Yanfang Ye's research page

Research Overview

Research Areas: AI/ML, Data Mining, Cybersecurity, and Public Health.

I and my research group are very passionate about developing novel yet elegant techniques to solve real-world problems that generate broader impacts. By harnessing large-scale, multi-source, multi-modality data, we discover new research problems, propose novel machine learning (especially deep graph learning) models, and develop advanced AI techniques for real-world driven applications in cybersecurity and public health. More specifically, we strive to advance knowledge and science in graph (and multimodal) learning, bridge AI/ML and cybersecurity concentrating on large-scale malware detection, AI security, and study of the evolving underground ecosystem, and develop AI and data-driven techniques to combat the opioid crisis and infectious disease outbreaks.

Integrating humanity and technology, our long-term goal of research is to advance capabilities and trustworthiness of AI to provide state-of-the-art innovations i) to secure cyberspace for its users, and ii) to improve health and well-being of people around the world.

Research Sponsors: National Science Foundation (NSF), Department of Justice (DoJ)/National Institute of Justice (NIJ).

"Innovation, research and education - for a better world!" We deeply appreciate the National Science Foundation (NSF) programs of Information and Intelligent Systems (IIS): Information Integration and Informatics (III), Secure and Trustworthy Cyberspace (SaTC), Office of Advanced Cyberinfrastructure (OAC), and Disrupting Operations of Illicit Supply Networks (D-ISN), and the Department of Justice (DoJ)/National Institute of Justice(NIJ) for the strong support on our research works. THANK YOU!

Current Research and Impacts

Developing New Machine Learning Paradigm Towards Effective yet Efficient Foundation Graph Learning Models

Ongoing Research: As graph data has been ubiquitous in various real-world applications, graph learning has drawn significant attention in recent years. Along this line of research, we discover new research problems, formulat them with rigor and elegance, and advance knowledge and science in graph learning by the development of novel techniques to address the grand challenges of heterogeneity, label-scarcity, noise, incompleteness, and sparseness for effective and robust graph representation learning. Scientific Impact: With significant support from the NSF III program, our research on fundamental graph learning has resulted in over 50 publications on the flagship venues in AI/ML and data science such as TMLR (2023), IEEE TBD (2021-2022), IEEE TKDE (2021), ICLR (2023), ICML (2023), NeurIPS (2021-2023), AAAI (2021-2023, 2019), WWW (2023, 2021, 2019), WSDM (2023), KDD (2020-2022, 2017-2018), CIKM (2019-2023), ICDM (2023, 2019-2020), etc. Notably, we have received the prestigious ACM CIKM 2021 Best Paper Award in Full Paper Track, the ACM CIKM 2021 Best Paper Runner-Up Award in Applied Paper Track, and the SIGKDD 2017 Best Paper Award and SIGKDD 2017 Best Student Paper Award in Applied Data Science Track. Promising Direction: Inspired by the success of foundation language models in applications such as ChatGPT, one can envision the far-reaching impacts that can be brought by a pre-trained Foundation Graph Learning Model (FGLM) with broader applications in the areas such as scientific research, social network analysis, anomaly detection, drug discovery, and e-commerce. Despite the significant progress of pre-trained graph neural networks, there has not yet a FGLM that can achieve desired performance on various graph-learning-related tasks. To bridge this gap, by continuing our effort and with the support of our project recently funded by the NSF, we will design and develop a new machine-learning paradigm (techniques, methods, and models) aiming to jointly solve the cross-task, cross-graph, and cross-domain challenges in graph learning towards effective yet efficient FGLMs, which will help researchers and practitioners in different domains to advance their work in a variety of real-world applications driven by the ubiquitous graph data.

