2024 MAD Design Fellows announced

Adelaide Zollinger | MIT Morningside Academy for Design • May 21, 2024

Since its launch in 2022, the MIT Morningside Academy for Design (MAD) has supported MIT graduate students with a fellowship , allowing recipients to pursue design research and projects while creating community. Pulling from different corners of design, they explore solutions in fields such as sustainability, health, architecture, urban planning, engineering, and social justice. 

On May 1, MAD announced the 2024 cohort of Design Fellows at the MIT Museum.

Sofia Chiappero , MCP student in the Department of Urban Studies and Planning and MITdesignX affiliate: Chiappero is working around the intersection of community development and technology, aiming to address the challenges faced by underserved communities at risk of displacement in Latin America. Through a blend of social science and digital inclusion, she seeks to design a new approach to researching human interactions and replicating them in virtual settings, with the ultimate goal of preserving the identity of these communities and giving them visibility for resilient growth.

Clemence Couteau , MBA candidate in the MIT Sloan School of Management : Couteau is tackling the rise of postpartum depression among U.S. mothers by aiming to develop a digital solution empowering at-risk pregnant women to improve mental health outcomes. This involves a self-directed therapy chatbot in a mobile app, based on the “ROSE” protocol.

Mateo Fernandez , MArch student in the Department of Architecture : Fernandez explores how to depart from the current construction industry, designing alternatives such as growing buildings with biomaterials, and deploying advanced 3D printing technologies for building.

Charlotte Folinus , PhD candidate in the Department of Mechanical Engineering : Folinus creates new methods for designing soft robots, using these tools to design soft robots for gentle interactions, uncertain environments, and long mechanical lifetimes. “I am really excited to be surrounded by people who can do things I cannot. That's when I'm the best version of myself. I think that's the community I'll find here,” she says.

Alexander Htet Kyaw , master's student in the Department of Architecture and the Department of Electrical Engineering and Computer Science and MITdesignX affiliate: Htet Kyaw's current research utilizes robotic assembly, multimodal interaction, and generative AI to challenge conventional manufacturing and fabrication practices. He is working on an AI-driven workflow that translates design intent into tangible objects through robotic assembly.

Dení López PhD candidate in the Department of Urban Studies and Planning : As a Design Fellow, López uses design research to evaluate and extend the scope of Bicheeche Diidxa’, a long-standing Participatory Action Research initiative for disaster resilience focused on five Zapotec communities along the Los Perros River in Oaxaca, Mexico.

Caitlin Morris , PhD candidate in media arts and sciences : Morris’s research explores the role of multisensory influences on cognition and learning, and seeks to find and build the bridges between digital and computational interfaces and hands-on, community-centered learning and teaching practices.

Maxine Perroni-Scharf , PhD candidate in the Department of Electrical Engineering and Computer Science : Perroni-Scharf is currently working on developing techniques that enable the discovery and design of extremal metamaterials — 3D printed materials that exhibit extreme properties arising not from their chemical composition, but rather from their structure. These can be applied to a variety of tasks, from battery design to accessibility.

Lyle Regenwetter , PhD candidate in the Department of Mechanical Engineering : Regenwetter develops methods to incorporate design requirements, such as safety constraints and performance objectives, into the training process of generative AI models.

Zane Schemmer , PhD candidate in the Department of Civil and Environmental Engineering : Schemmer's research aims to minimize the carbon footprint of the built environment by designing efficient structures that consider the availability of local materials.

A collage of four pictures of a yellow robot dog.

Helping robots practice skills independently to adapt to unfamiliar environments

By Alex Shipps | MIT CSAIL • August 8, 2024
A new algorithm helps robots practice skills like sweeping and placing objects, potentially helping them improve at important tasks in houses, hospitals, and factories.
A man wearing glasses and a blue shirt is smiling for the camera.

Dimitris Bertsimas named vice provost for open learning

By Sara Feijo | MIT Open Learning • August 8, 2024
Leveraging more than 35 years of experience at MIT, Bertsimas will work with partners across the Institute to transform teaching and learning on and off campus.
Two men are standing next to each other in front of a table with a robot on it.

Precision home robots learn with real-to-sim-to-real

By Rachel Gordon | MIT CSAIL • July 31, 2024
CSAIL researchers introduce a novel approach allowing robots to be trained in simulations of scanned home environments, paving the way for customized household automation accessible to anyone.
A bunch of green thermometer on a pink background.

Method prevents an AI model from being overconfident about wrong answers

By Adam Zewe | MIT News • July 31, 2024
More efficient than other approaches, the “Thermometer” technique could help someone know when they should trust a large language model.
A bunch of dice are flying in the air in a dark room.

Study: When allocating scarce resources with AI, randomization can improve fairness

By Adam Zewe | MIT News • July 24, 2024
Introducing structured randomization into decisions based on machine-learning model predictions can address inherent uncertainties while maintaining efficiency.
A computer generated image of a brain on a motherboard.

MIT researchers advance automated interpretability in AI models

By Rachel Gordon | MIT CSAIL • July 23, 2024
MAIA is a multimodal agent that can iteratively design experiments to better understand various components of AI systems.
A computer generated image of a molecule on a green background

Proton-conducting materials could enable new green energy technologies

By David L. Chandler | MIT News • July 23, 2024
Analysis and materials identified by MIT engineers could lead to more energy-efficient fuel cells, electrolyzers, batteries, or computing devices.
A hand is touching a screen with its finger.

Large language models don’t behave like people, even though we may expect them to

By Adam Zewe | MIT News • July 23, 2024
A new study shows someone’s beliefs about an LLM play a significant role in the model’s performance and are important for how it is deployed.
A nurse is looking at a computer screen while a woman is getting a mammogram.

AI model identifies certain breast tumor stages likely to progress to invasive cancer

By Adam Zewe | MIT News • July 22, 2024
The model could help clinicians assess breast cancer stage and ultimately help in reducing overtreatment.
A grid of colorful balls connected to each other on a white background.

Machine learning unlocks secrets to advanced alloys

By Poornima Apte | Department of Materials Science and Engineering • July 18, 2024
An MIT team uses computer models to measure atomic patterns in metals, essential for designing custom materials for use in aerospace, biomedicine, electronics, and more.
More Posts