Syed Billah, assistant professor in the Penn State College of Information Sciences in Technology, presented "Reinterpreting Fitts' Law for Non-Visual Interaction” on December 5, 2024, as part of the IST Research Talks series.

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Abstract

Fitts' Law has been a cornerstone of user interface design since the advent of graphical user interfaces. However, its traditional interpretation assumes certain user abilities, potentially limiting its applicability for diverse user groups, particularly those who are blind or have low vision. This talk presents a critical reexamination and reinterpretation of Fitts' Law to overcome these initial assumptions and extend its utility to non-visual interaction paradigms. We will present recent research that builds upon this reinterpretation, showcasing novel interaction techniques such as multi-linear and multi-wheel-based interactions, grid-based interfaces, and abacus-inspired mid-air gestures. We conclude by illustrating how this reinterpretation of Fitts' Law serves as a bridge between traditional HCI research and other disciplines, particularly control theory and reinforcement learning from human feedback, opening new avenues for research and design in accessible human-computer interaction.

About the Speaker Syed Billah’s research focuses on human-computer interaction with a strong emphasis on accessible computing, a topic that is broadly concerned with making computing devices and digital information accessible for people with special needs or people with special situations. Billah investigates the low-level accessibility issues in computer systems and designs efficient, robust, and extendible accessibility supports in modern operating systems. He also develops assistive technologies to make non-visual interaction fast, cross-platform, and ubiquitous. His research promotes equality for people with vision impairments and unlocks their opportunities in education and employment. More recently, he studies the impact and opportunities of AI, 3D fabrication, augmented reality, data sonification, and smart sensing technologies in accessibility and intelligent interactive system research.

Assistant Professor Minhao Cheng, Assistant Professor i n the Penn State College of Information Sciences in Technology, presented "Post-Hoc Security in Machine Learning Systems" on November 6, 2024, as part of the IST Research Talks series.

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Abstract

Machine learning systems, while powerful, remain vulnerable to diverse adversarial threats. Existing defenses, even those widely adopted, are frequently bypassed by more sophisticated attacks. In this talk, I will first demonstrate the vulnerability of a common defense technique against backdoors. Specifically, I will show how machine learning systems can swiftly relearn malicious behavior through minimal exposure, even from simple queries. To bolster machine learning security against such advanced threats, I will introduce my research on building digital forensics frameworks. These frameworks offer post-breach protection, complementing traditional defenses. They enable the tracing of AI-generated content origins, assisting regulators in ensuring the safe and responsible use of machine learning. Our works provides a new perspective, suggesting a shift from a purely preventive security mindset toward a more comprehensive approach that includes robust post-breach analysis and response capabilities.

About the Speaker

Minhao Cheng is an assistant professor in the Penn State College of Information Sciences and Technology. He works at the intersection of security and machine learning, with a particular focus on automated, efficient, and trustworthy machine learning systems. Before joining Penn State, Cheng was an assistant professor of computer science and engineering at Hong Kong University of Science and Technology. He holds a doctoral degree in computer science from the University of California, Los Angeles.

Mahir Akgun and Chris Gamrat, assistant teaching professors in the Penn State College of Information Sciences in Technology, presented "Integrating Advanced Technologies for Personalized and Effective Learning: From Generative AI to Micro-Credentials" on October 3, 2024, as part of the IST Research Talks series.

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Abstract

This session will explore innovative approaches to enhancing educational outcomes by integrating advanced technologies, focusing on two key areas: generative AI and micro-credentials. The first part of the session will discuss how combining pretesting with generative AI tools, such as ChatGPT, can significantly boost students' ability to retain and apply complex information. The second part will delve into the design and implementation of digital badges and micro-credentials, based on a decade of research and practical applications, to support personalized and independent professional learning. Together, these talks will provide valuable insights into optimizing learning environments and personalizing education to meet diverse learner needs.

About the Speakers

Mahir Akgun’s research interests include learning analytics, computer-supported collaborative learning, epistemic agency, knowledge creation and building, and expert heuristics. He earned his doctorate in learning, design, and technology from Penn State and a master’s degree in cognitive science from Middle East Technical University.

