Merging civic, public, and personal datasets of complaints, requests, and searches into a shared temporal schema, BADWIZARD restages data through vignettes shot on digital 4K and 16mm film. By rehearsing, performing, and filming at the actual coordinates embedded in the data, the project reenacts complaints, requests, searches, and queries as gestures through which we now navigate experience, recalling in queries rather than narratives. Data is not objective fact but rhetoric and performance, shaped by bias, institutional logics, and the limits of capture. It reveals how historical distortions recur within digital systems, how disparities become automated under the guise of neutrality, and how those most precariously positioned are often rendered absent within the very systems they sustain. Through this filmic and data-driven restaging, BADWIZARD explores how fragments of lived experience might become a form of collective memory or collective fiction, and what it means to reconstruct meaning inside computational structures that both depend on and overwrite the lives they record.

Artist Statement

How would you begin to describe the sound of New York? And does "noise" even mean anything in that context? Is it a specific sound, a texture, or just a kind of excess yet to be filtered or discarded?

The past year was spent listening, collecting, and scraping that excess. Or at least what we could trace of it, still residual within New York’s public data. 311 service requests, FOIL requests, forum archives, satisfaction surveys… Thousands of accounts, grievances and requests, filed and formatted, mostly forgotten. Noise, the city's most common complaint, but also what the documents most fail to hold: subjective, ephemeral, present in the record only as a grievance about itself.

These records became restaged as BADWIZARD. Through on-site reenactments, we returned these accounts to their original sites of report and to the spaces they describe.The “script” was
performed by two actresses and captured on three cameras: digital, 16mm, and behind-the-scenes. What resulted was not a singular narrative but a database of over 600 shots, one account becoming multiple versions, none the original.

BADWIZARD is part of a multi-year project tracing the tension between lived experience and the data systems that claim to preserve it. It began in 2020 as a personal counter-archive examining how accounts of truth, particularly those that are diasporic, unofficial, or "noisy," are produced, authorized, erased, or retold.

Institutional archives have long shaped how we understand and remember the past; today, data systems extend that authority into the prediction of the future. Yet data does not mirror reality. It only reflects how reality is described and valued, carrying forward the biases embedded in its schemas, encoded into algorithms, and repeated at scale. Life, "de-noised."

During my time at Backslash, I wanted to move behind the algorithmic interface through which data is generally experienced and into the processes beneath it: to trace the raw material ingested within machine learning models that increasingly shape how culture is produced and reality understood. How lived experience is compressed into a database entry, an annotation, a model, a research paper, a civic policy, a legitimized "fact." And what, and who, becomes the "noise" within the very systems they help sustain?

Working with Gabriel over the past year has been so wonderful. From our first meeting, we found ourselves asking the same questions just from different directions. Our concerns about technology and data processing were never really technical ones, but societal, cultural ones.

Slowly, I came to understand data processing as really, storytelling: of countless occurrences, only some are documented, fewer are selected, and from these something coherent is assembled. The difference is that a film admits to being edited. Data arrives as if no one made it.

So I approached the footage the way data is processed. Each edit becomes a "figure," a query run against the takes, the way data becomes research becomes proof. Each version proposes an alternative narrative, a slightly different account, perhaps actually closer to what was lived than what the record retains.

The vignettes will culminate as a multichannel installation activated by performance, where earlier queries screen alongside new ones being made. Rehearsal, blocking, retakes; onstage and off; live in the camera feed or edited footage. The apparatus of filmmaking as the visible stand-in for the apparatus of data. As only what can be felt can be questioned, remembered otherwise. Noise moves across walls, thresholds, and timestamps, interrupting the database as an unstable yet shared memory, a possible future, or a collective fiction.

Collaborator Statement

Through our discussions about BADWIZARD, Tianyi and I would always be amused by how much our workflows were alike. In both art and research, we thought of ourselves as data curators. Writing an academic paper and putting together a new art piece both required us to make sense of the connections, opacities, and insights in messy and problematic representations of an also messy and problematic world. We shared our grievances about data and how people would misinterpret datasets until we realized that, between navigating peer review or a gallerist’s request, subjectivity and inconsistencies in how we communicate our findings are better exposed than resolved. Working on BADWIZARD was an opportunity for me to reflect on my own 5-year long performance as a researcher.

I am very concerned about quantifying uncertainty and biases in spatial data, in particular when such data is used to inform urban policies that address socioeconomic disparities and resource allocation in our cities. When policymakers or researchers combine multiple datasets—a practice known as data fusion—, they need to be careful not to systematically reinforce data issues. So when Tianyi came to Backslash, set on exploring how data and new technologies can contribute to feelings of disembodiment or loss, I happily asked her what were her thoughts on 311 data: a channel through which residents report concerns about the city and request services to the appropriate agency, yielding a dataset laden with biases and noise.

This is a dataset that, in a way, one can never fully grasp. Myself and other students in my lab have written multiple papers addressing flaws and proposing solutions to better use this citizen request data for emergency management. But as much as these papers would tell one story about the data, they would also propel my thinking into new, more fundamental directions. Who is being heard and what does requesting something to a city mean? What are the implications of fabricating a request? What happens when data gets duplicated, imputed, or lost due to technical issues? Most of these meta-questions about the data didn’t find their way into my paper, but Tianyi would echo them and further pose me new questions about my work. We explored the data together using quantitative methods similar to those I use for my work and arrived at our most important shared research question: what is the role of the spatiotemporal data structure in shaping how the human experience of the city is collected, used, and importantly archived.

When Tianyi handed me her first draft of the script, I was fascinated by how well she captured not only the themes we saw together in the data but also the shape of the dataset, its restrictions and allowances. When we were filming, I think for the first time I saw the 311 dataset concretely and got closer to understanding it. For a researcher, seeing the data you worked on with a so-called objective stance be turned inside out with a more abstract lens can be daunting. I found it fascinating, and maybe even enlightening. This was the first time I could appreciate the certain uncertainty of urban data rather than meticulously attempt to tame it.