I'm Hossein Fathollahian

As a passionate PhD student at UIC, I'm deeply engaged in the fields of AI and Machine Learning, Visual Analytics, and Human-Computer Interaction, where I explore innovative ways to transform complex data and interactions into meaningful, impactful insights.

At UIC, my work focuses on biomedical data visualization, medical imaging, and brain connectomics. I apply AI, Machine Learning, and Human-Computer Interaction to design advanced visualization and computer vision techniques that uncover complex patterns in biomedical data. By integrating intelligent algorithms with human-centered design, I aim to make biomedical insights more interpretable, actionable, and impactful for both researchers and clinicians.

I'm a Ph.D. student in Computer Science at the University of Illinois Chicago (UIC) under the supervision of Prof. G. Elisabeta (Liz) Marai and co-advisor Dr. Saeed Boorboor. I obtained my master's in Telecommunication Engineering at Shahid Beheshti University of Tehran, Iran. I'm currently working as a Research Assistant at the Electronic Visualization Laboratory (EVL). My research interests are Visualization, Computer vision, and Image processing.

Looking for an opportunity to research in a challenging position combining my skills in visual analytics and computer vision, which provides professional development, interesting experiences and personal growth.

News & Recognition

  1. CAIDF team to receive the AMIA Patricia Flatley Brennan Award

    The CAIDF research team will receive the Patricia Flatley Brennan Award for Informatics Team Science. I contributed to the team’s computational and visual analytics work.

    About the AMIA Signature Awards

  2. “Mapping war trauma” published in SSM - Population Health

    Co-authored with Safiyeh Tayebi, Ayse Sert Oti, and Ubydul Haque, this study uses GeoAI and spatial data to predict the mental-health impacts of war across Ukrainian cities.

    Read “Mapping war trauma: A machine learning approach to predict mental health impacts in Ukraine”

  3. ConGAT accepted in Computers & Graphics

    Our paper presents a context-aware graph attention network for discovering regions of interest in 3D multiplexed microscopy images.

    Read “ConGAT: Context-aware graph attention network for 3D region of interest discovery in multiplexed microscopy images”

  4. Loom accepted to IEEE TVCG

    Our spatial transcriptomics visual computing system supports analysis of local neighborhoods, cross-sample comparisons, and global trajectories.

    Read “Loom: Multi-region analysis of spatial transcriptomics with local neighborhoods and global trajectories”

  5. Best Poster Award at the ESSB Conference

    Recognized for work on 3D visualization of multiplexed tissue data, including research related to BioSET.

    ESSB Conference 2026

  6. Award of Merit — “Attention-based ROI Discovery in 3D Tissue Images”

    Received an Award of Merit with Siyuan Zhao and Nafiul Nipu for our BioMedVis Challenge submission at IEEE VIS 2026.

    View the BioMedVis project

  7. Award of Merit — “From Peaks to Patterns”

    Received a second Award of Merit for the redesign submission, co-authored with Marziye Salahshour, to the BioMedVis Challenge at IEEE VIS 2026.

    View the BioMedVis project
1- Graduate Research Assistant

Electronic Visualization Laboratory (EVL) And Department Of Communication, UIC

Jan 2024 - Present | Chicago, IL
2- Graduate Teaching Assistant

University of Chicago Illinois

  • CS 141: Introduction to Computer Science(Prof. Mark Hodges)
  • CS 251: Data Structures and Algorithms(Prof. Daniel Ayala)
  • CS 211: Computer Programming practicum(Prof. Scott Reckinger)
Spring-Fall 2024, Spring 2025 |UIC, Chicago, USA
3- Head of Low Voltage System Design and Research and Development

MENIC Co., Tehran, Iran

  • Designing, consulting, and implementing telecommunication and low voltage systems in industrial plants and buildings (CCTV, telephone, speakers, fire alarms, paging, and computer networks)
  • Leading innovation and strategy in research and development to drive technological advancement and growth
Jan 2017 - Sep 2023 | Tehran, Iran
4- Image Processing Specialist

Shahid Beheshti University, Tehran, Iran

  • Image and video feature extraction and pattern recognition in textiles and painting quality measurement
  • MATLAB and Python programming for various image processing tasks
Nov 2011 - Dec 2012 | Tehran, Iran

How I turn data into insight

My work brings together software, machine learning, and visual analytics to help make complex biomedical data easier to explore and understand.

Programming languages

I build research tools and applications with Python, JavaScript, C++, and Java, supported by shell scripting and Unix.

  • Python
  • JavaScript
  • C++
  • Java
  • Shell
  • Unix

AI & machine learning

I use machine learning and deep learning to find patterns in data, working with scikit-learn, TensorFlow, PyTorch, and Keras, alongside NumPy and Pandas.

  • Machine learning
  • NumPy
  • Pandas
  • scikit-learn
  • TensorFlow
  • PyTorch
  • Keras

Visualization, visual analytics & computer vision

I design visual ways to examine biomedical images and complex data, combining computer vision, OpenCV, and Matplotlib with visual analytics.

  • Data visualization
  • Computer vision
  • OpenCV
  • Matplotlib

Other tools I use

MySQL · PostgreSQL · Django · Flask · Node.js · Bootstrap · Git · AWS · Heroku · Unity

1- ConGAT: Context-Aware Graph Attention Network For 3D Region Of Interest Discovery In Multiplexed Microscopy Images

A visual analysis system that uses 3D graph attention networks and self-supervised learning to identify cell–cell interactions and regions of interest in large multiplexed microscopy volumes. Experts can combine biomarkers interactively to explore spatial patterns without labeled data.