Chris Gamrat’s research interests include inclusive design, micro-credentials/digital badges, and the scholarship of teaching and learning. He holds three degrees from Penn State: a doctorate in learning, design, and technology; a master’s of education in instructional systems; and a bachelor’s degree in management information systems. He also serves as Penn State's EDUCAUSE ambassador. Before joining the College of IST, Gamrat served as an education technologist for NASA's Aerospace Education Services Project.

Jinghui Chen, assistant professor in the Penn State College of Information Sciences in Technology, presented "Trustworthy Machine Learning in the Era of Generative AI" on September 5, 2024, as part of the IST Research Talks series.

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Abstract

Generative AI models like ChatGPT have revolutionized data generation, information processing, and problem-solving across many fields. However, despite their impressive performance and vast potential, these models introduce significant challenges regarding trustworthiness, preventing us from reliably deploying them in real-world applications. This talk will explore the emerging trustworthiness challenges in the era of generative AI and share our approaches to addressing these issues. We will focus on two key areas: (1) evaluating and mitigating safety concerns in generative AI, and (2) steering the personality and behaviors of AI models. Additionally, we will touch upon other important directions surrounding the trustworthiness of generative AI.

About the Speaker Jinghui Chen is an assistant professor in the College of Information Sciences and Technology. He earned his doctoral degree in computer science from UCLA in 2021, and a bachelor of science degree in electronic engineering and information science from the University of Science and Technology of China in 2015. Chen’s research interests broadly include theory and applications in different aspects of machine learning, with a particular focus on building efficient and trustworthy machine learning models. His current research explores trustworthiness and safety issues in large language models, security and privacy issues for other emerging machine learning models, and efficient optimization strategies for training large-scale foundation models and federated learning.

Aiping Xiong, associate professor in the Penn State College of Information Sciences in Technology, presented "Hoax Springs Eternal: Exploring an Interdisciplinary Approach to the Mitigation" on March 14, 2024, as part of the IST Research Talks series.

Abrstract

The ubiquitousness of social media platforms and humans’ extended use of them for news consumption paved the way for the proliferation of misinformation in the 2016 and 2020 U.S. presidential elections and the COVID-19 pandemic. More recently, deepfake images and videos are on the rise in social media platforms, engaging disinformation campaigns. The potential that Large Language Models (LLMs), such as ChatGPT, can also be exploited to generate misinformation poses extra concerns regarding the trustworthiness of online content. In this talk, Xiong will describe recent interdisciplinary efforts for misinformation mitigation and discuss challenges and potential solutions for mitigating AI-generated content, such as hallucinations and deepfakes.

About the Speaker Dr. Aiping Xiong is an assistant professor in the Penn State College of Information Sciences and Technology. Her research spans cognitive psychology, human factors, and usable security and privacy. She studies human information processing in various security and privacy settings, examines psychological mechanisms contributing to individuals’ susceptibility and vulnerability, and investigates effective ways to help humans make informed decisions and take meaningful actions. Her research findings have been published in primary journals in cognitive psychology and top-tier conferences in security and privacy, computational social science, and data science. Xiong received her doctorate in cognitive psychology from Purdue University in 2017.

Cindy Lin, assistant professor in the Penn State College of Information Sciences in Technology, presented "Algorithmic Fairness in Collective Decision-Making" on February 8, 2024, as part of the IST Research Talks series.

Abstract

How can we comprehend rapidly shifting environments that exceed both longstanding patterns and historical trends? In Indonesia, environmental scientists and computer engineers in government and industry have turned to data science to broaden their range of emergency responses to fires they can no longer control. This talk draws on more than three years of ethnographic fieldwork with government and industry scientists to argue that environmental governance practices are shifting in the Global South. These changes bear important lessons for ongoing attempts to secure livable futures in the West. 

Prior technologies were designed to monitor and map large-scale shifts in fire patterns and land conditions from afar. However, data science in land mapping and fire prediction has opened new frames of reference for observing and preventing volatile fires. These shifts have shattered public and professional expectations of how environmental disasters can be handled. 

