Productive spring

This spring I’ve had a couple opportunities to share work with Ravi Sastry Ganti and Rebecca Willett on Matrix completion under monotonic single index model observations that we had at NIPS last December. In a week I’ll be at AI stats with my student Dejiao Zhang presenting our work on Global convergence of a Grassmannian gradient descent algorithm for subspace learning — we now have a proof that the GROUSE subspace learning algorithm converges globally from any random initialization to the global minimizer, or to a ball around the generating subspace in the case of noisy data. And last but never least, I had a great set of grad students in my Estimation, Detection, and Filtering class this year; they did some great projects on data science and statistical inference and did very well in the class overall.

Fall 2015 DSP Projects

The link to all the awesome projects in my fall DSP class is up — Once again the students impressed me with their creative ideas and excitement about advanced topics in signal processing. Great work everyone!

Computational Advances in Multi-Sensor Adaptive Processing

I just returned from a great CAMSAP meeting, where I learned about a probabilistic theory of deep learning, new work in tensor completion, and an importance sampling strategy for non-convex block coordinate descent. My student John and I also presented our new work on active labeling in the subspace clustering problem, where we showed that a small amount of label information helps tremendously in clustering noisy data when each cluster lies near a low-dimensional subspace. Thanks to the organizers for putting together such a great meeting!

Intel Early Career Faculty Honor Program

I am honored to have received the Intel Early Career Faculty Honor Program Award for my research in big data. The purpose of the program is to help Intel connect with early career faculty members who show promise as future academic leaders in disruptive computing technologies.

DSP undergraduate projects highlighted in EECS news

With every new fall semester comes the excitement of teaching EECS 351, Digital Signal Processing. This week the EECS news highlighted my initiative to incorporate data collection and analysis with DSP into 351 with a course project. It’s an initiative I began two years ago and will continue for as long as I teach DSP. They mentioned a few of the cool projects students have done, and there are many more at the links. I’m looking forward to seeing what the students will do in my class this fall!

Perry has hit the water

Check out this great video featuring my collaborator Branko Kerkez and his students Brandon Wong and Rachel Menge. My student John Lipor and I joined them last week out at Sister Lake to test our autonomous lake sampling watercraft.

Perry on the Sister Lake

Perry is a small watercraft whose main control hardware consists of an Arduino and an Android phone. These work together to navigate the boat using GPS to take samples of water temperature and oxygen levels. With the phone, the boat can be controlled from anywhere in the world. We are implementing adaptive sampling algorithms so that Perry will know where best to take a next sample based on previous measurements.

SFM code

Ryan Kennedy put our SAGE GROUSE code for Structure from Motion online. We’ve also updated the preprint of our paper on this topic. Please let us know if you have any questions!

Fall 2014 DSP Projects

The Digital Signal Processing class project links have now been posted! Click and prepare to be amazed. Great work students!

Productive December

December was chock full starting with a successful symposium at Global SIP on Information Processing in Big Data — thanks to my track co-organizers Yuejie Chi and Yao Xie, and thanks to Ryan Kennedy for presenting our paper on Matrix Completion for Ill-Conditioned Matrices. Then I attended FOCM and presented my work with Steve on the local convergence of GROUSE and its relationship to the ISVD. Besides all that I have almost wrapped up teaching Functional analysis and Digital Signal Processing, to two more outstanding groups of Michigan students.

Solving the Big Data Dilemma

MConnex has created a video highlighting my research in messy big data. We’re working on all kinds of applications to improve statistical methodologies with big data.