Graduate Student Research

Niraj Adhikari

Concentration: Construction and Infrastructure Management
Overview: Adhikari’s research develops large language model (LLM)-based agentic systems for construction, addressing both their capabilities and their risks. He builds self-correcting LLM agents that automate BIM workflows and multimodal systems for construction safety compliance. His dissertation investigates the inverse problem: how generative AI threatens the integrity of highway construction quality assurance (QA) data, developing adversarial simulation methodologies to expose and mitigate these vulnerabilities. The overarching goal is enabling trustworthy adoption of AI across the construction project lifecycle.

Advisor: Rayan H. Assaad
Smart Construction and Intelligent Infrastructure Systems (SCIIS) Laboratory

Ghadi Charbel 

Concentration: Construction and Infrastructure Management
Overview: Charbel’s research integrates simulation modeling and deep learning to advance infrastructure resilience. His dissertation develops novel models for optimizing maintenance decisions and extending bridge lifecycles. Beyond civil infrastructure, he contributes to environmental research, examining lithium-ion battery lifecycle management and recycling, as well as litter management and policy improvements in the U.S. These efforts aim to enhance decision-making, sustainability, and long-term outcomes across both infrastructure and environmental systems. 

Advisor: Rayan H. Assaad
Smart Construction and Intelligent Infrastructure Systems (SCIIS) Laboratory

Cyril Mendelek

Concentration: Construction and Infrastructure Management
Overview: Mendelek’s research advances artificial intelligence, machine learning, and data analytics for smarter infrastructure construction management. His dissertation develops a data-driven, expert-informed framework for production-rate estimation in transportation infrastructure projects by synthesizing daily work report analytics, multistate benchmarking, statistical production-rate analysis, and scheduling practice. By linking productivity assumptions to contract time determination, schedule reliability, delays, claims, and disputes, his work supports enhanced construction scheduling manuals, proactive risk governance, and more reliable, defensible decisions throughout the project delivery lifecycle.

Advisor: Rayan H. Assaad
Smart Construction and Intelligent Infrastructure Systems (SCIIS) Laboratory

Oscar Poudel

Concentration: Construction and Infrastructure Management
Overview: Poudel’s research focuses on developing AI-driven frameworks for next-generation construction automation, integrating human–robot interaction, intelligent sensing, and generative systems. The work spans over multimodal perception and control using deep learning, with ongoing research studies like audio-based monitoring, noise-robust voice control systems, wearable-based hand pose estimation for precision tasks etc. The ultimate goal is to enable context-aware, adaptive, and safe robotic systems that enhance productivity, safety, and real-time decision-making in dynamic construction environments. 

Advisor: Rayan H. Assaad
Smart Construction and Intelligent Infrastructure Systems (SCIIS) Laboratory

Matthew Sonibare

Concentration: Geotechnical Engineering
Overview: Sonibare’s research focuses on understanding the mechanical behavior of weak rock masses under different loading, environmental, and failure conditions to enhance their stability and performance in underground and geotechnical engineering applications. In particular, his research work investigates the effectiveness of grouting techniques in improving the strength, deformability, and resilience of fractured rocks. By integrating experimental testing, and numerical simulations, his research aims to optimize rock improvement strategies for challenging ground conditions. 

Advisor: Oladoyin Kolawole
Geomechanics for Geo-Engineering & Sustainability (GGES) Laboratory

Ronit Sthapit 

Concentration: Structural Engineering
Overview: Sthapit’s research focuses on understanding the behavior of reinforced high-performance fiber-reinforced cementitious composites (R/HPFRCC) columns through extensive experimental testing and numerical simulations. The effects of key parameters such as axial load ratio, longitudinal reinforcement, and material mechanical properties are investigated by subjecting R/HPFRCC columns to axial and reversed cyclic loadings. Based on the experimental and simulation results, plastic hinge expressions suitable for use in frame analysis are developed.

