Sungae's doctoral work in mechanical engineering involved solving constrained optimization problems — linear programming, gradient descent, Lagrange multipliers — as part of actual design processes. She teaches the theory behind objective functions and feasibility regions while showing students how solvers behave in practice, which makes the math feel purposeful instead of abstract.

Linear programming, gradient descent, and constrained optimization each require translating a real problem into precise mathematical language — then solving it. Irene's background spanning both mathematics and computer science means she tackles optimization from both the theoretical side (convexity, duality) and the computational side, connecting the two so the methods actually make sense.

A double major in Applied and Computational Mathematics and Statistics from Notre Dame means Abby spent serious time with the tools underlying optimization — linear algebra, multivariable calculus, and statistical modeling — before applying them to problems like constrained minimization and resource allocation. She's particularly strong at breaking down how to set up a problem correctly, connecting the computational steps back to the underlying math so the process doesn't feel like a black box.

Andrew's graduate work in electrical engineering put him deep into linear programming, constrained optimization, and gradient descent methods — the kind of problems where setting up the objective function correctly matters as much as solving it. He teaches students to translate real-world engineering constraints into mathematical formulations and then systematically find optimal solutions. Rated 5.0 by students, he brings both the theoretical rigor and applied intuition that optimization demands.

Linear programming models, constraint formulation, and objective function analysis are daily tools in industrial management — and Arun's doctorate is specifically in Engineering and Industrial Management. He teaches optimization not as a purely theoretical exercise but as a decision-making framework, walking through simplex methods and duality with real-world resource-allocation scenarios. That applied perspective makes even the most notation-heavy problems feel purposeful.

Linear programming, simplex methods, and constrained optimization problems all require a mix of calculus intuition and algebraic precision. Joseph's math coursework at the University of Chicago, combined with his programming skills in Python and Java, means he can walk through both the theory behind optimization and the computational side of implementing solutions. He's especially good at translating word problems into objective functions and constraint sets.

Chemical engineers optimize processes for a living — minimizing cost, maximizing yield, balancing constraints — so Sujana's degree gave her hands-on fluency with linear programming, Lagrange multipliers, and objective function formulation. She teaches students to translate real-world problems into mathematical models first, then apply the optimization technique that fits. That modeling step is where most students struggle, and it's where her engineering instincts are most useful.

I am an interdisciplinary educator with an Ed.M. from the Harvard Graduate School of Education and a B.A. from Dartmouth College. My background is primarily in integrated arts learning and museum education and I specialize in visual arts, history and art history, and object-based learning. In all subjects, I take a creative, inquiry-based and learner-centered approach, designing opportunities for each unique individual to meet their learning goals.

I'm not tutoring or buried in my textbooks, you will either find me rock climbing at the Triangle Rock Club, playing Ultimate Frisbee, working on my car, or enjoying the great outdoors (beaches, mountains, forests--you name it, I love it). On rainy weekends I enjoy tinkering with computers and old electronics, playing Pokemon, or picking at my guitar.

I am a recent graduate from a masters program in biostatistics at Columbia University. I received my Bachelor of Arts in biological sciences, with a focus in neurobiology at Northwestern University. In August, I will be starting a doctoral program in biostatistics at NYU. I was a teaching assistant at Columbia University in my department and also have tutored graduate students and undergraduates privately as well. My primary areas of tutoring are math and statistics coursework in addition to math sections on standardized tests such as the GRE and GMAT. I am very passionate about helping students feel more confident and excited about math. In my spare time, I enjoy running, playing piano, and spending time with friends and family.

I am a graduate of Wesleyan University, where I received my Bachelor of Arts in Sociology with High Honors. With eight years of experience working in education, I've tutored students in math, science, history, and English, as well as helped students prepare for standardized tests. I've guided adults towards passing the US Citizenship Exam and taught English in India, where I lived for six months. Whenever I work with a student I personalize the lessons to fit their particular learning style, since I know every student is unique and having the right fit can make all the difference in making learning fun and effective. My strengths are tutoring the social sciences and humanities, as well as making math and standardized tests approachable to students that normally don't like those subjects. In my spare time I like traveling, spending time in the outdoors (climbing & backpacking), meditation, and playing soccer. Next fall I will be beginning my PhD in Education at Harvard University.

I am proud to be a part of Varsity Tutors! I am originally from San Antonio, TX; I completed my undergraduate education at Rice University in Houston where I received a bachelor's degree in Biochemistry and Cell Biology. Currently, I am in my second year of medical school at Baylor College of Medicine.