Chief Scientist - Chief Technology Officer, ANDRO Computational Solutions, LLC
Founding Director of Marconi-Rosenblatt AI Innovation Lab
Adjunct Assistant Professor, Department of Electrical Engineering at the University at Buffalo
IEEE Senior Member | IEEE Region 1 Technological Innovation Awardee
AFCEA International Meritorious Rising Star Awardee | AFCEA 40 Under 40
Contact: jithinj26 (at) gmail (dot) com
Dr. Jithin Jagannath received his Ph.D. degree from Northeastern University. He is the Chief Scientist - Chief Technology Officer and the Founding Director of the Marconi-Rosenblatt AI Innovation Lab at ANDRO Computational Solutions. He is also the Adjunct Assistant Professor in the Department of Electrical Engineering at the University at Buffalo, State University of New York. He is an IEEE Senior member and serves as an IEEE SPS Applied Signal Processing Systems Technical Committee member. Dr. Jagannath was the recipient of 2021 IEEE Region 1 Technological Innovation Award with the citation, "For innovative contributions in machine learning techniques for the wireless domain". He is also the recipient of AFCEA International Meritorious Rising Star Award for achievement in Engineering and AFCEA 40 Under 40. Dr. Jagannath's recent research has led to over 40 peer-reviewed journal and conference publications and 18 patents (granted and pending).
Dr. Jagannath heads several of the ANDRO's research and development projects in the field of 5G and beyond, signal processing, signal intelligence, applied machine learning, protocol design for ad hoc networks, Internet-of-Things, and UAV automation. He has been the Principal Investigator of several Rapid Innovation Fund (RIF)s and Small Business Innovation Research (SBIR)s with the objectives of designing and developing novel solutions in the domain of intelligent wireless networking, machine learning, MIMO wireless communication, and signal detection and classification among others. These projects serve several customers including the U.S. Army, U.S. Navy, USSOCOM, and the Department of Homeland Security (DHS). He is currently leading several teams developing commercial products such as SPEARLink™, DEEPSpec™, among others. Dr. Jagannath has been invited to give various talks including Keynotes on the topic of machine learning and Beyond 5G wireless communication. He has been invited to serve on the Technical Program Committee (TPC), editor, and reviewer to several leading journals and conferences.
Please feel free to reach out if you are interested in,
- Collaboration
- Need editorial, TPC support.
- Technical talks in my area of expertise
- Students looking for general guidance in these areas.
- Or just having a discussion on how these areas are evolving!
Radio Frequency Machine Learning (RFML)
5G/6G
Signal Intelligence (SIGINT)
RF Fingerprinting
Applied Machine Learning for Wireless Communication.
Software Defined Radios
Cognitive Networks
Automation for UAS
Computer Vision
J. Jagannath, A. Jagannath, S. Furman, T. Gwin,"Deep Learning and Reinforcement Learning for Autonomous Unmanned Aerial Systems: Roadmap for Theory to Deployment" in Deep Learning for Unmanned Systems. Studies in Computational Intelligence, Koubaa A., Azar A.T. (eds), vol 984. Springer, Cham., 2021. [pdf][bibtex]
J. Jagannath, N. Polosky, A. Jagannath, F. Restuccia, T. Melodia, "Neural Networks for Signal Intelligence: Theory and Practice," in Machine Learning for Future Wireless Communications, F.‐L. Luo (Ed.). Wiley - IEEE Series, 2020, ISBN: 97811195622523. [pdf][bibtex]
A. Jagannath, Z. Kane, J. Jagannath, "Bluetooth and WiFi Dataset for Real World RF Fingerprinting of Commercial Devices", IEEE Communication Magazine, 2023.
