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.

Gov_Security

National Labs

Enabling researchers at national laboratories to solve any optimization problem.

Nuero_Network

Coursework and Boot Camps

Helping students train and tune models more efficiently, as they learn ML.

University of Fribourg Logo

 

“SigOpt is the most advanced and complete solution for experiment management and hyperparameter optimization we have encountered thus far, and it enables us to produce robust and reproducible research with reliable results. We built it into our modeling framework, DeepDIVA, as the standard method for experimentation and optimization.”

Michele Alberti
PhD Candidate
Department of Informatics
University of Fribourg

MIT Logo


“SigOpt removes the guesswork in designing models with apt hyperparameters and provides a conclusive answer in minimal runs. This has led to usage of far less computational resources without compromising on accuracy, and a stronger confidence in the otherwise mystical machine learning approach.”

Somesh Mohapatra
PhD Candidate
Materials Science and Engineering
Massachusetts Institute of Technology

University of Pittsburgh Logo

“We are excited about our collaboration with SigOpt and appreciate the recognition we have received on our work with a Materials Horizons publication, including the research being featured on the cover, and a couple of NeurIPS conference presentations.  The SigOpt team has helped to greatly accelerate our materials research from concept to demonstration.”

Paul Leu
Associate Professor
Department of Industrial Engineering
University of Pittsburgh

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 30 parameters and 500 observations
  • Teams of up to 3 users
  • All general availability advanced features
  • Access to the experiment insights dashboard
SigOpt Academic Logos

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 [email protected] to let us know about your work.

On Variable and Random Shape Gaussian Interpolations
Bayesian Optimization for Adaptive Experimental Design: A Review
Trainable Spectrally Initializable Matrix Transformations in Convolutional Neural Networks
MDP-based Shallow Parsing in Distantly Supervised QA Systems