Mohammed Shoaib

RESEARCHER

 

99/2613, Microsoft Research
One Microsoft Way,
Redmond, WA 98052

 

Phone: (425) 421-7114
Fax: (425) 936-7329

Email: myfirstname DOT mylastname AT microsoft DOT com

Background

 

I am a member of the of the Sensing and Energy Research Group at MSR, Redmond.

I build computing systems for portable devices, including wearables and internet of things (IoT). I am particularly interested in building specialized hardware architectures for real-time computer-vision and image-processing applications. My work applies various techniques of deep/shallow machine learning, signal processing and VLSI design to these problem domains.

For my Ph.D., I developed energy-efficient sensing systems, which could compute directly on compressed data. I received my Ph.D. and M.A. degrees in Electrical Engineering from Princeton University in 2013 and 2010, respectively. I have also received the B.Tech. and M.Tech. dual degree in electrical engineering with a specialization in microelectronics and VLSI design from the Indian Institute of Technology (IIT), Madras in 2008.

Projects

 

I enjoy working at the level of both software algorithms (C++, C#, Matlab, Weka, etc.) and hardware implementations (accelerator architectures, RTL design, etc.). At Microsoft, I have hands-on experience in developing computing systems for computer-vision applications on portable devices. I have also designed accelerators for image-signal processing and unsupervised inferences in IoT.

The following is a list of my projects:

- Context-aware SoC applications with object recognition/image classification on streaming video
- Algorithms and architectures for efficient machine learning through scalable-effort data processing
- Alternate inference algorithms that provide insight through multi-device sensing
- Tools that exploit hardware variability and heterogeneity for efficient cloud-server design

Recent Publications

 
  • M. Shoaib, J. Liu, and M. Phillipose, "Energy Scaling in Multi-tiered Sensing Systems Through Compressive Sensing", IEEE Custom Integrated Circuits Conference (CICC), Sep. 2014 (best paper award nomination).
  • M. Shoaib, K-H. Lee, N. K. Jha, and N. Verma, "0.6-107 µW energy-scalable processor for directly analyzing compressively-sensed EEG", IEEE Transactions on Circuits and Systems (TCAS) - 1, Apr. 2014.
  • M. Shoaib, N. K. Jha, and N. Verma, “Signal Processing with Direct Computations on Compressively-sensed Data", IEEE Transactions on VLSI (TVLSI) Systems, Feb. 2014
  • M. Shoaib, N. K. Jha, and N. Verma, “Algorithm-driven hardware-specialized architecture for low-energy biomedical sensor platforms,” IEEE Transactions on VLSI (TVLSI) Systems, Oct. 2013.
  • M. Shoaib, N. K. Jha, and N. Verma, “A compressed-domain processor for seizure detection to simultaneously reduce computation and communication energy,” IEEE Custom Integrated Circuits Conference (CICC), Sep. 2012.
  • M. Shoaib, G. Marsh, H. Garudadri, and S. Majumder, “A closed-loop system for artifact mitigation in ambulatory electrocardiogram monitoring,” IEEE Design Automation and Test in Europe (DATE) Conference, Mar. 2012.
  • M. Shoaib, N. K. Jha, and N. Verma, “Enabling advanced inference on sensor nodes through direct use of compressively-sensed signals,” IEEE Design Automation and Test in Europe (DATE) Conference, Mar. 2012.
  • M. Shoaib, N. K. Jha, and N Verma, “Sub-threshold computational circuits for high-order data-driven analysis of physiological signals,” Sub-threshold Microelectronics Conference, Sept. 2011.

Communities

 

I am a member of the IEEE and ACM. While I am interested in working across multiple disciplines, my interests are particularly aligned with the following research communities:

  • IEEE Circuits and Systems Society, Computer Society, Signal-processing Society
  • ACM SIGMOBILE, SIGBED

Service

 

The following is my past/current participation:

  • Reviewer: TCAS-II, TVLSI, DAC, TBME, and TBCAS
  • Member of organizing/program committee: CPS Week 2015, IPSN 2014