Dr. Alipour is co-organizing mini-symposium MS 0306 titled “Recent Advances in Sensing, SHM, and Automated Inspections for Infrastructure Condition Assessment: Toward Actionable Solutions” at the upcoming Engineering Mechanics Institute (EMI) Conference in Chicago, May 28-31, 2024. Abstract submissions are open until Dec. 31, 2023, here.

Dr. Alipour will be offering CEE 598- Deep Learning for CEE Sensing, Simulation & Prediction in the Spring of 2023. For more information, please visit here. 

Alipour Research Group presented a study titled “UAS-mounted radio-frequency-based soil moisture sensing for wildland fire characterization” at the 10th International Fire Ecology and Management Congress by the Association For Fire Ecology in Monterey, CA. Read more here.

Dr. Alipour was selected as an outstanding research mentor in the National Center for Supercomputing Applications (NCSA) Students Pushing for Innovation (SPIN) Program (2023). Read more here.

Alipour Research Group presents two papers at the 2023 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2023) in Pasadena, CA. Read more here and here. 

Our paper titled "A multimodal data fusion and deep learning framework for large-scale wildfire surface fuel mapping" was published in the Journal of Fire (I.F.= 3.2). Check it out here.

Alipour Research Group presents a paper titled "Developing Human Sensing Platforms for Digitizing Visual Inspections of Critical Infrastructure" at the 2023 Structures Congress in New Orleans, LA. Read more here. 

Dr. Alipour was recently appointed to the editorial board of the Journal of Nondestructive Evaluation as an associate editor. Excited to join this reputable journal and contribute to the NDE community!

Amazon Science Hub for Humanity and Artificial Intelligence at UCLA recently awarded our project titled "Fighting Wildfires with AI: Enabling Timely Wildfire Simulation using Probabilistic Geospatial Deep Learning". Thanks to Amazon for their support!

Dr. Alipour will be presenting research on "Inexpensive and Scalable Detection of Corrosion Using Semi-Supervised Deep Learning and Minimal Labeled Data" at the upcoming Engineering Mechanics Institute (EMI) Conference at Johns Hopkins University, May 31-June 3, 2022 in Baltimore, MD.

Our work titled "Mapping Wildfire Fuels using Deep Learning for High-fidelity Fire Spread Simulation" is accepted for presentation at the Fire&Climate Conference of the International Association of Wildland Fire in Pasadena, CA, May 23-27.

Our paper titled "Integrating Visual Sensing and Structural Identification Using 3D-Digital Image Correlation and Topology Optimization to Detect and Reconstruct the 3D Geometry of Structural Damage" is published in the Journal of Structural Health Monitoring (IF=5.93) and can be accessed here.

Dr. Alipour was invited to give a talk at the technical leadership meeting of the LANDFIRE program of the US Dept. of Agriculture Forest Service and Dept. of the Interior. The title of the talk was "Leveraging Deep Learning for Large-scale On-demand Wildfire Fuel Estimation".

Our paper titled "Leveraging Mixed Reality for Augmented Structural Mechanics Education" is accpeted for presentation and publication in the proceedings of the American Society for Engineering Education (ASEE) Annual Conference and Exposition.  

Our paper titled "Mapping Textual Descriptions to Condition Ratings to Assist Bridge Inspection and Condition Assessment Using Hierarchical Attention" is published in the Journal of Automation in Construction (IF = 5.669). It can be accessed here.

Our paper titled "Context-aware Sequence Labeling for Condition Information Extraction from Historical Bridge Inspection Reports" is published in the Journal of Advanced Engineering Informatics (IF = 3.879). The paper can be accessed from here. 

Our proposed computational framework titled "Using remote sensing and AI for data-driven wildfire modeling and management" was recently awarded a Microsoft AI for Earth Compute Grant for Azure Cloud services. Thanks to Microsoft for the support!

Research paper accepted for publication in the Journal of Transportation Research Record. "Deep Learning-based Visual Identification of Signs of Bat Presence in Bridge Infrastructure Images: A Transfer Learning Approach" is part of research by graduate student Tianshu Li and co-authored by Prof. Devin Harris, and Bridget Donaldson of Virginia Transportation Research Council as part of the project “Development of a Test Method to Determine the Source of Staining on Structures”.

Our recent paper coauthored by Mehrdad Shafiei Dizaji and Devin Harris was published in Engineering Structures. The paper introduces a powerful method that not only detects damage but can also quantify its location and 3D shape via the use of optical cameras and topology optimization. The article can be found here.

Research paper "E-scooter Availability Versus Utilization Insights: A Geospatial Analysis" was accepted for presentation at the 100th Annual Meeting of the Transportation Research Board (TRB ). This work by graduate student Tina Tang presents a data-driven geospatial analysis of dockless micromibity and was co-authored by Prof. Devin Harris, and Amanda Poncy (City of Charlottesville).

Our image-based sensing work was presented at the International Digital Image Correlation Society Conference (iDICS-2020) in a plenary talk by Prof. Devin Harris (“Civil Infrastructure – The Next Big Playground for Image-Based Assessment and Digital Image Correlation“), and a technical talk by Mehrdad Dizaji (“Full-Field Sensing and Topology Optimization for Reconstructing the 3D Geometry of Internal Structural Damage”).

Honored to receive the 2020 Young Professionals Award from the Structural Engineering Institute (SEI) of the American Society of Civil Engineers (ASCE). Special thanks to the SEI Futures Fund and its generous contributors for this great recognition. See news coverage on ASCE website here.

Our paper titled “A Big Data Analytics Strategy for Scalable Urban Infrastructure Condition Assessment Using Semi-supervised Multi-transform Self-training ” is published in Springer's Journal of Civil Structural Health Monitoring. The article is available at https://link.springer.com/article/10.1007/s13349-020-00386-4.

I successfully defended my Ph.D. dissertation titled “Deep Learning for Robust and Efficient Automated Defect Recognition in Critical Infrastructure”. Special thanks to my advisor Prof. Devin Harris and committee members Profs. Scott Acton, Laura Barnes, Arsalan Heydarian, and David Lattanzi.

Received the 2019 CEE Award for Research Excellence in recognition of superior research performance by a graduate student. Special thanks to the Department of Engineering Systems and Environment (ESE) at UVA for this recognition.

My research was recently featured in a March 2019 edition of UVA Today. See full article and coverage. The article discusses the applications of our work in the field of automated structural inspection.