Research

Research Statement

My research is situated at the intersection of artificial intelligence, computer vision, digital art history, computational creativity and computational and media art. I study how machine learning systems interpret visual culture, how they reproduce or transform cultural assumptions, and how computational methods can become tools for both analysis and artistic inquiry.

The work moves between technical research, critical methodology and practice-based experimentation. It includes computer vision for art history, critical evaluation of text-to-image models, generative image and video systems, artworld network analysis, affective image generation, robotics and autonomous systems, and multimodal visual reasoning.

Fields

  • Artificial Intelligence
  • Computer Vision
  • Generative AI
  • Digital Art History
  • Computational Creativity
  • Computational and Media Art
  • Robotics and Autonomous Agents
  • Digital Humanities

Research Areas

AI, Computer Vision and Art History

Computer vision and machine learning methods for the computational study of images, artworks, visual archives and cultural collections, with attention to the interpretive limits of automated visual analysis.

Generative AI and Critical Evaluation

Critical frameworks for studying text-to-image models, prompt practices, visual bias, model aesthetics and the social assumptions embedded in generated imagery.

Computational and Media Art

Artistic research using generative systems, found footage, social media data, neural networks, interactive media and AI image models as material for experimental moving-image, network and web-based works.

Cultural Data and Network Analysis

Methods for mining and visualizing cultural data, especially artworld networks, online visibility, influence and the computational modeling of contemporary art ecosystems.

Multimodal Visual Reasoning

Current doctoral supervision and research interests include robust visual reasoning, fine-grained visual recognition, visual language models, zero-shot learning, knowledge graphs, graph neural networks and explainable multimodal systems.

Robotics and Autonomous Systems

Earlier and continuing work on robotics, autonomous navigation, decision-making under uncertainty and agent-based systems informs my teaching and broader research in AI and autonomous agents.

Selected Research Directions

  • She Works, He Works

    Critical AI, Gender and Visual Bias

    Research on text-to-image models as cultural systems, combining feminist visual theory, critical prompt engineering and close analysis of generated images.

    Recent work such as She Works, He Works examines how generative AI depicts gender in professional contexts and how identical prompts can produce divergent visual narratives. This work is connected to broader methodological research on critical evaluation of text-to-image models.

    More on She Works, He Works

  • Bushwalking

    Generative Landscapes and Environmental Narratives

    Generative image and video methods for ecological reflection, landscape representation and AI-mediated environmental imagination.

    Bushwalking and related work use StyleGAN-based processes and AI-generated moving image to explore landscape, walking, environmental attention and the poetics of machine-generated terrain.

    More on Bushwalking

  • The Invisible Structures of the Artworld

    Artworld Networks and Cultural Data

    Computational analysis of contemporary art ecosystems through social media mining, ranking systems and network visualization.

    This line of work develops methods for identifying artworld actors, constructing networks of visibility and influence, and using cultural data to reflect on structures that are normally difficult to observe directly.

    Explore the artworld network

  • Forging Emotions

    Affective Image Generation

    Deep learning experiments on emotion, image datasets and the visual conventions through which affect is generated, classified and circulated.

    Forging Emotions explores GAN-generated images trained on affective social media datasets and connects technical image generation to art-historical and cultural questions around emotional expression.

    More on Forging Emotions

  • AI research

    Robust Visual Reasoning

    Visual language models, zero-shot recognition, knowledge graphs, graph neural networks and explainable multimodal reasoning for fine-grained visual understanding.

    This direction includes doctoral supervision on architectures for robust visual reasoning and fine-grained recognition, with emphasis on explainability, structured knowledge and domain-aware visual intelligence.