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Exploring communication complexity in private simultaneous messages protocols, examining quantum advantages and demonstrating gaps between classical and quantum models in secure multiparty computation.
Explores post-compromise security in self-encryption, proving its implications for state size in cloud storage, 0-RTT session resumption, and secure messaging. Presents a secure scheme matching the derived bound.
Explore innovative techniques for database joins that balance privacy and efficiency, overcoming limitations of fully oblivious algorithms in worst-case scenarios.
Explore novel constructions and asymptotic improvements in perfect ORAM/OPRAM performance metrics. Gain insights into advanced cryptographic techniques and their applications.
Explores honey encryption for unconditional security with short keys, presenting a new scheme for semantic security in the standard model and enabling multiple message encryption with a single key.
Explore cutting-edge cryptography research on ZK-PCPs derived from leakage-resilient secret sharing. Learn about innovative constructions combining the best aspects of zero-knowledge proofs and probabilistically checkable proofs.
Celebration of women and nonbinary researchers in computer science, featuring a welcome address and keynote presentation on innovative research and the vision behind Women's Research Day.
Explore cross-layer optimization for energy-efficient AIoT systems, integrating machine learning, wireless communication, signal processing, and VLSI architecture for ultra-low power IoT applications.
Explore integrated descriptions for automated scientific processes, extracting models and protocols for analysis and execution using the Kaemika app across multiple platforms.
Explore innovative approaches to robotic policy learning in spatial action spaces for complex manipulation tasks, addressing challenges and evaluating solutions in simulated and physical environments.
Explore causal inference in single-cell genomics, focusing on differential expression analysis and integrating bulk data for high-resolution insights into complex disease mechanisms.
Explores advancements in safe robot learning, focusing on imitation learning theory, scalable reward inference, and efficient policy evaluation to enable real-world deployment with safety guarantees.
Innovative computer vision algorithms for disease phenotyping across scales, from video-based heart function assessment to spatial transcriptomics and immune cell dynamics, with focus on robust and human-compatible AI.
Explore how formal methods and synthesis techniques can design, explain, and guarantee robot behavior, with examples of modular robots, swarms, and human-robot interaction.
Explore machine learning approaches for protein engineering, focusing on data-driven design to create proteins with desired properties and the challenges of extrapolation in predictive modeling.
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