Bridging AI and Cybersecurity for Malware Detection, AI Security, and Study of the Evolving Underground Ecosystem

Ongoing Research: Nowadays, society's overwhelming reliance on complex cyberspace makes its security more important than ever. By seamlessly integrating my expertise on the interdisciplinary areas in both AI and cybersecurity, our work mainly focuses on answering the following research questions: (1) How can we advance AI-driven innovations to protect users against evolving malware attacks? Malware (i.e., malicious software) has been used as a major weapon by cyberthreat actors to launch various attacks, such as the Colonial Pipeline shutdown forced by the ransomware attack in May 2021. By the long-term collaboration with industry partners, we are addressing several key challenges in malware analysis and detection by the development of: i) advanced static/dynamic analysis techniques for effective feature representations of binary executables in both PC and mobile platforms; ii) innovative models to abstract the complex ecosystem of application (app) development; and iii) novel yet effective AI-driven techniques for large-scale malware detection. (2) How can we improve resilience of machine learning models against adversarial attacks? As machine learning (ML) models have been deployed in various applications, the incentive for defeating them increases. In light of this, ranging from shallow learning to deep learning (including deep neural networks and graph neural networks), we conduct original research works by developing novel attack perception models based on diverse mixture of experts and adaptive defenses using randomization techniques and regularization algorithms designed based on min-max optimization strategy to improve resilience of ML models against both poisoning and evasion attacks. (3) How can we have a deep understanding of the evolving underground ecosystem for effective intervention against cybercrimes? Driven by considerable profits, cybercriminals have used various techniques to exploit weak links of cyberspace, who are organized within the online underground ecosystem - i.e., a loose federation of specialists selling capabilities, services, and resources explicitly tailored to Internet abuses. Within the ecosystem, underground markets emerging in the forms of underground forums and dark webs have played a central role for them to exchange knowledge and trade in illicit products or services. Supported by my NSF Career Award, we are developing an AI-driven framework for in-depth investigation of the online underground ecosystem to help secure cyberspace by producing effective interventions against cybercrimes. Research and Industrial Impacts: Supported by the NSF SaTC and OAC programs, our research has resulted in a series of publications on top-tier venues in AI/ML and cybersecurity such as ACM CSUR (2021, 2017), IEEE TNSE (2021), ECIR (2023), ICLR (2023), AAAI (2023), SIGKDD (2022, 2020, 2017-2018), CIKM (2021-2022 & 2019), ICDM (2020), USENIX Security (2022), ACSAC (2017), etc. In particular, our developed techniques and systems have been incorporated into popular commercial cybersecurity products such as Comodo Internet Security that protect millions of users worldwide against evolving malware attacks. Promising Direction: In addition to its bright side, AI can also be abused to do harm (e.g., casualties caused by misleading traffic signals, suicides encouraged by AI generated conversations). How can we enable trustworthy AI in response to the safety issues? Built upon our extensive research on the interdisciplinary areas of AI and cybersecurity, we will explore the new yet challenging direction of AI safety aiming at preventing AI from doing harm.

Advancing AI and Data-driven Techniques to Combat the Opioid Crisis and Infectious Disease Outbreaks

