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Data SCIENCE/AI Track
Data SCIENCE/AI Track
Build anything from classical ML (trees, boosting, SVMs) to deep learning for vision, audio, graphs, or time-series—plus unsupervised/self-supervised learning, reinforcement learning, and causal methods where they fit. Strong submissions show solid data work and production-minded touches like reproducible pipelines, monitoring, and deployability to edge or cloud.
Build anything from classical ML (trees, boosting, SVMs) to deep learning for vision, audio, graphs, or time-series—plus unsupervised/self-supervised learning, reinforcement learning, and causal methods where they fit. Strong submissions show solid data work and production-minded touches like reproducible pipelines, monitoring, and deployability to edge or cloud.
Ai AGENT TRACK
Ai AGENT TRACK
Build any innovative project that applies the AI-agent concept using LLMs. Use the tools and LLM tokens available via the QR code above. macOS users can run the package directly; Windows/Linux users should access it through the API. Evaluation matches the AI/DS track, with one extra requirement: your project must incorporate AI-agent technologies. Sponsored by Archia.io; teams may receive platform credits. (Click here for Credit info)
Build any innovative project that applies the AI-agent concept using LLMs. Use the tools and LLM tokens available via the QR code above. macOS users can run the package directly; Windows/Linux users should access it through the API. Evaluation matches the AI/DS track, with one extra requirement: your project must incorporate AI-agent technologies. Sponsored by Archia.io; teams may receive platform credits. (Click here for Credit info)
nrt Track
nrt Track
A broad, inclusive track focused on using AI, data science, and secure networked sensing to understand substance use. Open to everyone (with special outreach tob). Do anything from exploratory analyses and visual storytelling to modeling or sensor-based prototypes—use public datasets or bring your own, and keep the emphasis on meaningful, health-relevant insights.
A broad, inclusive track focused on using AI, data science, and secure networked sensing to understand substance use. Open to everyone (with special outreach tob). Do anything from exploratory analyses and visual storytelling to modeling or sensor-based prototypes—use public datasets or bring your own, and keep the emphasis on meaningful, health-relevant insights.
NSF LLM
NSF LLM
A dedicated space for projects built around large language models. Do anything from fine-tuning or distilling open models, building domain-specific LLMs, adding retrieval over your own data, multilingual or multimodal extensions, compression/quantization for edge, dataset/prompting strategies, or creating evaluation and analysis tools for LLM behavior—using any stack (open models or APIs).
A dedicated space for projects built around large language models. Do anything from fine-tuning or distilling open models, building domain-specific LLMs, adding retrieval over your own data, multilingual or multimodal extensions, compression/quantization for edge, dataset/prompting strategies, or creating evaluation and analysis tools for LLM behavior—using any stack (open models or APIs).
Quantum Track
Quantum Track
Explore the intersection of quantum computing and AI—anything from big-picture concepts and educational demos to exploratory prototypes and simulations. Emphasize clear storytelling about why the idea matters, what’s promising (and what’s not yet practical), and how your approach advances understanding or sparks new directions.
Explore the intersection of quantum computing and AI—anything from big-picture concepts and educational demos to exploratory prototypes and simulations. Emphasize clear storytelling about why the idea matters, what’s promising (and what’s not yet practical), and how your approach advances understanding or sparks new directions.
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