image-center My research builds practical systems to address core problems spanning embedded, cyber-physical systems, wireless systems and robotics. I am interested in pushing the limits of radio frequency sensing capabilities on embedded scale platforms. With high resolution, see-through occlusion sensing from such tiny compute platforms:

  • my work enables reliable perception in transportation, critical infrastructure monitoring and robotics in harsh conditions. I want to build on top of this perception and close the loop to create practical, full fledged autonomous systems.
  • my work augments next generation communication devices to be ultra-aware of their environment. I have built communication systems that learn from their environment in new ways to schedule resources efficiently, manifesting in large improvements in signal strength and throughput.
  • we can envision radio frequency sensing to be deployed on emerging portable, handheld and wearable devices. This opens up new possibilities to look through new types of occlusions in new scenarios, enabling new applications.

My research takes a full-stack approach building novel embedded hardware, machine learning and signal processing techniques, compute accelerators, and architecting end-end system to manage constraints on communication, real-time latency and reliability.

Selected Research Projects


image-center Tire wear affects safety and performance of automobiles. Past solutions are either inaccurate, require sensors embedded in tires or are prone to road debris. Osprey proposes a radio frequency tire wear sensing system design, based on automotive millimeter wave radars, to overcome challenges related to road debris. We design super resolution algorithms to measure millimeter-level changes due to wear and tear. In addition, the principles used also open up solutions for detecting and localizing harmful metallic foreign objects in the tire.

Full Paper | Demo Paper | Magazine | Slides | Talk | Video-1min | Video-3min

Press: CMU | Gizmodo | | That’s Cool News - Podcast| Weibold | Interesting Engineering | Wonderful Engineering |

Best Paper Honorable Mention (MobiSys 2020)
Best Demo (MobiSys 2020)
ACM GetMobile Research Highlight


image-center How can cars perceive critical roadside infrastructure even when weather conditions are not favorable for visual sensors? Millimetro tackles this by designing low power radio frequency tags that can be mounted on such infrastructure and builds decoding algorithms that run on radars in cars to decode, identify and accurately localize tags.

Full Paper | Demo Paper | Talk

Press: UIUC | Pioneering Minds

Best Demo Runner Up (MobiCom 2021)


image-center How can we enable single vantage point depth imaging that works at long ranges to build simple-to-deploy but high quality survelliance systems? Metamoran proposes a monocular camera-radar fusion solution that leverages the pros of each sensor to combat the cons and provides an accurate depth image of various objects even at long ranges.

Full Paper | Slides | Talk


image-center How can we enable high quality perception for robots navigating in harsh environments with smoke or fog? Camera or lidar based perception would suffer in these conditions. We explore a millimeter wave radar based perception for seeing past these occlusions. We combat the poor spatial resolution of these radars by training an end-to-end deep learning super-resolution network that outputs lidar-like point clouds from just a cheap, single-chip radar! We also show RadarHD’s robustness in smoke by testing with smoke bombs in a smoke chamber.

ICRA Paper | Extended Paper | Demo Link | Slides | Talk | Poster | Code and Dataset

Top-5 Demos (MobiCom 2023)


image-center Realistic radar simulation requires careful 3D modeling of a virtual world with accurate material properties and modeling all types of EM wave interactions with it. Previously we have seen explicit approaches for direct modeling or radar imaging followed by simulated view rendering. These explicit approaches are hard to design for practical, crowded scenes with heterogenous material composition and challenging multipath interaction. We propose an implicit neural rendering approach to produce accurate simulation of radar measurements from novel view points. As a by-product of our approach, we are also able to produce high quality radar images of scenes, akin to synthetic aperture imaging, but without any explicit modeling.

Full Paper | Code | Dataset | Video



image-center Low Earth Orbit cubesats and nanosats satellites have paved the way for missions with cheaper launch costs. But, ground infrastructure for communication remains bulky and expensive. Quasar proposes to alleviate this problem by democratizing access to satellite transmissions at the cost of a few RTL-SDRs. The key idea behind Quasar is to leverage a network of cheap radios to mimic one single expensive radio. Quasar achieves this by tackling challenges with respect to synchronizing the cheap radios, dealing with high doppler spread and central aggregation of high bandwidth raw radio streams.

Full Paper | Video

ACM GetMobile Research Highlight


image-center How can we continuously monitor the development of miniature cracks in today’s aging public infrastructure like bridges? This would inform decisions on safety and maintenance of these critical structures well in advance. Past work has looked at coarse grained structure health monitoring. Platypus builds novel signal processing and low power hardware tags to mount on bridges. We enable a new operating point that can measure micro-displacements of structures over years and in extreme weather conditions.

Full Paper | Demo Paper | Video

Best Demo Runner Up (IPSN 2023)


image-center How can we locate important and often misplaced objects-of-interest like keys, wallets, tools? Prior works that involve buildings tags for objects either need supporting custom infrastructure or the tags are power hungry for multi-year timescales. We turn to already existing and widely deployed WiFi networks for infrastructure support and build custom extremely low power tags. The user can simply forget about the tag for years. Our algorithms can locate tags (objects) with fine-grained accuracy and provide seamless context awareness with just unmodified WiFi.

Full Paper | Talk | Demo Video

Zoom Out

image-center As more and more edge computational tasks are performed at an embedded, chip-scale radar, we need accelerators that not only deal with signal processing blocks at Gbps datarates, but also adapt to the rising benefits of machine learning. Zoom Out proposes novel abstractions to map machine learning models from high level language to accelerators for easy and efficient implementations.

Full Paper