Computer Science Research

Lu Zhang

Deep Connectome is a graph-based neural network that integrates brain structure and function into a disease related network for individuals. The network topology is initialized with an individual's structural network and iteratively updated with disease-related functional information to optimize MCI classification. The resulting Deep Brain Connectome captures “deep relations” between brain structure and function in individuals, representing their disease status and offering insights into the disease's impact on brain networks.

Lu Zhang

Current brain mapping methods rely heavily on anatomical regularity and often overlook individualized structures. To simultaneously encode commonality and individuality, we developed a framework to establish correspondences of individual cortical folding patterns based on 3-hinge gyrus (3HG), encoding regularity in learned embedding vectors (cortex2vector), while preserving individuality by multi-hop combination coefficients. Each 3HG is then represented as an individually specified combination of embedding vectors.

Hyeju Jang

Visual metaphors are powerful communication tools that can be used to convey persuasive messages. They are often more impactful than verbal explanations, as they can appeal to the senses and trigger emotions. For example, smoking visual metaphors can be more effective in depicting the harmfulness of smoking than simply stating the facts. Despite their importance, visual metaphors have not been studied extensively. This research project aims to build computational models that can interpret and generate visual metaphors.

Lu Zhang

The continuous nature of Alzheimer’s disease (AD) development has been typically overlooked in AD prediction models. Our proposed Disease2Vec framework learns the intrinsic relations among AD stages from fMRI functional connectivity data and generates a disease embedding tree (DETree).

Arjan Durresi

This project aims to develop metrics to evaluate trustworthiness of AI systems involved in decision making processes and help calibrate the trust and acceptance of these systems.

Mohammad Al Hasan

The BINDER algorithm uses binary operations that effectively represent the logical 'is-a' relation that standard word embedding models often fail to capture. Also, it applies randomized optimization as an innovative learning algorithm in the discrete domain, flipping bits in each epoch with a carefully calculated probability generated from a proxy gradient designed for binary space.

Mohammad Al Hasan

CNN deep learning model can achieve 80% accuracy in classifying major cell-types in the human kidney cortex when trained on biopsy images of reference nuclei cells, but it failed to classify cells from diabetic patients, likely due to structural deformations and other biological changes unseen during training. We propose a novel Dual Encoder-Unseen Class Score (DE-UCS) outlier detection method, which embeds both images and cell transcriptomics (side information) and links them in a latent space. A test instance is considered an outlier if the distance between its embedding and the seen classes' embeddings is greater than a threshold.

Xukai Zou

The research project aims to develop a remote e-voting system that is transparent, verifiable, and privacy preserving. The system will enable voters to cast their ballots remotely while ensuring the integrity and accuracy of the voting process. It will allow voters to verify their individual plain vote and enable anyone to tally and verify the vote counts for every candidate. The system will be resistant to misbehavior and outside attacks, and any invalid votes or attacks can be detected with high probability. The project will also investigate receipt freeness, coercion-resistance, everlasting privacy, and quantum-safe e-voting solutions, and implement and deploy a secure remote electronic voting system in the real world.

Arjan Durresi

This study aims to design a framework to detect and mitigate fake and potentially threatening user communities in 5G social networks. By leveraging geo-location data, community trust dynamics, and AI-driven community detection algorithms, the framework identifies users who may pose harm by considering attributes that are challenging for malicious users to emulate.

Xukai Zou

The project focuses on enhancing security and privacy measures on JetStream, a cloud platform funded by National Science Foundation, to meet strict requirements for handling sensitive data like protected health information (PHI). It establishes a robust cybersecurity architecture on JetStream to facilitate secure PHI sharing among users in collaborative research and education projects. This architecture ensures comprehensive protection for PHI and its workflows through user authentication, precise data access control, confidentiality, integrity, and traceability. Notably, this versatile architecture can be seamlessly implemented in various data and resource sharing environments.

Yao Liang

CyberWater2 is an open and sustainable data/model integration framework to facilitate collaboration across disciplines within the water domain. The framework aims to integrate heterogeneous data sources and enable two-way couplings among diverse computational models, eliminating the need for complex glue code. CyberWater2 also introduces a novel web service architecture, making it easily accessible via web browsers and adaptable AI-driven data agents to accommodate future modifications to external data sources.

James Hill

This project aims to develop a low-cost acoustic gunshot detection network for civilian community intervention workers. The network uses off-the-shelf hardware and custom deep learning algorithms to recognize gunshots in real-time. Moreover, it incorporates a smartphone-based alert system to enable prompt responses from community intervention workers during violent incidents.

Shiaofen Fang

This project aims to create a visualization framework that reveals the shape patterns of machine learning models. These models, often perceived as black boxes operating in high-dimensional spaces, becomes more comprehensible to users through the framework’s multiple viewing options in lower-dimensional spaces. This framework not only helps users interpret model behavior but also ensures safety and instills trust in their use.

Shiaofen Fang

Collaborating with the Department of Radiology and Imaging Sciences at Indiana University, we developed image analysis and visualization tools for neuroimaging data. These tools facilitate visual exploration and analysis, aiding in the detection of diagnostic biomarkers. (By harnessing the power of visual analytics, we aim to enhance our understanding of brain health and contribute to improved clinical outcomes.)

Shiaofen Fang

In collaborates with researchers in the Regenstrief Institute, our team designed and implemented an interactive visualization system tailored for large healthcare datasets. This system offers real-time insights and operates as a web-based solution, effectively harnessing the vast scale of electronic health records. (By enabling seamless exploration and analysis, we empower healthcare professionals to make informed decisions and enhance patient care.)

Mohamed Abdel-Mottaleb

Neoadjuvant Systemic Therapy (NST) is the primary method to reduce tumor size and metastasis in breast cancer before surgery, potentially enabling breast-conserving procedure. Deep learning models can predict the effectiveness of NST using MRI scans, but many rely on manual tumor segmentation by experts. To improve practical use in clinical settings, we developed a multi-level spatiotemporal transformer (MST-Former) model that directly uses raw MRI scans and clinical report. Our model demonstrates state-of-the-art performance on the publicly available I-SPY-1 dataset.