alexiswilmot.com
About Alexis
Hello! My name is Alexis, and I am a student at Georgia Institute of Technology. I will be graduating with my Master of Science in Biomedical Engineering this May. I also received my undergraduate from Georgia Tech in May 2024 with a Bachelor of Science in Biomedical Engineering, and a minor in Computing and Intelligence, graduating with highest honors. I have a strong foundation in both engineering and machine learning, and am excited to apply my interdisciplinary skills as a R&D engineer in the healthcare industry!
Relevant Coursework
Here are some (not all) courses that I have completed during my time at Georgia Tech:
Biomedical Engineering Capstone Design:
Hands-on experience with project planning, concept and prototype development, design verification testing, FDA Quality Systems Regulations, design controls, and regulatory pathways for commercialization of medical devices. Business functions such as marketing, sales, manufacturing, intellectual property and their effects on the product development process.
Microelectromechanical Systems Devices:
Design for Microelectromechanical systems (MEMS), fabrication, and modeling. Exploration of the MEMS design cycle, including system modeling, microfabrication techniques, structural mechanics, and transduction mechanisms.
Acoustics:
In-depth understanding of the principles governing the generation, propagation, reflection, and transmission of sound waves in fluids, and their applications in engineering.
Artificial Intelligence:
Foundational techniques such as uninformed and informed search algorithms, optimization such as simulated annealing and adversarial game strategies using minimax and alpha-beta pruning. Explored probabilistic reasoning through Bayesian networks and Hidden Markov Models, as well as machine learning techniques including decision trees, random forests, and neural networks. Additionally logic based AI, constraint satisfaction problems, planning under uncertainty, etc. along with theory and practical applications.
Machine Learning:
Techniques in machine learning with an emphasis on algorithms and their applications to real-world data. Key mathematical concepts like linear algebra, probability, and optimization. Unsupervised learning such as clustering and dimensionality reduction, as well as supervised learning including tree-based models, support vector machines, and neural networks. Hands on experience structuring machine learning workflows.
Biomechanics:
Application of engineering principles to biological systems. Including statics, dynamics, and solid mechanics, with applications to musculoskeletal biomechanics, biofluid mechanics, and the mechanical behavior of biological tissues. Analytical problem solving and computational modeling emphasizing real-world biomedical applications.
Image Based Tissue Modeling:
Computational modeling of tissue biosolid and biofluid mechanics, and simulation of medical device tissue interaction. Advanced constitutive models and application of continuum mechanics to account for large deformation and nonlinearity.
Computer Organization and Programming:
Fundamentals of both computer hardware and software. Translation of a program into commands for execution on hardware, and how the hardware executes those commands using electrons.
Circuit Analysis and Electronics Laboratory:
Design, analysis, and measurement of analog and digital electronic circuits and instrumentation.
Robotics and Perception:
Problems and solutions to autonomous robot behavior - how a robot can perceive the world, and using that information to effectively operate.
Physiology of Cellular and Molecular Systems:
Cellular systems including gene expression, organelles, cell signaling, the cytoskeleton, cell life cycle, and the extracellular matrix. Application of current cell and molecular biological technologies to real-world problems.
Biotransport:
Transport phenomena in biological systems, including fluid dynamics, mass transport, and heat transfer. Topics include biofluid mechanics, oscillatory flow, transvascular and interstitial transport, rheology, computational modeling, and disease-related transport processes such as atherosclerosis and thrombosis. Applied to cardiovascular, respiratory, and organ systems, with a focus on in vivo and in vitro measurement techniques
Quantitative Engineering Physiology Laboratory:
Design and conducting of human-centered biomedical experiments, the creation of bioinstrumentation, including sensors and circuits for analyzing physiological data.
Rational Design of Biomaterials:
Integrating chemistry and cellular biology principles in the rational design of new biomaterials.
Systems Physiology:
Human physiology emphasizing biomedical engineering approaches to organ function, disease states, and medical intervention. Core physiological principles such as homeostasis and structe-function relationships, building onto how regulatory systems (nervous, endocrine, etc.) impact the cardiovascular, respiratory, and renal systems.