A. Jagannath, J. Jagannath, "Embedding-Assisted Attentional Deep Learning for Real-World RF Fingerprinting of Bluetooth", IEEE Transactions on Cognitive Communications and Networking, 2023. [pdf][bibtex]
S. Kafle, J. Jagannath, Z. Kane, N. Biswas, P. S. V. Kumar, A. Jagannath, "Generalization of Deep Reinforcement Learning for Jammer-Resilient Frequency and Power Allocation," in IEEE Communications Letters, 2023. [pdf][bibtex]
K. Ramezanpour, J. Jagannath, A. Jagannath, “Security and privacy vulnerabilities of 5G/6G and WiFi 6: Survey and research directions from a coexistence perspective”, Computer Networks (Elsevier), vol. 221, 109515, ISSN 1389-1286, 2023. [pdf][bibtex]
C. Farquhar, S. Kafle, K. Hamedani, A. Jagannath, J. Jagannath, "Marconi-Rosenblatt Framework for Intelligent Networks (MR-iNet Gym): For Rapid Design and Implementation of Distributed Multi-agent Reinforcement Learning Solutions for Wireless Networks", Computer Networks (Elsevier), vol. 222, 109489, ISSN 1389-1286, 2023. [pdf][bibtex]
A. Jagannath, J. Jagannath, PSPV Kumar, “A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges”, Computer Networks (Elsevier), vol. 219, 109455, ISSN 1389-1286, 2022. [pdf][bibtex]
K. Ramezanpour, J. Jagannath, “Intelligent Zero Trust Architecture for 5G/6G Networks: Principles, Challenges, and the Role of Machine Learning in the context of O-RAN”, Computer Networks (Elsevier), vol. 217, 109358, ISSN 1389-1286, 2022. [pdf][bibtex]
A. Jagannath, J. Jagannath, "Multi-task Learning Approach for Modulation and Wireless Signal Classification for 5G and Beyond: Edge Deployment via Model Compression", Physical Communications (Elsevier), vol. 54, 101793, ISSN 1874-4907, 2022. [pdf][bibtex]
J. Jagannath, A. Jagannath, J. Henney, T. Gwin, Z. Kane, N. Biswas, A. Drozd, "Design of Fieldable Cross-Layer Network using Embedded Software Defined Radios: Survey and Novel Architecture with Field Trials", Computer Networks (Elsevier), vol. 209, 108917, ISSN 1389-1286, 2022. [pdf][bibtex]
A. Jagannath, J.Jagannath, "Dataset for Modulation Classification and Signal Type Classification for Multi-task and Single Task Learning", Computer Networks (Elsevier), vol. 199,108441, ISSN 1389-1286, 2021.[pdf][bibtex]
A. Jagannath, J.Jagannath, T. Melodia, ''Redefining Wireless Communication for 6G: Signal Processing Meets Deep Learning with Deep Unfolding", IEEE Transactions on Artificial Intelligence, vol. 2, no. 6, pp. 528-536, Dec. 2021. [pdf][bibtex]
J. Jagannath, N. Polosky, A. Jagannath, F. Restuccia, T. Melodia, "Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey," Ad Hoc Networks (Elsevier) vol. 93, pp 101913, June 2019. [pdf] [bibtex]
J. Jagannath, S. Furman, A. Jagannath, L. Ling, A. Burger, A. Drozd, "HELPER: Heterogeneous Efficient Low Power Radio for Enabling Ad Hoc Emergency Public Safety Networks," Ad Hoc Networks (Elsevier) , vol. 89, pp 218-235, June 2019. [pdf][bibtex]
J. Jagannath, S. Furman, T. Melodia, A. Drozd, "Design and Experimental Evaluation of a Cross-Layer Deadline-Based Joint Routing and Spectrum Allocation Algorithm," IEEE Transactions on Mobile Computing, vol. 18, no. 8, pp. 1774-1788, August 2019. [pdf][bibtex]
N. Cen, J. Jagannath, S. Moretti, Z. Guan, T. Melodia, "LANET: Visible-Light Ad Hoc Network," Ad Hoc Networks (Elsevier), vol. 84, pp 107-123, March 2019. [pdf] [bibtex]
A. Jagannath, Z. Kane, J. Jagannath, “RF Fingerprinting Needs Attention: Multi-task Approach for Real-World WiFi and Bluetooth” in Proc. of IEEE Global Communications Conference (GLOBECOM), Rio de Janeiro, Brazil, December 2022. [pdf][bibtex]
