Past Events

A Benchmark for Brain Network Analysis with Graph Neural Networks

Monday, November 21, 2022 - 10:00 am
Online

Deepa Tilwani will deliver the talk.
 
One of the most common paradigms for neuroimaging analysis is the mapping of the human connectome utilizing structural or functional connectivity. Due to their proven ability to represent complicated networked data, Graph Neural Networks (GNNs), motivated by geometric deep learning, have recently gained a lot of attention. The best way to create efficient GNNs for brain network research has not yet been thoroughly studied, despite their better performance in many disciplines. This work provides a benchmark for brain network analysis with GNNs, to fill this gap by summarizing the pipelines for building brain networks for both structural and functional neuroimaging modalities and by modularizing the execution of GNN designs. Overview Paper.
 
Zoom Link

Meeting ID: 860 1921 3021
Passcode: 12345

Towards Safe and Trustworthy Cyber-Physical Systems

Friday, November 18, 2022 - 02:20 pm
Online

Virtual Meeting Link

Abstract:
Cyber-physical systems (CPS) are smart systems that include co-engineered interacting networks of physical and computational components. Prominent examples of CPS include autonomous robots, self-driving cars, smart cities, and medical devices. CPS are increasingly everywhere, providing new capabilities to improve quality of life and transform many critical areas. However, significant challenges are posed for assuring the safety and trustworthiness of CPS. In this talk, I will present some of my recent work to tackle these challenges , such as Trust in Human-CPS, and Safety of AI-enabled CPS.

Speaker's Bio: 
Lu Feng is an Assistant Professor at the Department of Computer Science and the Department of Engineering Systems and Environment at the University of Virginia. Previously, she was a postdoctoral fellow at the University of Pennsylvania and received her PhD in Computer Science from the University of Oxford. Her research focuses on assuring the safety and trustworthiness of cyber-physical systems, spanning many different application domains, from autonomous robots to smart cities to medical systems. She is a recipient of NSF CAREER Award.
Webpage: http://www.cs.virginia.edu/~lufeng/
 

Regression analysis of arbitrarily censored data subject to potential left truncation

Friday, November 11, 2022 - 02:30 pm
Storey Innovation Center 1400

Online Meeting Link

Abstract: 
Survival analysis is a branch of statistics that studies time-to-event data or survival data. The main feature of survival data is that the response variable is only partially observed and subject to censoring and/or truncation caused by the nature of study design. In this talk, I will briefly discuss different types of survival data and existing popular semiparametric survival models in the literature. Then I will discuss my recent work in detail on regression analysis of arbitrarily censored data and left truncated data. The proposed estimation approaches are developed based on EM algorithms and enjoy several nice properties such as being easy to implement, robust to initial values, fast to converge, and providing variance estimates in closed form.

Speaker's Bio:
Dr. Lianming Wang is an Associate Professor in the Department of Statistics at University of South Carolina. His research areas include survival analysis,  longitudinal data analysis, categorical data analysis, multivariate analysis, statistical computing, nonparametric and semiparametric modeling, and biomedical applications. His research goal is to develop sound statistical approaches for analyzing complex data with various structures in real life studies of all fields.
 

Learning Object Detection from Repeated Traversals

Friday, November 4, 2022 - 02:20 pm
Online

Virtual Meeting Link


Abstract:
Recent progress in autonomous driving has been fueled by improvements in machine learning. Ironically, most autonomous vehicles do not learn while they are in operation. If a car is used in the same location multiple times, it will act identically every single time. We propose to leverage and learn from repetition by allowing a neural network to save some of its activations in a geo-referenced data base that can be retrieved later on. If a vehicle is used in the same location multiple times, it builds up a rich data set of past network activations that aid object detection in the future. This allows it to recognize objects from afar when they are only perceived by a few pixels or LiDAR points. We further demonstrate that it is in fact possible to completely bootstrap an object detection classifier only based on repetition. Our approach has the potential to drastically improve the accuracy and safety of self-driving cars, enable them for sparsely populated areas, and allow them to adapt naturally to their local environment over time.

