Corporate Talk



Loretta Cheeks

(Founder, Strong TIES and DS Innovation)

Bio: Dr. Cheeks is on a mission to create a better world with technology. Before earning a Ph.D. in Computer Science at Arizona State University, the STEAM advocate was developing, deploying and leading various teams within the communications, avionics, instrumentation & control and chemical industries for Fortune 500 corporations. She is the CEO of DS Innovation, an independent research, training and consulting Artificial Intelligence and Machine Learning organization. But this Doctor of Philosophy isn’t just paving the way for up-and-coming engineers, Dr. Cheeks is also committed to improving higher education for underserved and underrepresented communities to follow in her scientific footsteps. To do that, Dr. Cheeks created “Strong TIES,” a non-profit.

Title of the talk: Data Trust Development

Abstract: The Internet is the premier platform for the proliferation of vast amounts of data and has been compared to a huge social and psychological laboratory. It has enabled a globally connected world which is greatly shaping and transforming individual and group standards, values, attitudes, and beliefs about societal, economic, and environmental systems and efficiencies. Data and technological advancements are promising for ushering in the fourth industrial revolution. Yet, there exists tension over access, ownership, and rights to data, which is a risk to society and democracy. This workshop explores properties, conditions, governance, and guiding principles for responsible data sharing where the rights of the people’s data are within the structure of a trust-data trust.


Walid Rjaibi

(Distinguished Engineer and Chief Technology Officer (CTO), IBM Canada)

Bio: Dr. Walid Rjaibi is a Distinguished Engineer and CTO for Data Security at IBM. He drives the research for data security as well as the technical architecture for several products including Guardium Data Protection, Guardium Data Encryption, Guardium Insights, Security Key Lifecycle Manager, and Data Risk Manager. Prior to his current role, Walid held several technical and management roles within IBM including Research Staff Member at the Zurich Research Lab, Security Architect for DB2, and Chief Security Architect for IBM Data & AI. His Data Security work resulted in several commercial capabilities, 26 granted patents and various publications in leading scientific and academic journals/conferences. Walid also serves on the professional advisory board for the department of Electrical Engineering & Computer Science at York University, the department of Computer & Information Systems at the University of Michigan and the Cybersecurity Centre at the University of Missouri.

Title of the talk: The Next Frontier for Data Security: Protecting Data in Use

Abstract:  Sensitive data can exist in three states: In transit, in storage and in use. Tremendous progress has been made over the last several years to protect sensitive data in transit and in storage. But sensitive data may still be vulnerable when it is being worked on (data in use). For example, consider Transparent Database Encryption (TDE). While TDE ensures sensitive data is protected in storage, that sensitive data must be stored in cleartext in the database bufferpool in order for SQL queries to be processed. This renders the sensitive data vulnerable because its confidentiality may be compromised in several ways, such as by memory-scraping malware or by privileged users dumping memory content to a file. This concern around protecting sensitive data while it is being worked on has been the primary reason holding back many organizations from saving on IT infrastructure costs by delegating certain computations to the cloud and from sharing sensitive data with collaborators. Confidential Computing and Fully Homomorphic Encryption (FHE) are promising emerging technologies for addressing this concern and enabling organizations to unlock the value of sensitive data. This paper explores and contrasts both technologies.

Vikas Sharma

(Sr. Principal Engineer and Sr. Director, Pathfinding, INTEL R&D)

Bio: Dr. Vikas Sharma is Sr. principal engineer and Sr. Director of Pathfinding in Intel R&D, responsible for developing math algorithms in AI/ML for a variety of business problems across Intel. Vikas has focused on problems ranging from product planning, development, execution, valuation, Intel Capital investment modeling, new revenue streams for Intel and yield improvement. Associated algorithms are patented or deemed Intel secret, and add significantly to Intel top and bottom lines. Previously, Vikas led the development of many innovative test solutions over 10+ Silicon technology nodes. Vikas earned a doctorate from the Massachusetts Institute of Technology with a focus on combining physics models, statistical modeling and predictive algorithms. He also has a Masters from MIT with a focus on housing construction, and a bachelors from the Indian Institute of Technology, Delhi, with a thesis on robot design and vibration analysis.

Title of the talk: Data and algorithms help leaders solve problems

Noam Aigerman

(Research Scientist , ADOBE Research)

Bio: Dr. Noam Aigerman is a research scientist in Adobe. He conducts fundamental research in computer graphics, geometry processing, deep learning, and optimization, with a focus on the interplay between these fields. His research is geared towards providing techniques for solving practical 3D problems, which can be used in creative tools and the movie industry. His work is published in venues such as CVPR, SIGGRAPH, ICCV and ECCV. Before joining Adobe, Noam earned his PhD from the Weizmann Institute of Science under the supervision of Prof. Yaron Lipman.

Title of the talk: Deep Learning of 3D geometry made easy

Abstract: Although deep learning has become the dominant ML technique of the last decade by achieving uncanny results which can be used within practical application, the same cannot be said about its performance on 3D geometry. This is due to factors such as irregularity of data, and memory constraints limiting performance in 3D, as opposed to 2D. In this talk I will give a very intuitive review of why that is the case, as well as various approaches taken in my work and papers to circumvent these issues, and provide techniques that push the state of the art in 3D deep learning. The talk does not assume any prior knowledge on 3D geometry processing and minimal knowledge of deep learning.


Important Deadlines

Full Paper Submission:20th September 2021
Acceptance Notification: 7th October 2021
Final Paper Submission:15th October 2021
Early Bird Registration: 15th October 2021
Presentation Submission: 19th October 2021
Conference: 27 - 30 October 2021

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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