About Me

I am an Associate Research Scientist at the Department of Electrical and Computer Engineering, Northeastern University. I work at Next Generation Networks and Systems (GENESYS) lab, directed by Prof. Kaushik Roy Chowdhury. I received my PhD degree in Computer Science under the supervision of Dr. Mainak Chatterjee in Spring 2020 from Department of Computer Science, University of Central Florida. I also received Master of Science (Spring 2018) in Computer Science from the same university. [More in my CV]


Research Interests

  • Multimodal Sensor Fusion: Deep learning based fusion of multimodal sensing data from LiDAR, camera, GPS, acoustic, radar, and radio frequency sensors for situational assessment, enabling digital twins and autonomous vehicles; Practical demonstration on millimeter wave band and V2X networks
  • Deep Spectrum Learning in NextG Communication: Deep learning based solutions in wireless spectrum sensing, sharing, signal detection, and RF fingerprinting; Applied research in the CBRS and sub-6 GHz band.
  • Networked Robotics: Reinforcement learning for coordination, computation and communication for unmanned autonomous systems; Real-world and emulation study on automated factory floor in Industry 4.0.

Recent Accomplishments and Highlights


Listed are some of the projects I have been working with Prof. Kaushik Chowdhury and astounding members of Genesys lab.

Non-RF Sensor Guided Beam Selection in mmWave V2X Network

In this project we solve the problem of fast beam selection in mmWave band by using non-RF sensor data such as LiDAR, camera images and GPS coordinates. We have 2 papers in conference proceedings, 2 in IEEE magazine and transactions, and multiple under submission as a result of this project.

Next-Generation Deep Spectrum Learning in Sub-6 GHz

In this project we solve the problem of signal detection and identification from overlapped signals using machine learning based solutions. This project is funded through IARPA and NSF SWIFT program. We have 2 papers accepted for publication in IEEE conferences from this project.

Autonomous Edge based Robot Connectivity and Mobility

In this project we solve the problem of delivering low-latency, reliable connectivity in Industry 4.0 autonomous systems using reinforcement learning based solutions. This is a collaboration project with Intel Corporation. We have one article under submission in IEEE Transactions on Mobile Computing, and one under preparation.

Multiverse at the Edge: Interacting Virtual and Real Worlds for Automative Wireless Beamforming

In this project we solve the problem of automative beamforming in mmWave band using an interactive communication between real and virtual world which provides a multiverse of digital twins. We have one article under submission in Journal of Selected Areas in Communications, and one in preparation.

Awards and Services


  • Doctoral Research Support Award from College of Graduate Studies of University of Central Florida, USA, 2019, Amount: ~$5000 .
  • Best Paper Runner-up , IEEE International Performance, Computing, and Communications Conference, USA, 2018.
  • NSF Student Travel Grant , IEEE Global Communication Conference (GLOBECOM), USA, 2019, Amount: ~$1200 .
  • NSF Student Travel Grant , IEEE International Symposium on dynamic Spectrum Access Networks (DySPAN), USA, 2019, Amount: ~$800 .


  • Member of 'Women in AI' panel at the NSF AI Edge Institute.
  • Publicity Co-chair at IEEE CCNC, 2023, Virtual.
  • Publicity Co-chair at IEEE CCNC, 2022, Virtual.
  • Webchair at IEEE DySPAN, 2021, Virtual.
  • Session Chair at IEEE INFOCOM, 2021, Virtual.
  • Online and Poster Co-chair at IEEE LANMAN, 2021, Virtual.
  • Member of Graduate Grade Appeal Committee, University of Central Florida, Fall '19-Spr '20.

Editorial Board

TPC Member

  • In ACM 'International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing' (MobiHoc '22, '23).
  • In IEEE `Internet of Things and Cyber Physical Systems' at IEEE International Conference on Distributed Computing Systems (ICDCS '22, '23).
  • In IEEE 'Mobile and Wireless Networks Symposium' (IEEE ICC '22, '23).
  • In IEEE 'Consumer Communications & Networking Conference’ (IEEE CCNC '22, '23).
  • In IEEE 'International Symposium on Local and Metropolitan Area Networks' (IEEE LANMAN '22).
  • In IEEE 'International Symposium on Dynamic Spectrum Access Networks' (IEEE DySPAN '21).
  • In IEEE 'Green Communications and Networks' track at IEEE 95th Vehicular Technology Conference (IEEE VTC '22).
  • In International Conference on Computer Communications and Networks (IEEE ICCCN '21').
  • In 'Serverless To sErvE moRe at Scale' workshop (ACM CCGrid '21).
  • In 'SAGE: Green Solutions for smArt Environment' workshop (ICC '21).
  • In 'AI/ML in Wireless Sensing, Communication and Applications' track (CCNC '21').
  • In 'Traffic Congestion in Beyond 5G/6G Networks' (TCB6GN)' workshop, in conjunction with IEEE CCNC '21.

