Scholar
Alec Helbling
Google Scholar ID: hwY7DI8AAAAJ
Machine Learning PhD Student, Georgia Tech
Citations & Impact
Citations
397
H-index
5
i10-index
3
Publications
15
Co-authors
12
list available
Publications
Resume (English only)
Academic Achievements
- - Published 'ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features' at ICML 2025, won oral presentation (top 1%) and Best Paper Award at CVPR 2025 Workshop.
- - Developed ManimML, an open-source Python library, presented at IEEE VIS 2023, won Best Poster Award.
- - Developed Diffusion Explorer, presented at IEEE VIS 2025.
- - Contributed to Transformer Explainer, presented at IEEE VIS 2024, won Best Poster Award.
- - Co-authored 'Non-Robust Features are Not Always Useful in One-Class Classification', presented at CVPR 2024.
Research Experience
- - Worked with Chris Rozell at Georgia Tech on methods for guiding generative models based on human feedback.
- - Interned at Adobe on the Firefly team under Oliver Brdiczka.
- - Interned at IBM Research with Achille Fokue, focusing on applying graph neural networks to large-scale language models for document summarization.
- - Interned at Microsoft, working on scaling a data analytics service.
- - Interned at NASA Jet Propulsion Laboratory with Lukas Mandrake, developing web-based visualization tools for interacting with ML models.
- - Started research career at the University of Pittsburgh with David Koes, working on machine learning applications to computational drug discovery, specifically trying to understand protein-ligand interactions.
Education
- PhD student at Georgia Tech's College of Computing, advised by Polo Chau, supported by the National Science Foundation Graduate Research Fellowship.
Background
- ML PhD student, interested in generative models, multimodal machine learning, and ML interpretability. Previously worked on methods for guiding generative models based on human feedback, and also interested in the application of data visualization and HCI to understanding and guiding machine learning systems.