Speaker's Bio: 
Kilian Weinberger is a Professor in the Department of Computer Science at Cornell University. He received his Ph.D. from the University of Pennsylvania in Machine Learning and his undergraduate degree in Mathematics and Computing from the University of Oxford. During his career he has won several best paper awards at ICML (2004), CVPR (2004, 2017), AISTATS (2005) and KDD (2014, runner-up award). In 2011 he was awarded the Outstanding AAAI Senior Program Chair Award and in 2012 he received an NSF CAREER award. He was elected co-Program Chair for ICML 2016 and for AAAI 2018 and currently serves as a board member and president-elect of the ICML society. In 2016 he was the recipient of the Daniel M Lazar '29 Excellence in Teaching Award. In 2021 he became a finalist for the Blavatnik National Awards for Young Scientists. Kilian Weinberger's research focuses on Machine Learning and its applications, in particular, metric learning, Gaussian Processes, computer vision, perception for autonomous vehicles, and deep learning. Before joining Cornell University, he was an Associate Professor at Washington University in St. Louis and before that he worked as a research scientist at Yahoo! Research in Santa Clara.
 

Applications of Machine Learning for Improved Patient Selection and Therapy Recommendation

Monday, October 31, 2022 - 06:00 pm
Online

DISSERTATION DEFENSE

Author : Brendan Odigwe

Advisor : Dr. Homayoun Valafar

Date : Oct 31, 2022

Time: 6:00 pm

Place : Meeting Link

Abstract

The public health domain continues to battle with illness and the growing need for continuous advancement in our approach to clinical care. Individuals experiencing certain conditions undergo tried and tested therapies and medications, practices that have become the mainstay and standard of care in clinical medicine. As with all therapies and medications, they don't always work the same way and do not work for everyone. Some Treatment regimens come with some adverse side effects due to the nature of the medication. This would be particularly disappointing if the patients must be subjected to such medications without improving their health and quality of life. Asides from the physical toll patients could be subjected to; there is the matter of the economic impact of these therapies on the patients, their family members, insurance companies and even the government. Some life-saving therapies are cost intensive in addition to requiring risky, invasive procedures. It would be great if we had more ways of identifying patients that are most likely to receive significant benefits from recommended therapies before they are subjected to them. The datasets used in our work were varied in size as well as the hypothesis guiding our experiments, and as such, our approach to predictive analysis also varied. We have employed a series of machine learning techniques to create models that can indicate a patient's response pattern to recommended therapy. To ensure that our approaches are widely applicable, we have investigating multiple pressing healthcare problems, namely; Chronic Kidney Disease, Heart Failure, Sickle Cell Anemia, and Peripheral Arterial Disease. These approaches and others like it will positively influence medical decision-making, and administration of intervention procedures, and further the practice of precision medicine. The approaches and the rules generated produce a means of prioritizing patient data parameters and present us with the opportunity to extend medical practice and ultimately improve patient outcomes.

Human Activity Recognition (HAR) Using Wearable Sensors and Machine Learning 

Monday, October 31, 2022 - 03:00 pm
2265 Innovation

DISSERTATION DEFENSE 

Author : Chrisogonas Odero Odhiambo 

Advisor : Dr. Homayoun Valafar 

Date : Oct 31, 2022 

Time: 3:00 pm  

Place : 2265 Innovation and Teams

Teams Meeting Link

Abstract 

Humans engage in a wide range of simple and complex activities. Human Activity Recognition (HAR) is typically a classification problem in computer vision and pattern recognition, to recognize various human activities. Recent technological advancements, the miniaturization of electronic devices and the deployment of cheaper and faster data networks have propelled environments augmented with contextual and real-time information, such as smart homes and smart cities. These context-aware environments, alongside smart wearable sensors, have opened the door to numerous opportunities for adding value and personalized services to citizens. Vision-based and sensory-based HAR find diverse applications in healthcare, surveillance, sports, event analysis, Human-Computer Interaction (HCI), rehabilitation engineering, occupational science, among others, resulting into significantly improved human safety and quality of life. 

Despite being an active research area for decades, HAR still faces challenges in terms of gesture complexity, computational cost on small devices, energy consumption, as well as data annotation limitations. In this research, we investigate methods to sufficiently characterize and recognize complex human activities, with the aim to improving recognition accuracy, reducing computational cost, energy consumption, and creating a research-grade sensor data repository to advance research and collaboration. This research examines the feasibility of detecting natural human gestures in common daily activities. Specifically, we utilize smartwatch accelerometer sensor data and structured local context attributes, and apply AI algorithms to determine the complex activities of medication-taking, smoking, and eating gestures 

A major part of my work centers around modeling human activity and the application of machine learning techniques to implement automated detection of specific activities using accelerometer data from smartwatches. Our work stands out as the first in modeling human activity based on wearable sensors in a linguistic representation with grammar and syntax to derive clear semantics of complex activities whose alphabet comprises atomic activities. We apply machine learning to learn and predict complex human activities. I demonstrate the use of one of our unified models to recognize two activities using smartwatch: medication-taking and smoking. 

