About

Megan Hofmann is an Assistant Professor at Northeastern University, with a joint appointment in the College of Engineering and the Khoury College of Computer Sciences. She earned her PhD in Human-Computer Interaction from Carnegie Mellon University in 2022. Her research focuses on accessibility and fabrication, digital fabrication in healthcare, and automatic machine knitting. She is involved with the Accessible Creative Technologies (ACT) Lab, which develops digital fabrication tools such as design and 3D modeling systems aimed at producing assistive technologies and medical devices accessible to people with disabilities. Her work also explores the use of fabrication technology in healthcare settings, including efforts to manufacture personal protective equipment during the COVID-19 pandemic. Hofmann's research includes advancing real-time monitoring and behavioral interventions through intelligent healthcare garments and creating tools for designing interactive smart textiles. She has received grants for her projects, including NSF awards for developing adaptive healthcare garments and interactive knitted textiles.

Research topics

• Computer Science
• Artificial Intelligence
• Political Science
• Social Science
• Sociology
• Engineering
• Human–computer interaction
• Engineering drawing
• Materials science
• Mechanical engineering
• Programming language
• Software engineering
• World Wide Web
• Psychology
• Applied psychology
• Gender studies
• Composite material

Selected publications

• KnitA11y: Fabricating Accessible Designs with Machine Knitting

  2025-04-23 · 2 citations

  article
  PublisherDOI

• Demonstration of MechStyle: Augmenting Generative AI with Mechanical Simulation to Create Stylized and Structurally Viable 3D Models

  2025-11-20

  article

  Recent developments in Generative AI enable creators to stylize 3D models based on text and image prompts. These methods change the 3D model geometry, which can compromise the model’s structural integrity once fabricated. We present MechStyle, a system that enables creators to stylize 3D printable models while preserving their structural integrity. MechStyle accomplishes this by augmenting the Generative AI-based stylization process with feedback from a Finite Element Analysis (FEA) simulation. As the stylization process modifies the geometry to approximate the desired style, feedback from the FEA simulation reduces modifications to regions with increased stress. In this demonstration, attendees can interact with MechStyle’s prompt-based UI, observe simulation-informed stylization results, and explore a curated collection of 3D printed objects generated using MechStyle. These artifacts showcase the system’s ability to produce physically plausible, stylized designs across a range of prompts and model types. This hands-on experience highlights MechStyle’s potential to bridge high-level design intent with low-level fabrication constraints in generative workflows.

  PublisherDOI

• Curl Quantization for Automatic Placement of Knit Singularities

  2025-07-23 · 2 citations

  articleOpen access
  PublisherDOI

• A Feedback-Controlled Jamming Approach for Variable-Stiffness Actuators in Untethered Soft Robots

  2025-04-22 · 1 citations

  article

  Jamming actuators have been proposed for many portable or wearable applications, yet the performance of these actuators will vary widely with fluidic leaks that degrade vacuum pressure and therefore maximum stiffness and stiffness over time. We investigate the power consumption and pressure in a series of leaky jamming actuators using four approaches: continuous jamming, jamming once, and re-jamming at regular intervals or if the pressure falls outside a specified range. We demonstrate the pressures and power consumptions of these approaches in a soft gripper and an active robotic elbow brace. We found that re-jamming when pressure fell below a target range reduced power consumption by more than a factor of 7.5 over continuous jamming while maintaining performance. These findings, and other efficient re-jamming approaches, will be crucial to jamming robots that can operate after damage and untethered for multiple hours.

  PublisherDOI

• MechStyle: Augmenting Generative AI with Mechanical Simulation to Create Stylized and Structurally Viable 3D Models

  2025-11-14 · 4 citations

  preprintOpen access

  Recent developments in Generative AI enable creators to stylize 3D models based on text prompts. These methods change the 3D model geometry, which can compromise the model’s structural integrity once fabricated. We present MechStyle, a system that enables creators to stylize 3D printable models while preserving their structural integrity. MechStyle accomplishes this by augmenting the Generative AI-based stylization process with feedback from a Finite Element Analysis (FEA) simulation. As the stylization process modifies the geometry to approximate the desired style, feedback from the FEA simulation reduces modifications to regions with increased stress. We evaluate the effectiveness of FEA simulation feedback in the augmented stylization process by comparing three stylization control strategies. We also investigate the time efficiency of our approach by comparing three adaptive scheduling strategies. Finally, we demonstrate MechStyle’s user interface that allows users to generate stylized and structurally viable 3D models and provide five example applications.

