August 2023

Young Scholar TechTalk – Secure and High-performance AI Serving: Protecting AI Secretes, Accelerating AI Insights

Innovation Wing Two, TechTalk / By

September 19, 2023 (Tuesday) 4:30-5:30pm
Driven by the remarkable success of artificial intelligence (AI) and edge computing, the deployment of well-trained private AI models on third-party edge devices for mission-critical applications has become increasingly prevalent. Safeguarding these private models on untrusted devices, while simultaneously speeding up model serving (i.e., inference) through accelerators like GPUs, has escalated in urgency.
We introduce SOTER, a new AI serving system that, for the first time, achieves both high security and high performance. Harnessing the associativity property of AI operators, SOTER presents an innovative approach—transforming computationally expensive AI operators into parameter-morphed equivalents for secure execution on untrusted but fast GPUs, and losslessly restoring inference results within trusted execution environments (TEEs) in CPUs. Experimental results on six prevalent AI models in the three most popular categories show that, even with stronger model protection, SOTER achieves comparable performance with baselines while retaining the same high accuracy as insecure AI model inference.

SIG – NestSpace

Projects, SIG project / By

The history of civilization originates from the progression of human technology. However, technology is never the sole driver of social innovations. No engineering marvel nor innovative inventions speak for themselves, and neither can the masses who will benefit the most from this technology know how to harness its power. NestSpace believes that it is the ministry of society for technology to manifest itself in ergonomic, responsible, and modern ways.

SIG – Design, Build and Fly

Projects, SIG project / By

HKU Design, Build & Fly (DBF) Team is formed by undergraduate students of the University of Hong Kong. The team joins the American Institute of Aeronautics and Astronautics (AIAA) Design/Build/Fly Competition and the British Model Flying Association (BMFA) University and Schools Payload Challenge each year. Last year, champion and 1st runner-up awards were taken by the DBF teams in the BMFA competition. In 2019, the best result of the Hong Kong teams was established by the HKU DBF Team, ranking 13th among the 104 eligible teams participating in the AIAA competition. It was the first rank among teams from East Asia. Supervised by the Faculty Supervisor from the Department of Mechanical Engineering, the DBF team is supported by the Tam Wing Fan Innovation Wing under the Faculty of Engineering.

TechTalk – Simulation, Optimization and Artificial Intelligence for On-demand Ride Service Operations

Innovation Wing Two, TechTalk / By

September 14, 2023 (Thursday) 4:30-5:30pm
On-demand ride services or ride-sourcing services, offered by transportation network companies like Uber, Lyft and Didi, have been experiencing fast development and steadily reshaping the way people travel in the past decade. Various mathematical models and optimization algorithms, including reinforcement learning approaches, have been developed in the literature to help ride-sourcing platforms design better operational strategies to achieve higher operational efficiency. However, due to cost and reliability issues (implementing an immature algorithm for real operations may result in system turbulence), it is commonly infeasible to validate these models and train/test these optimization algorithms within real-world ride sourcing platforms. Acting as a useful test bed, a simulation platform for ride-sourcing systems will thus be very important for both researchers and industrial practitioners to conduct algorithm training/testing or model validation through trails and errors. While previous studies have established a variety of simulators for their own tasks, it lacks a fair and public platform for comparing the models/algorithms proposed by different researchers. In addition, the existing simulators still face many challenges, ranging from their closeness to real environments of ride-sourcing systems, to the completeness of different tasks they can implement. To address the challenges, we propose a novel multi-functional and open-sourced simulation platform for ride-sourcing systems, which can simulate the behaviors and movements of various agents (including drivers and passengers) on a real transportation network. It provides a few accessible portals for users to train and test various optimization algorithms, especially reinforcement learning algorithms, for a variety of tasks, including on-demand matching, idle vehicle repositioning, and dynamic pricing. Evaluated by experiments based on real-world datasets, the simulator is demonstrated to be an efficient and effective test bed for various tasks related to on-demand ride service operations.

HKU Robocon 2023

Industrial and Manufacturing Systems Engineering, SIG project / By

The annual Robocon competition project allows HKU engineering students from different disciplines to design and fabricate innovative robots with an integration of various advanced technologies, including IoT sensors, AI, computer vision, and mobile computing. Besides, it provides hands-on experiences on product design, prototyping, CNC machining, design and fabrication of electric circuit and PCB, control program development, etc.

Young Scholar TechTalk – Modeling Uncertainty of Connected Vehicle Penetration Rate: Theory and Application

Innovation Wing Two, TechTalk / By

September 12, 2023 (Tuesday) 4:30-5:30pm
The rapid development of communication technologies enables real-time information exchange between vehicles, thus being virtually connected. However, the full connected vehicle (CV) deployment will take a long time and may never be achievable, due to privacy, security, and willingness. Knowledge of the CV penetration rate is thus crucial for realizing numerous beneficial applications during the prolonged transition period. Although several novel models have been proposed for CV penetration rate estimation, they are solely point estimators. Direct use of these point estimators without considering their variability can lead to biased models or suboptimal solutions. To bridge this research gap, this study proposes a series of analytical models to accurately estimate the variability of CV penetration rate. Comprehensive VISSIM simulations, real-world applications, and a simple CV-based adaptive signal control scheme demonstrate the readiness of the models for use in real-world situations and the potential of the models to improve system optimizations.

Ultrastrong and multifunctional aerogels with hyperconnective network of composite polymeric nanofiber

Innovation Wing Two, Third exhibition / By

Aerogels are lightweight materials with extensive microscale pores, which could be used in thermal insulation, energy devices, aerospace structures, as well as emerging technologies of flexible electronics. However, traditional aerogels based on ceramics tend to be brittle, which limits their performance in load-bearing structures. Due to restrictions posed by their building blocks, recently developed classes of polymeric aerogels can only achieve high mechanical strength by sacrificing their structural porosity or lightweight characteristics.

TechTalk – Quantitative Predictive Theories through Integration of Quantum, Statistical, and Irreversible Thermodynamics

Innovation Wing Two, TechTalk / By

August 28, 2023 (Monday) 2-3pm
Thermodynamics is a science concerning the state of a system, whether it is stable, metastable, or unstable. Its derivatives to natural variables give fundamental physico-chemical properties of the system. It is historically divided into four categories: equilibrium thermodynamics by Gibbs, statistical thermodynamics by Gibbs and Landau, irreversible thermodynamics by Onsager and Prigogine, and quantum mechanics. The development of density function theory (DFT) enabled the quantitative prediction of properties of the ground state of a system from quantum mechanics. Their integration into predictive theories will be discussed in this presentation along with future perspectives. It will be shown that the zentropy theory combines the bottom-up DFT predictions with the revised top-down statistical thermodynamics, while the theory of cross phenomena keeps the entropy production due to irreversible processes in the combine law of thermodynamics to revise the Onsager flux equations. The zentropy theory is capable of quantitatively predicting free energy landscape, singularity and emergent divergences of properties at critical point free of parameters, while the theory of cross phenomena can predict the coefficients of internal processes between non-conjugate variables.