Studying economics at Brown meant Carter lived inside datasets — running regressions, testing hypotheses, and interpreting distributions long before he started tutoring. That firsthand experience makes him especially effective at teaching concepts like standard deviation, normal models, and conditional probability in ways that feel grounded rather than abstract. He's rated 5.0 by students.

Having run statistical analyses on lab data throughout his biochemistry program at Texas A&M, Gabriel understands distributions, regression, and probability as practical tools, not just textbook exercises. He walks through problems by tying each formula — whether it's standard deviation or a chi-square test — to what it actually reveals about a data set.

Discrete math — Laila's strongest area within her UT mathematics degree — is built on combinatorics and probability, which means she arrives at statistics with the counting and logic skills that make topics like probability distributions and expected value click at a deeper level. She teaches students to trace a formula back to the combinatorial reasoning behind it, turning problems that look like memorization into something they can actually derive. Rated 4.5 by students.

Probability distributions, hypothesis testing, and confidence intervals require a different kind of mathematical thinking than most students are used to — less computation, more interpretation. Roozbeh's graduate training in industrial management leaned heavily on statistical analysis, so he brings firsthand experience with how these tools actually get used in research and decision-making. He's especially sharp at demystifying p-values and helping students read output from statistical software.

Applied math training means Rakhi doesn't just know the formulas for variance or regression — she understands the linear algebra and calculus that make them work, which lets her explain statistical concepts at whatever depth a student needs. Her 1550 SAT and competition math background reflect sharp quantitative instincts she channels into breaking down tricky topics like probability distributions and hypothesis testing. Rated 4.8 by students.

As a data science major at the University of Rochester, Lloyd lives in statistics — probability distributions, hypothesis testing, regression modeling, and everything in between. He teaches the reasoning behind each method so students can look at a problem and know which test to run and why. That blend of theoretical understanding and applied practice is exactly what makes stats stop feeling like guesswork.

Fieldwork in archaeology is surprisingly data-heavy — Brandi has used statistical methods on National Science Foundation grants to analyze artifact distributions, date findings, and draw conclusions from incomplete datasets. She brings that practical grounding to topics like probability, standard deviation, and hypothesis testing, making abstract formulas feel purposeful.

Computer science coursework means Rowdy encounters statistics where it actually lives — probability driving algorithm analysis, distributions shaping data structures, and regression powering machine learning models. He teaches concepts like expected value and variance by connecting them to the computational problems where they matter, giving the math a concrete anchor. Rated 5.0 by students.

I am a graduate petroleum engineering student at The University of Texas at Austin. My graduate research is focused on modeling friction losses between the drill string and wellbore during drilling operations. I did my undergrad at UT as well and majored in petroleum engineering and Plan II Honors (an interdisciplinary honors liberal arts program).

Running Monte Carlo simulations and building DCF valuation models during his finance research gave Mahan a hands-on relationship with probability, distributions, and statistical inference that most tutors don't have. He connects textbook concepts like p-values, standard deviation, and sampling methods to tangible examples from engineering and economics, making the reasoning behind each formula concrete. Rated 4.6 by students.

I am excited to work with all students, particularly those with special needs (including G/T). Please contact me with any questions you may have, and I look forward to working with you and/or your child!

I'm currently living in Austin Texas, and will be commuting to Houston regularly to visit my family and friends.