Designing Quantum Programming Languages with Types

Wednesday, April 26, 2023 - 10:15 am
online

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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Meeting ID: 241 582 962 702 Passcode: ytidHn

Learning Efficiently and Robustly in Data-Scarce Regimes

Friday, April 21, 2023 - 01:00 pm
online

AIISC Seminar (Invited Talk)

Zoom: https://us06web.zoom.us/j/8440139296?pwd=b09lRCtJR0FCTWcyeGtCVVlUMDNKQT…

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

Challenges and Opportunities in Quantum Networks

Tuesday, April 18, 2023 - 10:00 am
online

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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CSE Research Symposium

Friday, April 14, 2023 - 11:30 am
550 Assembly St, Room 2277

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

Agenda

Time Location Event
11:30 am – 12:45 pm

Room 2277

(If no seating is available in room 2277, please visit rooms 2265, 2267 and 2268)

Networking and refreshments
12:45 pm – 2:00 pm 2nd floor hallway Graduate student poster session
2:00 pm – 2:15 pm Break
2:15 pm – 4:00 pm Room 1400 (1st floor) 7 Minute Madness
4:00 pm – 4:20 pm Room 1400 (1st floor) Closing Notes and Poster Awards

 

Poster Session Authors

Poster Number Advisor Name Poster Title
1 Amit Sheth Towards Rare Event Prediction in Manufacturing Domain
2 Amit Sheth Alleviate: Artificial Intelligence Enabled Virtual Assistance for Telehealth: The Mental Health Case
3 Amit Sheth FACTIFY3M - A benchmark for multimodal fact verification with explainability through 5W Question-Answering
4 Amit Sheth and Forest Agostinelli Inductive Logic Programming for Explainable Artificial Intelligence
5 Biplav Srivastava Rating of AI Systems Through a Causal Lens
6 Biplav Srivastava Group Recommendation and a Case Study in Team Formation with ULTRA
7 Biplav Srivastava Planner Performance Improvement using Ontology
8 Chin-Tser Huang Smarkchain: An Amendable and Correctable Blockchain Based on Smart Markers
9 Chin-Tser Huang Investigation of 5G and 4G V2V Communication Channel Performance Under Severe Weather
10 Christian O'Reilly Deep Ensemble Learning: A Synergistic Approach for Ultrasonic Vocalization Analysis in Post-Traumatic Stress Disorder Study
11 Christian O'Reilly Characteristics of cerebrospinal fluid in Autism Spectrum Disorder (ASD): A systematic review
12 Christian O'Reilly and Amit Sheth Interpretable Machine Learning for Predicting the Likelihood of Autism from Infant ECG Recordings​​
13 Forest Agostinelli Explainable AI for Solving Pathfinding Problems through Collaborative Education
14 Ioannis Rekleitis SM/VIO: Robust Underwater State Estimation Switching Between Model-based and Visual Inertial Odometry
15 Ioannis Rekleitis Confined Water Body Coverage under Resource Constraints
16 Ioannis Rekleitis Weakly Supervised Caveline Detection For AUV Navigation Inside
Underwater Caves
17 Jianjun Hu crystalTransformer
18 Jianjun Hu Composition based Oxidation State Prediction of Materials using Deep Learning Language Model
19 Jianjun Hu DeepXRD, a Deep Learning Model for Predicting XRD spectrum from Material Composition
20 Jianjun Hu Scalable deeper graph neural networks for high-performance materials property prediction
21 Pooyan Jamshidi Partitioning and Mapping for ASIC AI Accelerators
22 Pooyan Jamshidi Not Just a Rose by Any Other Name: Differential Privacy as an Instrumentality of Effective Regulation Thwarting the Subterfuge of Differential Privacy by another Name and Undue Influence
23 Qi Zhang, Christopher Sutton Maximizing Learning Efficiency in Material Science through Domain Adaptation
24 Ramtin Zand Reliability-Aware Deployment of DNNs on In-Memory Analog Computing Architectures
25 Sanjib Sur MatGAN: Sleep Posture Imaging using Millimeter-Wave Devices
26 Sanjib Sur Towards Robust Pedestrian Detection with Roadside Millimeter-Wave Infrastructure
27 Sanjib Sur Outdoor Millimeter-Wave Picocell Placement using Drone-based Surveying and Machine Learning
28 Sanjib Sur Outdoor Small Scale Point Cloud Reconstruction Using Drone-based Millimeter-Wave FMCW Radar System and CFAR
29 Sanjib Sur MmSight: Millimeter-Wave Imaging on 5G Handheld Smart Devices
30 Sanjib Sur Enabling Integrated Networking and Activity Sensing in Indoor Millimeter-Wave Networks
31 Sanjib Sur mmWaveNet: Indoor Point Cloud Generation from Millimeter-Wave Devices
32 Sanjib Sur, Srihari Nelakuditi SSCense: A Millimeter-Wave Sensing Approach for Estimating Soluble Sugar Content of Fruits
33 Song Wang Few-shot 3D Point Cloud Semantic Segmentation via Stratified Class-specific Attention Based Transformer Network
34 Song Wang Parametric Surface Constrained Upsampler Network for Point Cloud
35 Song Wang MISF: Multi-level Interactive Siamese Filtering for High-Fidelity Image Inpainting
36 Stephen Fenner Implementing the fanout operations with simple pairwise interactions
37 Homayoun Valafar "Revolutionizing Cardiovascular Health: Harnessing the Power of Deep Learning for Automatic Calcification Calculation in Vascular Systems"
38 Homayoun Valafar Smartwatch-Based Smoking Detection Using Accelerometer Data and Neural Networks
39 Homayoun Valafar Analysis of cancer patients’ molecular and clinical data using Machine Learning approaches
40 Vignesh Narayanan On Safe and Usable Chatbots for Promoting Voter Participation
41 Vignesh Narayanan Building a Digital Twin for Information Environment
42 Yan Tong Unlocking the Potential of Consumer Wearables for Predicting Sleep in Children: A Device-Agnostic Machine Learning Approach
43 Yan Tong Cascade Feature Fusion Network for Facial Expression Recognition
44 Ramtin Zand Facial Expression Recognition at the edge: CPU vs GPU vs VPU vs TPU
45 Ramtin Zand Static American Sign Language Recognition Using Neuromorphic Hardware

