Corporate Talk


Kristen M. Altenburger

(Research Scientist - Core Data Science, Facebook Meta)

Bio: Kristen M. Altenburger (she/her/hers) is a Research Scientist on the Networks & Behavior group within Meta’s Core Data Science team and is a Non-Resident Fellow with the RegLab at Stanford Law School.

Her research focuses on developing statistical methods for characterizing social structures in networks, with applications to data privacy, and focuses on promoting equitable digital systems that feature complex cultural and political considerations. At Meta, she is also involved in the co-teaching program with Georgia Tech, which is aimed at increasing pathways into AI.  She received her Ph.D. (January 2020) in Computational Social Science in the Management Science & Engineering Department at Stanford University advised by Johan Ugander. Her graduate work was supported in part by a National Defense Science and Engineering Graduate Fellowship. She received her Bachelor of Science (B.S.) in Mathematics from Ohio University in 2012 where she was also a Barry M. Goldwater Scholar, completed a research fellowship at Stanford Law School in 2012-2014, and received her Master of Arts (A.M.) in Statistics from Harvard University in 2015. She was previously a Member of Technical Staff in the Data Science and Cyber Analytics Department at Sandia National Laboratories and was a 2016 SPOT Award recipient based on her research. During the summer of 2017, she was the first intern for the Social Science & Algorithm team at Netflix. 
Title: Network Science & Human Behavior for Product Innovation
Abstract: Technological innovations have fundamentally transformed information flow and social behavior. As society’s digital platforms continue to promote instantaneous connectivity, network science can help advance machine learning and causal inference methods to better account for complex user behavior. This talk will discuss my work that applies and develops machine learning and causal inference methods for business decisions, taking into account the unique features of networked systems and consumer behavior. The talk will also give an overview of how we measure online conversations, new methods for addressing interference in A/B tests on networks, and ongoing work including studying the emergence of conflicts in groups. The talk will conclude with thoughts on future directions in social systems research.  In sum, this discussion will highlight the importance of network science in advancing machine learning and causal inference approaches and demonstrate the unique role of computational social science in product innovation.

Ross Cutler
(Partner Applied Scientist Manager, Microsoft, USA)

Bio: Ross Cutler is a Partner Applied Scientist Manager at Microsoft in the IC3 group where he manages the IC3-AI team of applied scientists and software engineers with the focus of improving Teams/Skype audio/video quality and reliability and enabling new functionality with AI. He has been with Microsoft since 2000, joining as a researcher in Microsoft Research. He has published 60+ academic papers and has 100+ granted patents in the areas of computer vision, speech enhancement, machine learning, optics, and acoustics. Ross received his Ph.D. in Computer Science (2000) in the area of computer vision from the University of Maryland, College Park.

Title for Talk: Developing machine learning based speech enhancement models for Microsoft Teams and Skype

Abstract: Microsoft Teams and Skype are used daily by hundreds of millions of users and have become critical tools for working remotely and communicating with friends and family. In this talk we will describe how we are replacing traditional digital signal processing (DSP) speech enhancement components in those products with machine learning (ML) based models.  We will describe how we have replaced the echo canceller, noise suppressor, packet loss concealment and added dereverberation. The new ML based models significantly outperform their old DSP components, but how we developed them is even more interesting. We used a Software 2.0 development methodology and created the first large scale datasets for training and testing these models, the first scalable systems to accurately label this type of data, and the first objective functions that are highly correlated to human perception to help train and evaluate these models. We also created 11 academic challenges at ICASSP and INTERSPEECH to engage with academic and industry researchers which significantly accelerated the process and raised the state of the art in these areas. We are applying this process to other areas such as ML based video codecs and ML based bandwidth control.

Important Deadlines

Full Paper Submission:12th September 2022
Acceptance Notification: 26th September 2022
Final Paper Submission:5th October 2022
Early Bird Registration: 3rd October 2022
Presentation Submission: 6th October 2022
Conference: 12 - 15 October 2022

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• Conference Proceedings will be submitted for publication at IEEE Xplore® digital library .

• Best Paper Award will be given for each track.

• Conference Record No 53756