Personal information
Full name: Jakob Schoeffer
User name: jakobschoeffer
Organization memberships
Not a member of any organization.
First 1000 volunteers
Volunteer background
Education: Master's on Operations Research (Georgia Institute of Technology)
LinkedIn profile
Volunteer availability
Start date: None
End date: None
Hours available per week: None
Volunteering interests
No stated preferences.
Computer Science (Algorithms)
Text Data (NLP)
Data in Relational Databases
Data > 1TB
Data from Sensors
Multimedia Data (Video or Audio)
Network/Graph Data
Data in Text Files
Experience working on real-world problems using data
Data Visualization
Social Science (Economics, Sociology, etc.)
Experimental Methods (RCTs, A/B testing, etc.)
Data Analysis
Random Forests
Decision Trees
Unsupervised models
Time Series Models
Neural Networks / Deep Learning
Graphical models
Causal inference
Semi-Supervised models

Volunteer projects

Project name Organization name Social impact area Project summary Task name Task status
Covid Response Simulator // Scenario Planning for Non-Pharmaceutical Interventions (NPIs) Covid Act Now Education The COVID Response Simulator is a localized, customizable version of the public Covid Act Now (CAN) model. With it, you can take a powerful SEIR epidemiology model and customize it for your county to help plan your response to COVID. The inputs and assumptions in the simulator are modifiable and can be changed to reflect your local realities. In addition, you can project the impact of specific Non-Pharmaceutical Interventions (NPIs) for your county, such as closing schools, restricting business activities, and canceling large events. Based on your inputs, the simulator generates data and graphs illustrating COVID forecasts with and without these NPIs, including estimated case numbers and hospitalizations. Project scoping Started
Cloud Enabled Social Media Analytics Framework for Crisis Management Artificial Intelligence and Data Engineering (AIDE)Lab Education This project aims to develop a cloud-based computing framework that will systematically monitor, collect and integrate disaster and crisis-related data streams from diverse social network channels, and turn them into actionable intelligence in a knowledge-base for intelligent inferencing, crisis handling, and decision making. The key components of the proposed framework are three: First, the collection of multilingual and multimodal data (text, audios, videos, images, and location information or other sensor-data) and their conversion into a unified machine-understandable dynamic knowledge-base. Second, the application of Natural Language Processing (NLP), Data Mining, Machine Learning, and Artificial Intelligence techniques for automatic identification, prediction, and detection of any type of crisis or disaster using the unified knowledge-base from the previous stage. 3) Third, the development of an interactive dashboard visualization to facilitate crisis monitoring and management. Project scoping done_all Completed