About the lab
A research lab, not a product company.
AutismAI Lab is a small research group building AI tools that bring research-grade assessment within reach of families, clinicians, and researchers using devices they already own. We treat published methods as the input and clinically-grounded, openly documented tools as the output.
Why we exist
Assessment-grade tools, in the browser.
Autism spectrum disorder affects 1 in 36 children in the US, and motor and cardiorespiratory differences extend the same population into adulthood and aging. Yet most assessment tools are subjective, time-consuming, and inaccessible to those who need them most. They sit behind specialist clinics, expensive hardware, and long waitlists.
We believe AI can help bridge these gaps, not by replacing human expertise, but by augmenting it with objective, scalable, and accessible tools that run on devices people already own.
The lab was founded to make that vision concrete. We translate peer-reviewed methods into deployed tools, validate them against clinical references, and publish the methodology so peers can scrutinise and extend our techniques.
- →Browser-native assessment tools that work without specialist hardware.
- →Methods documented publicly so peers can scrutinise and extend them.
- →Privacy by construction: inference on-device, raw data stays local.
- →Continuous statistical layer (GAMMs, GP normative modeling) shared across every tool.
- →Active collaboration with K-CART, ITTC, and clinical partners at KUMC.
Our story
A small lab, with a personal origin.
Founded 2025 · University of Kansas
AutismAI Lab was started by a parent of a child on the spectrum who also happened to be an AI architect. The original motivation was unglamorous: the tools the family needed (for adaptive learning at home, for objective motor screening, for low-friction in-home monitoring) were not yet available in a deployable form. So they had to be built.
The lab is small and intentionally so. A lead researcher (PhD candidate in AI at the University of Kansas) carries the build work; a rotating circle of academic collaborators contributes domain expertise in motor neuroscience, special education, signal processing, and disability-inclusive design.
What has changed in the last five years is the methods stack. Computer vision, large language models, modern signal processing, and rigorous statistical modelling have matured to the point where research-grade assessment can run on devices people already own. The lab's job is to translate that maturity into tools that families, clinicians, and researchers can actually use, and to publish the methodology so peers can scrutinise it.
The four current tools, Grovalin, Volitiq, WAVE, and PULSE, are the practical output of that translation, with more in the pipeline. They are released as research instruments, not products: built with peer-reviewed methods, validated against clinical references, and shared with the community that motivated them in the first place.
People
The lab + collaborators.
A small team backed by a circle of academic collaborators. Everyone listed below contributes on a volunteer basis as a technical or domain advisor.
PhD (ABD) in AI at the University of Kansas; dissertation on attention-guided motion profiling for autism detection and intervention personalisation. Enterprise AI architect with 20+ years building production ML and LLM systems across healthcare and life-sciences data; AWS-certified (Solutions Architect + ML Specialty). Parent of a child on the spectrum, the original reason the lab exists.
LinkedIn →-
Dr. Matthew Mosconi →
KU Center for Clinical & Psychoneuroscience Programs. Dissertation co-advisor; motor and oculomotor biomarkers in autism.
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Dr. Sumaiya Shomaji →
KU Electrical Engineering & Computer Science. Dissertation co-advisor; signal processing and applied machine learning.
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Dr. Lisa Dieker →
KU School of Education. Special-education research and autism intervention design.
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Dr. Trey Vasquez →
KU FLITE. Educator-facing technology and inclusive learning.
Collaborators contribute their time and expertise to autism detection and intervention work across multiple projects.
How we got here
Timeline
Early development of two research prototypes at the University of Kansas: E-MotionSpec for video-based motor analysis, and ContextLearn for AI-assisted contextual learning. Both would later move under the AutismAI Lab umbrella.
Founded by Shailesh Pandey (PhD candidate at the University of Kansas, EECS) to consolidate the research-grade tooling under a single brand and bring assessment-grade tools to anyone with a device.
First deployed tool: quantitative motor-movement analysis (rebrand and refactor of E-MotionSpec) launched at volitiq.com.
AI-powered contextual learning platform (rebrand of ContextLearn) moves into active development at grovalin.com.
The lab's work extends from the clinic into the home, with two complementary passive-sensing modalities: WAVE (WiFi channel-state inference) and PULSE (RGB rPPG vitals).
GAMM trajectory analysis, GP normative modeling, and individual deviation scoring formalised as the shared statistical layer across Volitiq, HEARTH, and Grovalin.
Contact
Get in touch
Questions, ideas, collaboration inquiries: reach out through any of the channels below, or fill out the collaboration form.
- Apps →Grovalin, Volitiq, WAVE, PULSE: what's live and what's proposed.
- Research →Active research lines and the statistical foundation shared across all programs.
- HEARTH report →The full long-form report on the WAVE + PULSE program.
- Collaborate →How developers, researchers, clinicians, and families can contribute.