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Restricted Eavesdropping Analysis in Quantum CryptographyAbstract: Quantum computing is a fast developing field, but it poses threats to the modern cryptography system, thus research in quantum cryptography is of great importance for near-term applications. However, traditional security analysis assumes that the eavesdropper is omnipotent, with her "abilities" only limited by the laws of quantum physics. In this research talk I will introduce my work on "Geometrical Optics Restricted Eavesdropping Analysis of Secret Key Distillation and its applications to practical scenarios", which extended traditional secret key distillation security analysis scheme to a more realistic scenario where the eavesdropper is assumed with a limited power collection ability. Such a restricted-eavesdropping scenario is highly applicable on wireless communication links like wireless microwave or free space optics communications. We will start from a quantum wiretap channel to establish lower bounds and upper bounds based on Hashing Inequality and Relative Entropy of Entanglement. We will then apply this model to realistic channel conditions and analyze eavesdropping and defense strategies from both the eavesdropper's and communication parties' sides. Respective conclusions will be presented and discussed in detail during the presentation.

Ziwen Pan is currently a wireless systems applications engineer, mainly working with auto-testing solutions for Qualcomm chipsets on technologies such as WiFi, BT, GPS, etc. He obtained his Ph.D. degree at the Electrical & Computer Engineering department from the University of Arizona in 2022. His major research work focuses on quantum communication/cryptography, including security analysis of generic secret key distillation schemes and protocol designs for quantum key distributions. He has also worked on other projects such as quantum computation simulation and experimental work on diamond oscillator arrays and microtoroids, FPGA-embedded LDPC channel coding, and entanglement-assisted communication protocol design. He has published in and served as a reviewer for multiple IEEE, Optica (OSA), and APS journals.

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Quantum computing presents many challenges for the programming language community. How can we program quantum algorithms in a way that ensures they behave correctly? In this talk, I will explore how types can be used to enforce various properties of quantum programs. I will highlight my research on combining linear types and dependent ttypes to create more expressive type systems for quantum programming languages. I will also discuss my work on dynamic lifting, which is a construct in Quipper/Proto-Quipper that enables programming quantum algorithms such as magic state distillation and the repeat-until-success paradigm.

Bio: Dr. Frank (Peng) Fu is a postdoctoral researcher at Dalhousie University. He received his Ph.D. from University of Iowa in 2014. His research interests  include type theory, the design and implementation of quantum programming languages. He has served on program committees for international conferences such as FSCD and PLanQC.

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AIISC Seminar (Invited Talk)

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The unprecedented processing demand, posed by the explosion of big data, challenges researchers to design efficient and adaptive machine learning algorithms that do not require persistent retraining and data annotation and avoid learning redundant information. This capability is essential in adopting AI in healthcare and biomedical applications. Inspired by learning techniques of intelligent biological agents, identifying transferable knowledge across learning problems has been a significant research focus to improve machine learning algorithms. Towards this mission, this talk covers how the challenges of knowledge transfer can be addressed through embedding spaces that capture and store hierarchical knowledge.We first focus on the problem of cross-domain knowledge transfer and show how this idea can address the challenges of learning with unannotated data, including, in medical image segmentation.We then investigate the problem of cross-task knowledge transfer in sequential learning settings. Here, the goal is to identify relations and similarities of multiple machine learning tasks to improve performance across tasks that are encountered temporally one at a time. We show how the core idea can help to address catastrophic forgetting and learning from distributed private data.Finally, we focus on potential and new research directions to expand past results.

About the Speaker: Mohammad Rostami is a faculty member at the USC Department of Computer Science with a joint appointment at the Department of Electrical and Computer Engineering. He is alsoa research leadat the USC Information Sciences Institute. Before USC, he received his PhD from the University of Pennsylvania, where he was awarded the Joseph D'16 and Rosaline Wolf Best PhD Dissertation Award. His research focus is on machine learning in data scarce regimes, focusing on practical applications in healthcare.His research has been recognized by several awards, including, IJCAI Distinguished Student Paper Award, AAAI New Faculty Highlights, and Keston Research Award. More at: https://viterbi.usc.edu/directory/faculty/Rostami/Mohammad

Abstract:   The vision of a quantum Internet, a global network capable of transmitting quantum information, brings with it the promise of implementing quantum applications such as quantum key distribution (QKD), quantum computation, quantum sensing, clock synchronization, quantum-enhanced measurements, and many others. Developing such an infrastructure needs to address major challenges, such as channel and operational noise, limited quantum information lifetime, and long-distance transmission losses. In this talk, I will present my work that tackles these major challenges and designs performance benchmarks, architectures, and resource allocation policies for first-generation quantum networks.

Bio:  Dr. Nitish Kumar Panigrahy is currently a postdoctoral researcher at NSF ERC Center for Quantum Networks, working jointly with Prof. Leandros Tassiulas (Yale University) and Prof. Don Towsley (University of Massachusetts Amherst). He earned his Ph.D. degree in Computer Science at the University of Massachusetts Amherst in 2021. Nitish’s research interests lie in modeling, optimization, and performance evaluation of networked systems with applications to the Internet of Things (IoT), cloud computing, content delivery, and quantum information networking.

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Graduate students will be selected by their advisor to present a research poster. Monetary awards will be given to the top 3 posters.

