CORPORATE TALK SERIES
(Senior Graphics Researcher at Pixel Labs)
Bio: Yixin Hu is now a senior Graphics Researcher at Pixel Lab, Tencent America. She obtained her doctoral degree from Courant Institute of Mathematical Sciences, New York University where she joined Geometric Computing Lab and started working with professor Daniele Panozzo. Her research interests are Computer Graphics and 3D Geometry Processing. Her research has been recognized by Adobe Research Fellowship (2019), Jacob T. Schwartz Ph.D. Fellowship (2019), and Sandra Bleistein Prize (2021).
Title for Talk: WildMeshing: Unstructured Mesh Generation and Repairing in the Wild
Abstract: WildMeshing is a series of meshing algorithms I proposed during my PhD, which solved a long-standing yet fundamental problem in Geometry Processing: generating high-quality triangle/tetrahedral meshes and repairing imperfect geometries robustly and automatically. WildMeshing has been tested on over ten thousand real-world inputs and achieved a 100% success rate. The implementation of WildMeshing is open-source and is currently used in the released products of companies like nToppology, AREVO, Mechanical Finder, and Rhino3D. WildMeshing can also be used as a black-box for generating large collections of clean geometries for geometric deep learning.
Bio: Sophia Paleologou is a seasoned IT executive with over 20 years of experience developing software platforms for a wide spectrum of applications. She has been an integral part of the design and development of several large, enterprise-grade commercial products. She is currently the Chief Technology Officer of UNS, an identity and access management solution provider. Sophia holds a BS in
Mathematics from the University of Athens, Greece and a PhD in Computer Science from Yale University. She has also co-authored multiple patents in Cryptography and Error Correction.
Title for Talk: Enabling Better Medical Research through Privacy-Preserving Record Linking
Abstract: The outcomes of medical research heavily rely on the quality of the data it is based on. Although healthcare providers collect a vast amount of good-quality medical data, they
are severely restricted in how they can share this data because of existing privacy regulations. Before patient data can be shared for research purposes, it has to be
stripped from all information that could potentially be used to identify the associated patients. Once personal identifiable information (PII) is stripped, linking of medical data
across healthcare providers becomes very challenging. In this work, we present a solution that uses a new cryptographic primitive to facilitate linking of medical data without using PII. We also present an implementation of our novel solution over a network of security nodes that further bolsters the security and privacy characteristics of our solution as well as the results of a series of pilot tests engaging with a small number of providers and a state registry that explore potential integration and adoption challenges.
(Research Scientist at Facebook AI Research)
Bio: Roberto Calandra is a Research Scientist at Facebook AI Research. Previously, he was a Postdoctoral Scholar at the University of California, Berkeley (US) in the Berkeley Artificial Intelligence Research Laboratory (BAIR) working with Sergey Levine. His education includes a Ph.D. from TU Darmstadt (Germany) under the supervision of Jan Peters and Marc Deisenroth, a M.Sc. in Machine Learning and Data Mining from the Aalto university (Finland), and a B.Sc. in Computer Science from the Università degli studi di Palermo (Italy). His scientific interests are broadly at the conjunction of Decision-making, Robotics and Machine Learning. Research topics that he is currently developing include Model-based Reinforcement Learning, Tactile Sensing, Morphology Adaptation, and Bayesian Optimization.
|Full Paper Submission:||23rd September 2022|
|Acceptance Notification:||6th October 2022|
|Final Paper Submission:||13th October 2022|
|Early Bird Registration||15th October 2022|
|Presentation Submission:||20th October 2022|
|Conference:||26 - 29 October 2022|
• Conference Proceedings will be submitted for publication at IEEE Xplore® digital library.
• Best Paper Award will be given for each track.
• Conference Record No 53757