99/2613, Microsoft Research
Phone: (425) 421-7114
Email: myfirstname DOT mylastname AT microsoft DOT com
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). In particular, I am interested in developing specialized hardware architectures that support real-time computer-vision and image-processing applications on such devices. 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 are able to 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.
I enjoy working at the level of both software (C++, C#, Python, Matlab, etc.) and hardware implementations (accelerator architectures, RTL design, etc.). At Microsoft, I primarily work on developing next-generation embedded computer-vision systems on portable devices. I also design hardware IP for image processing and machine-learning with IoT data.
The following are my active projects:
- Context-aware SoC applications with real-time object recognition and detection on streaming video
- Cloud-server design methodologies that exploit heterogeneity and variability in hardware
- S. Venkataramani, J. Liu, A. Raghunathan, and M. Shoaib, "Scalable-effort classifiers for energy-efficient machine learning", IEEE Design Automation Conference (DAC) Jun. 2015 (to appear).
- S. Venkataramani, V. Bahl, X. Hua, J. Liu, J. Li, M. Phillipose, B. Priyantha, and M. Shoaib, "SAPPHIRE: An always-on context-aware computer vision system for portable devices", IEEE Conf. Design Automation and Test in Europe (DATE) Mar. 2015.
- 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.
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
The following is my past/current participation:
- Reviewer: TCAS-II, TVLSI, DAC, TBME, and TBCAS
- Organizing/program committees: ES Week (IoT Symposium) 2015, CPS Week 2015, IPSN 2014