Another major part of my work addresses the problem of HAR activity misalignment through edge-based computing at data origination point, leading to improved rapid data annotation, albeit with assumptions of subject fidelity in demarcating gesture start and end sections. Lastly, I propose a theoretical framework for the implementation of a library of shareable human activities. The results of this work can be applied in the implementation of a rich portal of usable human activity models, easily installable in handheld mobile devices such as phones or smart wearables to assist human agents in discerning daily living activities. This is akin to a social media of human gestures or capability models. The goal of such a framework is to domesticate the power of HAR into the hands of everyday users, as well as democratize the service to the public by enabling persons of special skills to share their skills or abilities through downloadable usable trained models. 

Semantics-based Data Security Models   

Monday, October 31, 2022 - 02:00 pm
online

DISSERTATION DEFENSE

Author : Theppatorn Rhujittawiwat

Advisor : Dr. Csilla Farkas

Date : Oct 31, 2022

Time :  2:00 - 3:30 pm

Meeting Link


Abstract

In this dissertation, we studied how an adversary could attack databases and how the system could prevent or recover from such an attack. Our motivation to improve the current security capabilities of database management systems. We provided better recovery capabilities of database management systems by incorporating data provenance. We also expand our study to express security and privacy needs of data in the Internet of Things (IoT) environments such as a smart home environment. For this, we proposed a stream data security model to theoretically represent the data in the IoT network. We built a dynamic authorization model on our context-aware architecture and stream data model. We demonstrated the capabilities of our dynamic security policy to address security needs due to the changes in the context. Furthermore, we demonstrated the applicability of our approach by implementing our framework in a smart home IoT network. For our proof-of-concept implementation, we used a commercial and open-source home automaton software. Our approach to improve the system is expanding it by incorporating third party applications, such as a dynamic access control engine. We aim to incorporate a logic reasoner into smart home automaton to provide situation-aware capabilities to the system in this study.

Empirical Studies on Automated Software Testing Practices   

Monday, October 31, 2022 - 10:30 am
Online

DISSERTATION DEFENSE

Author : Alireza Salahirad

Advisor : Dr. Gregory Gay

Date : Oct 31, 2022

Time :  10:30 am

Place : Virtual (teams/zoom link below)

Meeting Link


Abstract

 

 Software testing is notoriously difficult and expensive, and improper testing carries economic, legal, and even environmental or medical risks. Research in software testing is critical to enabling the development of the robust software that our society relies upon. This dissertation aims to lower the cost of software testing, with a focus on the use of automation to lower the cost of testing without decreasing the quality. The dissertation consists of three empirical studies on aspects of software testing. Specifically, these three projects focus on (1) mapping the connections between research topics and the evolution of research topics in the field of software testing, (2) an assessment of the metrics used to guide automated test generation and the factors that suggest when automated test generation can detect real faults, and (3) examination of the semantic coupling between synthetic and real faults in service of improving our ability to cost-effectively generate synthetic faults for use in assessing test case quality.

   Project 1 (Mapping): Our main goal for this project is to better understand the emergence of individual research topics and the connection between these topics within the broad field of software testing, enabling the identification of new topics and connections in future research. For achieving this goal, we have applied co-word analysis in order to characterize the topology of software testing research over three decades of research studies, based on the keywords provided by the authors of studies indexed in the Scopus database.

    Project 2 (Automated Input Generation): We have assessed the fault-detection capabilities of unit test suites generated by automated tools with the goal of satisfying eight fitness functions representing common testing goals. Our purpose was to not only identify the particular fitness functions that detect the most faults, but to further explore the factors that influence fault detection. To do this, we gathered observations on the generated test suites and metrics describing the source code of the faulty classes and applied a rule-learning algorithm to identify the factors with the strongest influence on fault detection.

    Project 3 (Mutant-Fault Coupling): Synthetic faults (\textit{mutants}), which can be inserted into code through transformative \textit{mutation operators}, offer an automated means to assess the effectiveness of test suites and create new test cases. However, mutants can be expensive to utilize and may not realistically model real faults. To enable the cost-effective generation of mutants, we investigate this semantic relationship between mutation operators and real faults.

