Amirhossein Zaji
Biography:
Dr. Amirhossein Zaji has a PhD in Data-Driven Civil Engineering from the University of British Columbia, a Master of Science degree in Hydrology and Water Resources Science and a BSc in Civil Engineering.
Expertise and Experience
Amirhossein has research and work experience as a Research Assistant at the School of Engineering at UBC, where he is developing a high-throughput phenotyping system to localize and count wheat spikes, measuring plant height using computer vision, deep learning, and stereo cameras, and optimizing traffic lights using knowledge-based ontology systems and evolutionary algorithms. He has also worked as a teaching assistant and lab instructor for the Instrumentation and Data Analysis course at UBC.
As an instructor with 10 years of experience in the education industry, Amirhossein has developed a passion for teaching and inspiring students to reach their full potential. With expertise in curriculum development, classroom management, and student assessment, he has a proven track record of delivering engaging and effective instruction across a range of subjects.
His experience spans both online and in-person teaching environments, and he is committed to leveraging the latest technologies and teaching methodologies to create dynamic and interactive learning experiences for my students. Whether working with students at the elementary, middle, or high school level, he is dedicated to fostering a love of learning and helping students develop the skills and knowledge they need to succeed.
Publications and Scholarly Activity
Amirhossein’s research interests include deep learning models, evolutionary optimization algorithms, high-throughput phenotyping, remote sensing, time series prediction, and numerical methods. He has published over 70 papers in various data application fields, including more than 30 in Q1 journals. His publications have been cited more than 1,700 times and he has an H-index of 25.
Publications:
- Zaji, A., Liu, Z., Xiao, G., Bhowmik, P., Sangha, J. S., & Ruan, Y. (2023). “AutoOLA: Automatic Object Level Augmentation for Wheat Spikes Counting.” Computers and Electronics in Agriculture. (https://www.sciencedirect.com/science/article/pii/S016816992300011X?dgcid=coauthor)
- Zaji, A., Liu, Z., Bando, T., & Zhao, L. (2023). Ontology-Based Driving Simulation for Traffic Lights Optimization. ACM Transactions on Intelligent Systems and Technology. (https://dl.acm.org/doi/abs/10.1145/3579839)
- Zaji, A., Liu, Z., Xiao, G., Bhowmik, P., Sangha, J. S., & Ruan, Y. (2022). “Wheat spike localization and counting via hybrid UNet architectures.” Computers and Electronics in Agriculture, 203, 107439. (https://www.sciencedirect.com/science/article/abs/pii/S0168169922007475)
- Zaji, A., Liu, Z., Xiao, G., Sangha, J. S., & Ruan, Y. (2022). “A survey on deep learning appli- cations in wheat phenotyping.” Applied Soft Computing, 109761. (https://www.sciencedirect.com/science/article/abs/pii/S1568494622008109)
- Zaji, A., Liu, Z., Xiao, G., Bhowmik, P., Sangha, J. S., & Ruan, Y. (2023). Wheat Spikes Height Estimation Using Stereo Cameras. IEEE Transactions on AgriFood Electronics. (https://ieeexplore.ieee.org/abstract/document/10105651)
- Zaji, A., Liu, Z., Xiao, G., Bhowmik, P., Sangha, J. S., & Ruan, Y. (2022, May). Wheat spikes counting using object-level data augmentation. In 2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (pp. 1-6). IEEE. (https://ieeexplore.ieee.org/abst ract/document/9806479)
- Lin, J., Fernández, J. A., Rayhana, R., Zaji, A., Zhang, R., Herrera, O. E., & Mérida, W. (2022). Predictive analytics for building power demand: day-ahead forecasting and anomaly prediction. Energy and Buildings, 255, 111670. (https://www.sciencedirect.com/science/article/abs/pii/S0378778821009543)
- Zaji, A., Liu, Z., Xiao, G., Bhowmik, P., Sangha, J. S., & Ruan, Y. (2021, August). Wheat Spike Counting Using Regression and Localization Approaches. In 2021 IEEE International Conference on Imaging Systems and Techniques (IST) (pp. 1-6). IEEE. (https://ieeexplore.ieee.org/abstract/d ocument/9651407)
- Ebtehaj, I., Bonakdari, H., Zaji, A., & Gharabaghi, B. (2021). Evolutionary optimization of neu- ral network to predict sediment transport without sedimentation. Complex & Intelligent Systems, 7(1), 401-416. (https://link.springer.com/article/10.1007/s40747-020-00213-9)
