The Media Arts and Technology Program at UCSB presents Deep Cuts, our 2025 End of Year Show:

• UCSB | Tuesday, June 3 | 5-8pm
  California NanoSystems Institute, Elings Hall (2nd Floor)

  Research, exhibitions, demos

  Explore the latest work of MAT’s research labs in their full splendor and experience the unique and justly famous AlloSphere.

  Paid parking is available in lot 10 (adjacent to Elings Hall)

  Click here for a map to Elings Hall

• SBCAST | Thursday, June 5 | 6-10pm (Live performances begin 8pm)
  Santa Barbara Center for Art, Science and Technology (531 Garden Street)

  Installations, performances, urbanXR

  Enjoy Installations, performances, urbanXR, in a unique and festive setting at Santa Barbara’s extraordinary SBCAST compound.

  This second event takes place as part of Santa Barbara’s “First Thursday,” highlighting the collaboration of the MAT Alloplex Studio/Lab@SBCAST with the SBCAST and the Santa Barbara creative community. Live music performances (from 8-10 p.m) will feature the MAT Create Ensemble. Large-scale projection-mapping will be presented after dark.

  Paid parking is available in city lots 10 and 11.

  Click here for a map to SBCAST

  sbcast.org

Deep Cuts

1. Obscure or lesser-known pieces of work by a musician, artist, technologist, designer, researcher, etc. —often overlooked, yet deeply meaningful to those who know them.
2. Severe reductions in funding or support, particularly in education, the arts, and research—threatening the survival of programs that exist at the edge of visibility.

Deep Cuts are heard in boardrooms and whispered through backdoor channels. They echo in creative, thought-provoking work that’s been both cherished and cast aside. They’re felt by those with eyes on the margins and hands in the process—those shaping what’s next from the edge.

Existing between abstraction and lived experience, Deep Cuts is a warning, a wound, a hidden gem. It’s a call to attention.

MAT is a deep cut.

Media Arts and Technology is a graduate program at UC Santa Barbara that bridges the humanities and sciences. We design and deploy cutting-edge tools to create work that lives in the gradients between and beyond disciplines. Our practices span fabrication, musical composition and performance, computer graphics, immersive installation, and more. We harness the power of digital technology to produce bold, experimental, and defiantly original works that cut deep in every sense of the word. Creative and critical, from the edge of campus and the cutting edge of ideas, our fresh light illuminates the deep cuts of our complex world.

This exhibition is our reveal. It surfaces what’s often unseen: the quietly radical, the structurally complex, the playfully subversive. Here, algorithms dance, light speaks, and code performs. In our hands, technology is not just a tool—it’ s a creative co-conspirator shining light into the unknown.

Deep Cuts is both a reckoning and a celebration. It honors what has been lost or overlooked, refuses to be silenced, and boldly carves bright new pathways through the unknown.

"There is a crack, a crack, in everything. That’ s how the light gets in."
- Leonard Cohen

Both EoYS events will showcase our students' cutting-edge research and new media artworks and work from our MAT courses.

Artist List

Sabina Hyoju Ahn
Alejandro Aponte
Sam Bourgault
Emma Brown
J.D. Brynn
Deniz Caglarcan
Pingkang Chen
Payton Croskey
Ana Cárdenas
Colin Dunne
Diarmid Flatley
Devon Frost
Yuehao Gao
Amanda Gregory
Joel A. Jaffe
Nefeli Manoudaki
Ryan Millett
Erik Mondrian
Megumi Ondo
Lucian Parisi
Iason Paterakis
Weihao Qiu
Marcel Rodriguez-Riccelli
Jazer Sibley-Schwartz
Mert Toka
Ashley Del Valle
Shaw Yiran Xiao
Karl Yerkes
Emilie Yu
Anna Borou Yu
Yifeng Yvonne Yuan

See the article in UCSB's news magazine "The Current" MAT end of year show fuses art and engineering.

Abstract

The integration of Artificial Intelligence (AI) into mass cultural production has brought its underlying limitations—stereotypical representations, distorted perspectives, and misinformation—into the spotlight. Many of the risks of AI disproportionately impact minority cultural groups, including Black people. Now that AI is embedded in institutions like education, journalism, and museums, it is transforming how we access and reproduce sociocultural knowledge, while continuing to exclude or misrepresent many aspects of Black culture, if they are included at all. To reimagine AI systems as tools for meaningful and inclusive creative production, we must look beyond conventional technical disciplines and embrace a radical, decolonized imagination. I am to use Afrofuturism—a methodological framework rooted in Afrodiasporic visions of the future and grounded in critical engagement with race, class, and power—as a rich foundation for creating more equitable and imaginative technological innovations.

