Qualcomm Innovation Fellowship finalists

I feel extremely exciting to have a chance to deliver a presentation and meet other professors and Phd students in those top CS and EE departments. And it always feel good to got an opportunity to share research ideas and results within the society. Have been working on language vision stuff for almost one year, the beauty of combining these two main cognition processes within a computational framework makes me until now have not get bored by PhD life. So far so well.

QInF 2011 East Coast Finals: Bridgewater, NJ

School	Students	Recommenders	Innovation Title
UMD	Ching L Teo Yezhou Yang	Yiannis Aloimonos Hal Daumé III	Robots Need Language: A computational model for the integration of vision, language and action
Princeton	Mohammed Shoaib Kyong Ho Lee	Naveen Verma Niraj K. Jha	Algorithm-driven Platforms for Low-energy Intelligent Biomedical Systems
MIT	Soheil Feizi Georgios Angelopoulos	Muriel Medard Vivek Goyal	Energy-Efficient Time-Stampless Adaptive Nonuniform Sampling
UMD	Timir Datta Filiz Yesilkoy	Martin Peckerar Pamela Abshire	An Ultra-low Power Infrared Scavenging Autonomous Silicon Micro-robot
MIT	Adam Marcus Eugene Wu	Samuel Madden	Qurk: A Crowdsourced Database for Query Processing with People
Princeton	Zhen Xiang Hao Xu	Peter Ramadge	A Cloud-based Low Bandwidth Machine Learning Service
Rutgers	Sejong Yoon Shahriar Shariat	Vladimir Pavlovic	A Novel Multimodal Mobile Media Rank System for Faster Media Flow
Rutgers	Akash Baid Tam Vu	Dipankar Raychaudhuri	NASCOR: Network Assisted Spectrum Coordination Service for Coexistence between Heterogeneous Radio Systems
UCB	Reza Naima Pablo Paredes	John Canny	The Mobile Stress Platform: Detection, Feedback & Mitigation
MIT	Ahmed Kirmani Andrea Colaco	Vivek K Goyal Franco Wong	Single Pixel Depth Sensing and 3D Camera
MIT	Sushmit Goswami Sungwon Chung	Joel Dawson Anantha Chandrakasan	A Frequency Agile Architecture for Fully Integrated Radio Frontends

• Venue: Qualcomm Research Center New Jersey
• Address: 500 Somerset Corporate Blvd, Bridgewater, NJ 08807
• Date: Tue Apr 1

We are planning to drive to NJ, to visit Princeton and enjoy the presentation. ^^

Hal's Legacy

I am reading the book "Hal's Legacy" (2001) and found something interesting, on page 8 when the author tried to summarize Prof. Rosenfeld's vision on Computer Vision in one paragraph:

" Imagine, for example, a computer that could look at an arbitrary scene - anything from a sunset over a fishing village to Grand Central Station at rush hour - and produce a verbal description. This is a problem of overwhelming difficulty, relying as it does on finding solutions to both vision and language and then integrating them. I suspect that scene analysis will be one of the last cognitive tasks to be performed well by computers."

ICCV Done

Is the PhD life all about deadline?

Well, I am such a guy who always more willing to look at the bright side. Even the paper doesn't make it finally, still we learned a lot of things during each process. Discussion, implementation and struggling about the way to present the idea.

Maybe we can learn like 50% through class about one specific method, but after writing a paper using that method, we can leverage it to say 80%. This time we use design a EM algorithm intuitively, but how to formalize it into the right way cost more time than implementing the method.

Where is the data, and what is the parameter and what is the indicator? What does the E step do, how can we get there? And what does the M step do, the deducing path is correct or not? Still, we are very lucky that Hal helped us get through this painful process....

It seems to be a normal routine. We usually design the method first, and can't wait to implement it to see whether it gets better result or does what we expected. Then if yes, we sit down and try to figure out how to formalize what we've already did in a formal way...


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