Lin will discuss recent attempts to integrate data science into fire and land mapping and prediction in Indonesia and demonstrate how competing methods to measure environmental change have altered the legitimacy of government science and engineering institutions, with universal claims to cartographic exactitude narrowly pitted against data science, rather than a broader understanding of emergent environmental risks.

About the Speaker Cindy Lin is an assistant professor in the social and organizational informatics area of the Penn State College of Information Sciences and Technology.  Lin’s research draws on long-term fieldwork with environmental scientists, computer engineers, and cloud architects in government and industry to examine the politics of computational labor and data architectures for subterranean peatland fire control in Indonesia.  She is currently working on a book that examines ground truth within the history of machine learning as a shifting political and scientific category. This book asks how ground truth and its claim to accuracy and evidentiality have shaped and been shaped by transnational exchanges of mapping, surveying, and computing expertise between Southeast Asia and the United States.  As an information scholar, Lin’s work is located at the intersection of postcolonial and feminist science and technology studies, critical data studies, the history of computing, and environmental justice. Her work has been published in leading computing venues, including ACM CHI, DIS, and PD, and has been featured in Social Text and CoDesign. Her graduate studies and research have been funded by the National Science Foundation, Dow Sustainability Fellows Program, Rackham Graduate School, and the International Institute at the University of Michigan.  Lin is the co-author of Technoprecarious, a multigraph written with Precarity Lab analyzing the role of digital technology in multiplying precarity. She was also the co-director of DoIIIT, an interactive design and making studio.  Before joining Penn State, Lin was a postdoctoral fellow at the Cornell Atkinson Center for Sustainability, affiliated with the Department of Information Science. She was also a Digital Life Initiative visiting fellow at Cornell Tech. She holds a doctoral degree from The University of Michigan School of Information and a graduate certificate from The University of Michigan’s Science, Technology, and Society Program.

Hadi Hosseini, assistant professor in the Penn State College of Information Sciences and Technology, presented"Algorithmic Fairness in Collective Decision-Making" on January 30, 2024, as part of the IST Research Talks series.

Abstract

As artificial intelligence continues to rapidly transform the ways social, political, and commercial decisions are governed, fairness has become a pivotal concern in algorithmic and collective decision-making. It plays an instrumental role in the allocation of public and private resources both in centralized and distributed settings, ranging from the distribution of scarce medical resources (e.g., vaccines) in federated healthcare and the distribution of tasks in digital gig economy (e.g., ridesharing platforms) to the collective development of foundation models in data-oriented systems. These decisions require input from multiple self-interested entities (a.k.a. agents) that often have conflicting preferences. In this talk, Hosseini will describe some of the nuances in designing fair and robust algorithmic solutions for collective decision-making, give several examples for achieving approximate fairness through techniques that are rooted in economics and computer science, and discuss recent advances in bringing human value judgments into designing fairness axioms.  

About the Speaker Hadi Hosseini is an assistant professor in the College of Information Sciences and Technology at Penn State. A member of the Data Science and Artificial Intelligence area, Hosseini’s research focuses on problems related to artificial intelligence and multiagent systems, including problems at the interface of computer science and economics. He studies different aspects of self-interested players in multiagent systems and develops algorithms and theoretical techniques to achieve certain computational and game-theoretical properties. Hosseini’s research is supported by an NSF CAREER Award, an NSF Medium RI Award, and an NSF CRII Award. He is an associate director of the Center for Artificial Intelligence Foundations and Engineered Systems (CAFE) and affiliated with both the Computer Science Theory Group and the Institute for Computational and Data Sciences. Prior to joining Penn State, Hosseini was an assistant professor in the Department of Computer Science at Rochester Institute of Technology and a postdoctoral research fellow at Carnegie Mellon University. He earned his doctorate in computer science at the University of Waterloo.

Hong Hu, assistant professor in the Penn State College of Information Sciences and Technology, presented "Spotting Syscall-Guard Variables for Data-Only Attacks" on December 12, 2023, as part of the IST Research Talks series. 

