AI in Research Consultation

What we cover

This is a research methodology consultation. Our staff scientists bring both AI/ML expertise and domain research experience, so the conversation is grounded in your specific field, not just the technical mechanics of a model or tool.

Consultations can touch any stage of your research, from framing your question to sharing your results. Here’s what that can look like across the research lifecycle:

• Research design: Is AI/ML the right approach for your question? We help you think it through.
• Data access & discovery: Guidance on what data you need and where to find it.
• Data preparation: Support for cleaning, structuring, or extracting information from your data, including using AI tools for this step.
• Compute resources: We help you figure out what you need (GPU type, VRAM, RAM) so you can request the right resources. (We don’t provision compute ourselves; for that, we point you to ARC/ITS).
• Baseline analysis: Feedback on your initial modeling approach.
• Advanced analysis: Support for more complex methods, including generative AI and agentic workflows.
• Custom engineering: Guidance on architecture and approach for custom pipelines or tools.
• Outputs & reproducibility: Help interpreting results and making sure your approach holds up.
• Knowledge sharing: Support thinking through how to present or document your AI/ML methods.

Examples of what this looks like in practice

1. AI strategy & study design for a multi-year research grant
   Description: A research team was drafting a major grant proposal to study complex biological systems and needed to define where AI methods could add rigor and value. We helped structure the overall AI integration roadmap, identified feasibility risks early, and reviewed the planned computational methodology to ensure a rigorous approach for the funding proposal.
   Stages: Research design → AI strategy & grant planning
   
2. Extracting structured insights from unstructured medical records
   Description: A research group wanted to extract specific diagnosis indicators from thousands of narrative health records to study long-term outcomes. We assisted in designing an LLM-based parsing pipeline, evaluating prompt strategies, and structuring validation workflows to ensure high extraction accuracy.
   Stages: Data preparation → Advanced analysis
   
3. Benchmarking vision models for ecological monitoring
   Description: An environmental research team needed to select an optimal open-source computer vision architecture for detecting rare wildlife species in massive image archives. We helped establish an evaluation framework to benchmark candidate detection models against annotated ground-truth data.
   Stages: Research design → Baseline analysis → Outputs & reproducibility
   
4. Integrating generative summaries into multimodal pipelines
   Description: A project combined high-dimensional imaging data with tabular metrics, requiring concise, human-interpretable summaries of model outputs. We advised on the end-to-end model architecture and integrated an LLM summarization step tailored for non-technical stakeholders.
   Stages: Custom engineering → Outputs & reproducibility
   
5. Sizing compute and hardware specs for large-scale vision training
   Description: A multidisciplinary team was scaling up deep learning workflows on a massive multi-gigabyte image collection and needed to request HPC resources. We helped estimate GPU memory (VRAM), node allocation, and training time based on model architecture and batch constraints.
   Stages: Compute resources

Some researchers come to us just looking for the right collaborator, someone with the specific technical expertise your project needs. We’re happy to help make that connection too.

Looking for something else?

• Looking for help with grant boilerplate or Letters of Support? Visit Proposal Support instead.
• Looking for help with study design, sampling, or traditional statistical analysis? CSCAR@ISR is U-M’s dedicated statistical consulting service.
• Not sure where to start, or looking for something else entirely? The AI Campus Resource Map can help you find AI training, research funding, and technical support across U-M.