Open Research Position: Data Science / Machine Learning for Design

Location: Cambridge MA (remote option is possible but must have a US work visa/authorization)
Position Type: Part-time, Temporary
Compensation: part-time 10 – 20 hours/week, hourly rate dependent on experience.
Duration: Fall 2024
Job Description:
We are seeking a highly motivated and talented graduate student with expertise in Data Science and Machine Learning to join our research team for a short-term research project. The primary focus of this position is to develop predictive models that can forecast preferences and ratings based on image data, in the context of an on-going design study on the aesthetic perception of wall and floor tiles. This role will provide an excellent opportunity to apply advanced machine learning techniques to a practical problem while contributing to cutting-edge research. This work is part of the Laboratory of Design Technologies and the Material Processes and Systems Group at the Harvard Graduate School of Design.
Key Responsibilities:
• Model Development: Design and implement machine learning models to predict user preferences and ratings from image data.
• Data Processing: Clean, preprocess, and augment mid-size datasets of images to prepare them for model training and evaluation.
• Model Training and Evaluation: Train, validate, and tune models to ensure high accuracy and robustness. Conduct performance evaluations using appropriate metrics.
• Research Documentation: Document methodologies, experiments, and results in a clear and concise manner for both internal use and potential publication.
• Collaboration: Work closely with interdisciplinary team members including designers, architects, industry experts, and supervisors to refine models and achieve research goals.
• Literature Review: Stay updated with recent advancements and trends in machine learning and related fields to incorporate best practices into the project.
Required Qualifications:
• Education: Currently enrolled in, or recent graduate of a graduate program (Master’s or Ph.D.) in Data Science, Computer Science, Machine Learning, or a closely related field.
• Technical Skills:
o Proficiency in machine learning frameworks such as TensorFlow, PyTorch, or Keras.
o Strong programming skills in Python.
• Analytical Skills: Strong understanding of statistical and machine learning algorithms, including supervised and unsupervised techniques.
• Research Experience: Proven experience in conducting research projects, with a strong emphasis on machine learning or data science.
• Problem-Solving: Strong analytical and problem-solving skills with the ability to work independently and in a team environment.

Preferred Qualifications:
• Experience with deep learning frameworks (e.g., TensorFlow, PyTorch).
• Experience with computer vision techniques and libraries such as OpenCV, scikit-image, or similar.
• Publications or significant coursework in machine learning, computer vision, or related areas.
• Familiarity with data visualization tools and techniques.
Application Process:
Interested candidates should submit the following documents:
1. Resume/CV: Detailing relevant educational background and research experience.
2. Statement: Explaining your interest in the position and highlighting any specific expertise that aligns with the job description. Max. 250 words.
3. References: Contact information for at least one academic or professional reference.
Please send your application materials to [email protected] by September 15th 2024.

LDT Research Studio Reports

Three research reports atop a model

LDT launched a new research studio in Summer 2022, which was an exploratory dive into research topics for continued development and investigation. The studio involved interdisciplinary collaboration between faculty, researchers, students, and members of the Industry Advisors Group (IAG), and culminated in three research reports:

  • Sustainable Material Systems

  • REAL / Urban Stack Collaboration: Projects on Human Experience

  • NLP and the Built Environment

Aecom Joins the IAG

LDT is pleased to announce that Aecom has joined the Industry Advisors Group (IAG). “AECOM is the world’s trusted infrastructure consulting firm, partnering with clients to solve the world’s most complex challenges and build legacies for generations to come” (Aecom.com).

LDT Industry Advisors Kick-Off Meeting

We are looking forward to hosting our first in person meeting with the LDT Industry Advisors, kicking off the next phase of research and engagement! We are eager to start working on new ideas and prototypes for a better built environment! This year we will begin with a summer research studio that includes current students in the Master in Design Engineering Program at Harvard, and we will interact frequently with our industry advisors. The goal for the summer is to map out the space of interest, research precedents, and generate ideas for research agendas that will then be pursued during the remainder of the year! Our theme will be ‘Artificial Intelligence, Internet of Things, and the Build Environment’, and we will focus on the threefold aspects of data, materials, and the human experience.

Air Travel Design Guide

The Air Travel Design Guide is a guidebook for airport stakeholders, designers, and air travel enthusiasts, which describes the design of artifacts, spaces, and systems that impact the passenger experience of air travel. 

