1. Project 1 – Twitter Slang Term Extraction

2. Project -2 Reddit Opinion Mining
Use reddit as a source of opinion and public discussion around the opioid epidemic
Manually compiled list of subreddits corresponding to many of the fifty states, as well as many of the more populous US cities (more than 100,000 per the last census

3. Project 3 Healthcare Data
Prescriptions
Nonprescriptions
Machine learning
Data Analytics

4. Project – 4 Location in Mojo Environment
Mojo APs monitor the RSSI for all devices connected to the network – as well as those that are not connected to Mojo network
The RSSI values can be extracted using an API we have access to
Goal –
determine the location of all devices continuously
Display the locations on demand
Access to Mojo API

5. Project 5 – Augmented reality for Wifi
Mojo APs can give a device its location on demand
Goal – Based on its location, provide augmented reality system in which additional information about the WiFi is displayed, such as the location of APs, their current load, etc.
Augmented Reality headsets

6. Project 6 Uses of Continuous Location Tracking
Locus system as used for SPH project can track the location of the user(s) continuously
Goal – design an app that makes use of this capability for the benefit of the user.
Locus App Source Code

7. Project 7 – Building Energy Management
New Building Energy Management systems provide detailed information about the use of energy in the building. Many have a number of sub-meters to localize the usage.
A common use of such monitoring has been to manage the bills received from the utility company and to make sure they are correct.
Given the detailed historic information what else can be derived?
DataSet
Capitol Building Historic

8. Project 8 – Fraud detection in web and mobile app advertising
Summary : When advertisers spend money on digital advertising, there are a lot of fraudulent activity to generate fake views, clicks, impressions as well as app installs. An advertiser feels that genuine people are looking at ads and engaging with the campaign. In actuality, the campaign is being viewed by bots and engagement is faked. Digital ad fraud is a billion $ problem.
Mobile App:Identifying potential fraudulent transactions by fraud patterns
We have multiple datasets about an app installs (eg device make, model, operator, timestamps, IP addresses, user-agents, location etc) which can be used to generate patterns of frauds. Also, one issue is that fraudsters keep jumping behind different Ad networks to hide themselves. Detecting similar behaviour across different AdNetworks will help identify potential common fraudsters who are dividing their fraud among multiple AdNetworks.
Web : We run a fingerprint javascript which identifies and tries to fingerprint a unique user (without depending on a cookie). Multiple datasets about a user and his device (eg IP Address, OS, browser, fonts supported, Extensions installed, java version, languages, timestamps, timezone etc) is collected by the script. Using ML, understanding the repetitive patterns of a web user can identify duplicates and frauds. Two areas are important :
User Fraud : In this case, end-customers carry out fraud to take benefit of coupons or referral programs by repeatedly making fake accounts via bot
Publisher fraud : where publishers generate fake visits, views into a website by bots

9. Project 9 - YouTube Safety and Recommendation
Youtube has faced a lot of heat due to it running advertisers ads on non-brand-safe videos. Eg, no brand wants its ads to be running on a video showcasing violence and hatred. Youtube has released APIs by which different aggregators can collect information on different videos and understand brand-safe and brand-alike channels. This needs to be linked to potential channels which advertisers will find their audience.
Problem Statement :
Provide a regular list of channels on youtube where the brand is “safe”
Provide a list of channels on youtube where it is most likely to find relevant audience and “look-alikes”

10. Project – 10 User/Consumer Behavior
Characterization of user behavior
Level
Data Sets

11. User Behavior Typical Interaction Model Command – Response Command
Input Time
Output Time
Think Time
Response Time

12. User Behavior Typical Interaction Model Command – Response
Interleaving
Command
Response
Command
Response
Response
Response
Input Time
Output Time
Think Time
Response Time

13. User Behavior Commands E-Commerce Social Media Streaming Sequence
Markovian Model?
Linguistic Model?
Depend on response?
E-Commerce
Social Media
Streaming
Audio
Video

14. User behavior
Terms
Interaction
Activity
Session