1. Next Generation Data Mining Thinking out of the box hyper-rectangle
Michael Pazzani
Division Director, Information and Intelligent System
NSF
11/10/2018

2. Successes of Data Mining
Research results in medicine, biology etc.
Reducing expenses on fraud and increase revenue by recommendation systems
Good interplay between theory and practice
Well-defined metrics and envious collections of benchmark problems
11/10/2018

3. Data Mining: Looking where the light is good?
classification and regression problems dominate: What about plan recognition?
Learn from single record in database (or file): What about transactions, text, images, audio, video…
learn single concepts in isolation
learn from many examples
unaware of goals of learner (or adversaries)
don’t make ongoing contributions to knowledge base of agent or take advantage of it
can’t explain results of learning to laypeople or scientists
assume identically and independently distributed examples
11/10/2018

4. New problems need new metrics
Plan recognition: # of steps in plan (e.g., number of transactions)
Number of examples required to achieve a given accuracy vs. asymptotic
Distance (or similarity) between production environment and training examples
Privacy?
11/10/2018

5. NSF Types of proposals/awards
Unsolicited Proposal Deadlines  11/18 & 3/1
Information Technology Research (ITR)
Small (<$500K) Dec 12, 2002
Medium (<$4M) Feb 12, 2002
Large (<$15M) Nov 18, 2002 (Pre-proposal) Mar 24, 2003 (Full Proposal)
11/10/2018

6. How you can help? Submit High Quality Proposals
Innovative, but achievable
Well-written
Address broader impacts
Participate in Reviewing/Panels
Keep Program Manager informed of findings
CISE Newsletter
Joint Press Releases
PowerPoint of Conference Presentations
Participate in Workshops
Rotate in as a Program Manager (IPA)
11/10/2018