Award-Winning Statistics Graduate Level Tutors
serving Spokane, WA

Currently, I am a student at the University of Windsor, pursuing a PhD of Mechanical Engineering under the supervision of Dr. Altenhof. In 2005, I earned a Bachelor of Science degree in Mechanical Engineering from Azad University. The undergraduate curriculum in Mechanical Engineering introduced me to a wide variety of subjects, providing me with a strong foundation in the theoretical concepts of this major. Since I was interested in expanding my knowledge, acquiring skills in new technologies and methods, and opening pathways into additional employment opportunities, I applied for a master's degree in Solid Mechanic Engineering at Azad University. In 2006, I got admitted to the MSc program in Solid Mechanic Engineering at Azad University. During the first year, the coursework helped me to obtain a more in-depth understanding of the field. I engaged in research projects, presentations, and report submissions in the second year. In addition, as a Teaching Assistant, I developed pedagogical knowledge and skills. My MSc thesis project, "Experimental and numerical study of in-plane loading of thin-walled tubes," exposed me to new skills in experimental testing and software programs (LS- DYNA). I successfully earned my MSc in 2008 and ranked in the top 10% of students in my class. This two-year graduate program not only made me an expert in the field but also enhanced my critical thinking, analytic abilities, time management, and research skills. In 2009, Azad University of Tuyserkan offered me a Lecturer position. As a researcher who couldn't leave the dynamic atmosphere of the scholastic world and as a potential teacher who was constantly engaged in critical reflection, I accepted the offer. In the beginning, I took "Assessment & Evaluation in Higher Education" and " University Teaching Skills" courses to be eligible for teaching. These two mandatory courses increased my knowledge of evaluation methods, testing, and measurement in education. I learned how to motivate and activate students, encourage personal initiative and choose the most suitable teaching style. As a lecturer, I gained a great experience in teaching. I have taught Statics, Strength of Materials, and Design of machine elements courses at the undergraduate level. I believe that it has been an excellent opportunity to provide a great learning experience for a diverse group of students. As a faculty member, I have worked cooperatively and collaboratively with the campus community, including faculty members, employees, students, and others. I have contributed to the field as a researcher by conducting scholarly activities. I received two research grants from the university, conducted research and published three papers in prestigious ISI journals, and presented my research at 20 national and international conferences on Mechanical Engineering. I have valuable experience in optimizing energy absorption in thin-walled aluminum and composite tubes. I am also experienced working with Hopkinson instruments and gas gun testing equipment. In addition, I have significant experience working with the Santam instrument and the Instron testing machine. This machine can obtain the tensile and compressive strength of the specimens, input for LS Dyna software. I also became interested in Metal Matrix Composite after designing and manufacturing diffusion instruments to prepare metal matrix composite productions. As a faculty member, I provided administrative services. In 2011, I was appointed as the head of the Mechanical Engineering department for two years. I was responsible for faculty recruitment and development, faculty evaluation, program development, program review, curriculum development, class schedule planning, etc. In 2013, I was appointed as the "Vice Chancellor for Academic Affairs" for three years. In this position, I was the chair of the Academic Affairs committee. I was responsible for planning, developing, organizing, directing, and evaluating academic programs, policies, procedures, and guidelines. These two administrative services enhanced my management skills, such as strategic thinking, planning, communication, decision-making, motivating, and interpersonal skills. Driven by my dedication to sustainable engineering, I decided to pursue a Ph.D. in mechanical engineering overseas. My goal is to expand my expertise and confront fresh challenges in the realm of composite materials. If granted the chance to engage with Long Fiber Thermoplastic (LFT) composite materials during this internship, I am excited to explore industrial-scale manufacturing and design processes. I look forward to collaborating closely with fellow research engineers, anticipating that such collaboration will significantly enhance my skills and knowledge in the field. S

Graduate-level statistics demands comfort with proofs and distributions that most intro courses barely touch — moment-generating functions, maximum likelihood estimation, and Bayesian inference all require a different kind of mathematical maturity. Liban's dual training in mathematics and economics gives him fluency in both the theoretical framework and the applied modeling that graduate programs expect. Rated 4.8 by students, he brings the rigor without losing the intuition.

Hi! I'm Alexandre, I am a Machine Learning Engineer, so I write code to make AI do all sorts of stuff everyday. I have degrees in Applied Mathematics and Computer Science so if curious about how an area of math is useful in the real world I'd be happy to give a list of examples!

Graduate-level statistics demands comfort with concepts like maximum likelihood estimation, Bayesian inference, and hypothesis testing frameworks that go well beyond intro courses. Abhi's M.S. in Data Science from UIUC and current PhD work at NYU mean he uses these tools daily in research — he teaches the theory alongside the practical intuition for when and why each method applies.

Graduate-level statistics often means wrestling with multivariate methods, hierarchical models, and software like SPSS, Stata, or R while simultaneously trying to apply them to a thesis or dissertation dataset. Hidefusa's doctoral work in clinical neuropsychology gave him hands-on experience designing studies, running complex analyses, and interpreting output — skills he now breaks down for other graduate students navigating their own research.

Graduate-level statistics lives at the intersection of theory and application — ANOVA designs, regression diagnostics, multilevel modeling, and knowing when each tool fits. Joshua's master's work in cognitive psychology at Purdue required heavy use of these methods on real experimental data, so he teaches statistical reasoning the way researchers actually use it. He's especially sharp on translating between the math and the conceptual logic behind hypothesis testing.

I am a Molecular Engineering major at the University of Chicago, I am currently taking time off to focus on other aspects of my career but I don't want to stop tutoring outside college campus!. I am a child of immigrants and have spent my life tutoring my siblings and younger students, and I loved working with them! See y'all in class!

Graduate-level statistics is where Matthew lives professionally — his Master's in Educational Measurement and Statistics at the University of South Florida has him deep in topics like multivariate analysis, psychometrics, and inferential modeling on a daily basis. He's also served as a T.A. and instructor trainee for undergraduate statistics, so he knows how to unpack dense concepts like maximum likelihood estimation or ANOVA assumptions for students who are encountering them for the first time at the graduate level.

Graduate-level statistics demands comfort with proofs and derivations that most intro courses skip — maximum likelihood estimation, Bayesian inference, and the mathematical foundations behind common tests. Brian's Caltech background in economics and computer science gave him deep exposure to these methods in both theoretical and applied contexts, and he breaks down dense notation into intuitive steps.

Graduate-level statistics pushes well beyond t-tests into maximum likelihood estimation, Bayesian inference, and multivariate regression diagnostics — territory where intuition from introductory courses often breaks down. Kathleen's experience across biostatistics and bioinformatics means she can ground abstract theory in real analytical workflows. Rated 4.9 by students, she brings both mathematical rigor and applied perspective to advanced coursework.

Graduate-level statistics throws students into multivariate analysis, ANOVA designs, and regression modeling where the intuition behind each test matters as much as running it. Macklin applies these methods daily as a medical student analyzing clinical research data, so he teaches the reasoning behind choosing a test — not just the formulas. Rated 5.0 by students.

Duncan's master's degree in statistics makes him a natural fit for graduate-level coursework in regression analysis, hypothesis testing, ANOVA, and Bayesian methods. He approaches each topic by connecting the mathematical theory to the practical decisions students need to make — choosing the right model, interpreting output, and defending assumptions. His 5.0 client rating speaks to how clearly he breaks down even the most notation-heavy material.