Research Profile

Portrait of Allira Lu

Allira Lu

Postdoctoral Fellow, AI and Art

Short Bio

I am an artist-engineer studying how generative systems transform creative practice, authorship, and exhibition design. My work asks how models, datasets, and interfaces shape what audiences perceive as meaningful or legitimate in contemporary art. Since 2019, I have collaborated with curators, choreographers, and machine learning engineers on public installations and museum projects. I currently hold a postdoctoral fellowship at the Embedded Institute and teach as a visiting lecturer. Outside the lab, I record urban soundscapes and print small-run zines.

Research Interests

  • Generative art and design
  • Human–AI co-creation
  • Computational aesthetics
  • Curatorial strategies for AI art
  • Dataset curation and ethics
  • Interactive installations

Short CV

  • 2022–present: Postdoctoral Fellow in Creative Computation, Embedded Institute
  • 2019–2022: Doctoral Researcher, Department of Media Systems, Isobar City Institute of Technology
  • 2018–2019: Research Assistant, Laboratory for Computational Aesthetics, Polydora School of Design
  • 2016–2018: Interaction Designer and Resident Artist, Meridian Arts & Code Lab

Affiliations

  • Embedded Institute
  • City Conservatory of Media Arts
  • Laboratory for Computational Aesthetics, Polydora School of Design

Education

  • PhD, Computational Arts, Isobar City Institute of Technology , 2022
  • MSc, Human–Computer Interaction, Northbridge Institute of Technology , 2018
  • BFA, Visual Communication, Aurora City College of Arts , 2015

Teaching

  • Generative Art: Methods and Practice
  • Human–AI Co-Creation Studio
  • Data, Bias, and Creative Systems

Awards

  • Early Career Prize, Forum on Creative AI , 2023
  • Installation Award, New Media Triennial of Borealis , 2021
  • Best Demo, Symposium on Interactive Machine Learning , 2020

Publications

  • Lu, A., Weaving with Models: Curatorial Strategies for Generative Exhibitions, Journal of Computational Aesthetics , 2024.
  • Lu, A.; Nayar, C., Listening to Datasets: A Method for Auditing Creative ML Corpora, Pacific Creative AI Conference , 2023.
  • Lu, A., Choreographic Interfaces for Human–AI Collaboration, Transactions on Media Systems and Society , 2022.
  • Lu, A.; Deka, R.; Moss, J., The Careful Dataset: Community Sourcing for Ethical Generative Art, Workshop on Responsible Creative Technologies , 2021.
  • Lu, A., Co-Authoring with Algorithms in Public Space, Isobar City Institute of Technology Press , 2022.

Abstract

This project investigates how audiences, artists, and curators negotiate authorship and accountability in AI-driven artworks exhibited in public spaces. Drawing on three mixed-method case studies—two museum installations and one outdoor festival piece—the study combines ethnographic observation, interaction logging, and iterative system prototyping. It examines how model transparency, adjustable autonomy, and dataset narrative framing influence audience trust and perceived legitimacy. Findings indicate that (1) audiences attribute greater creative agency to installations when control parameters are exposed, (2) concise storytelling about dataset provenance reduces skepticism, and (3) co-creative interfaces that surface constraints rather than conceal them lead to longer engagement and higher curator satisfaction. The work proposes design guidelines for human–AI co-authorship in exhibition contexts and a lightweight audit protocol for creative ML datasets.