Solution

SigOpt for Academia

SigOpt is committed to supporting researchers who are publishing peer-reviewed techniques and results.

Complimentary Platform Access for Researchers

SigOpt was founded to empower the world’s experts. We are committed to advancing and enabling academic research by supporting academics, researchers, and qualified non-profits through our academic program. SigOpt was founded in part because our CEO needed to manually solve difficult optimization problems as a Ph.D. student, and thought that there needed to be a better solution.

Our academic program gives eligible researchers complimentary access to our optimization solution so they can focus on discovering new methods and techniques and not worry about parameter optimization. Numerous peer-reviewed papers using SigOpt as part of this program have already been published, in areas ranging from drug discovery to water pump operations.

Have you already published a paper that uses SigOpt? Please be sure to follow our citation guidelines.

Academics

Universities

Give your entire university research group access to our product.

Non-Profit

Non-Profits

Accelerating research for non-profits working on social good.

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National Labs

Enabling researchers at national laboratories to solve any optimization problem.

Apply for the Academic Program

You may be eligible for complimentary access to SigOpt through the academic program if you are engaged in publishing research. The academic program provides access to the SigOpt Optimization Solution and enables qualifying researchers to focus on their domain of research.

  • Plan limits comparable to our first-tier Enterprise plan
  • 50 experiments per month
  • Up to 15 parameters and 500 observations
  • Teams of up to 3 users
  • All general availability advanced features
  • Access to the experiment insights dashboard
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Researchers Everywhere Love Using SigOpt

SigOpt has been utilized to automate parameter optimization and supercharge research in a wide range of research areas, from machine learning to molecular biology. Check out the publications that SigOpt has been cited in below. If you are a researcher who has already used SigOpt, please make sure you have cited us accordingly to these guidelines in your publication and reach out to us at contact@sigopt.com to let us know about your work.

Deep Learning: The Revolution in AI
Hybrid Stochastic GA-Bayesian Search for Deep Convolutional Neural Network Model Selection
Improving CNN-based activity recognition by data augmentation and transfer learning
Improving Reproducible Deep Learning Workflows with DeepDIVA