  PublisherDOI

• “As Someone Who is Disabled, I am so thankful for Sex Work”: Alternative Approaches to Access Among Disabled Sex-Workers

  2025-10-22 · 2 citations

  articleOpen accessSenior author
  PublisherDOI

• Beyond Beautiful: Embroidering Legible and Expressive Tactile Graphics

  2025-10-22 · 2 citations

  articleOpen access
  PublisherDOI

• "It's like Goldilocks:" Bespoke Slides for Fluctuating Audience Access Needs

  2024-10-20 · 6 citations

  articleOpen access

  Slide deck accessibility is often studied for people who are blind or visually impaired, but rarely for other people with access needs. We first conducted focus groups with 17 people with slide deck access needs and found that their access needs differed greatly and often conflicted. Moreover, some people’s access needs changed throughout the day (e.g., needing lower contrast colors at night). Therefore, we conducted a design probe with 14 of the existing participants to understand the experience of using a plug-in that lets audience members at a presentation modify a local copy of the slides to meet their accessibility needs. We then interviewed four slide deck authors and presenters to offer a preview of the perspectives that other stakeholders of this tool might have. Finally, we created a functional prototype as a Google Slides plug-in with a subset of the features requested by the participants.

  PublisherDOI

• Singular Foliations for Knit Graph Design

  2024-07-12 · 6 citations

  articleOpen access

  We build upon the stripes-based knit planning framework of [Mitra et al. 2023], and view the resultant stripe pattern through the lens of singular foliations. This perspective views the stripes, and thus the candidate course rows or wale columns, as integral curves of a vector field specified by the spinning form of [Knöppel et al. 2015]. We show how to tightly control the topological structure of this vector field with linear level set constraints, preventing helicing of any integral curve. Practically speaking, this obviates the stripe placement constraints of [Mitra et al. 2023] and allows for shifting and variation of the stripe frequency without introducing additional helices. En route, we make the first explicit algebraic characterization of spinning form level set structure within singular triangles, and replace the standard interpolant with an “effective” one that improves the robustness of knit graph generation. We also extend the model of [Mitra et al. 2023] to surfaces with genus, via a Morse-based cylindrical decomposition, and implement automatic singularity pairing on the resulting components.

  PublisherDOI

• KODA: Knit-program Optimization by Dependency Analysis

  2024-10-11 · 6 citations

  articleOpen access1st authorCorresponding

  Digital knitting machines have the capability to reliably manufacture seamless, textured, and multi-material garments, but these capabilities are obscured by limiting CAD tools. Recent innovations in computational knitting build on emerging programming infrastructure that gives full access to the machine’s capabilities but requires an extensive understanding of machine operations and execution. In this paper, we contribute a critical missing piece of the knitting-machine programming pipeline–a program optimizer. Program optimization allows programmers to focus on developing novel algorithms that produce desired fabrics while deferring concerns of efficient machine operations to the optimizer. We present KODA, the Knit-program Optimization by Dependency Analysis method. KODA re-orders and reduces machine instructions to reduce knitting time, increase knitting reliability, and manage boilerplate operations that adjust the machine state. The result is a system that enables programmers to write readable and intuitive knitting algorithms while producing efficient and verified programs.

  PublisherDOI

Frequent coauthors

• Jennifer Mankoff

  University of Washington

  28 shared

• Scott E. Hudson

  Carnegie Mellon University

  16 shared

• Kelly Mack

  University of Washington

  8 shared

• U. K. Lakshmi

  6 shared

• Rosa I. Arriaga

  Georgia Institute of Technology

  6 shared

• Amy Hurst

  New York University

  4 shared

• Erin Buehler

  Google (United States)

  3 shared

• Chris Barker

  3 shared

Labs

• Accessible Creative Technologies (ACT) LabPI

Education

• PhD Human Computer Interaction, Human Computer Interaction Institute

  Carnegie Mellon University

  2022

• Masters of Human Computer Interaction, Human Computer Interaction Institute

  Carnegie Mellon University

  2021

• Bachelors of Science, Computer Science

  Colorado State University

  2017

Awards & honors

• NSF grant for 'Adaptive Intelligent Healthcare Garment: Adva…
• NSF grant for 'Tools for Programming and Designing Interacti…