Speakers

Evgeni Gousev is a Senior Director of Qualcomm AI Research. He leads Qualcomm R&D organization in the Silicon Valley Center and is also responsible for developing ultra low power embedded computing and machine learning platform, including always on machine vision. He is a co-founder of tinyML Foundation (www.tinyML.org), a non-profit organization of 3500+ professionals and 200+ organizations worldwide. The Foundation is focused on supporting and nurturing the fast-growing branch of ultra-low power machine learning technologies and approaches dealing with machine intelligence at the very edge. He currently serves on the Board of Directors. Evgeni joined Qualcomm in 2005 and led Technology R&D in the MEMS Research and Innovation Center commercializing mirasol display technology. He earned a Ph.D. degree in the solid-state physics and M.S. in Applied Physics from Moscow Engineering Physics Institute. In 1993, Evgeni joined Rutgers University first as a Postdoctoral Fellow and then as a Research Assistant Professor. While at Rutgers, he performed fundamental research in the area advanced gate dielectric for CMOS devices which, a decade later, became industry wide standards. In 1997, he was a Visiting Professor with the Center for Nanodevices and Systems, Hiroshima University, Japan. Shortly after, he joined IBM, where he led projects in the field of advanced silicon technologies in Semiconductor Research and Development Center in East Fishkill and T.J. Watson Research Center in Yorktown Heights, NY. He has co-edited 26 books and published more than 166 papers (with over 10k citations and h-index of 46: Google Scholar). He is a holder of more than 50 issued and filed patents. Dr. Gousev is a member of several professional boards, committees, panels, and societies.

Josh Meyer, PhD is a Mozilla Machine Learning Fellow and the Lead Scientist for Speech Technology at Artie, Inc. His expertise is in automatic speech recognition, speech synthesis, and natural language processing. Josh is an advocate of democratizing speech technology for all the world's languages.

Jane is a New Product Lead at Mozilla. Jane brings international experience in the intersection of deep research, breakthrough technology, policy and new products across all types of organisations - startups, governments and not-for-profits, and industry. With a computer engineering and psychology background, and over a decade of product experience, Jane brings a unique problem solving approach to the most challenging technical and social challenges.

Jane is passionate about leveraging technology to break through digital divides. And that’s exactly why she is devoting her career to building out tech in the open, starting with voice tech, and encouraging a more equitable and diverse approach to this domain.

Dr Sacha Krstulović was a Senior Research Engineer at Nuance’s Advanced Speech Group (Nuance ASG) before joining Audio Analytic. At Nuance, he worked on pushing the limits of large-scale speech recognition services such as Voicemail-to-Text and Voice-Based Mobile Assistants (Apple Siri type services). Prior to that, he was a Research Engineer at Toshiba Research Europe Ltd., developing novel Text-To-Speech synthesis approaches able to learn from data. He is the co-author of three book chapters, author of several international patents and several articles in international journals and conferences. Sacha is using his extensive audio analysis expertise to drive forward Audio Analytic’s technology. He is passionate about researching and developing automatic recognition of sound where Audio Analytic is building significant leadership.

Alex is a Principal Software Engineer at Google, where he leads the team of machine learning researchers and engineers who develop the technologies behind hotword detection ("Ok Google"), speaker identification, acoustic echo cancellation, embedded large vocabulary speech recognition, and endpointing and integrate them into Google products.

Prior to joining Google in 2009, Alex received B.S. and M.S. degrees in Symbolic Systems from Stanford and a Ph.D. in Computer Science from M.I.T.

Prateek Jain is a Senior Principal Researcher at Microsoft Research India and an adjunct faculty member at IIT Kanpur. He obtained his PhD degree from the Computer Science department at UT Austin and his BTech degree from IIT Kanpur. He works in the areas of large-scale and non-convex optimization, high-dimensional statistics, and ML for resource-constrained devices. He wrote a monograph on Non-convex Optimization in Machine Learning summarizing many of his results in non-convex optimization. Prateek regularly serves on senior program committee of top ML conferences and is an action editor for JMLR. His research has won several awards at the top conferences like ICML, CVPR, SDM. Recently, his work on alternating minimization has been selected as one of the best research papers in last five years by the IEEE Signal Processing Society.

Bio coming soon!

