Dan Feldman's Home Page

Postdoctoral at MIT,

in the Artificial Intelligence Lab (CSAIL)

of the Electrical Engineering and Computer Science Department (EECS)

I am a post-doc researcher at the group of Distributed Robotics Laboratory (DRL) at MIT.

Current research

· Google your life: activity learning from GPS and sensor networks of smartphones and robots.

· Design and implementation of streaming algorithm in cloud/GPU systems.

· I am especially excited about reducing the gap between theoretical and practical algorithms, using my experience in the industry and academy.

Areas of interest

Big Data, Machine Learning, Robots, Sensor networks, Streaming, Distributed cloud computing,
Optimization algorithms, Compressed sensing, Private data analysis.

Main technique

Core-sets/Sketches: Semantic compression of data sets into small sets that provably approximate the original data for a given problem. Using merge-reduce (e.g. Hadoop) the small sets can then be used for solving hard machine learning problems in parallel (on the cloud/network) and on Big streaming data.

Publications and Papers

· Big Data for Robots: Online HMM Coresets for Sensor Streams,
with Cathy Wu, Brian Julian, Cynthia Sung, and Daniela Rus.
Submitted to the IEEE International Conference on Robotics and Automation (ICRA) 2013

· K-Robots Clustering of Moving Sensors using Coresets,

with Stephanie Gil, Ross Knepper, Brian J. Julian, and Daniela Rus.
Submitted to the IEEE International Conference on Robotics and Automation (ICRA) 2013

· My Long and Winding Road: From Big (GPS) Data to a Searchable Diary,
with Cynthia Sung, and Daniela Rus.
Submitted to the ACM Special Interest Group on Information Retrieval (SIGIR) 2013

· Learning Big (Image) Data via Coresets for Dictionaries,

with Micha Feigin, and Nir Sochen.

The International Biomedical and Astronomical Signal Processing (BASP) Frontiers workshop, 2013, to appear
Submitted to The Journal of Mathematical Imaging and Vision (JMIV)

· The Single Pixel GIS:
Learning Big Data Signals from Tiny Coresets,
with Cynthia Sung and Daniela Rus.
Proc. 20^th ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) 2012

[Slides]

· Turning Big Data into Tiny Data:
Constant-size Coresets for k-means, PCA and Projective Clustering,
with Melanie Schmidt and Christian Sohler.
Proc. 24^th Annu. ACM Symp. on Discrete Algorithms (SODA) 2013, to appear,
and in the 4^th Workshop on Massive Data Algorithms (MASSIVE) 2012

· Trajectory Clustering for Motion Prediction,

with Cynthia Sung and Daniela Rus.

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’12)

[Slides]

· Communication Coverage for Independently Moving Robots,

with Stephanie Gil and Daniela Rus.

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’12)

[Slides]

· An Effective Coreset Compression Algorithm for Large Scale Sensor Networks,

with Andrew Sugaya and Daniela Rus.

Proc. 11^th ACM/IEEE Conf. on Information Processing in Sensor Networks (IPSN) 2012

[Slides]

· Data Reduction for Weighted and Outlier-resistant Clustering

with Leonard J. Schulman.

Proc. 23^th Annu. ACM Symp. on Discrete Algorithms (SODA) 2012

[Slides]

· Scalable Training of Mixture Models via Coresets,

with Andreas Krause and Matthew Faulkner.

Proc. 25th Conference on Neural Information Processing Systems (NIPS) 2011 [Slides]