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Ph.D. Students
Ph.D. Students
Kasra Borazjani
Kasra Borazjani
Ph.D. Student
I am a PhD Candidate who is currently working on Federated Learning (FL). My research focus is on the intersection of Federated Learning and other areas such as Computer Vision and Medical Image Analysis. I am interested in explainability and traceability of FL networks in both model level and network level.
Fardis Nadimi
Fardis Nadimi
Ph.D. Student
I have a deep passion for wireless communications, reinforcement learning, federated learning, and combinatorics, turning these interests into an exciting lifelong journey! I’ve spent several years in academia and now I’m fully immersed in my Ph.D. journey. My research playgrounds? They’re nothing short of exhilarating! I’m delving into cutting-edge areas like mathematical modeling, distributed machine learning, and the next-generation networks that will shape our future. Whether it’s making computing systems more reliable or exploring the fascinating intersections of combinatorics with advanced technologies, I’m all in!
Payam Abdisarabshali
Payam Abdisarabshali
Ph.D. Student
I earned my M.Sc. in Computer Engineering from Razi University, Iran, where I graduated at the top of my class in 2018. Currently, I'm a Ph.D. student at the University at Buffalo (SUNY), NY, USA. My research focuses on dynamic system analysis, with a keen interest in the mathematical modeling of federated learning and distributed machine learning over highly complex dynamic networks.
M.Sc. Students
M.Sc. Students
Imani Muhammad‑Graham: Studying federated learning over non‑uniform datasets.
Koushani Chakrabarty: Studying multi‑modal machine learning over medical datasets.
Arnab Das (Thesis): Studying reliable federated learning in wireless networks.
– Thesis: Reliable Distributed Learning through Cooperative Federated Learning over Wireless Networks, 2024.
Crystal Vesneske: Studying federated machine learning over smart power grids.
Jean‑Jose Aull: Studying federated machine learning over smart power grids.
Allan Salihovic: Studying federated machine learning over irregular networks.
External Collaborations
External Collaborations
External Collaborations with Ph.D. Students:
• Su Wang: Studying on device sampling for federated learning and UAV‑assisted online federated learning.
• Sheikh Shams Azam: Studying privacy‑preserving representation learning and federated learning.
• Houyi Qi: Studying matching‑based resource provisioning for edge computing.
• Zhang Liu: Studying learning‑based DAG task scheduling for edge computing.
• Frank Po‑Chen Lin: Studying semi‑decentralized federated learning.
• Junghoon Kim: Studying intelligent reflecting surfaces (IRS) and linear encoding/decoding techniques.
• Myeung Suk Oh: Studying wireless positioning and pilot assignment in cell‑free massive MIMO.
• Bhargav Ganguly: Studying edge‑assisted federated leaning with floating aggregator server.
• Satya Wagle: Studying unsupervised federated learning.
• Shahryar Zehtabi: Studying event‑triggered decentralized federated learning scheme.
• Yun‑Wei Chu: Studying federated student performance prediction techniques.
• Bingshuo Guo: Studyin hybrid scheduling of graph structure big data applications.
• Zhan‑Lung Chang: Studying multi‑model federated learning.
External Collaborations with Postdoctoral Researchers:
• Dinh C. Nguyen: Studying blockchain‑empowered federated learning.
• Rohit Parasnis: Studying connectivity‑aware semi‑decentralized federated learning.
• Dong‑Jun Han: Studying non‑terrestrial federated learning.
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