Agenda

Poster Session Authors

7 Minute Madness

Closing Notes and Poster Awards

4:30 – 4:45 Dr. Homayoun Valafar

Advances in artificial intelligence (AI) and the increasing digitization of healthcare data promise significant advances in disease understanding, therapeutic development, patient treatment and, ultimately, improvement in health outcomes. However, many technical and anthropological challenges must be addressed if AI is to fulfill this potential. In this talk, we will first discuss a conceptual framework for conducting AI based clinical decision support (CDS) research that includes qualitative research to understand clinician needs, AI method development and applications research, and aspects of implementation science to address barriers to system adoption. In the context of this framework, we will consider three research studies focused on AI utilization in healthcare applications: (1) development of a sepsis early warning system for neonatal intensive care units; (2) automated recognition of adverse event descriptions in social media and electronic health records (EHRs); and (3) subtyping of traumatic brain injury (TBI). For the sepsis study, we will discuss challenges related to AI systems that must continuously update predictions for patients including concerns over false alarm rate and model interpretability. Relative to adverse event detection, we will discuss natural language processing and deep learning methods. For TBI subtyping, we will see an application of unsupervised learning on EHR data and correlation between baseline subtypes and long-term outcomes. Along the way, we will discuss topics related to predictive model development, unsupervised learning, explainable AI, and the need for domain expert collaboration. Finally, we will discuss ideas for future directions for each of these studies.

Dr. Aaron Masino is currently the Senior Director of Clinical Data Science at AiCure where he provides scientific leadership for novel digital biomarker development and clinical data science research. Previously, he was an Assistant Professor in the Department of Anesthesiology and Critical Care in the Perelman School of Medicine at the University of Pennsylvania and The Children's Hospital of Philadelphia where he conducted research focused on the application and development of machine learning methods to challenges in pediatric medicine. Dr. Masino also served as a senior scientist at MZA Associates Corporation where he developed advanced adaptive optics control algorithms. He holds a PhD in Applied Mathematics and a Masters of Engineering in Aerospace Engineering.Specialties: Machine learning, deep learning, NLP, data science, software development, biomedical informatics, python, pytorch, tensorflow

Full Details Here

ACM Club and Cybersecurity Clubs with be hosting a guest speaker from John Deere on Wednesday, April 5th at 5:30 pm in Swearingen, Room 2A15/17.

The speaker is Josh Beck, Application Security Engineer, from their Raleigh office. Mr. Beck will discuss how Deere & Co approaches cybersecurity to protect tractors and other equipment. He will also discuss career paths into cybersecurity and software engineering.

For more information, please contact Diana StMarie at dstmarie@mailbox.sc.edu.

A core aspect of our social lives is often embedded in the communities we are situated in. Our shared experiences and social ties intertwine our situated context with our wellbeing. A better understanding of wellbeing can help devise timely support provisions. However, traditional forms of wellbeing measurements have limitations, motivating an increasing interest in supporting wellbeing through passive sensing technologies. Parallelly, social media platforms enable us to connect and express our personal and social lives with others. Given its ubiquity, social media can be considered a “passive sensor” to obtain naturalistic data, which can also be combined with various multimodal sensing to comprehensively measure wellbeing. However, wellbeing sensing technologies can lead to unintended outcomes and cause harms. Therefore, despite the potential, are we ready to deploy these wellbeing sensing technologies in the real world yet?

In this talk, Koustuv Saha will present theory-driven computational and causal methods for leveraging social media in concert with complementary multisensor data to examine wellbeing, particularly in situated communities such as college campuses and workplaces. He will also interrogate the meaningfulness of the data and inferences and reflect on how these approaches can potentially be misinterpreted or misused without additional considerations. To bridge the gap between the theoretical promise and practical utility, he will present the importance of evaluating the needs, benefits, and harms of wellbeing sensing technologies in practice. This talk will propel the vision toward questioning the underlying assumptions and in responsible design and deployment of wellbeing sensing technologies (if at all) for situated communities and the future of work.

Koustuv Saha was a Senior Researcher at Microsoft Research, Montreal, in the Fairness, Accountability, Transparency, and Ethics in AI (FATE) group. He completed his Ph.D. in Computer Science from Georgia Tech in 2021, advised by Prof. Munmun De Choudhury. His research interest is in social computing, computational social science, human-centered machine learning, and FATE. He adopts machine learning, natural language, and causal inference analysis to examine human behavior and wellbeing using different forms of digital data, including social media and multimodal sensing data. His work questions the underlying assumptions of data-driven inferences and the possible harms such inferences might lead to. His research is situated in an interdisciplinary and human-centered context and bears implications for various stakeholders. His work has been published at various venues, including CHI, CSCW, ICWSM, IMWUT (UbiComp), Scientific Reports, JMIR, FAT* (now FAccT), among others. He is a recipient of the 2021 Outstanding Doctoral Dissertation Award from the College of Computing at Georgia Tech, Foley Scholarship Award from the GVU Center, Snap Research Fellowship, and a finalist of the Symantec Graduate Fellowship. His research has won the Outstanding Study Design Award at ICWSM, and has been covered by several media outlets, including the New York Times, Vox, CBC Radio, NBC, 11Alive, the Hill, and the Commonwealth Times. During his Ph.D., he did research internships at Snap Research, Microsoft Research, Max Planck Institute, and Fred Hutch Cancer Research. Earlier, he completed his B.Tech (Hons.) in Computer Science and Engineering from the Indian Institute of Technology (IIT) Kharagpur. He was awarded the NTSE Scholarship by the Govt. of India, and he has six years of overall Industry research experience.

Link to his website:

https://koustuv.com/

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