Danilo Pau, graduated at Politecnico di Milano, on 1992 in Electronic Engineering. He joined SGS-THOMSON (now STMicroelectronics) on 1991 and worked on mpeg2 video memory reduction, then video coding, embedded graphics, computer vision, and currently on deep learning. During his career helped in transferring those developments into company products. Also funded and served as 1st Chairman of the STMicroelectronics Technical Staff Italian Community; he is currently Technical Director into System Research and Applications and a Fellow Member of ST. Since 2019 Danilo is an IEEE Fellow, serves as Industry Ambassador coordinator for IEEE Region 8 South Europe, is vice chair of the Task Force on “Intelligent Cyber-Physical Systems” within IEEE CIS and Member for the Machine learning, Deep learning and AI in CE (MDA) Technical Stream Committee IEEE Consumer Electronics Society (CESoc).

Contributed with 113 documents the development of Compact Descriptors for Visual Search (CDVS), CDVS successfully developed ISO-IEC 15938-13 MPEG standard. He was Funding Chair of MPEG Ad Hoc Group on Compact Descriptor for Video Analysis (CDVA), formerly Compact Descriptors for Video Search (CDViS). He also contributes (applications) to MPAI.community recently started by L. Chiariglione. His scientific production consists of 91 papers to date, 78 granted patents and more than 23 invited talks/seminars at various universities and conferences. He was also principal investigator into numerous funded projects at European and Italian level on embedded systems.

Danilo tutored lots of undergraduate students (till Msc graduation), Msc engineers and PhD students from various universities in Italy and India, one of the activities that he likes at most

Lead of the TensorFlow Mobile/Embedded team, former CTO of Jetpac, acquired by Google in 2014. O'Reilly author, and blogger at petewarden.com.

Felix Johnny is the maintainer of Arm’s open source CMSIS-NN library that targets optimized Neural Network kernels for Cortex-M CPUs. He has spent most of the last 15 years in the wireless domain working with software design and optimizations in memory and cycle constrained systems. Outside of work, he is an active music photographer.

Tomas Edsö, Senior Principal Engineer, is currently acting as HW Tech Lead within the Arm machine learning group for the embedded NPU product line. Tomas joined Arm Sweden Lund in 2008 as one of the founding members of the video IP company Logipard, and has a wealth of knowledge within Video standards, digital signal compression and signal processing.

For the latter years, Tomas has transitioned into the machine learning group within Arm, where he has been part of designing as well as defining and scoping Arm NPU products.

Tomas holds a master’s degree in engineering physics from the University of Lund, with one additional year of scholarship studies in signal processing and artificial intelligence at the University of California, Irvine. Tomas currently holds 17 patents.

Erich Plondke has been designing signal processor architectures for almost 20 years. For the last 16 years he has been working at Qualcomm on the Hexagon signal processor architecture. Over 10 billion Hexagon cores have shipped into phones and other devices. For the last several years he has been working on accelerating and improving the efficiency of machine learning workloads. He has a Bachelor’s and a Master’s in Electrical and Computer Engineering from Georgia Tech.

Nat is a Software Engineer on the Tensorflow team at Google. He specializes in neural network optimization for embedded devices. Nat represents Google at the TinyMLPerf industry working group, and authored the benchmarking and profiling tools in Tensorflow for Microcontrollers.

Susan Kennedy is a postdoctoral fellow of Embedded EthiCS in the Philosophy Department at Harvard University. Her research interests lie in the ethics of procreation and parenthood with a particular focus on reproductive technologies.

Tulsee Doshi is the product lead for Google’s ML fairness and Responsible AI efforts, where she partners with product teams to drive improvements and develop Google-wide resources and best practices for more inclusive, diverse, and ethical products. Previously, Tulsee worked on the YouTube recommendations team. She received her BS in Symbolic Systems and MS in Computer Science from Stanford University.

Dan leads embedded machine learning engineering at Edge Impulse, a platform that allows developers to train machine learning models that run on tiny, low-power devices. He's co-author of the book TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers, published by O'Reilly, and sits on the tinyML Foundation's community organizing committee. He has previously worked on the TensorFlow team at Google, as CEO of Tiny Farms Inc., and as a lecturer in Automatic Identification and Data Capture at Birmingham City University.

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