Ongoing Research: By harnessing big data revolutions and developing novel AI techniques, we aim to improve public health with the focus on the following two topics. (1) Developing a holistic framework to combat the opioid crisis. Battling the devastating and lethal opioid epidemic is a national priority. By collaboration with various partners including healthcare professionals and law enforcement, we strive to advance data science and AI to fight the opioid crisis through two-fold research objectives: i) (from user perspective): we are developing novel data-driven models for opioid overprescribing prediction and drug-drug interaction discovery to reduce opioid overdose risks; ii) (from supplier perspective): we are advancing AI technologies to combat online opioid trafficking. (2) Developing robust science-based decision support systems in responses to infectious disease outbreaks like the pandemic. With the large-scale, disease-related data, we are developing a new interactive decision support framework allowing in silico exploration of extensive possible non-pharmaceutical interventions prior to the potential field implementation phase responding to future natural or health-related disasters. Research and Societal Impacts: With significant support from the NSF III and D-ISN programs as well as the DoJ/NIJ grant, the series of our original works have advanced the knowledge and science in the interdisciplinary fields of AI/data science and public health by addressing the critical issues facing our society. Our research has resulted in a series of publications on top-rank venues in AI/ML, data science, and cybersecurity such as Decision Support Systems (2023), ACM TIST (2021), IEEE J-BHI (2020), ICDM (2023), NeurIPS (2021-2022), KDD (2022), CIKM (2020-2021), WWW (2021, 2019), ACSAC (2020). Besides the scholarly impact, we have engaged with local communities and various partners to broaden societal impacts, e.g., via ND WWYFF opportunity, we have engaged with the Drug Enforcement Administration (DEA) and worked with NBC for a filming on the combat against the opioid crisis that will be disseminated to the general public with millions of audiences in the Drug Awareness Month (i.e., Oct 2023), which will help facilitate getting the message out about this very important public health issue. To combat the pandemic, in early March 2020, we developed an AI and data-driven system (named a-Satellite) to provide near real-time COVID-19 risk assessment in the US for community mitigation. After the system was launched for public tests, it has attracted positive media attention by news media outlets like WKYC, Ideastream, Fox8, WTAM, and received the MetroLab Innovation of the Month (May 2020). As of June 19, 2023, the system has had 654,522 users with positive feedback on its utility of dynamic risk estimations, which demonstrates its significant societal impact when facing health-related disasters like the pandemic. Promising Direction: It has been argued that combating opioid epidemic takes long-term commitment and effort. In addition to the medication assisted treatment (MAT), with accessibility of big data and advances of AI technologies, supported by our recently funded NSF grant, we aim to facilitate personalized dietary nutrition in the prevention and intervention against opioid misuse and addiction. We believe for our generation, for the next generation, and the future of this country, the line of this research work is vital and will save lives.

Media Press

Press Coverage: Our works on fondamental research in AI/ML (especially graph learning) and bridging AI and data science with cybersecurity and public health have generated significant industrial and societal impacts, which have attracted positive media attentions by various news media outlets. For examples,

Fighting to Combat the Opioid Crisis with Artificial Intelligence (AI)

Award-winning "What Would You Fight For?" series: Fighting to Combat the Opioid Crisis (10/28/2023)

Our work on the combat against the opioid crisis by advancing AI technologies received the award-winning "What Would You Fight For (WWYFF)?" series, which was promoted by the Drug Enforcement Administration (DEA) and aired on NBC and the stadium during the Notre Dame football game with millions of audiences on October 28, 2023 in the "Red Robbin Week". The story can be accessed here and the filmed video aired on NBC during the football game can be found here.

The University of Notre Dame's award-winning "What Would You Fight For (WWYFF)?" series showcases the work, scholarly achievements, and global impact of Notre Dame faculty, students, and alumni. These two-minute segments, each originally aired during a home football game broadcast on NBC, highlight the University's proud moniker, the Fighting Irish, and tell the stories of the members of the Notre Dame family who fight to bring solutions to a world in need.

After the filmed video was aired, we have been receiving emails from the audiences who generously shared their stories and appreciated our research on the fight against the opioid crisis by advancing AI technologies. For example,

"I wanted to give my heartfelt thanks to Prof. Fanny Ye. I lost my 19-year-old son on April 22nd, 2019. ... Our family has only three boys and my son was the only child with our family name. ... One of our last conversations was how devastated she was to lose her grandson that carried our family name. It was a burden that I carried in silence. Needless to say the loss of my precious son has devastated my life and the lives of my siblings. ... Prof. Fanny Ye, I think what you are doing is phenomenal. I am often invited to various events to commemorate victims of fentanyl poisoning. I think these events are important. However, solutions are the best way we can honor our children. The AI program is amazing. I know that my son and his friends often bought drugs from the black web. ... To the students and staff who are working on this program know that with my tears and prayers I say thank you."