Mark Fuge
Full Professor
Leads the Artificial Intelligence in Engineering Design lab, developing generative and probabilistic models for data-driven engineering design and optimization.
Mark Fuge's lab at ETH Zurich works on learning theory & optimization and probabilistic & causal inference. 17 papers since 2023, mostly Neural Networks. 7 PhD students. 11 alumni.
At a glance
- papers, 2023–2026
- 4/ year
- at T1 venues
- 0%
- PhD students
- 7
- MS students
- 0
- alumni listed
- 11
Papers by yearfrom the lab homepage, 2023–2026
- 6 papers2026 · T1 0 · collecting
- 5 papers2025 · T1 0
- 4 papers2024 · T1 0
- 2 papers2023 · T1 0
T1 venuesOtherVenues: Neural Networks
Research focuspaper titles that use each topic's words
- design7 papers
- Learning theory & optimization5 papers
- Probabilistic & causal inference1 paper
- engineering design1 paper
- process engineering0 papers
Paper list
From the lab homepage.
20266 papers
- Journal of Mechanical DesignAgentic Large Language Models for Conceptual Systems Engineering and Design (opens in a new tab)
- International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC-CIE ), Houston, TX, USA, August 23-26, .EngiAI: A Multi-Agent Framework and Benchmark Suite for LLM-Driven Engineering Design
- Medical PhysicsMachine learning model for fast prediction and uncertainty quantification of needle deflection during prostate biopsy (opens in a new tab)
- AIAA SciTech Forum , Orlando, FL, USAOptiWing3D: A Diverse Dataset of Optimized Wing Designs (opens in a new tab)
- Journal of Mechanical DesignTo Quantize or Not to Quantize: Effects on Generative Models for Topology Optimization Problems (opens in a new tab)
- Journal of Mechanical DesignWhen is it Actually Worth Learning Inverse Design? (opens in a new tab)
20255 papers
- Computers in Biology and MedicineAutomatic Laplacian-based shape optimization for patient-specific vascular grafts (opens in a new tab)
- Neural NetworksBayesian inverse problems with conditional Sinkhorn generative adversarial networks in least volume latent spaces (opens in a new tab)
- Behavior Research MethodsCAP: The creativity assessment platform for online testing and automated scoring (opens in a new tab)
- Journal of Mechanical DesignExploring Human and Language Model Alignment in Perceived Design Similarity Using Ordinal Embeddings (opens in a new tab)
- Acta MaterialiaGrainPaint: A multi-scale diffusion-based generative model for microstructure reconstruction of large-scale objects (opens in a new tab)
20244 papers
- International Conference on Design Theory and Methodology (DTM ), Washington, DC, USAA Picture or a Thousand Words: Design Description Crafting to Replicate Human Similarity Judgments in Large Language Models (opens in a new tab)
- Conference on Human Factors in Computing Systems (CHI ), Honolulu, HI, USAFormulating or Fixating: Effects of Examples on Problem Solving Vary as a Function of Example Presentation Interface Design (opens in a new tab)
- AIAA SCITECH Forum, Orlando, FL, USAOptimizing Diffusion to Diffuse Optimal Designs (opens in a new tab)
- International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE ), Washington, DC, USATo Quantize or Not to Quantize: Effects on Generative Models for 2D Heat Sink Design (opens in a new tab)
20232 papers
- International Conference on Design Theory and Methodology (DTM ) part of ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE ), Boston, MA, USAFewer Triplets Than You Think: Novelty Error Converges Faster Than Triplet Violations in Ordinal Embeddings (opens in a new tab)
- Journal of Applied PhysicsMachine learning for shock compression of solids using scarce data (opens in a new tab)
Where alumni went11 listed
Apply to ETH ZurichRolling
Not announced yet
Computer Science Doctorate (D-INFK)
- Round
- Rolling (no deadline)
- Opens
- —
- GRE
- See program page
- English
- Proficiency in written and spoken English
- Fee
- See program page
Confirmed on the program page.
Similar labsnearest by research summary
- SGNTU
Yingzhen Li
- Probabilistic & causal inference
- CNTsinghua
Wenhui Fan
- Reasoning & neuro-symbolic AI
- SGNUS
Bryan Kian Hsiang Low
- Probabilistic & causal inference
- USNYU
Jennifer Hill
- Probabilistic & causal inference
- GBUCL
Arthur Gretton
- Learning theory & optimization
- USCMU
Nicholas Boffi
- Generative & foundation models
Your notes
Save this lab to keep notes.
Data
Checked against the lab homepage.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.