Online Rumors, Misinformation and Disinformation - The Perfect Storm of COVID-19 and Election 2020
Explores mechanics of online misinformation during COVID-19 and Election2020, highlighting vulnerabilities in current information systems and the threat to democratic societies. Calls for action to address this critical issue.
Privacy by Design for Agile Development - Uber's Approach
Integrating privacy by design principles in agile development at Uber: challenges, strategies, and practical approaches for ensuring data protection in a fast-paced tech environment.
Deeply Learning Derivatives - From Hilbert to Riskfuel
Explore advanced derivatives pricing using deep learning, from Hilbert's 13th problem to modern neural networks for valuing high-dimensional contingent claims in quantitative finance.
On the Role of Data Structure in High-Dimensional Learning
Explores high-dimensional learning challenges, focusing on geometric function classes, optimization aspects, and computational hardness. Discusses algorithmic approaches and implications for deep learning.
PCA for High-Dimensional Heteroscedastic Data
Explore optimal weighting for PCA in high-dimensional heteroscedastic data, improving recovery of underlying components and addressing challenges in modern applications with heterogeneous datasets.
Neural SDEs, Deep Learning and Stochastic Control
Explore neural SDEs, deep learning, and stochastic control in finance, covering optimal transport, neural networks, calibration, and simulations with practical applications.
Privacy Policies - Effective Communication and User Education - Panel Discussion
Panel discussion exploring creative approaches to inform users about data rights beyond traditional privacy policies, featuring experts from tech, law, and privacy advocacy.
Content-Oblivious Trust and Safety Techniques - Survey Results from Online Service Providers
Survey results on trust and safety techniques used by online service providers, highlighting the prevalence of content-oblivious methods and implications for policy debates on anti-abuse measures and end-to-end encryption.
Disinformation: The State of Informational Trust and Integrity - Panel Discussion
Panel discussion on disinformation tactics, actors, and impacts. Experts analyze campaigns, offer tips to spot false information, and explore the security community's role in preserving data integrity.
SEOWNING the Internet - Part 2
Explore advanced SEO techniques and strategies for dominating search engine rankings, presented by industry expert Robert Hansen at LASCON.
Hunting Twitter Bots at Scale - 2018
Explore techniques to identify and track automated Twitter accounts using data collection, attribute analysis, and machine learning classifiers for large-scale bot detection.
Digital Afterlife - Ethical Considerations and Data Legacy
Exploring the ethical implications of digital afterlives, data legacies, and the future of personal information in the age of pervasive internet use.
Mathematical and Computational Challenges in Quantum Computing - IPAM UCLA
Explore mathematical and computational challenges in quantum computing through workshops and collaborations, focusing on empowering mathematics to impact quantum information science and vice versa.
Gunnar Carlsson: Topological Deep Learning
Explore topological data analysis in deep learning, reducing data needs and enhancing transparency. Focuses on image and video analysis, network architectures, and applications in various fields.
How Optimal Transport Can Help Us to Determine Curvature of Complex Networks
Explore Ollivier Ricci curvature in complex networks, its applications in network analysis, and implementation in chemical reaction networks using optimal transport theory.
Cuneyt Akçora - TDA on Networks – Applications and Scalability Issues
Explore Topological Data Analysis on networks, focusing on applications in blockchain for ransomware detection, price prediction, and anomaly detection. Learn about persistent homology and TDAMapper algorithms.
Bala Krishnamoorthy - Steinhaus Filtration and Stable Paths in the Mapper
Explore Steinhaus filtration and stable paths in Mapper, a topological data analysis tool. Learn applications in recommendation systems and explainable machine learning for data-driven insights.
Alexander Wagner - Nonembeddability of Persistence Diagrams into Hilbert Spaces
Explore nonembeddability of persistence diagrams in Hilbert spaces, examining stability, metrics, and implications for machine learning applications in topological data analysis.
Geometric and Topological Data Analysis of Enzyme Kinetics
Explore geometric and topological data analysis techniques applied to enzyme kinetics, focusing on ERK molecular dynamics and its implications for cancer and developmental defects.
Hengrui Luo - Generalized Penalty for Circular Coordinate Representation
Explore dimension reduction using circular coordinates and generalized penalties in topological data analysis. Enhance visualization of high-dimensional datasets while preserving topological structures.
Mean Estimation in High Dimension
Exploring recent advances in high-dimensional mean estimation, focusing on statistical challenges and computational aspects for independent, identically distributed data.