Team
Our Team
Golsar brings together professional engineering leadership and interdisciplinary research collaboration to address complex infrastructure challenges.
Professional engineering responsibility remains under appropriately licensed professionals, while research advisors and collaborators provide complementary scientific and technical perspectives within their respective areas of expertise.
Research Advisory Team
Golsar’s Research Advisory Team brings together researchers and professionals from complementary disciplines to provide independent scientific and technical perspectives supporting applied research, interdisciplinary collaboration, and research-to-practice initiatives.
Advisors may contribute expertise to the development of research ideas, multidisciplinary collaborations, technical discussions, proposal development, and specific research projects based on their expertise, interest, availability, and applicable institutional requirements.
Advisory participation does not, by itself, constitute employment, ownership, authority to bind Golsar, or responsibility for professional engineering services. Participation in individual research projects, proposals, publications, or funded activities is established separately for each opportunity.
Areas of Expertise
- Professional Civil Engineering
- Water Resources & H&H
- Infrastructure Engineering
- Sustainable Construction & Materials
- Geotechnical Systems
- GIS & Spatial Analysis
- Data Science & Machine Learning
- Engineering AI Evaluation
- Applied Engineering Research
Mohammad Nikookar, D.Eng., P.E. (Founder & Principal Engineer)
Dr. Mohammad Nikookar is the Founder and Principal Engineer of Golsar Construction, Testing and Exploration PLLC, a Texas-registered professional engineering firm.
His professional experience spans civil and water-resources engineering, hydrologic and hydraulic modeling, infrastructure design, transportation and drainage systems, sustainable construction materials, geotechnical systems, GIS, data analysis, and applied engineering research.
His academic and research background includes extensive interdisciplinary research and publication experience involving civil engineering, sustainable construction, infrastructure systems, environmental applications, and data-driven methods.
In addition to professional engineering practice and academic research, Dr. Nikookar has served as a contracted engineering domain expert and Ph.D.-level engineering expert through Mercor and Handshake, evaluating and validating AI-generated technical content across civil, geotechnical, structural, and related engineering domains.
This work has included evaluation of engineering concepts, methodologies, calculations, technical reasoning, and research-level content, as well as providing expert technical feedback supporting the development and evaluation of advanced artificial intelligence systems.
His combination of professional engineering practice, applied research, computational analysis, and emerging AI technologies supports Golsar’s broader interest in research-informed and data-driven approaches to infrastructure engineering.
Vahid Najafi Moghaddam Gilani, Ph.D.
Research Advisor — Transportation, Pavement & Data-Driven Engineering
Dr. Vahid Najafi Moghaddam Gilani is a civil and transportation engineering researcher with expertise in transportation systems, pavement engineering, road safety, civil engineering materials, and data-driven methods.
His research has addressed pavement and asphalt-material performance, traffic operations, road-safety analysis, railway infrastructure, and the application of statistical and machine-learning methods to transportation engineering problems.
As a Research Advisor to Golsar, Dr. Gilani provides complementary expertise supporting interdisciplinary research and collaboration involving transportation infrastructure, pavement systems, road safety, engineering materials, and data-driven engineering.
Research Expertise: Transportation Engineering • Pavement Engineering • Road Safety • Civil Engineering Materials • Traffic Analysis • Machine Learning • Data-Driven Engineering
Mubarak Adesina, D.Eng., CFM
Research Advisor — Flood Resilience & Infrastructure Systems
Dr. Mubarak Adesina is a civil engineering researcher with experience in flood resilience, infrastructure systems, flood monitoring technologies, geospatial analysis, and sustainable construction materials.
His research has addressed regional flood-monitoring networks, flood-risk assessment, infrastructure resilience, GIS and spatial analysis, and the sustainable use of alternative materials and water resources in cementitious systems.
As a Research Advisor to Golsar, Dr. Adesina contributes expertise in flood resilience, infrastructure monitoring, data-driven assessment, and applied civil engineering research, supporting interdisciplinary initiatives that connect engineering research with practical infrastructure applications.
Research Expertise: Flood Resilience • Infrastructure Systems • Flood Monitoring • GIS & Spatial Analysis • Infrastructure Resilience • Sustainable Construction Materials • Applied Civil Engineering Research
Esmat Jafarpour Lashkami, M.S.
Research Advisor — Data Science, AI & Computational Methods
Esmat Jafarpour Lashkami is a data science and mathematics professional with interdisciplinary experience in machine learning, statistical analysis, computational methods, optimization, and data-driven research.
Her academic background combines graduate education in data science with a strong foundation in mathematics. Her research experience includes applications of optimization and computational methods to engineering problems, as well as machine learning and deep learning applications.
As a Research Advisor to Golsar, she contributes expertise in data analytics, machine learning, statistical modeling, optimization, and computational methods supporting interdisciplinary engineering research and data-driven infrastructure applications.
Research Expertise: Data Science • Machine Learning • Statistical Modeling • Optimization • Computational Methods • Applied Mathematics • Data-Driven Research
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