Cadre Research Labs (CRL) is a Contract Research Group specializing in Contract Research Solutions and Technology Transfer. Our focus is on the use of computation in non-computational fields (e.g., medicine, finance, retail). Whether it's predicting molecular interactions, future sales, or customer behavior, we provide companies actionable intelligence using tools that allow them to better understand their data. Let us worry about the algorithmic details.
Data Scientists & Big Data
There's currently a lot of hype about leveraging 'Data Scientists' and 'Big Data'. Companies of all sizes are mining their data to gain sales advantage and research leverage. Data Scientists are people like us, PhDs in Computer Science, who specialize in data mining, machine learning, and statistical analysis.
Who We Are
Cadre researchers balance their depth of knowledge and focused expertise with a breadth of experience across the research spectrum. The team is not simply a 'group of programmers', true to the name Cadre, we are a SWAT team designed to tackle the world's toughest computational problems. Team members love the challenge of interdisciplinary research, are passionate about pushing the technology envelope, and are driven to have impact. We live to develop and deploy custom research solutions to the problems faced by industry.
Cadre consists of a core group of PhDs within a broader associate network (see Our Team). We maintain an elite group of creative problem solvers with a wealth of experience in the computational sciences. Our core strengths are complemented with the expertise of a network of external research associates, typically university faculty and others with doctoral-level experience.
Contract Research Services
Companies of all sizes, from new startups to multi-national corporations frequently encounter computational challenges that do not have 'textbook' solutions and whose complexity forces one to look beyond 'tried-and-true' methods. Cadre’s contract research services provide your company a competitive advantage by reducing both development and ongoing costs as well as enabling novel functionality.
Most academic technologies are too early stage for industrial adoption. A significant challenge lies in successfully 'leaving the sandbox'; moving moving from easier proof-of-concept examples to complex real-world environments. This transition is part of Cadre’s core competencies. We work directly with academic research groups both to extend their technology and to identify industrial partners. Our goal is to help all organizations successfully integrate research advances.
Our Areas of Expertise
Cadre has a wealth of experience in scientific computing, defined as the use of computational techniques to solve modeling and analysis problems in interdisciplinary applications. These techniques often employ methods from Machine Learning and Artificial Intelligence adn can be grouped into several general classes.
- Search and Optimization - The exploration of very large solution spaces. Typically one is trying to find the best answer as defined by an objective or scoring function. Success requires the rapid evaluation of a large number of candidate solutions against observed data. These methods can also identify operational parameters to optimize a process.
- Predictive Modeling - The creation of predictive models capable of answering questions and testing hypotheses. Exploits existing knowledge of the system and relies on accurate assumptions. May involve the modeling of physical systems.
- Experiment Planning - The optimization of an experimental protocol or investigation to minimize resource use, to reduce experimental time, or to increase information yield. Builds from the subfield of planning where a goal is achieved via an optimal sequence of actions.
- Statistical Modeling - The principled use of data to form conclusions and the ability to express a confidence in multiple hypotheses or models. This process is known as statistical inference.
- Pattern Matching and Detection - The identification of patterns or features whose occurrence indicates the presence of a specific event, object, or phenomenon. The desired patterns may be known or unknown. When applied to experiment monitoring these methods can detect the occurrence of an event.
- Clustering, Labeling, and Regression - The task of clustering and labeling involves grouping similar samples and labeling them with a descriptive annotation. For example, tissue samples could be labeled as healthy or cancerous based on gene expression profile. Regression is the task of making an informed numerical prediction based on previous data. For example, the cellular growth rate of a sample can be predicted by combining previous experimental data with the sample’s observed features.