معرفی
Veena Mendiratta serves as an Adjunct Professor in the Master of Science in Machine Learning and Data Science Program at Northwestern University's McCormick School of Engineering and Applied Science. She recently retired from Nokia Bell Labs (and its predecessor entities AT&T, Lucent, and Alcatel-Lucent) where she held the position of Research Lead for Network Reliability and Analytics, bringing extensive industry expertise to her academic role.
Her educational background includes:
- PhD in Operations Research from Northwestern University
- BTech in Engineering from the Indian Institute of Technology, New Delhi, India
Professor Mendiratta's research spans telecom data analytics, system and network dependability analysis, network resiliency, and software reliability engineering. Her work applies advanced machine learning techniques to solve critical problems in telecommunications network performance, with particular focus on anomaly detection, root cause analysis, and predictive maintenance in mobile and software-defined networks. She bridges theoretical reliability models with practical telecom applications through data visualization and visual analytics.
Her publication record from 2011-2020 reveals a consistent trajectory in leveraging data-driven methodologies for network management. Key contributions include unsupervised anomaly detection in mobile networks, deep reinforcement learning for network slicing optimization, outage prediction using log analytics, and churn modeling through social network analysis. Her research demonstrates strong industry-academia collaboration with practical implementations at Bell Labs.
While specific details on current advising activities are not provided, her role in the Machine Learning and Data Science program suggests involvement in curriculum development and student mentorship. During her tenure at Bell Labs, she led research initiatives that secured substantial industry funding for network reliability projects.
Her prior leadership at Nokia Bell Labs involved directing research on network reliability and analytics, where she developed methodologies for dependability analysis in complex telecom systems. This industry experience directly informs her academic contributions, particularly in translating real-world network challenges into data science solutions for next-generation communication infrastructures.