J. Jagannath, K. Ramezanpour, A. Jagannath, "Digital Twin Virtualization with Machine Learning for IoT and Beyond 5G Networks: Research Directions for Security and Optimal Control", in Proc. of ACM Workshop on Wireless Security and Machine Learning (WiseML), San Antonio, Texas, USA May, 2022. [pdf][bibtex]
J. Jagannath, K. Hamedani, C. Farquhar, K. Ramezanpour, A. Jagannath, "MR-iNet Gym: Framework for Edge Deployment of Deep Reinforcement Learning on Embedded Software Defined Radio", in Proc. of ACM Workshop on Wireless Security and Machine Learning (WiseML), San Antonio, Texas, USA May, 2022. [pdf][bibtex] [Best Paper Award]
N. Polosky, BC. da Silva, M. Fiterau, J. Jagannath, "Constrained Offline Policy Optimization", in Proc. of the 39th International Conference on Machine Learning (ICML), Baltimore, MD, July, 2022. [pdf][bibtex]
A. Jagannath, J. Jagannath, Y. Wang, T. Melodia, "Deep neural network goes lighter: A case study of deep compression techniques on automatic RF modulation recognition for Beyond 5G networks" in Proc. of SPIE Defense and Commercial Sensing, Orlando, Florida, USA, May, 2022. [pdf][bibtex]
C. Farquhar, PSPV Kumar, A. Jagannath, J. Jagannath, "Distributed Transmission Control for Wireless Networks using Multi-Agent Reinforcement Learning" in Proc. of SPIE Defense and Commercial Sensing, Orlando, Florida, USA, May, 2022. [pdf][bibtex]
N. Polosky, T. Gwin, S. Furman, P. Barhanpurkar, J. Jagannath, "Machine Learning Subsystem for Autonomous Collision Avoidance on a small UAS with Embedded GPU", in Proc. IEEE International Workshop on Communication and Networking for Swarms Robotics, 2022. [pdf][bibtex]
J. Jagannath, A. Jagannath, J. Henney, N. Biswas, T. Gwin, Z. Kane, A. Drozd, "Fieldable Cross-Layer Optimized Embedded Software Defined Radio is Finally Here!", in Proc. of IEEE Conference on Military Communications (MILCOM), San Diego, CA, USA, Nov 2021. [pdf][bibtex]
K. Hamedani, L. Liu, J. Jagannath, Y. Yi, “Adversarial Classification of the Attacks on Smart Grids Using Game Theory and Deep Learning”, in Proc. of ACM Workshop on Wireless Security and Machine Learning (WiseML), Abu Dhabi, UAE, July 2021. [pdf][bibtex]
A. Jagannath, J. Jagannath. "Multi-task Learning Approach for Automatic Modulation and Wireless Signal Classification", in Proc. of IEEE International Conference on Communications (ICC), Montreal, Canada, June 2021. [pdf][bibtex][video]
A. Jagannath, J. Jagannath, “Jam-Guard: Low-Cost, Hand-held Device for First Responders to Detect and Localize Jammers” in Proc. of 14th International Conference on Signal Processing and Communication Systems (ICSPCS), Adelaide, Australia, Dec 2020. [pdf][bibex]
A. Jagannath, J. Jagannath, A. Drozd “High Rate-Reliability Beamformer Design for MIMO-OFDM System under Hostile Jamming”, in Proc. of 29th International Conference on Computer, Communication and Networks (ICCCN), Honolulu, HI, USA, Aug, 2020. [pdf][bibtex]
A. Jagannath, J. Jagannath, A. Drozd, “Towards Higher Spectral Efficiency: Rate-2 Full-Diversity Complex Space-Time Block Codes,” in Proc. of IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, December 2019. [pdf][bibtex]
A. Jagannath, J. Jagannath, A. Drozd, “Artificial Intelligence-based Cognitive Cross-layer Decision Engine for Next-Generation Space Mission,” in Proc. of IEEE Cognitive Communication for Aerospace Applications (CCAA) Workshop, Cleveland, OH, June 2019. [pdf][bibtex]
J. Jagannath, T. Melodia, “VL-ROUTE: A Cross-Layer Routing Protocol for Visible Light Ad Hoc Network,” in Proc. of IEEE Symp. on a World of Wireless, Mobile, and Multimedia Networks (WoWMoM), Washington D.C., USA, June 2019. [pdf][bibtex]
