventing collisions and protecting the satellites we rely on every day for communications, navigation, and national security.

What an incredible honor for our SIE community! 🐻💙❤️

Read the full story here: https://sie.engineering.arizona.edu/news-events/furfaro-joins-united-nations-outer-space-committee

🏆 First place! Huge congratulations to SIE PhD students Nazmul Hasan, Mohamed Ibrahim, and Ryan Mowlai for winning first place in the IISE Quality Control and Reliability Engineering (QCRE) Data Challenge!

The competition included 33 teams representing 85+ students and researchers from 29 institutions across 4 countries and our Wildcats came out on top! 🐻💙❤️ This was a fully student-led effort based on real-world industry data, judged by both company and academic judges. We couldn't be more proud!

We showed up and showed out at the IISE Annual Conference, the premier gathering for industrial & systems engineering professionals from around the world! It was a fantastic opportunity to share research, connect with colleagues, and showcase the great work happening here at the University of Arizona SIE department. So proud of our faculty and students who attended. Go Wildcats! 🐻❤️

We gathered to celebrate and award our 2026 Systems & Industrial Engineering graduates! 🎓✨ It was a wonderful evening recognizing the hard work and dedication of this outstanding class. Congratulations to the Class of 2026 we are proud of each and every one of you! 🎉❤️

And last but certainly not least, we are thrilled to close out our Outstanding Students recognition with Ximena Peregrino, our SIE Outstanding Graduate Student! 🎊 Ximena is finishing her MS in Industrial Engineering and is headed all the way to Germany to pursue a career in the pharmaceutical industry. Her favorite memory from SIE was stopping by the building to say hi to her professors and advisors, because sometimes it really is those small, casual moments that make your experience truly special. We could not be more proud. Congratulations and best of luck on your next adventure! 🌍

We are so excited to recognize Josh Nau as our next SIE Outstanding Seniors! Josh is finishing his BS in Engineering Management and he will be joining SteelFab as a Project Engineer. One of his favorite memories from his time in SIE was his group project for SIE 305, where he got to work closely with fellow students on a real-life statistics project. Josh, we are so proud of everything you have accomplished congratulations!

Congratulations to Majed Alasiri on being named an SIE Outstanding Teaching Assistant! Majed has shown incredible dedication to his students and the SIE community, and we are proud to recognize his hard work. Well deserved, Majed!

Congratulations to our SIE Outstanding Senior, Michael Jones! 🎉 Michael is completing his BS in Systems Engineering and will be commissioning into the United States Air Force as a Second Lieutenant. His favorite memory from SIE was working on a supply chain project alongside his closest friends — Corbin Austin, Marshall Gwillim, Brandon Tong, and Dylan Wojtyna. We are so proud of everything Michael has accomplished and cannot wait to see all that he does next. Congratulations, Michael!

this is who you’re emailing “just a quick question”

Join us on 4/22/2026 to support Ximena Peregrino for her Master Defense!

Excited to share my thesis research on advancing the reliability of Laser Powder Bed Fusion (LPBF)! 🚀
The Challenge: While nominal Volumetric Energy Density (VED) is used to set machine parameters, it doesn’t capture the actual, local thermal conditions materials experience during a build. This creates a gap between expected settings and actual melt-pool behavior.
The Solution: I developed a "VED Proxy"—a monitoring-oriented ML framework that maps melt-pool features from in-situ coaxial imaging to track the effective process state.
Key Highlights of the Work:
🔹 Methodology: Introduced a "Reliable Zone" logic to train models on stable, least-confounded conditions. Melt-pool features were extracted and aggregated layer-wise.
🔹 The Model: Evaluated multiple supervised-learning models and selected Linear Regression for its optimal balance of predictive performance, interpretability, and layer-wise stability.
🔹 The Results: The VED Proxy successfully captured complex variations—including short-feeds, geometry-driven changes, and thermal-history effects (build-height/interlayer-time).
🔹 Cross-Material Transferability: Excitingly, a model trained on Inconel 718 retained useful interpretive value when applied directly to Haynes 282!

Ultimately, this establishes a physically grounded foundation for translating rich sensor data into a single, process-centered metric for better monitoring and future control in metal AM.

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