Machine Learning for Internet of Things

  • Webinar Date

    10 January, 2021

  • Webinar Time

    12:00 - 13:30 ET

Webinar Overview

Panelists:
Omesh Tickoo, Principal Engineer, Intel Labs
Ahmad Beirami, Research Scientist, Facebook AI
Anirudh Badam, Principal Researcher, Microsoft Research
Oguz Elibol, Applied Science Manager, Amazon Alexa AI
Tugba Erpek, Lead Scientist, Intelligent Automation, Inc. (IAI)

Panelist Biographies:

OMESH TICKOO :
Principal Engineer, Intel Labs

Omesh is a Principal Engineer and Research Manager in Intel Labs. His research is focused on next generation algorithms and platforms solutions for human-computer interaction using Computer Vision and associated sensing modalities. Omesh’s current research interests include probabilistic computing, interactive multi-modal scene understanding and contextual learning. In the past Omesh has worked on projects related to low power hardware acceleration, contextual knowledge management and systems optimization for different Intel platform solutions.

Omesh received his PhD from Rensselaer Polytechnic Institute for his thesis on Analysis and Improvement of Multimedia Transmission over Wireless Networks. Omesh has authored more than 30 papers in premier international Journals and Conferences and holds more than 30 patents. Omesh has served as chair of multiple committees for IEEE conferences. He has co-organized Computational Intelligence and Soft Computing workshops alongside PACT. Omesh regularly serves as a Technical Program Committee member and reviewer for international conferences and journals.

AHMAD BEIRAMI :
Research Scientist, Facebook AI

Ahmad Beirami is a research scientist at Facebook AI, solving state-of-the-art challenges in Conversational AI to power the next generation of virtual digital assistants. Prior to that, he led the AI agent research program for automated playtesting of video games at Electronic Arts. Before moving to industry in 2018, he held a joint postdoctoral fellow position at Harvard & MIT, focused on problems in the intersection of core machine learning and information theory. He is the recipient of the 2015 Sigma Xi Best PhD Thesis Award from Georgia Tech, for his work on the fundamental limits of network traffic compression.

Homepage: https://sites.google.com/view/beirami

ANIRUDH BADAM :
Principal Researcher, Microsoft Research

Dr. Anirudh Badam is a Principal Researcher at Microsoft Research where he leads multiple storage and data intensive computing projects spanning the edge and the cloud. He has a Ph.D. in Computer Science from Princeton University where his thesis work was recognized as one of the top ten emerging technologies of 2010 by MIT’s Technology Review and the corresponding IP was adopted by a leading storage technology company. His work on storage and edge computing systems since joining Microsoft Research have won multiple best paper awards and recognitions. His work on new battery technology and ways to leverage them in systems small and large has made it into Microsoft laptops as well as data centers. More recently, he is working on problems pertaining to storage and data in the commercial industry sectors including agriculture, retail, energy, media, and financial services.

OGUZ ELIBOL :
Applied Science Manager, Amazon Alexa AI

Oguz received his BSME, BSEE, MSEE and PhD degrees from Purdue University. He was with Intel Labs from 2008 to 2017 where his contributions included building an all electronic DNA sequencing system and an ultra-low power intelligent analog front end for IoT applications. Later he was with Intel AI Lab from 2017 to 2020 researching deep learning based solutions to various audio applications and enabling training of large deep learning models. Since May 2020 he is with Amazon Alexa, focusing on advancing Speaker ID systems, including training and inference at the edge.

TUGBA ERPEK :
Lead Scientist, Intelligent Automation, Inc. (IAI)

Dr. Tugba Erpek is a Lead Scientist at the Networks and Security Division of Intelligent Automation, Inc. (IAI). She is the Network Communications Technical Area Lead. She is also an Adjunct Research Professor at the Hume Center at Virginia Tech. She received her Ph.D. degree in Electrical and Computer Engineering from Virginia Tech. She has been developing machine/deep learning algorithms to improve the performance, situational awareness and security of wireless communications systems. She has been the Principal Investigator for numerous government-funded projects. Her R&D work covers wireless communications, 5G and 6G communications systems, IoT security, cognitive radio, machine/deep learning, adversarial machine learning, network protocol design and implementation, and resource allocation.