Technical Talks

  • RF Transmitter Fingerprinting Exploiting Spatio-temporal Properties in Raw Signal Data, ICMLA '19.
  • Primary User Activity Prediction in DSA Networks Using Recurrent Structures, DySPAN '19.
  • Adaptive Video Encoding and Dynamic Channel Access for Real-time Streaming over SDRs, IPCCC '18.
  • GNURadio Support for Real-time Video Streaming over a DSA Network, GRCon '18.
  • A Fuzzified Approach Towards Global Routing in VLSI Layout Design, FUZZ IEEE '13.

Reviewer in Journals

  • IEEE Transactions on Wireless Communications, IEEE Transactions on Networking, IEEE Transactions on Mobile Computing, IEEE Transactions on Vehicular Technology, IEEE Transactions on Information Forensics & Security, IEEE Transactions on Cognitive Communications and Networking, IEEE Communications Magazine, IEEE Communications Letters, IEEE Networking Letters, Elsevier Pervasive and Mobile Computing, Elsevier Computer Communications.

Reviewer in Conferences

  • IEEE INFOCOM '21, Wi-DroIT '20, IEEE ICC '20, IEEE TSP '20, ICDCN '20, IEEE DySPAN '19, ACM MMSYS '19, ACM NOSSDAV '18, '19, ACM SIGMM '17, IEEE ISVLSI '14, '15.




  • Presentation Fellowship from University of Central Florida, USA, 2019.


  • GATE Scholarship from Ministry of Human Resource Development (MHRD), India, August 2011- June 2013.


  • Kaushik Chowdhury, and Debashri Roy, System for Software-Based Emulation of Wireless Communication Environments for Autonomous Vehicles, (U.S. Patent 2023, January), Status: Provisional Patent Application (63/441,175).
  • Kaushik Chowdhury, and Debashri Roy, FLASH: Federated Learning for Automated Selection of High-band mmWave Sectors, (U.S. Patent 2022, February), Status: Provisional Patent Application (63/314, 336).
  • Tathagata Mukherjee, Eduardo Pasiliao, Debashri Roy, Alec Riden, and Jared Paquet , GANSAT: A GAN and SATellite Constellation Signal-based Framework for Detecting GPS Spoofers, (U.S. Patent 2020, November), Status: Provisional Patent Application (63/086, 126).
  • Debashri Roy, Tathagata Mukherjee, Mainak Chatterjee, and Eduardo Pasiliao, Transmitter Identification Using Adversarial Networks and IQ Data, (U.S. Patent 2020, March), Status: Provisional Patent Application (62/993, 748).
  • Debashri Roy, Tathagata Mukherjee, Mainak Chatterjee, Erik Blasch, and Eduardo Pasiliao, Adversarial Learning for RF Transmitter Identification and Classification , (U.S. Patent 2020, March), Status: Provisional Patent Application (62/993, 751).


Copyright Notice. Citation details: Google Scholar

Book Chapters

Journals and Transactions

Conference Proceedings

Professional Experience


Below are the different datasets that were created in the projects where I have been involved. The link to download the data is given in the corresponding dataset.

Massive MIMO Channel Estimations Datasets

Massive multiple input and multiple output (mMIMO) is a critical component in upcoming 5G wireless deployment as an enabler for high data rate communications. mMIMO is effective when each corresponding antenna pair of the respective transmitter-receiver arrays experiences an independent channel. We publish a simulation dataset containing CSI information of mmWave band with a 32-element array base station, and a user equipment with a 4-element array, collected in MATLAB. (More Details)

Multimodal Fusion for NextG V2X Communications Datasets

The V2X communication spans a variety of applications such as collision avoidance safety systems, traffic signal timing, safety alerts to pedestrians, and real-time traffic. In all of the above applications, the communication system must meet the requirement of either low latency or high data rate for safety or quality of service reasons. We publish different real-world datasets to the research community for using non-RF data such as camera images, LiDAR, and GPS to solve different RF related problems to meet those requirements. (More Details)

ICARUS Datasets

ICARUS presents a machine learning based framework that offers choices at the physical layer for inference with inputs of (i) in-phase and quadrature (IQ) samples only, (ii) cycle-frequency features obtained via cyclostationary signal processing (CSP), and (iii) fusion of both, to detect the underlay DSSS signal and its modulation type within LTE frames. We release three real-world datasets that include signals captured in cellular bands in the wild and the NSF POWDER testbed for advanced wireless research (PAWR). (More Details)