View Demo → Live App → GitHub Repository →

2- Loom: Multi-Region Analysis Of Spatial Transcriptomics With Local Neighborhoods And Global Trajectories

A spatial transcriptomics visual computing system for pseudo-temporal trajectories, cross-sample comparison, and local microenvironments. A novel glyph and computational backbone support discovery of cellular transitions and spatiotemporal gene-expression dynamics.

View Demo → Live App → GitHub Repository →

3- BioSET: Interactive Visual Analysis Of Biomarker Co-Localization In Large Multi-Volume Tissue Data

An interactive visual analytics application for terabyte-scale multi-volume tissue data and biomarker co-localization at cellular resolution. Scalable 3D visualization, guided exploration, and reporting help experts find cellular interactions linked to disease such as cancer.

View Demo → Live App → GitHub Repository →

4- Communication Department: AI‑Enhanced Modeling of Human Communication Behaviors (Prof. Zizi Papacharissi)

Location: Chicago, IL | Position: Researcher (Since Fall 2025)

Contribution: I am pleased to serve in the Department of Communication under the supervision of Professor Zizi Papacharissi. In this role, I support projects that utilize AI and machine learning to enhance our understanding of human communication, integrating computational methods with communication research to identify meaningful patterns in how people interact.

5- Argonne National Laboratory: SAGE Project (Prof. Michael E. Papka)

Location: Chicago, IL | Position: Researcher (Since Summer 2025)

Contribution: Contributed to the NSF‑funded SAGE project in collaboration with Argonne National Laboratory. Developed AI pipelines for sensor data using NVIDIA Jetson edge devices. Focused on real‑time data integration and deployment of ML models.

6- BI‑LAVA: A Visual Analytics and Active Learning System for Biomedical Image Taxonomies

Contribution: Developed a Computer vision caption finding and Frontend upgrading.

Tools: Web development, Database, Machine Learning

7- Camera Best View Positioning for Multidimensional Biomedical Imaging

Contribution: Propose and conduct new Algorithm based on 3D Gaussian splatting.

Tools: MATLAB, Python, Deep‑Learning

8- Early stage Axillary Lymph node detection in mammography image

Contribution: Leveraging radiomics extraction instead of only segmentation for improving machine learning efficiency.

Tools: MATLAB, Python, Deep Learning, data labeling

Bio+MedVis Challenge 2026

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View Demo → GitHub Repository →

Human Computer Interaction

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View Demo → GitHub Repository →

Computer Vision

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View Demo →

Stat NLP

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Database

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Game Design

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Other Categories

Machine Learning

Machine Learning

AI Research

AI Research

J: journal, C: conference/symposium, W: workshop

1. [J] Hossein Fathollahian, Siyuan Zhao, Nafiul Nipu, Marziye Salahshour, Lei Duan, Wei Tang, Saeed Boorboor, and G. Elisabeta Marai. "ConGAT: Context‑Aware Graph Attention Network for 3D Region of Interest Discovery in Multiplexed Microscopy Images." Computers & Graphics, 2026, 104728. doi:10.1016/j.cag.2026.104728

2. [J] Siyuan Zhao, Nafiul Nipu, Hossein Fathollahian, Olga Karginova, Hao Chen, Ameen Salahudeen, and G. Elisabeta Marai. "Loom: Multi‑Region Analysis of Spatial Transcriptomics with Local Neighborhoods and Global Trajectories." IEEE Transactions on Visualization and Computer Graphics, 2026. arXiv:2607.22505

3. [J] Tayebi, Safiyeh, Ayse Sert Oti, Hossein Fathollahian, and Ubydul Haque. " Mapping War Trauma: A Machine Learning Approach to Predict Mental Health Impacts in Ukraine." SSM–Population Health, 2025:101879. doi:10.1016/j.ssmph.2025.101879

4. [C] Hossein Fathollahian, Siyuan Zhao, Marziye Salahshour, Saeed Boorboor, and G. Elisabeta Marai. "ConformaLens: Conformational States in Adaptive Molecular Dynamics" IEEE Bio+MedVis Challenge 2026, Boston, USA, Nov 2026, pages 1‑3. doi.org/10.2139/ssrn.7359719

4. [C] Hossein Fathollahian, Siyuan Zhao, Nafiul Nipu, and G. Elisabeta Marai. "Attention‑Based ROI Discovery in 3D Tissue Images." IEEE Bio+MedVis Challenge, Vienna, Austria, Nov 2025, pages 2‑4 doi.org/10.48550

5. [C] Hossein Fathollahian, Marziye Salahshour. " From Peaks to Patterns." IEEE Bio+MedVis Challenge, Vienna, Austria, Nov 2025, pages 22‑24 doi.org/10.1101/2025.11.09.687456

6. [J] Farah Torkamani‑Azar, Hassan Imani, Hossein Fathollahian. "Video Quality Measurement Based on 3‑D Singular Value Decomposition." Journal of Visual Communication and Image Representation, Volume 27, February 2015, Pages 1–6. doi:10.1016/j.jvcir.2014.12.004.

University of Illinois Chicago

Chicago, USA

Degree: PhD student in Computer Science
GPA: 3.88/4.0

Relevant Coursework:

  • Human-Computer Interaction/ Data Visualization/ Video Game Design
  • Computer Algorithms/ Database/ Data Structure/ Software Engineering
  • Artificial Intelligence/ Machine Learning
  • Computer Vision/ Advance Computer Vision (3D)/ Advance Stat NLP

University of Shahid Beheshti

Tehran, Iran

Degree: M.Sc. in Telecommunication Engineering
GPA: 3.8/4.0

Relevant Coursework:

  • Image Processing
  • Signal Processing
  • Source and Channel Coding