I am a 5th year graduate student in the Department of Computer Science at the University of North Carolina Chapel Hill. My research interest lies in deep neural network based multi-modal sensing, using acoustic and RF signal. In a broader sense, I work on semantic knowledge transfer from external sources, along with domain adaptation to nullify sensor and environment bias, that yields robust and scalable machine learning classifiers than traditional supervised models for signal processing.


  • My paper " Sound-Adapter: Multi-Source Domain Adaptation for Acoustic Classification Through Domain Discovery." has been accepted to IPSN '21.
  • Successfully completed internship at Amazon Lab 126 as an applied scientist intern working with multi-channel audio data and deep learning for sound source localization.
  • My work as a research intern at Microsoft Research (Audio and Acoustics Group) has been publised in AES AVAR 2020.
  • Successfully finished Summer Internship at Microsoft Research (Audio and Acoustics Group) under Ivan Tashev's mentorship.
  • My paper "SoundSemantics: Exploiting Semantic Knowledge in Text for Embedded Acoustic Event Classification." has been accepted to IPSN '19.
  • Finished Summer Internship at Bosch Research, working at Smart Campus Initiative.
  • My paper "Duty-Cycle-Aware Real-Time Scheduling of Wireless Links in Low Power WANs." has been accepted to DCOSS '18
  • Our paper "Glimpse.3D: A Motion-Triggered Stereo Body Camera for 3D Experience Capture and Preview." has been accepted to IPSN '18
  • Our paper "PAWS: A Wearable Acoustic System for Pedestrian Safety." has been accepted to IOTDI '18
  • My paper has been accepted to MobiSys '17
  • Our demo " got BEST DEMO RUNNER UP at Sensys'16