PhD (in progress), Yale University
Focus on Computer and Communication Networks, and Machine Learning Methods
Posts by Collection
education
M.S. and M.Phil., School of Engineering and Applied Science, Yale University
Completion: May 2021
B.S in Engineering with concentration in Electrical Engineering (with Honors), B.S. in Physics(with Honors), Trinity College
Completion: May 2018 (Summa Cum Laude)
Releted Course Works
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experience
Yale University, Smart Networks Lab
Researcher, PhD student, September 2018 - Present
Bell labs, NJ, USA
Research Intern, June-August 2021
Qualcomm, CA, USA
Engineering Intern, May-August 20222
NEC Laboratories America, NJ, USA
Research Intern, June-August 2023
publications
Ultrafast electron microscopy for investigating fundamental physics phenomena
Recommended citation: P. Dahal, A. Mudvari, A. Basnet, P. W. Brown, and B. Barwick, "Ultrafast electron microscopy for investigating fundamental physics phenomena," Ultrafast Nonlinear Imaging and Spectroscopy IV, vol. 9956, pp. 12-18. SPIE, 2016 unltrafast_electron_microscopy_for_investigating_fundamental_physics_phenomena.pdf
Respiration estimation and apnea detection using fuzzy logic
Recommended citation: A. Mudvari and T. Ning, "Respiration estimation and apnea detection using fuzzy logic," 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju, Korea (South), 2017, pp. 2818-2821, doi: 10.1109/EMBC.2017.8037443. Respiration_estimation_and_apnea_detection_using_fuzzy_logic.pdf
Heart Murmurs Clustering Using Machine Learning
Recommended citation: K. Chen, A. Mudvari, F. G. G. Barrera, L. Cheng and T. Ning, "Heart Murmurs Clustering Using Machine Learning," 2018 14th IEEE International Conference on Signal Processing (ICSP), Beijing, China, 2018, pp. 94-98 Heart_Murmurs_Clustering_Using_Machine_Learning.pdf
Brownian Motion of Radioactive Particles: Derivation and Monte Carlo Test of Spatial and Temporal Distributions
Recommended citation: M. P. Silverman, A. Mudvari, "Brownian motion of radioactive particles: derivation and Monte Carlo test of spatial and temporal distributions", World Journal of Nuclear Science and Technology, 2018 brownian_motion_of_radioactive_particles_ derivation_and _monte_carlo_test_of_spatial_and_temporal_distributions.pdf
Magnalium: Highly Reliable SDC Networks with Multiple Control Plane Composition
Recommended citation: G. Li, A. Mudvari, K. Gokarslan, P. Baker, S. Kompella, F. Le, Kelvin M. Marcus, J Tucker, Y. R. Yang, P Yu, " Magnalium: Highly Reliable SDC Networks with Multiple Control Plane Composition," IEEE International Conference on Smart Computing (SMARTCOMP), Washington, DC, USA, 2019 Magnalium_Highly_Reliable_SDC_Networks_with_Multiple_Control_Plane_Composition.pdf
ML-driven scaling of 5G Cloud-Native RANs
Recommended citation: A. Mudvari, N. Makris, L. Tassiulas, " ML-driven scaling of 5G Cloud-Native RANs," IEEE Global Communications Conference (GLOBECOM), 2021 ML-driven_scaling_of_5G_Cloud-Native_RANs.pdf
Exploring ML methods for Dynamic Scaling of beyond 5G Cloud-Native RANs
Recommended citation: A. Mudvari, N. Makris, L. Tassiulas, " Exploring ML methods for Dynamic Scaling of beyond 5G Cloud-Native RANs," IEEE International Conference on Communications (ICC), 2022 Exploring_ML_methods_for_Dynamic_Scaling_of_beyond_5G_Cloud-Native_RANs.pdf
Constrained Reinforcement Learning for Adaptive Controller Synchronization in Distributed SDN
Recommended citation: I. Panitsas, A. Mudvari, L Tassiulas, “Constrained Reinforcement Learning for Adaptive Controller Synchronization in Distributed SDN” arXiv preprint arXiv:2404.08113, 2024, in submission Constrained_Reinforcement_Learning_for_Adaptive_Controller_Synchronization_in_Distributed_SDN.pdf
Robust SDN Synchronization in Mobile Networks using Deep Reinforcement and Transfer Learning
Recommended citation: A. Mudvari, K. Poularakis, L. Tassiulas, “Robust SDN Synchronization in Mobile Networks using Deep Reinforcement and Transfer Learning,” IEEE International Conference on Communications (ICC), 2023 Robust_SDN_Synchronization_in_Mobile_Networks_Using_Deep_Reinforcement_and_Transfer_Learning.pdf
Fog Computing for Deep Learning with Pipelines
Recommended citation: A. Vainio, A. Mudvari, D. Kiedanski, S. Tarkoma and L. Tassiulas, "Fog Computing for Deep Learning with Pipelines," 2023 IEEE 7th International Conference on Fog and Edge Computing (ICFEC), Bangalore, India, 2023, pp. 64-72, doi: 10.1109/ICFEC57925.2023.00017 Fog_Computing_for_Deep_Learning_with_Pipelines.pdf
Joint SDN Synchronization and Controller Placement in Wireless Networks using Deep Reinforcement Learning
Recommended citation: A. Mudvari, L. Tassiulas, "Joint SDN Synchronization and Controller Placement in Wireless Networks using Deep Reinforcement Learning", IEEE/IFIP Network Operations and Management Symposium (NOMS), 2024 Joint_SDN_Synchronization_and_Controller_Placement_in_Wireless_Networks_using_Dee_Reinforcement_Learning.pdf
Predictive Handover Strategy in 6G and Beyond: A Deep and Transfer Learning Approach
Recommended citation: I. Panitsas, A. Mudvari, A Maatouk, L Tassiulas, “Predictive Handover Strategy in 6G and Beyond: A Deep and Transfer Learning Approach” arXiv preprint arXiv:2404.08113, 2024, in submission Predictive_handover_strategy_in_6g_and_beyond_A_deep_and_transfer_learning_approach.pdf
Compiler for Distributed Quantum Computing: a Reinforcement Learning Approach
Recommended citation: (co-first author) P. Promponas, A. Mudvari, L. D. Chiesa, P. Polakos, L. Samuel, L. Tassiulas, “Compiler for Distributed Quantum Computing: a Reinforcement Learning Approach” arXiv preprint arXiv:2404.17077, 2024, in submission process Compiler_for_Distributed_Quantum_Computing_a_Reinforcement_Learning_Approach.pdf
Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency
Recommended citation: A. Mudvari, A. Vainio, I. Ofeidis, S. Tarkoma, L. Tassiulas, " Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency", ACM transactions on Internet Technology (2024) adaptive_compression_aware_split_learning_and_inference_for_enhanced_network_efficiency.pdf
SplitLLM: Collaborative Inference of LLMs for Model Placement and Throughput Optimization
Recommended citation: A. Mudvari, Y. Jiang, L. Tassiulas, et. al., " SplitLLM: Collaborative Inference of LLMs for Model Placement and Throughput Optimization", in submission process SplitLLM_Collaborative_Inference_of_LLMs_for_Model_Placement_and_Throughput_Optimization.pdf
talks
Talk 1 on Relevant Topic in Your Field
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.