Active Projects

A full list of my publications on Google Scholar

Visual exploration and analysis of multimodal healthcare data

This research focuses on addressing several prevalent challenges related to the design of interactive visual analysis tools for healthcare systems. These challenges include, but are not limited to: heterogeneity of data types and data modalities; dimensionality; scalability; complexity; and data quality; incorporating other dynamic and contextual indicators such as demographics (i.e., population density, individual and community movements, distribution of age, etc.), time-series data, and real-time visual imaging. The goal is to enhance the capability of healthcare professionals to explore, analyze, and derive meaningful insights from complex and diverse healthcare data, ultimately improving decision-making and patient outcomes.

Visual exploration and analysis of high dimensional spatial ensembles

Exploring and analyzing simulation ensemble facilitates understanding parameter sensitivity and the correlations between different ensemble members. In many cases, visual analysis of spatial ensembles highlights the differences in the ensemble using aggregated or descriptive statistics ignoring the correlations and local differences between different spatial regions, which could hinder the exploration process. This research focuses on designing visual analysis tools tailored for exploring and analyzing spatial and spatiotemporal high dimensional simulation ensembles. The primary objectives include quantifying patterns, identifying trends, and uncovering relationships within the spatiotemporal data. Additionally, the research involves the development of predictive models aimed at forecasting spatiotemporal patterns and understanding how the phenomenon may evolve in the future.

Publications:

Dahshan, M., Polys, N., House, L., Youssef, K., & Pollyea, R. Human-Machine Collaboration for the Visual Exploration and Analysis of High-Dimensional Spatial Simulation Ensembles. Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2024). - Volume 1: IVAPP, 678–689. https://doi.org/10.5220/0012405100003660

Dahshan M, Polys NF, Jayne RS, Pollyea RM. Making sense of scientific simulation ensembles with semantic interaction. InComputer Graphics Forum 2020 Sep (Vol. 39, No. 6, pp. 325-343). https://doi.org/10.1111/cgf.14029

Dahshan M, House L, Polys N. High-dimensional spatial simulation ensemble analysis. InProceedings of the 9th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data 2020 Nov 3 (pp. 1-4). https://doi.org/10.1145/3423336.3429344

Computational Thinking and Data Science integrating K-12 STEM Education

This research is dedicated to building teacher capacity for integrating computational thinking and data science education in K–12 settings. The overarching goal is to empower students with essential skills and mindsets pertinent to both computational problem-solving and the analysis of data. This research has two main directions: first, to provide teachers with professional development opportunities that enhance their understanding and proficiency in computational thinking and data science; and second, to support teachers in the development of integrated lesson plans that integrate computational thinking and data science seamlessly into existing STEM curricula.

Publications:

Dahshan, M. and Galanti, T., 2024. Teachers in the Loop: Integrating Computational Thinking and Mathematics to Build Early Place Value Understanding. Education Sciences, 14(2), p.201.

Dahshan, M. and Galanti, T.M., 2022, March. Designing Integrated Math+ CT Activities to Promote Sensemaking about Place Value in Grades K-2. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 2 (pp. 1321-1321).

Past Projects

Visual exploration and analysis of high dimensional Image Based ensembles

The explosive growth in supercomputers capacity has changed simulation paradigms. Simulations have shifted from a few lengthy ones to an ensemble of multiple simulations with varying initial conditions or input parameters. Thus, an ensemble consists of large volumes of multidimensional data that could go beyond the exascale boundaries. However, the disparity in growth rates between storage capabilities and computing resources results in I/O bottlenecks. This makes it impractical to utilize conventional post-processing and visualization tools for analyzing such massive simulation ensembles. In-situ visualization approaches alleviate I/O constraints by saving predetermined visualizations in image databases during simulation. Nevertheless, the unavailability of output raw data restricts the flexibility of post-hoc exploration of in-situ approaches. This research focuses on designing visual analysis tools tailored for exploring and analyzing image based high dimensional simulation ensembles.

Publications:

Dahshan, M., Polys, N., House, L., North, C., Pollyea, R.M., Turton, T.L. and Rogers, D.H., 2024. Human–machine partnerships at the exascale: exploring simulation ensembles through image databases. Journal of Visualization, pp.1-19 https://doi.org/10.1007/s12650-024-00999-7

Dahshan, M., Turton, T.L. and Polys, N.F., 2022. Exploration and Analysis of Image-base Simulation Ensembles. In EuroVis (pp. 91-93). https://doi.org/10.2312/evp.20221128

Framework for Securing Data in Cloud Storage Services

Cloud storage, offered by companies like Amazon, Google, Microsoft, Dropbox, and Box.net, is popular for its affordability and accessibility, enabling file synchronization, sharing, versioning, and backups. However, it carries risks like data exposure and tampering. To mitigate these risks, data should be encrypted before outsourcing. However, this alone doesn't ensure fine-grained access control. Public cloud services, managed by untrusted providers, can't guarantee secure data sharing or user privacy. Our work aims to enhance cloud storage security by designing a system that ensures data confidentiality, fine-grained access control, and scalable user revocation, independent of the provider. We propose a third-party service that encrypts data before uploading and decrypts it after downloading, managing encryption keys securely. This service uses multi-authority ciphertext policy attribute-based encryption (CP-ABE) and attribute-based signatures (ABS) for fine-grained access control and efficient user privilege revocation. It offloads re-encryption tasks to proxy servers, ensuring data confidentiality even during unauthorized access attempts. Our architecture is validated through a detailed security analysis, proving its effectiveness in maintaining data confidentiality throughout user interactions with the cloud.

Publications:

Dahshan, M. and Elkassass, S., 2014. Framework for securing data in cloud storage services. In 2014 11th International Conference on Security and Cryptography (SECRYPT) (pp. 1-8). IEEE.

Dahshan, M. and Elkassass, S., 2014. Data Security in Cloud Storage Services. In The Fifth International Conference on Cloud Computing (pp. 1-5).