Hi, I am Alireza Bagheri.
A
About
Alireza Bagheri currently holds the position of Vice President Applied AI ML Lead at JPMorgan Chase & Co., located at 383 Madison Avenue, New York, NY 10001. Before joining JPMorgan Chase, he served as a Senior Machine Learning Engineer at Roots Automation, situated at 85 Broad Street, New York, NY 10004. Prior to that role, he worked as a Machine Learning Engineer at the American Express AI Labs, located at 200 Vesey Street, New York, NY. He earned his PhD in Electrical Engineering from New Jersey Institute of Technology (NJIT), Newark, New Jersey, in 2019. Alireza Bagheri also holds an MSc degree in Electrical Engineering with first-class honors from Semnan University, Semnan, Iran, conferred in 2013.
Dr. Bagheri's research focus encompasses Wireless Communication, Optimization, and Machine Learning. During his doctoral studies, he aimed to develop a theoretical foundation facilitating the creation of adaptable bio-inspired computing algorithms specifically designed for addressing both supervised and unsupervised cognitive tasks. His research involved supervised learning, unsupervised learning, and adversarial training applied to Spiking Neural Networks (SNNs). Additionally, as part of his doctoral and master's degrees, he contributed to research in Mobile Cloud Computing (MCC) and Cognitive Radio Networks (CRNs). Most of his research works funded by grants from leading agencies such as the National Science Foundation (NSF) of US, the European Research Council (ERC), the Engineering and Physical Sciences Research Council (EPSRC) of UK, the National Foundation for Science and Technology (NAFOSTED) of Vietnam, the Academy of Finland, the TEKES of Finland, and the Research Program DGRES of Greece.
Dr. Bagheri served as a reviewer for IEEE Communications Letters, IET Communications Journal, IEEE International Conference on Communications (ICC), IEEE Wireless Communications & Networking Conference (WCNC), IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), and IEEE Vehicular Technology Conference (VTC).
Experience
Education
New Jersey Institute of Technology
Newark, NJ 07102
Degree: PhD in Electrical Engineering
GPA: 3.83/4
Dissertation:
Probabilistic Spiking Neural Networks: Supervised, Unsupervised and Adversarial Trainings
Semnan, Semnan, Iran
Degree: MSc in Electrical Engineering
GPA: 17.62/20 with first class honors
Thesis:
Energy Detection Based Cooperative Spectrum Sensing in Cognitive Radio Networks
Publication
Adversarial Training for Probabilistic Spiking Neural Networks
A. Bagheri, O. Simeone, and B. Rajendran IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2018.
Training Probabilistic Spiking Neural Networks with First-to-spike Decoding
A. Bagheri, O. Simeone, and B. Rajendran IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.
Joint Uplink/Downlink Optimization for Backhaul-Limited Mobile Cloud Computing with User Scheduling
A. AL-Shuwaili, O. Simeone, A. Bagheri and G. Scutari IEEE Transactions on Signal and Information Processing over Networks, vol. 3, issue 4, pp 787-802, December 2017.
A Comprehensive Framework for Spectrum Sensing in Non-Linear and Generalized Fading Conditions
P.C. Sofotasios, A. Bagheri, T.A. Tsiftsis, S. Freear, A. Shahzadi and M. Valkama IEEE Transactions on Vehicular Technology, vol. 66, issue 10, pp 8615-8631, Octobor 2017.
Energy detection based spectrum sensing over enriched multipath fading channels
A. Bagheri, P.C. Sofotasios, T.A. Tsiftsis, K. Ho-Van, M.I. Loupis, S. Freear and M. Valkama IEEE Wireless Communications and Networking Conference (WCNC), Doha, Qatar, 3-6 April 2016.
Joint uplink/downlink and offloading optimization for mobile cloud computing with limited backhaul
A.N. Al-Shuwaili, A. Bagheri and O. Simeone IEEE Conference on Information Science and Systems (CISS), Princeton, NJ, USA, 16-18 March 2016.
Area under ROC curve of energy detection over generalized fading channels
A. Bagheri, P.C. Sofotasios, T.A. Tsiftsis, A. Shahzadi, S. Freear and M. Valkama IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Hong Kong, China, 30 August-2 September 2015.
Spectrum sensing in generalized multipath fading conditions using square-law combining
A. Bagheri, P.C. Sofotasios, T.A. Tsiftsis, A. Shahzadi and M. Valkama IEEE International Conference on Communications (ICC), London, UK, 8-12 June 2015.
AUC study of energy detection based spectrum sensing over η-μ and α-μ fading channels
A. Bagheri, P.C. Sofotasios, T.A. Tsiftsis, A. Shahzadi and M. Valkama IEEE International Conference on Communications (ICC), London, UK, 8-12 June 2015.
Another look at performance analysis of energy detector with multichannel reception in Nakagami-m fading channels
A. Bagheri and A. Shahzadi Wireless personal communications, vol. 79, issue 1, pp 527-544, November 2014.
An analytical model for primary user emulation attacks in IEEE 802.22 networks
S. Tabatabaee, A. Bagheri, A. Shahini and A. Shahzadi IEEE International Conference on Connected Vehicles and Expo (ICCVE), Las Vegas, NV, USA, 2-6 December 2013.
A unified approach to performance analysis of energy detection with diversity receivers over Nakagami-m fading channels
A. Shahini, A. Bagheri and A. Shahzadi IEEE International Conference on Connected Vehicles and Expo (ICCVE), Las Vegas, NV, USA, 2-6 December 2013.
Analytical and learning-based spectrum sensing over channels with both fading and shadowing
A. Bagheri, A. Shahini and A. Shahzadi IEEE International Conference on Connected Vehicles and Expo (ICCVE), Las Vegas, NV, USA, 2-6 December 2013.
Cooperative Spectrum Sensing over κappa - mu Fading Channel
A. Bagheri and A. Shahzadi IEEE International Conference on Broadband, Wireless Computing, Communication and Applications (BWCCA), Victoria, BC, Canada, 12-14 November 2012.