The project is an analysis of the toolkit of tactics deployed by airports and airlines to guide passengers along. It catalogs a range of artifacts that passengers interact with during air travel: 1) documenting the design decisions embedded within them, 2) identifying their impact on passenger perception, and 3) speculating on alternative scenarios for design and passenger interaction.

The Air Travel Design Guide represents the culmination of the first phase of research within the Future of Air Travel project (an LDT focus project) at the Harvard Graduate School of Design.

Visit the guide: https://airtraveldesign.guide/

The Future of Air Travel Content Release

On Flying Cover      Atlas of Urban Air Mobility Cover

Anyone remember air travel? In early 2020, as the COVID-19 pandemic swept across the globe and international flights were hurriedly cancelled, the Harvard Graduate School of Design’s Laboratory for Design Technologies (LDT) pivoted its three-year focus project, The Future of Air Travel, to respond to new industry conditions in a rapidly changing world. With the broad goal of better understanding how design technologies can improve the way we live, the project aims to reimagine air travel for the future, recapturing some of its early promise (and even glamour) by assessing and addressing various pressure points resulting from the pandemic as well as more long-term challenges.

So far, the project has resulted in two research books: the Atlas of Urban Air Mobility and On Flying: The Toolkit of Tactics that Guide Passenger Perception (and its accompanying website www.airtraveldesign.guide). 

As part of their research, the labs consulted with representatives from Boeing, Clark Construction, Perkins & Will, gmp, and the Massachusetts Port Authority, all members of the GSD’s Industry Advisors Group.

Read the full article from Mark Hooper at the Harvard University Graduate School of Design website: https://www.gsd.harvard.edu/2021/01/the-future-of-air-travel%e2%80%a8/

Crossing Borders: Life & Science Across Disciplines

As part of the Master in Design Engineering (MDE) public lecture series, Dr. Bahareh Azizi, a consultant with the Director General’s Office at the Kuwait Foundation for the Advancement of Sciences (KFAS), spoke to her personal experiences and career trajectory across disciplines and cultures. The lecture, which took place on October 23, 2020, included Dr. Azizi’s experiences as Head of Basic Science and Director of Business Development at the Dasman Diabetes Institute (DDI), as well as her launching of the Experience Science program in collaboration with the Dar Al-Athar Al-Islamiyah.

Dr. Azizi received a Bachelor of Science degree in Biochemistry/Biotechnology from Michigan State University and her doctoral degree in Biochemistry from the School of Chemistry and Biochemistry from the Georgia Institute of Technology (Georgia Tech) in Atlanta, Georgia. After completing a post-doctoral fellowship, she joined the General Faculty in the School of Chemistry and Biochemistry at Georgia Tech, where she was actively involved in teaching undergraduate and graduate courses and maintained a research program, focused on the proteins implicated in several diseases including diabetes and cancer.

Marine sponges inspire the next generation of skyscrapers and bridges

Sponge Research at SEASMatheus C. Fernandes, Joanna Aizenberg, James C. Weaver, and Katia Bertoldi, have co-authored a paper on their research into lattices inspired by deep-sea glass sponges. As Leah Burrows writes in Harvard’s press release, “When we think about sponges, we tend to think of something soft and squishy. But researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) are using the glassy skeletons of marine sponges as inspiration for the next generation of stronger and taller buildings, longer bridges, and lighter spacecraft.”

Fernandes, M.C., Aizenberg, J., Weaver, J.C. et al. Mechanically robust lattices inspired by deep-sea glass spongesNat. Mater. (2020). https://doi.org/10.1038/s41563-020-0798-1

What Is a Forecast for?

Transit ForecastsFor Carole Turley Voulgaris, 2020 has been a year of research on trends in transit ridership forecasts and incentives to produce or not produce accurate ridership forecasts. Read more about her work via research papers in Transportation and the Journal of the American Planning Association.

Voulgaris, Carole Turley. “What Is a Forecast for? Motivations for Transit Ridership Forecast Accuracy in the Federal New Starts Program.” Journal of the American Planning Association, 2020. Vol 86, 4: 458-469.

Voulgaris, Carole Turley. “Trust in forecasts? Correlates with ridership forecast accuracy for fixed-guideway transit projects.” Transportation, 2020. Vol 47: 2439-2477