Computer Science

Young Scholar TechTalk – GRAINS: Proximity Sensing of Objects in Granular Materials

October 17, 2023 (Tuesday) 4:30-5:30pm
Proximity sensing is a method of detecting the presence of objects without making physical contact. However, this concept has not been widely explored in the context of granular materials, which are materials composed of small particles like sand or gravel. This is because granular materials have complex properties and the sensing needs to work without the aid of vision. In this presentation, I will introduce a system called GRAINS (Granular Material-Embedded Autonomous Proximity Sensing). GRAINS is designed to sense objects buried within granular materials by utilizing fundamental principles related to the behavior of granules, such as how they flow like a fluid, how they can become jammed. GRAINS uses force signals to determine the proximity of buried objects. It achieves this by analyzing force anomalies that occur when granules become jammed due to their proximity to objects. These force anomalies are learned in real-time by the system using a mathematical technique called Gaussian process regression. To capture these patterns, a probe is moved along a spiral trajectory within the granular material. The results of our experiments demonstrate that GRAINS can adaptively adjust its parameters to effectively work with different types of granules. It can perceive objects in the nearby vicinity, approximately 0.5 to 7 cm ahead, without the need for direct contact with the buried obstacles.
(project page:

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

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.

Young Scholar TechTalk – HOF2 – Interact with Device through Simple and Robust Hand-Over-Face Gesture

Mobile devices have been like an extended part of ourselves, but can we really operate a mobile device just as naturally as how we control our fingers or body? We present HOF2, a novel input modality that uses simple gestures over your face to interact with your device. Unlike other gesture-based modalities, HOF2 is highly robust and can avoid false triggering caused by many unconscious gestures like scratching or wiping, while is still easy, comfortable and natural to use. Moreover, HOF2 is highly available and can be implemented on any mobile phone/tablet/computer with a single camera and without remote servers. In this TechTalk, we will present a live demo on iOS/iPadOS demonstrating the performance of HOF2 scheme in practice and explore some real-life use cases such as virtual conferencing, selfie, or TV controller. We believe there are far more possibilities waiting to be explored with this novel interaction scheme.

TechTalk – Learning Optimal Auctions from Data

The design of optimal auctions for revenue maximization is a central topic in Economics. Classical optimal auction theory assumes that bidders’ values are drawn from a known distribution. In reality, the source of such prior information is really past data. Cole and Roughgarden (2014) modeled past data as i.i.d. samples from the value distribution and asked: How many samples are sufficient/necessary to learn a near optimal auction? This TechTalk will introduce a unified theory that yields sample-efficient algorithms with optimal sample complexity for auctions with homogeneous goods, and state-of-the-art sample complexity for auctions with heterogeneous goods. Unlike conventional statistical learning theory which focuses on the complexity of hypothesis classes, our new theory relies on the simplicity of data distributions and a monotonicity property of these problems.

Young Scholar TechTalk – Learning to Control and Coordinate Hybrid Traffic Through Robot Vehicles at Complex and Unsignalized Intersections

Intersections are essential road infrastructures for traffic in modern metropolises; however, they can also be the bottleneck of traffic flows due to traffic incidents or the absence of traffic coordination mechanisms such as traffic lights. Thus, various control and coordination mechanisms that are beyond traditional control methods have been proposed to improve the efficiency of intersection traffic. Amongst these methods, the control of foreseeable hybrid traffic that consists of human-driven vehicles (HVs) and robot vehicles (RVs) has recently emerged. We propose a decentralized reinforcement learning approach for the control and coordination of hybrid traffic at real-world, complex intersections–a topic that has not been previously explored. Comprehensive experiments are conducted to show the effectiveness of our approach. We show that using 5% RVs, we can prevent congestion formation inside the intersection under the actual traffic demand of 700 vehicles per hour. When there exist more than 50% RVs in traffic, our method starts to outperform traffic signals on the average waiting time of all vehicles at the intersection.