As a starting point, I build on existing calls to develop technology that does more than mitigate harm—a space I define as liberatory technology. Within this space, I identify liberatory collections—community-led repositories that amplify Black voices—as powerful models of data curation that empower communities historically marginalized by traditional AI and archival systems. My survey of fourteen such collections reveals innovative, culturally rooted approaches to preserving and sharing knowledge. I use these findings to argue for consent-driven training models, sustained funding for community-based initiatives, and the meaningful integration of Black histories and cultures into AI systems.

Expanding on this foundation, I have conducted preliminary interviews with Afrofuturist data stewards—Black technologists who carefully collect, curate, store, and use data related to Black speculative projects. My early analysis shows that these creators approach AI with a strong sense of cultural responsibility—not only to critique it, but to retool it in service of Black life. They navigate this historically fraught technological space by reclaiming tools once used for harm and repurposing them toward Black joy, rest, and healing. Through Afrofuturist perspectives, they reimagine historical data and artifacts, envisioning futures grounded in possibility rather than oppression.

Abstract

LLMs have revolutionized digital content production, automating text generation at unprecedented scale and fundamentally restructuring media markets.

Language agents are AI planning systems that break down complex problems using natural language reasoning. However, they face persistent challenges with hallucinations and memory retention in extended conversations—core technical barriers driving current research. Most critically, LLMs—the backbone of language agents—struggle with a deceptively simple creative challenge: producing realistic, long-running dialogue that captures authentic conflict, tension, and the unpredictable dynamics of human interaction.

This project applies language agents to narrative planning for synthetic entertainment. It builds upon foundational work in computational narratology, computer planning, and agent-based AI. It addresses key challenges in narrative AI: maintaining long-term narrative structure, demonstrating character agency, and generating contextually appropriate story progressions that adhere to causal logic. Specifically, it seeks to generate an agent-based drama – generative reality television and entertainment featuring natural, believable characters with internalities and actions consistent with their principles and psyche.

My software creates a conversation modeled as a turn-based game such as chess, where each turn ends with a character’s utterance. It uses existing television tropes for characters to identify themselves and each other. These tropes are used to synthesize a personal narrative for each agent. Each character’s interpretation of the conversation creates a rich step-based “chain of thought” that drives the character's motivations and actions. It attempts to improve the quality of LLM output by providing the structural benefits of traditional formal computer planning techniques and data pipelines while enriching the “reasoning” abilities and believability of the agents.

This project also takes a position on the dire need for transparency and explainability in AI. The sudden explosion of language agents in particular has created a still-nascent market of agent-based software products with a disorienting amount of vaporware that risks creating a bubble that, if burst, risks ushering in a new AI winter.

By visualizing each step of the pipeline as well as the natural text being used within the recursive workflow, this project reveals its entire inner workings layer, rather than obfuscating further an under-recognized technology by layering it within a new black box. The result is emergent storytelling through a transparent pipeline of machine cognition.

Abstract

Most of the world’s megacities are located in the Low Elevation Coastal Zone (LECZ), which represents 2% of the world’s total land area and 11% of the global population. The number of people living in the LECZ has increased by 200 million from 1990 to 2015 and is projected to reach 1 billion people by 2050. These areas are especially vulnerable to the effects of coastal processes such as sea level rise, coastal erosion, and flooding, exacerbated by warming global climates. The goal of coastal sciences is to characterize these processes to inform coastal management projects and other applied use cases. Coastal environments are exceptionally dynamic—there are several marine and land processes that occur on a range of spatio-temporal scales to influence the hydro- and morphological profile to various extents across discrete coastal sections. The spatio-temporal sampling requirements for characterizing coastal processes, coupled with the volatile nature of the area, make in-situ sampling difficult and traditional remote sensing techniques ineffective. Therefore, bespoke remote sensing solutions are required.

Our goal is to aggregate knowledge and review modern practices that pertain to remote sensing of coastal environments for oceanographic, morphological, and ecological field research in order to provide a framework for developing low-cost integrated remote sensing systems for coastal survey and monitoring.

A multidisciplinary approach that incorporates biological, physical, and chemical data gathered through a combination of remote sensing, ground truth observations, and numerical models subject to data assimilation techniques is currently the optimal method for characterizing coastal phenomena. Data fusion algorithms that integrate data from disparate sensor types deployed in conjunction are used to produce more accurate and detailed information. There is an international network of remote sensing systems that deploy a variety of sensors (e.g., radar, sonar, and multispectral image sensors) from a range of platforms (e.g., spaceborne, airborne, shipborne, and land-based) to compile robust open-source datasets that facilitate coastal research. However, models are currently limited by a lack of data pertaining to particular environmental parameters, and the extent and regularity of high-resolution data collection projects. The scientific coastal monitoring and survey network must be expanded to address this need. Recent technological advancements—including heightened accuracy and decreased footprint of sensors and microprocessors, increased coverage of GNSS and internet services, and implementation of machine learning techniques for data processing—have enabled the development of scalable remote sensing solutions that may be used to expand the global network of environmental survey and monitoring systems.