 

7 Minute Madness

Number Time slot Speaker
1 2:15 – 2:22 Ramtin Zand
2 2:30 - 2:37 Pooyan Jamshidi
3 2:40 – 2:47 Sanjib Sur
4 2:50 – 2:57 Forest Agostinelli
5 3:00 – 3:07 Vignesh Narayanan
6 3:10 – 3:17 Ioannis Rekleitis
7 3:20 – 3:27 Song Wong (presented by Ping Ping cai)
8 3:30 – 3:37 Christian O’Reilly
9 3:40 – 3:47 Biplav Srivastava
10 3:50 – 3:57 Jianjun Hu
11 4:00 – 4:07 Yan Tong
12 4:10 – 4:17 Steve Fenner
13 4:20 – 4:27 Homayoun Valafar

 

Closing Notes and Poster Awards

4:30 – 4:45 Dr. Homayoun Valafar

Toward AI Augmented Healthcare

Tuesday, April 11, 2023 - 11:15 am
Innovation Center, 550 Assembly Street, Room 2277 (2nd floor)

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.

Cybersecurity at Deere & Co

Wednesday, April 5, 2023 - 05:30 pm
Swearingen, Room 2A15/17

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.

Measuring Wellbeing in Situated Context with Social Media and Multimodal Sensing: Promises and Perils

Tuesday, April 4, 2023 - 11:15 am
online

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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An Introduction to Neuromorphic Computing and Spiking Neural Networks (SNNs)

Friday, March 24, 2023 - 12:00 pm
online

Time: Mar 24, 2023 12:00 PM Eastern Time (US and Canada)

Abstract: This short talk will be on Neuromorphic Computing by Ramashish Gaurav (Ram for short). He is a Ph.D. candidate at Virginia Tech - ECE, working on Spiking NeuralNetworks (SNNs) -- a subdomain of Neuromorphic Computing, under the supervision of Prof. Yang (Cindy) Yi at MICS. The talk will be an introduction to Neuromorphic Computing, followed by spiking networks and how they relate to the current generation of neural networks. It will then steer towards recent progress in SNNs, and will be concluded with opportunities and challenges towards energy-efficient AI. Ram's blog can be found at https://r-gaurav.github.io/

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Meeting ID: 871 7752 0455

Facial Expression Recognition Using Edge AI Accelerators

Wednesday, March 15, 2023 - 10:00 am
Room 2267 Innovation building

DISSERTATION DEFENSE 

Author : Heath Smith

Advisor : Dr. Ramtin Zand

Date : March 15, 2023 

Time: 10:00 am  

Place : Room 2267 Innovation building

Abstract 

Facial expression recognition is a popular and challenging area of research in machine learning applications. Facial expressions are critical to human communication and allow us to convey complex thoughts and emotions beyond spoken language. The complexity of facial expressions creates a difficult problem for computer vision systems, especially edge computing systems. Current Deep Learning (DL) methods rely on large-scale Convolutional Neural Networks (CNN) which require millions of floating point operations (FLOPS) to accomplish similar image classification tasks. However, on edge and IoT devices, large-scale convolutional models can cause problems due to memory and power limitations. The intent of this work is to propose a neural network architecture inspired by deep CNNs which is tuned for deployment on edge devices and small-form-factor edge AI accelerators. This will be carried out by strategically reducing the size of the network while still achieving good discrimination between classes. Additionally, performance metrics such as latency, accuracy, throughput, and power consumption will be captured and compared with several popular deep CNN models. It is expected that there will be trade-offs between network size and performance when the model is deployed and running model inference on edge AI accelerators such as the Intel Movidius Neural Compute Stick II and the NVIDIA Jetson Nano GPU accelerator. An additional benefit of smaller-scale convolutional models is that they are better suited to be converted into spiking neural networks and deployed on neuromorphic hardware such as the Intel Loihi neuromorphic chip. Furthermore, this work will also examine various image processing techniques across multiple datasets in an effort to increase the performance of the edge-efficient model.