Physical Layer Reliability for Air-Ground and Air-Air Networking

Friday, October 28, 2022 - 02:20 pm
Storey Innovation Center 1400

In-Person Meeting Location:
Storey Innovation Center 1400
 
Live Meeting Link for Virtual Audience

Abstract:
Aviation is growing rapidly, and the need for reliable and robust wireless signaling for communications, navigation, and surveillance is growing accordingly. As with all communication systems, the physical layer (PHY) forms the foundation—higher-layer operation is irrelevant if the PHY fails. In this talk, we briefly describe the growth in aviation and in related areas of wireless communications, some nascent applications and national/international programs aimed to support these applications, and then turn our attention to the PHY, where we describe some of the unique challenges of aviation networking, focusing on the air-ground and air-air channels themselves. These elements of the communication system can be rapidly time-varying, distorting, and lossy, hence quantification of channel effects is critical to enable design of effective PHY techniques to ameliorate them. We show example results from a prior and recently-completed NASA projects that illustrate some of these challenges. The talk concludes with a summary and identification of some key future work.

Speaker's Bio:
David W. Matolak received the B.S. degree from The Pennsylvania State University, M.S. degree from The University of Massachusetts, and Ph.D. degree from The University of Virginia, all in electrical engineering. He has over 25 years’ experience in communication system research, development, and deployment, with industry, government institutions, and academia, including AT&T Bell Labs, L3 Communication Systems, MITRE, and Lockheed Martin. He has over 250 publications and nine patents. He was a professor at Ohio University (1999-2012), and since 2012 has been a professor at the University of South Carolina. He has been Associate Editor for several IEEE journals, and has delivered several dozen invited presentations at a variety of international venues. His research interests are radio channel modeling, communication techniques for non-stationary fading channels, and secure and covert communications. Prof. Matolak is a Fellow of the IEEE, a member of standards groups in RTCA and ITU, and a member of Eta Kappa Nu, Sigma Xi, Tau Beta Pi, URSI, ASEE, and AIAA.
 

Harnessing Mean Field Game and Physics-Informed Deep Learning for Emerging Transportation Modeling

Friday, October 21, 2022 - 02:20 pm
Seminar in Advances in Computing

Virtual Meeting Link

Abstract:

Emerging transportation technology is expected to revolutionize the future transportation ecosystem. My research aims to understand the social implications of these emerging technologies in transportation and develop optimal interventions to achieve desirable outcomes. In this talk, I will present two topics: to design optimal controls for autonomous vehicles, and to introduce physics-informed deep learning into classical transportation problems. In the first topic, I will talk about how I address the technological challenges of vehicle automation leveraging the core concepts of game theory, control, and machine learning. In the second topic, a novel framework - physics-informed deep learning - will be introduced and applied to traffic state estimation and uncertainty quantification.

 

Speaker's Bio: 

Dr. Xuan (Sharon) Di is an Associate Professor in the Department of Civil Engineering and Engineering Mechanics at Columbia University in the City of New York and serves on a committee for the Smart Cities Center in the Data Science Institute. Prior to joining Columbia, she was a Postdoctoral Research Fellow at the University of Michigan Transportation Research Institute (UMTRI). She received her Ph.D. degree from the Department of Civil, Environmental, and Geo-Engineering at the University of Minnesota, Twin Cities in 2014. Dr. Di received a number of awards including NSF CAREER, the Transportation Data Analytics Contest Winner from Transportation Research Board (TRB), the Dafermos Best Paper Award Honorable Mention from the TRB Network Modeling Committee, Outstanding Presentation Award from INFORMS, and the Best Paper Award and Best Graduate Student Scholarship from North-Central Section Institute of Transportation Engineers (ITE). She also serves as the reviewer for a number of journals, including IEEE ITS, Transportation Science, Transportation Research Part B/C/D, European Journal of Operational Research, Networks and Spatial Economics, and Transportation.

Dr. Di directs the DitecT (Data and innovative technology-driven Transportation) Lab @ Columbia University. Her research lies at the intersection of game theory, dynamic control, and machine learning. She is specialized in emerging transportation systems optimization and control, shared mobility modeling, and data-driven urban mobility analysis. Details about DitecT Lab and Prof. Sharon Di’s research can be found in the following link: http://sharondi-columbia.wixsite.com/ditectlab