Prof. Eric Xing from CMU

We are pleased that Professor Eric Xing from CMU visited McGill and our lab.

Dr. Eric Xing is a Professor of Machine Learning in the School of Computer Science at Carnegie Mellon University, and Director of the CMU/UPMC Center for Machine Learning and Health. His principal research interests lie in the development of machine learning and statistical methodology, and large-scale computational system and architecture; especially for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds in artificial, biological, and social systems. Professor Xing received a Ph.D. in Molecular Biology from Rutgers University, and another Ph.D. in Computer Science from UC Berkeley. He servers (or served) as an associate editor of the Annals of Applied Statistics (AOAS), the Journal of American Statistical Association (JASA), the IEEE Transaction of Pattern Analysis and Machine Intelligence (PAMI), the PLoS Journal of Computational Biology, and an Action Editor of the Machine Learning Journal (MLJ), the Journal of Machine Learning Research (JMLR). He was a member of the DARPA Information Science and Technology (ISAT) Advisory Group, a recipient of the NSF Career Award, the Sloan Fellowship, the United States Air Force Young Investigator Award, and the IBM Open Collaborative Research Award. He served as the Program Chair of ICML 2014.

Dr. Pieter Johannes Mosterman visit

We are pleased that Dr. Pieter Johannes Mosterman from MathWorks visited our lab.

Pieter Johannes Mosterman is Senior Research Scientist at MathWorks in Natick, Massachusetts. He also holds an Adjunct Professorship at the School of Computer Science at McGill University in Montreal, Canada. His primary research interests are in Computer Automated Multi-paradigm Modeling with principal applications in design automation, training systems, and fault detection, isolation, and reconfiguration.

Current projects

VSmart: DSRC-based Smart Vehicle Testbed

VSmart is a DSRC-enabled smart vehicle testbed established at Cyber-Physical Systems Laboratory (CPS-Lab), McGill University, and is supported by NSERC and General Motors Company. VSmart explores and illustrates the potential to enhance driving safety and traffic efficiency with V2V communications (especially DSRC).


Sustainable Datacenters

The computing capacity and scale of data centers are increasing to meet the soaring demand for IT applications and services. Mega data center can host thousands of servers and require up to tens of megawatts of electricity. The high power consumption causes two serious consequences. First, data center operators may face millions of dollars annually charged by the electrical grid. Second, the enormous energy consumption can lead to negative environmental impacts. This project focuses on data center sustainability, to cut operational cost and increase renewable integration for data centers.


AppAudit: Detecting Data-leaking Mobile Apps

We design AppAudit, a program analysis framework that checks if an Android application leaks sensitive personal data. AppAudit is designed with minimalism, using least possible memory and least amount of time. Current prototype could vet a real app with 256MB memory in 5 seconds on average. AppAudit can be used for three use cases:
  • mobile app developers could use AppAudit to check if their apps include any data-leaking libraries or modules
  • the app market could use AppAudit to vet newly uploaded apps and remove data-leaking ones
  • mobile users could use AppAudit to avoid installing data-leaking apps
AppAudit appears at IEEE Symposium on Security and Privacy (S&P) 2015.


EV: Green Charging

The power flow incurred by electric vehicle(EV) and renewable energy are both crucial to the future smart grid. Yet how to integrated them into power systems remains largely unexplored. In the Cyber-Physical Systems Laboratory at McGill University, we are working on developing a series of innovative technology and market strategies to achieve this integration in an efficient, reliable and real-time manner.


A Study of Facebook Likes

This is a project revealing the flaws of the Facebook Like system. These flaws widely exist in most of the websites that take use of the Facebook Like button, and can be exploited by spammers to automatically generate large amount of fake Likes for profits. Meanwhile, these flaws make legitimate users unintentionally generate Facebook Likes against the online contents they don't like or even have negative feelings about. These flaws endanger the Facebook Like ecosystem and the benefits of both legitimate users and advertisers. Many famous websites, such as FoxNews, abcNews, ESPN, HuffingtonPost etc., are victims of these flaws. We hope this project can help people understand these flaws, and also inspire both the researchers and related companies to address them and eliminate the potential threat.


Apps Drain Battery Because of Memory Leaks

Mobile operating systems embrace new mechanisms that reduce energy consumption for common usage scenarios. The background app design is a repre sentative implemented in all major mobile OSes. The OS keeps apps that are not currently inter acting with the user in memory to avoid repeated app loading. This mechanism improves responsive ness and reduces the energy consumption when the user switches apps. However, we demonstrate that application errors, in particular memory leaks that cause system memory pressure, can easily cripple this mechanism. In this paper, we conduct experi ments on real Android smartphones to 1) evaluate how the background app design improves respon siveness and saves energy; 2) characterize memory leaks in Android apps and outline its energy im pact; 3) propose design improvements to retrofit the mechanism against memory leaks.

SafeVChat: Safety and Security in Online Video Chat Systems

Online video chat systems such as Chatroulette have become increasingly popular as a way to meet and converse one-on-one via video and audio with other users online in an open and interactive manner. At the same time, safety and security concerns inherent in such communication have been little explored. Our research group at University of Colorado, Boulder, USA and McGill University, Montreal, Canada seeks to conduct ground-breaking research in the context of an online video chat system.


ISC: Adult Account Detection on Twitter

The widespread of adult content on online social networks (e.g., Twitter) is becoming an emerging yet critical problem. An automatic method to identify accounts spreading sexually explicit content (i.e., adult account) is of significant values in protecting children and improving user experiences. Traditional adult content detection techniques are ill-suited for detecting adult accounts on Twitter due to the diversity and dynamics in Twitter content. In this article, we formulate the adult account detection as a graph based classification problem and demonstrate our detection method on Twitter by using social links between Twitter accounts and entities in tweets. As adult Twitter accounts are mostly connected with normal accounts and post many normal entities, which makes the graph full of noisy links, existing graph based classification techniques cannot work well on such a graph. To address this problem, we propose an iterative social based classifier (ISC), a novel graph based classification technique resistant to the noisy links. Evaluations using large-scale real-world Twitter data show that, by labeling a small number of popular Twitter accounts, ISC can achieve satisfactory performance in adult account detection, significantly outperforming existing techniques.

mZig: Multi-Packet Reception in ZigBee

mZig is a novel physical layer design that enables a receiver to simultaneously decode multiple packets from different transmitters in ZigBee. As a low-power and low-cost wireless protocol, the promising ZigBee has been widely used in sensor networks, cyber-physical systems, and smart buildings. Since ZigBee based networks usually adopt tree or cluster topology, the convergecast scenarios are common in which multiple transmitters need to send packets to one receiver. For example, in a smart home, all appliances report data to one control plane via ZigBee. However, concurrent transmissions lead to the severe collision problem. The conventional ZigBee avoids collisions using backoff time, which introduces additional time overhead. Advanced methods resolve collisions instead of avoidance, in which the state-of-the-art ZigZag resolves one m-packet collision requiring m retransmissions.
©CPSLab 2015