MIT-trained PhD engineer making math and physics finally click

Legena H.

Calculus

MATH AND MATH-BASED COURSES TAKEN ================================= HOWARD UNIVERSITY - BSc Mechanical Engineering (2001-2005) MATH 156 Calculus I (transfer credit, St Marys College) MATH 157 Calculus II MATH 158 Calculus III MATH 159 Differential Equations MATH 180 Intro to Linear Algebra MATH 167 Probability and Statistics (Univ of Wollongong exchange, 2004) MEEG 303 Mathematical Methods SYCS 165 Elementary Computation MASSACHUSETTS INSTITUTE OF TECHNOLOGY - MSc Mechanical Engineering (2005-2011) 18.085 Math Methods for Engineers I 18.086 Computational Science and Engineering II 18.336 Numerical Methods: Partial Differential Equations 18.355 Fluid Mechanics 18.445 Intro to Stochastic Processes 6.431 Applied Probability 6.438 Algorithms for Estimation and Inference 12.714 Computational Data Analysis 12.864 Time Series Methods 12.515 Data and Models 12.870 Air-Sea Interaction UNIVERSITY OF THE WEST INDIES - PhD Mechanical Engineering (2012-2018) MATH 6190 Numerical Analysis CZEM 6112 Coastal Zone Modelling MATH AND MATH-BASED COURSES TAUGHT ================================== EXPLICITLY MATHEMATICS PHYS 2400 Math Methods in PHYS I BSc Level II, UWI Cave Hill - Primary Course Lecturer, Semester I 2019 MATHEMATICAL MODELLING CZEM 6112 Coastal Zone Modelling MSc Level, UWI St. Augustine - Primary Course Lecturer, Semester II 2018 CZEM 6100 Coastal Processes (taught as ocean-based Renewable Energy Power module) MSc Level, UWI St. Augustine - Primary Course Lecturer, Semester I 2017 APPLIED MECHANICS / ENGINEERING MATHEMATICS MENG 1010 Statics and Strengths UWI St. Augustine - TA and labs, Semester II 2016 MENG 1004 Engineering Dynamics UWI St. Augustine - TA, Semester II 2013, Semester II 2016 MENG 2010 Strength of Materials UWI St. Augustine - Primary Course Lecturer and labs, Semester I 2013 MENG 2003 Mechanical Vibrations UWI St. Augustine - Primary Course

Mechanical Engineering

I hold three degrees in Mechanical Engineering (BSc magna cum laude, Howard University; MSc, MIT; PhD, University of the West Indies), with graduate coursework in applied mathematics including numerical methods for partial differential equations, stochastic processes, applied probability, time series methods, and computational data analysis. My doctoral and master's research was fundamentally mathematical — the analytical treatment of statistical moments in large ocean waves and phase-resolved simulation of wave fields — and produced peer-reviewed publications on Wiener chaos expansions and ocean wave prediction. I have been the primary instructor for twenty-three university courses, among them PHYS 2400 Math Methods in Physics I, Engineering Dynamics, Mechanical Vibrations, Strength of Materials, and Coastal Zone Modelling, and I served as a teaching assistant for Marine Hydrodynamics at MIT. That combination of research-level mathematics and sustained undergraduate teaching lets me move fluidly between rigorous derivation and the concrete applications that keep students engaged.

Physics

I have experience, materials and full solution sets to coach any student at any level, to move from a failing grade to getting an A in AP Physics, Physics 1, and Physics 2 - Now - the real question is - are you willing to put in the work? If so - come along! Pro tip - Physics is not a spectator sport - you have to get on the field and start chasing the ball

SAT Math

My own SAT Math scores were all close to 800. I hold a BSc magna cum laude, an MSc from MIT, and a PhD in Mechanical Engineering, with a mathematics background spanning algebra, geometry, trigonometry, statistics, and advanced calculus, which means every concept tested on the SAT sits well within territory I can teach from multiple angles. Over more than a decade I have served as primary instructor for twenty-three university courses and taught hundreds of students, most of them undergraduates whose success depended on solid command of exactly the algebra, ratio, and data-interpretation skills the SAT measures. That experience has made me quick at identifying where a student's reasoning breaks down and patient in rebuilding it, and I place particular emphasis on the problem-solving habits — reading carefully, choosing an efficient method, checking that the answer is plausible — that separate students who know the math from students who score well on it. My aim with any student is to build genuine confidence in the material, so that performance on test day reflects real understanding rather than fragile memorization.

Statistics

PhD engineer with MIT-trained expertise in applied probability, computational data analysis, and time series analysis. I've supervised graduate research, analyzed complex datasets, and taught quantitative courses at university level across engineering and sciences. I specialize in making statistics accessible and purposeful—helping you understand data, design robust experiments, and communicate uncertainty clearly in real-world contexts.