Caltech's economics curriculum drills the same optimization and quantitative modeling that sits at the heart of industrial engineering — linear programming, resource allocation under constraints, and systems-level efficiency analysis. Brian pairs that analytical training with his computer science background to tackle the computational side of IE, from simulation techniques to algorithm-driven process design. His comfort moving between mathematical formulation and real-world problem framing makes him effective across most IE coursework.

I am graduated from Penn State University in Industrial Engineering in 2017. I've tutored ever since I was in high school, and I love helping people! I like to help my students understand math (and other topics) instead of just doing it blindly. My goal is to help my students improve their math (and other topics) and build skills that will help them find learning easier in the future! Fun fact, I used to work for Disney and I like to salsa dance!

I am a recent graduate of Princeton University's Mechanical and Aerospace Engineering Department. I am passionate about teaching and mentoring and have done so in multiple capacities over the last four years, including a fellowship during which I taught pre-algebraic math to a group of middle school students from traditionally underserved backgrounds in Saint Paul, MN. I love interacting with students and seeing them grow over the course of their studies. I'm ecstatic at the opportunity to learn alongside them as we venture into educational rabbit holes and uncover key concepts about math, science, and everything else.

Studying industrial engineering at the University of Florida isn't just a line on Juan's résumé — it means he's actively working through optimization models, queuing theory, simulation, and operations research right now. That real-time familiarity with IE coursework makes him especially useful for students wrestling with linear programming formulations or stochastic process homework. His statistics training adds depth when courses shift into quality control and experimental design.

As a mathematics undergrad at NYU with a strong applied math and economics background, Nikhil tackles industrial engineering topics like optimization and probability modeling by grounding them in the underlying mathematical logic — why a particular linear programming formulation works, not just how to set it up. His coursework in applied mathematics and statistics maps directly onto core IE tools like stochastic modeling and data-driven decision analysis. Rated 4.8 by students.

As a senior in Columbia's industrial engineering program, Meghana is immersed in the discipline right now — optimization, operations research, linear programming, queueing theory, and supply chain modeling are all part of her active coursework. That currency matters because she can walk students through both the mathematical foundations and the real-world applications professors expect in project work. She's a natural fit for anyone navigating an IE curriculum for the first time.

Georgia Tech's ISyE program is one of the top-ranked industrial engineering departments in the country, and Allison graduated from it — so topics like linear programming, production planning, and systems optimization come directly from her own coursework. She's especially effective at teaching the modeling side of IE, where students need to translate messy real-world constraints into clean mathematical formulations using tools like MATLAB and Python.

Decision sciences — the focus of Benedetto's MBA — is essentially industrial engineering's analytical engine: the same optimization models, probabilistic reasoning, and quantitative decision-making that drive operations research and resource allocation problems. He breaks down topics like linear programming and statistical quality control through the lens of managerial trade-offs, connecting the math to the business decisions it's meant to inform. Rated 4.7 by students.

Matt earned his bachelor's in Industrial Engineering, so topics like linear programming, queuing theory, operations research, and facility layout aren't abstract concepts — they're problems he's solved firsthand. He breaks down optimization models step by step, connecting the math to real production and logistics scenarios so the reasoning behind each technique becomes clear. Rated 5.0 by students.

Joscelyn's B.S. in Industrial Engineering means she's worked through the full IE toolkit firsthand — facility layout, operations research, statistical quality control, and production planning aren't theoretical for her but problems she's modeled and solved. She's especially sharp at teaching students how to structure objective functions and identify constraints in messy, ambiguous scenarios, which is where most IE students get stuck. Rated 5.0 by students.

Regina earned her Bachelor of Science in Industrial Engineering, meaning she built her academic foundation on the exact curriculum IE students are navigating — operations research, process optimization, facility layout, and statistical quality control. She teaches the modeling side of these problems with particular clarity, showing students how to structure objective functions and interpret results rather than just crank through algorithms. Rated 5.0 by students.

Mechanical engineering and industrial engineering share a common language around systems design, process efficiency, and production constraints — and Joe's ME degree means he can ground IE topics like workflow optimization and capacity planning in the physical realities of how machines and materials actually behave. He's particularly useful for students who need help connecting the mathematical side of IE (linear programming setups, objective functions) to tangible manufacturing scenarios.

I'm a Texas A&M University graduate with a BS in Industrial & Systems Engineering.