J. Jagannath, S. Furman, A. Jagannath, A. Drozd, “Energy Efficient Ad Hoc Networking Devices for Off-the-Grid Public Safety Networks”, in Proc. of IEEE Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, January 2019. [pdf][bibtex]
A. Jagannath, J. Jagannath, B. Sheaffer, A. Drozd, “Developing a Low Cost, Portable Jammer Detection and Localization Device for First Responders”, in Proc. of IEEE Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, January 2019. [pdf][bibtex]
J. Jagannath, N. Polosky, D. O’Connor, L. Theagarajan, B. Sheaffer, S. Foulke, P. Varshney, “Artificial Neural Network based Automatic Modulation Classifier for Software Defined Radios,” in Proc. of IEEE International Conference on Communications (ICC), Kansas City, MO, USA, May 2018.[pdf][bibtex]
J. Jagannath, T. Melodia, "An Opportunistic Medium Access Control Protocol for Visible Light Ad Hoc Networks," in Proc. of International Conference on Computing, Networking and Communications (ICNC), Maui, HI, USA, March 2018.[pdf] [bibtex]
N. Polosky, J. Jagannath, D. O'Connor, H. Saarinen, S. Foulke, “Artificial Neural Network with Electroencephalogram Sensors for Brainwave Interpretation: Brain-Observer-Indicator Development Challenges,” in Proc. of 13th International Conference & Expo on Emerging Technologies for a Smarter World (CEWIT), Stony Brook, NY, USA, November 2017.[pdf] [bibtex]
J. Jagannath, D. O'Connor, N. Polosky, B. Sheaffer, L. N. Theagarajan, S. Foulke, P. K. Varshney, S. P. Reichhart, "Design and Evaluation of Hierarchical Hybrid Automatic Modulation Classifier using Software Defined Radios," in Proc. of IEEE Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, January 2017. [pdf] [bibtex]
J. Jagannath, T. Melodia, A. Drozd, "DRS: Distributed Deadline-Based Joint Routing and Spectrum Allocation for Tactical Ad-hoc Networks" in Proc. of IEEE Global Communications Conference (GLOBECOM), Washington D.C., USA, December 2016. [pdf][bibtex]
J. Jagannath, H. Saarinen, T. Woods, J. O’Brien, S. Furman, A. Drozd, T. Melodia, "COmBAT: Cross-layer Based Testbed with Analysis Tool Implemented Using Software Defined Radios," in Proc. of IEEE Conference on Military Communications (MILCOM), Baltimore, MD, USA, November 2016. [pdf] [bibtex]
T. Wimalajeewa, J. Jagannath, P. K. Varshney, A. L. Drozd, W. Su, "Distributed Asynchronous Modulation Classification Based on Hybrid Maximum Likelihood Approach," in Proc. of IEEE Conference on Military Communications (MILCOM) , Tampa, FL, USA, Oct 2015. [pdf] [bibtex]
J. Jagannath, H. M. Saarinen, A. L. Drozd, "Framework for Automatic Signal Classification Techniques (FACT) for Software Defined Radios," in IEEE Symposium on Computational Intelligence in Security and Defense Applications (CISDA) , Verona, NY, USA, May 2015. [pdf] [bibtex]
S. Foulke, J. Jagannath, A. L. Drozd, T. Wimalajeewa, P. K. Varshney, W. Su, "Multisensor Modulation Classification (MMC)," in Proc. of IEEE Conf. on Military Communications (MILCOM), Baltimore, MD, October 2014. [pdf] [bibtex]
A. L. Drozd, T. Arcuri, J. Jagannath, D. A. Pados, T. Melodia, E. Demirors, G. Sklivanitis, "Network Throughput Improvement in Cognitive Networks by Joint Optimization of Spectrum Allocation and Cross-layer Routing," in NATO Symposium on "Cognitive Radio and Future Network " (IST-123), The Hauge, The Netherlands, May 2014. [pdf] [bibtex]
J. Jagannath, A. Saji, H. Kulhandjian, Y. Sun, E. Demirors, T. Melodia, "A Hybrid MAC Protocol with Channel-dependent Optimized Scheduling for Clustered Underwater Acoustic Sensor Networks," in Proc. of ACM Conference on Underwater Networks and Systems (WUWNet), Kaohsiung, Taiwan, November 2013. [pdf] [bibtex].