Young Scholar TechTalk – Flexible Learning of Quantum States with Generative Query Neural Networks

Deep neural networks are a powerful tool for the characterization of quantum states. Existing networks are typically trained with experimental data gathered from the specific quantum state that needs to be characterized. In this talk, Mr. Yan Zhu, from Department of Computer Science, will introduce a model of network that can be trained with classically simulated data from a fiducial set of states and measurements, and can later be used to characterize quantum states that share structural similarities with the states in the fiducial set. With little guidance of quantum physics, the network builds its own data-driven representation of quantum states, and then uses it to predict the outcome statistics of quantum measurements that have not been performed yet. The state representation produced by the network can also be used for tasks beyond the prediction of outcome statistics, including clustering of quantum states and identification of different phases of matter.

TechTalk – Causes and Effects in the Microscopic World

Identifying cause-effect relations is a fundamental primitive in a variety of areas of science and technology. The identification of causal relations is generally accomplished through statistical trials where alternative hypotheses about the causal relations are tested against each other. Traditionally, such trials have been based on classical statistics. But while classical statistics effectively describes the behavior of macroscopic variables, it becomes inadequate at the microscopic scale, described by quantum mechanics, where a richer spectrum of causal relations is accessible. In the past years, there has been an increasing interest in the study of causal relations among quantum variables. In this talk, Professor Giulio Chiribella will show that the counterintuitive features of quantum mechanics can be turned to our advantage, providing speed-ups in the identification of causal relations. These speed-ups have applications to the design of automated quantum machines and new quantum communication protocols.

TechTalk – Autonomous Excavation: Manipulation and Perception of Granular Materials

Autonomous excavators are an essential part of the goal of “building the robots that build the world”. One unique problem in autonomous excavation is how to deal with the granular materials like soils and sands, which is seldom studied in robotics. In this talk, Dr. Pan will present his team’s recent work about how to achieve efficient manipulation of soils by optimizing the trajectory of the excavator’s bucket, and how to enable the excavator to be aware of the objects buried in the soils by using a proximity sensing mechanism based on jamming in granular materials.

Young Scholar TechTalk – Subgraph Federated Learning with Missing Neighbor Generation

In computer science, a graph is a network modeling objects and their unique interactions. The graph learning model is a specialized machine learning model that learns on graphs. Similar to traditional machine learning models, a well-performed graph learning model can capture the global data distribution with sufficient and unbiased training data. However, in a distributed subgraph system, most data owners only possess small amounts of the data (small subgraphs) in their local systems and can have unpredictable biases.
In this talk, the speaker will introduce this novel yet realistic setting – subgraph federated learning, which aims to let distributed data owners collaboratively train a powerful and generalized graph learning model without directly sharing their subgraphs. Towards this setting, two major techniques are proposed by the research team. (1) FedSage, which trains a GraphSage model based on FedAvg to integrate node features, link structures, and task labels on multiple local subgraphs; (2) FedSage+, which trains a missing neighbor generator along FedSage to deal with missing links across local subgraphs. Empirical results and theoretical analysis of proposed models respectively demonstrate the effectiveness and prove the generalization ability.

Tech Talk – dPRO: A Generic Performance Diagnosis and Optimization Toolkit for Expediting Distributed DNN Training

Distributed training using multiple devices (i.e., GPU servers) has been widely adopted for learning DNN models over large datasets. However, the performance of large-scale distributed training tends to be far from linear speed-up in practice. Given the complexity of distributed systems, it is challenging to identify the root cause(s) of inefficiency and exercise effective performance optimizations when unexpected low training speed occurs. To date, there exists no software tool which diagnoses performance issues and helps expedite distributed DNN training, while the training can be run using different machine learning frameworks. This paper proposes dPRO, a toolkit that includes: (1) an efficient profiler that collects runtime traces of distributed DNN training across multiple frameworks, especially fine-grained communication traces, and constructs global data flow graphs including detailed communication operations for accurate replay; (2) an optimizer that effectively identifies performance bottlenecks and explores optimization strategies (from computation, communication and memory aspects) for training acceleration. We implement dPRO on multiple deep learning frameworks (PyTorch, TensorFlow, MXNet) and representative communication schemes (AllReduce and Parameter Server architecture). Extensive experiments show that dPRO predicts performance of distributed training in various settings with<5% errors in most cases and finds optimization strategies with up to87.1%speed-up over the baselines.