Advisor: Matthew J. Bandelt
Materials and Structures Laboratory (MatSLab)

Bishnu Pandey

Overview: Pandey’s research focuses on addressing PFAS (“forever chemicals”) contamination in water. These persistent, potentially carcinogenic compounds are widespread across soil, air, and water. His work explores sustainable and practical methods to remove PFAS from water, aiming to protect both public health and the environment. Specifically, he investigates treatment strategies involving coagulation-flocculation and foam fractionation to improve PFAS removal efficiency.

Advisor: Arjun Venkatesan
Emerging Contaminants Research Laboratory (ECRL)

Jiahe Zhang

Concentration: Electrochemistry, Advanced Surface Analysis (AFM), Water Treatment & Resource Recovery
Overview: Zhang’s research focuses on novel electrochemical separation and resource recovery by valorizing conventional wastewater into useful chemical products. Specifically, he designs and tests gas-permeable electrodes that convert/capture nutrients with minimal chemical inputs and energy, leveraging interfacial acid–base microenvironments to drive selectivity. He also employs cutting-edge characterization tools such as atomic force microscopy (AFM) in combination with IR, KPFM, and electrochemical scanning microscope to perform nanoscale characterization for catalysts and acquire reactivity mapping and quantification, essential for understanding of nanoscale transport, fouling, and wetting.

Advisor: Wen Zhang
Sustainable Environmental Nanotechnology and Nanointerfaces Laboratory

Yining Zhang 

Concentration: Sustainable Agriculture and Advanced Water/Soil Treatment Technologies
Overview: Zhang’s research focuses on developing advanced methods for quantifying plant growth and health using 3D scanning technologies integrated with AI-based data analysis. By creating more accurate and efficient approaches to monitoring plant development, his work supports sustainable agricultural practices. He also explores the use of nanobubble-enriched hydrogels for improved water retention and nutrient delivery in soils. Together, these technologies aim to address key environmental challenges and promote resilient, sustainable food systems.

Advisor: Wen Zhang
Sustainable Environmental Nanotechnology and Nanointerfaces Laboratory

Abolfazl Afshari

Concentration: Intelligent Transportation Systems (ITS), Artificial Intelligence for Transportation, Transportation Data Analytics, Digital Twins, Autonomous AI Agents, Large Language Models (LLMs), Vision-Language Models (VLMs), and Smart Mobility Systems
Overview: Abolfazl Afshari’s research focuses on advancing intelligent transportation systems through the integration of artificial intelligence, generative AI, large language models (LLMs), vision-language models (VLMs), autonomous AI agents, computer vision, LiDAR sensing, digital twins, connected vehicle technologies, and traffic simulation. His work centers on developing intelligent and interpretable transportation systems for traffic operations, safety, mobility, and infrastructure management. By combining real-time sensing, data analytics, and AI-driven decision-making, his research aims to create safer, more efficient, and more adaptive transportation networks capable of responding to complex real-world conditions. 

Advisor: Joyoung Lee
NJDOT Intelligent Transportation Systems Resource Center (ITSRC)

Md Tufajjal Hossain

Concentration: Traffic Flow Modeling, Intelligent Transportation Systems, Physics-Informed Neural Network (PINN), and AI-Driven Traffic Safety Analysis
Overview: Hossain’s research focuses on advancing intelligent and data-driven traffic management by integrating connected-vehicle (CV) telemetry, crowdsourced sensing, and artificial intelligence. His work includes traffic count estimation from high-resolution trajectories, real-time incident detection using Waze data, optimal Safety Service Patrol routing from historical crash data, and crash-risk modeling through harsh-braking telematics. He was also involved in applying large language models (LLM) for crash severity prediction. By combining statistical modeling, GIS, and AI, his research enables proactive, cost-efficient, and real-time decision-making for safer and smarter transportation infrastructure.

Advisor: Joyoung Lee
Intelligent Transportation Systems Resource Center (ITSRC)