J.Jagannath, "Machine Learning for RF Signal Intelligence – Where We Are and What Challenges and Opportunities Lie Ahead?", in the Industry Expert Session at the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May, 2022. [pdf][bibtex]
J. Jagannath, "Energy efficient cluster based hybrid MAC protocol with channel dependent optimized intra-cluster scheduling," M.S. Thesis , Department of Electrical Engineering, State University of New York at Buffalo, May 2013. [bibtex]
J. Jagannath, "Cross-layer Optimized Networking for Next-Generation 5G Ad Hoc Networks," Ph.D. Dissertation , Department of Electrical and Computer Engineering, Northeastern University, August 2019. [pdf][bibtex]
Routing data through distributed communication network. U.S. Patent Number: US 10,721,162 B2. Patent Granted.
Vertebral assist spinal medical device. U.S. Patent Number US 10,966,757 B2. Patent Granted.
Apparatus and method for jammer resistant protocol stack design. U.S. Patent Number: US 11,115,090 B2. Patent Granted.
Rate-2 complex space-time block codes for 3×3 and 4×4 MIMO systems. U.S. Patent Number: US 11,271,681 B2. Patent Granted.
Transceiver with machine learning for generation of communication parameters and cognitive resource allocation: US 11,490,273 B2 Patent Granted.
Multi-task learning neural network framework for RF spectrum sensing and classification. April 2020. Patent Granted
Sensitivity enhancing emitter description technique using machine learning. US20230164572A1. Patent Pending
Machine learning framework for control of autonomous agent operating in dynamic environment. April 2020. Patent Pending.
Zero trust architecture for networks employing machine learning engines. April 2021. Patent Pending.
System and methodology for coexistence between wireless fidelity and cellular network. Aug 2021. Patent Pending.
Identifying Transponder Hardware or Waveform In Wireless Network Via Machine Learning Module. Oct 2022. Patent Pending.
Machine Learning System For Identifying And Countering Non-Friendly Radar Networks. Patent Pending
Distributed Authentication with Network Segmentation and Dynamic authorization for networks. Provisional Patent.
Signal Intelligence System to Integrate Spectrum Prediction with Emitter Classification and PNT. Provisional Patent.
Method And System To Evaluate Channel Occupancy Via Encoder-Decoder Network And Artificial Neural Network. Provisional Patent.
Association for the Advancement of Artificial Intelligence (AAAI) Spring Symposium 2024, “Federated Learning on the Edge”, Stanford University, USA.
“Army Applied Small Business Innovation Research (SBIR) and xTech Panel”, Invited Panel in AUSA Annual Meeting & Exposition, Oct 2023
"Role of Machine Learning in Next Generation Wireless Networks", Invited Panel in IEEE Consumer Communications & Networking Conference, Jan 2023
"Machine learning for next-generation software defined radio technologies", Invited Academic Speaker at Software Defined Radio (SDR) Industry Collider, Air Force Armament Directorate- Disruptive Futures Division (EBZ), Aug 2022
"Machine Learning enabled Future for Wireless Communication – Recent Advances and Open Challenges", ECE Graduate Seminar Lecture, March, 2022.
"Security Vulnerabilities in Coexistence of 5G and WiFi 6/6E", FCC's Communications Security, Reliability, and Interoperability Council (CSRIC) VIII WG3 Meetings, Feb, 2022.
KEYNOTE : “Demystifying the Spectrum with Machine Learning for Beyond 5G?”, Keynote Address in the Special Session, Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication (MDCWC 2021) at IEEE International Conference on Information and Automation for Sustainability (ICIAfS) 2021, Sri Lanka, Aug 2021. [video]
"Leveraging Deep Learning for Next-Generation of Wireless Communication", 2nd Buffalo Day for 5G and Wireless Internet of Things, University at Buffalo, Buffalo, NY, Nov, 2020.
KEYNOTE : “Will Machine learning Revolutionize Wireless Communication?”, Keynote Address at the Machine Learning, Deep learning and Computational intelligence for wireless communication (MDCWC) 2020, NIT Tiruchirappalli, India, Oct 2020.
“Next-Generation Intelligent Wireless Networking for Tactical and Commercial Application”, 1st Buffalo Day for 5G and Wireless Internet of Things, University at Buffalo, Buffalo, NY, Nov, 2019.
George. W. Thorn Award
IEEE Region 1 Technology Innovation
IEEE Senior Member
AFCEA 40 under 40
AFCEA International Meritorious Rising Star Award