Hannah Burd | Recent Grad and Current Explorer of Places
Welcome To My College Portfolio
Theorized during a three-week product redesign sprint, I worked on a project that an airplane seat redesign to assist passengers with limited strength, height, or mobility who need help lifting their carry-on into overhead bins. We propose a rotatable, locking armrest that can be used for temporary support in the lifting process.
My responsibilities included brainstorming, physical-mechanical prototyping, and engineering analysis using FEA on SOLIDWORKS.
Conceptualized, designed, and prototyped in six weeks during a graduate Industrial Ecology course, I worked on a project that, inspired by biomimicry, developed a novel approach to ready-to-eat food packaging. The product is a self-sealing bag that takes advantage of bistability and the physical properties of recyclable plastics.
Using cereal as a demo, the product would contribute to a reduction in food waste and ~20% reduction in the environmental impact of the entire product while minimally impacting the manufacturing process. The analysis demonstrated the product retains its bistability over time and creates a closed system in its closed state.
My responsibilities included need-finding, literature research, concept development, prototyping, product testing, and marketing strategy.
Theorized during a three-week Internet Of Things product design sprint, I worked on a project that proposed a guided maintenance system for car owners that need an easy way to identify and solve mechanical issues on their own in order to ensure car and passenger safety while empowering vehicle owners and reducing stress.
The vehicle system:
My responsibilities included need-finding, brainstorming, concept development, product design, and creating the concept video shown below.
Designed and prototyped as a part of a six-week project in a product design course, I developed a prototype for a lung monitoring wearable for ALS patients that is comfortable, robust, and easy to use.
After weeks of iterative prototyping, consulting with advisors, and human-factors testing, we proposed a wearable that featured two layers of electrodes sewn into the top and bottom of a spandex-nylon band. Two layers, as opposed to one, increase the data available without compromising functionality or usability. The electrodes are sewn into the fabric in casing shells to minimize disconnection. The wearable includes adjustable clips on the back to accommodate different sizes.
This project strove to consider female anatomy, long-wearability, and designing for repair in a biomechanical device. We also offered user testing data aimed to assist the downstream development of this device, which may include textile circuitry and wireless data communication.
My responsibilities in the project included research, ideating, mechanical design and rapid prototyping using textiles and SOLIDWORKS, engineering analysis through electrode contact quantification, assembling the proposed prototype, and user testing.
My first project with CSAIL focused on analyzing datasets associated with Massive Online Open Courses (MOOCs) to gain insight into the learning behaviors of millions of students in large-scale online programming education environments. Using python data science packages and looking at attempts and final accuracy, we found that (unsurprisingly) students with no programming experience generally struggle more than students with prior programming experience. Of more interest, we observed a potential turning point in the course where students of all experience levels begin to struggle. The final analysis demonstrated that two groups of students perform differently and inform question design by demonstrating which question types are particularly difficult for students.
I first-authored a paper on this topic, found here: https://dl.acm.org/doi/10.1145/3386527.3406728
My second project consisted of accelerating neuron detection in a neuronal analysis pipeline with the University of Cambridge through the same group at CSAIL. Using interactive game design, python, and machine learning techniques, I accelerated a step in a pipeline for studying the neuronal causes of Rett Syndrome and Autism. This work allowed researchers at Cambridge to process information in minutes instead of hours/days.
Designed, analyzed, and built in a ten-week solid mechanics course, this bridge is a scaled-down model of a pedestrian truss bridge. The goals of the project were to create the most economic bridge that can withstand 1kN of applied force and to accurately predict deformation and maximum load through finite element analysis (FEA) and hand-calculations, respectively.
This bridge received the highest marks in the class as it best met the economic requirement (used least material of all bridges) and most accurately predicted the deformation using FEA (4.7% error).
My responsibilities in the project included mechanical design, piece and assembly modeling in SOLIDWORKS, FEA for deformation, and assembling the physical bridge.
My 25-week capstone project consisted of a collaboration with the Saucony Innovation Lab and four other team members to identify and test new technologies and methods for a system to map the internal geometry of athletic shoes for quality control and customer fit, focusing on 3-D imaging tech.
My responsibilities in the project include content writing and review, mechanical design using SOLIDWORKS and 3D Printing, statistical analysis (DOE, ANOVA), project management (motivating informed decision making, submitting quality deliverables on time, staying under budget), and sustainability considerations.
Project Abstract:
There currently exists no commonly used technology or system designed to measure the internal volume and geometry of an athletic shoe. Information regarding internal shoe geometry has potential applications in the fields of quality control, customer fit, and shoe design, among others. To develop a system capable of measuring internal areas of the shoe, we investigate several types of technologies and methodologies, determine the requirements of such a measurement system, and lay the foundation for downstream automation of the measurement process. We propose using CT Scanning to take 3D images of up to five shoes at a time and we develop a script that identifies individual shoes and isolates their internal volumes. The MATLAB script allows the user to analyze the internal volume of a shoe in three dimensions or study attributes of individual planes. The script builds a 3-D point cloud in MATLAB, extracting key measurements and volumes and exporting this information as a data matrix or 3D point cloud for further analysis. We also consider the standardization of the shape and stretch of an athletic shoe. We offer two viable methods, an HDPE toe insert and a latex inflatable, as potential standardization tools. While the HDPE toe insert displays the most predictable stretch pattern in relation to shoe sizes, we recommend the latex inflatable as it is easier to filter the material in the image analysis process in MATLAB. Our system offers both a solution and a methodology for the measurement of the internal geometry of athletic shoes.
The following paper was published in Learning @ Scale 2020.
Or view here https://dl.acm.org/doi/10.1145/3386527.3406728
The following is an analysis of Hans Rosling's book Factfulness through a statistical lens.
Select examples of videography work. All work produced in Adobe Premiere Pro.