Machine Learning Techniques for Network Analysis

#machine #learning #network #analysis

Dr. Lateef will provide a high-level overview of the various Machine Learning Techniques and their application to large data networks analysis. He will cover the current start of the art techniques and the challenges and shortcomings. A few techniques will then be presented to address these challenges and demonstrate their application in network analysis.

  Date and Time




  • Date: 29 Apr 2022
  • Time: 06:00 PM to 07:00 PM
  • All times are (GMT-05:00) US/Eastern
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  • Starts 20 April 2022 02:21 PM
  • Ends 29 April 2022 07:00 PM
  • All times are (GMT-05:00) US/Eastern
  • No Admission Charge


Dr. Irfan Lateef


Dr. Irfan Lateef is currently the chair of IEEE

NJ Coast Section and working as a Principal Solution Architect at NEC, where he supports the global 5G business development activities, End-to-End solution engineering, partnership and alliances and sales engineering.

He has over 25 years of experience in the Telecom industry in wireline and wireless technologies including, evolving compute/network virtualization (SDN/NFV) and wireless (4G,5G) technologies directing multi-sized global teams to build new products and complex solutions for driving new revenues. Leverages trusted relationships with senior executives for driving pre-sales, solution architecture, and business development activities.

Most recently, he has been focusing on 5G Open RAN, massive MIMO, and Machine Learning technologies deployments in global Tier1 operators. He is a global technology leader who leverages technical strategy, innovative thinking, and business experience to create and sell transformative solutions in the telecom industry.

Before NEC, Irfan has worked in AT&T, Verizon, Juniper, Lucent Bell Labs, and several other startups and Tier1 telecom equipment vendors.

He has a Ph.D. in Computer Engineering and a Master’s in Telecommunications, and his topics of interest include Machine Learning, Artificial Intelligence, Eigendecomposition, Signal processing, Anomaly Detection, and RAN Cross-Layer Optimization.


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