Expertise
Don't worry if the list below is completely unfamiliar to you, it's our responsibility to worry about the algorithmic details. We'll utilize the appropriate techniques for you. So if you're inspired then read on, otherwise get in touch with us and let us help you navigate these waters!
Cadre has a wealth of experience in big data analysis and scientific computing, defined as the use of computational techniques to solve problems in the life sciences. These techniques often employ methods from Machine Learning and Artificial Intelligence. Some typical problems include:
- 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.
- 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.
A more complete, yet still non-technical, list of areas of expertise includes:
- Scientific Visualization
- Scientific Modeling
- Experiment Interpretation (model building, parameter estimation, data mining, trend detection, prediction)
- Computational Chemistry (molecular modeling, molecular similarity, spectroscopy)
- Computational Biology (genomics, structural biology, proteomics)
- Computational Medicine (expert systems, image analysis, outcomes analysis)
- Algebraic Algorithms
- Distributed Computing
- Classifiers
- Regression
- Sampling
- Computer Vision (feature detection, structure from motion, object recognition, appearance-based matching)
- Logic-Based Reasoning (inference and deduction)
- Search
- Probabilistic and Causal Reasoning
- Planning and Scheduling
- Explanation-Based Learning
- Feature Subset Selection
- Sensors, Data Processing, and Control
- Algorithmic and Runtime Optimization
- Data Structures
- Information Management
- Grammars, Automata, Turing machines
- Graph and Network Algorithms
- Cryptography
- Parallel Algorithms
- Computational Geometry
- Randomized Algorithms
- Pattern Matching and Compression
- Combinatorial Optimization
- Integer / Linear Programming