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Abstract

In this talk, Hu identifies an important category of critical data—syscall-guard variables—and proposes novel solutions to automatically detect such variables in a scalable manner. Syscall-guard variables determine to invoke security-related system calls—syscalls—and altering them will allow attackers to request extra privileges from the operating system. Hu proposes branch force, which intentionally flips every conditional branch during execution and checks whether new security-related syscalls are invoked. If so, one can conduct data-flow analysis to estimate the feasibility to flip such branches through common memory errors.

About the Speaker Hong Hu is an assistant professor in the College of Information Sciences and Technology of PennState University. His main research area is system and software security, focusing on exploring new attack vectors of memory errors and developing effective defense mechanisms. His work has appeared in top venues of system security, including IEEE S&P, USENIX Security, CCS and NDSS. He received the Best Paper Award from CCS 2019 and ICECCS 2014. Dr. Hu obtained his Ph.D. degree from the National University of Singapore in 2016. After that, he was a postdoctoral fellow and research scientist at Georgia Tech from 2017 to 2020.

Kelley Cotter, assistant professor in the Penn State College of Information Sciences and Technology, presented “Manifesting AI: Mystical Mentalities in Human-Machine Interactions" on November 16, 2023 as part of the IST Research Talks series.

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Abstract

What people know about data-centric technologies shapes human-machine interactions with regard to trust, reliance, agency, and control. While a conventional interpretation of "knowledge" focuses on understanding how a system operates, Cotter positions this as one of many ways of seeing and knowing data-centric technologies.

About the Speaker Kelley Cotter is an Assistant Professor in the College of Information Sciences and Technology at The Pennsylvania State University. She received her Ph.D. in information and media from Michigan State University and a master’s degree in library and information science from Drexel University. Her research explores how data-centric technologies shape social, cultural, and political life, and vice versa. Her most recent work focuses on how people learn about and make sense of algorithms, and how such insight may be mobilized in efforts to govern platforms. Dr. Cotter’s work has been published in New Media & Society, Information, Communication & Society, and the proceedings of the ACM Conference on Human Factors in Computing Systems (CHI).

Xinning Gui, assistant professor in the Penn State College of Information Sciences and Technology, presented "Exploring and Enhancing Transparency and Explainability of Consumer-Facing Health Technology" on October 12, 2023, as part of the IST Research Talks series.

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Xinning Gui Xinning Gui received her B.A. in Chinese Literature and Linguistics from Peking University, her M.A. in Chinese Modern and Contemporary Literature from Beijing Normal University, and completed her Ph.D. in Information and Computer Science from the University of California Irvine. Gui's research focuses on health informatics in the context of human-computer interaction.

Peng Liu, Raymond G. Tronzo, MD Professor of Cybersecurity in the Penn State College of Information Sciences in Technology, presented "AI for Cybersecurity: Current Status and the Role of GPT-4" on September 14, 2023, as part of the IST Research Talks series.

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Abstract

In this talk, Liu will briefly summarize the past 30 years of academic research in systems security. The summary is anchored by a few "broken heart" findings, which partially motivate the applications of AI/ML technologies. Second, he will review the current status of AI for cybersecurity. Finally, Liu will discuss why LLMs such as GPT-4 are likely the biggest X factor in the field of cybersecurity.

About the Speaker Peng Liu received his BS and MS degrees from the University of Science and Technology of China, and his PhD from George Mason University in 1999. Dr. Liu is a Professor of Information Sciences and Technology, founding Director of the Center for Cyber-Security, Information Privacy, and Trust, and founding Director of the Cyber Security Lab at Penn State University. His research interests are in many areas of computer security. He has published a monograph and over 270 refereed technical papers. His research has been sponsored by NSF, ARO, AFOSR, DARPA, DHS, DOE, AFRL, NSA, TTC, CISCO, and HP. He has served as a program (co-)chair or general (co-)chair for over 10 international conferences (e.g., Asia CCS 2010, CNS 2018) and workshops (e.g., ACM MTD 2016).