Are you looking for a data scientist to work with you on your data?
Are you a data scientist looking for interesting projects?
If so, you have come to the right place. MATCHPOINT is a matching sevice provided by the Data Science Hub at SDSC. Its goal is to match Domain experts and students with Methods experts and students to create interdisciplinary working teams that can take on challenging problems in data science and analytics. Learn more about how MATCHPOINT works.
Below you'll find a list of MATCHPOINT past, current and future projects. If you are a data scientist, you can find leads to interesting projects here. If you are a PI with a data science problem, create an entry so that people can find you. To see full project details and expert contact information, and to submit new projects or student applications, you will need to create an account on this site and login.
Displaying 1 - 20 of 20 projects. Click on the column headers to sort.
|Project||Short Description||Status||Domain Expert||Domain Expert Department||Methods Expert||Methods Student Funding||Methods Student Openings||Participating Students||Last Date Updated|
|Honey Bee Waggle Dance||Automate the analysis of videos capturing the honey bee waggle dances.||Archived||James Nieh||Behavioral Ecology||Yoav Freund||no||2017-04-06|
|Clinical NLP and Medical Note Analysis||Clinical Natural Language Processing (cNLP) to large corpora of medical notes.||Active||Chun-Nan Hsu||Biomedical Informatics||Julian McAuley||no||2017-04-06|
|Active Brain Atlas||Construction of an active digital atlas of the mouse brain, with current emphasis on the brainstem. This is a full stack project that spans from data acquisition to imaging processing to computational anatomy. Involves Prof. Y. Freund in CSE, Prof. D. Kleinfeld in Physics and Neurobiology, and their professional associates.||Archived||David Kleinfeld||Physics||Yoav Freund||yes||
|YouTube Video Analytics||Creating an automatic labeling scheme for YouTube Data based on social comments and Video Features using a set of Weak Learners and Association Engines||Active||Amarnath Gupta||San Diego Supercomputer Center, Computer Science and Engineering||Amarnath Gupta||no||
|Whale classification from echo-location clicks||Develop a classifier of whale species using underwater recording of echo-location clicks||Archived||John Hildebrand||Scripps Institution of Oceanography||Yoav Freund||no||2018-11-27|
|Global Bathymetry: Machine Learning for Data Editing||Develop a machine learning algorithm to edit seafloor bathymetry soundings.||Archived||David Sandwell||Scripps Institution of Oceanography||Yoav Freund||no||
|San Diego Housing Value Prediction||Developing Machine Learning based techniques to predict the property values in San Diego County||Active||Volkan Vural||San Diego Supercomputer Center||Volkan Vural||2020-07-24|
|Event Based Traffic Prediction||Event based traffic prediction using Machine Learning||Active||Maksim Peckerskiy||City of San Diego||Volkan Vural||no||2018-11-27|
|Online medical crowdfunding: Correlates of funding success||Exploring the phenomenon of web-based crowdfunding for medical treatment.||Archived||Cinnamon Bloss||Department of Psychiatry, School of Medicine||no||2018-11-27|
|Precision Medicine: Predict the Effect of Genetic Variations||Large-scale genome sequencing aims to uncover the role of genetic variants. In this project we will develop machine learning models to predict the effect of missence mutations.||Active||Peter Rose||San Diego Supercomputer Center||
|Molecular Diagnosis of Pediatric Brain Disease||Our lab at the UCSD School of Medicine is looking for data scientists interested in learning about genomic data for analysis and algorithm design. We work at the interface of neurogenomics and computational analysis. One of our major projects is on computationally identifying mosaic genetic mutations, which are genetic changes in only a small percent of the DNA molecules, but that can have devastating consequences in the brain. Detecting these mutations is computationally very challenging, and is a active area of research.||Active||Joseph Gleeson||Neurosciences||no||
|BrainSharer||Seeking an expert in Typescript for a project in three dimensional brain anatomy.||Active||David Kleinfeld||Physics||Yoav Freund||yes||
|Histology Browser||Software for viewing, analyzing and aligning brain image stacks.||Archived||David Kleinfeld||Physics||Yoav Freund||yes||
|Water Data Analysis||The goal of this project is to analyze datasets for water and waste water data related to a number of problems in utility and infrastructure management||Active||Raj Patil||AEEC||Volkan Vural||no||2018-11-27|
|Boosting Neural Networks||This goal of this project is to compare the capabilities of a cascade of shallow neural networks relative to a single deep neural network.||Active||Yoav Freund||Computer Science and Engineering||Yoav Freund||no||
|Discovering the Ocean Floor Using Data Science||This project aspires to extend ocean exploration into the data dimension by utilizing modern and historic open-access data . We will apply machine learning techniques to ocean floor imagery to explore sea floor geology, ocean sediments, sea life and more!||Active||Vicki Ferrini||Lamont-Doherty Earth Observatory||Ilkay Altintas||no||
|Whale and Dolphin sound classification||Underwater sound recordings of whales and dolphins have been manually annotated for known sounds to development of automated classification methods.||Archived||John Hildebrand||Scripps Institution of Oceanography||Yoav Freund||no||
|Unsupervised analysis of freely moving subjects in behavioral videos||Use computer vision and probabilistic modeling to characterize behavior in freely-moving human participants.||Active||Arpi Minassian||Psychiatry||Gal Mishne||no||
|Machine learning for ocean sound sources and environment||Using underwater sound pressure from active and passive sources to train machine learning models for various applications.||Active||Emma Ozanich||Scripps Institution of Oceanography||Peter Gerstoft||no||
|Predictive model of campus foot traffic||We collect foot traffic data from phones in an area with our sensors. we make a predictive foot traffic model for the area using past data and elements that impact such as weather, day of the week, local events. Sensors are installed in Geisel and Biomed Library, RIMAC and Main Gym, and other locations.||Active||Nicholas Halverson||None||Peter Gerstoft||no||2018-11-27|