- Ebtehaj, I., Bonakdari, H., Safari, M. J. S., Gharabaghi, B., Zaji, A., Madavar, H. R., & Mehr, A. D. (2020). Combination of sensitivity and uncertainty analyses for sediment transport modeling in sewer pipes. International Journal of Sediment Research, 35(2), 157-170. (https://www.sciencedirect.com/science/article/abs/pii/S1001627918303810)
- Zaji, A., Bonakdari, H., Khameneh, H. Z., & Khodashenas, S. R. (2020). Application of opti- mized Artificial and Radial Basis neural networks by using modified Genetic Algorithm on discharge coefficient prediction of modified labyrinth side weir with two and four cycles. Measurement, 152, 107291. (https://www.sciencedirect.com/science/article/abs/pii/S0263224119311558)
- Bonakdari, H., Zaji, A., Soltani, K., & Gharabaghi, B. (2020). Improving the accuracy of a remotely-sensed flood warning system using a multi-objective pre-processing method for sig- nal defects detection and elimination. Comptes Rendus. Géoscience, 352(1), 73-86. (https://comptes-rendus.academie-sciences.fr/geoscience/articles/10.5802/crgeos.4/)
- Rayhana, R., Jiao, Y., Zaji, A., & Liu, Z. (2020). Automated vision systems for condition assess- ment of sewer and water pipelines. IEEE Transactions on Automation Science and Engineering. (https://ieeexplore.ieee.org/abstract/document/9200333)
- Bonakdari, H., Zaji, A., Gharabaghi, B., Ebtehaj, I., & Moazamnia, M. (2020). More accurate prediction of the complex velocity field in sewers based on uncertainty analysis using ex- treme learning machine technique. ISH Journal of Hydraulic Engineering, 26(4), 409-420. (https://www.tandfonline.com/doi/abs/10.1080/09715010.2018.1498753)
- Gholami, A., Bonakdari, H., Zaji, A., & Akhtari, A. A. (2020). A comparison of artificial intelligence- based classification techniques in predicting flow variables in sharp curved channels. Engineering with Computers, 36(1), 295-324. (https://link.springer.com/article/10.1007/s00366-018-00697-7)
- Anyaoha, U., Zaji, A., & Liu, Z. (2020). Soft computing in estimating the compressive strength for high-performance concrete via concrete composition appraisal. Construction and Building Ma- terials, 257, 119472. (https://www.sciencedirect.com/science/article/abs/pii/S095006182031477X)
- Khateri, M., Shabanzade, F., Mirzapour, F., Zaji, A., & Liu, Z. (2020). A variational approach for fusion of panchromatic and multispectral images using a new spatial–spectral consistency term. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 3421- 3436. (https://ieeexplore.ieee.org/abstract/document/9119131)
- Bonakdari, H., Qasem, S. N., Ebtehaj, I., Zaji, A., Gharabaghi, B., & Moazamnia, M. (2020). An expert system for predicting the velocity field in narrow open channel flows using self-adaptive extreme learning machines. Measurement, 151, 107202. (https://www.sciencedirect.com/science/article/abs/pii/S0263224119310681)
- Gholami, A., Bonakdari, H., Mohammadian, M., Zaji, A., & Gharabaghi, B. (2019). Assess- ment of geomorphological bank evolution of the alluvial threshold rivers based on entropy concept parameters. Hydrological Sciences Journal, 64(7), 856-872. (https://www.tandfonline.com/doi/full/10.1080/02626667.2019.1608995)
- Bonakdari, H., Zaji, A., Binns, A. D., & Gharabaghi, B. (2019). Integrated Markov chains and uncertainty analysis techniques to more accurately forecast floods using satellite signals. Journal of Hydrology, 572, 75-95. (https://www.sciencedirect.com/science/article/abs/pii/S00221694193 01842)
- Gholami, A., Bonakdari, H., Zaji, A., & Akhtari, A. A. (2019). An efficient classified radial basis neural network for prediction of flow variables in sharp open-channel bends. Applied Water Science, 9(6), 1-17. (https://link.springer.com/article/10.1007/s13201-019-1020-y)
- Safarzadeh, A., Zaji, A., & Bonakdari, H. (2019). 3D flow simulation of straight groynes using hybrid DE-based artificial intelligence methods. Soft Computing, 23(11), 3757-3777. (https://link.springer.com/article/10.1007/s00500-018-3037-9)
- Zaji, A., Bonakdari, H., & Gharabaghi, B. (2019). Developing an AI-based method for river discharge forecasting using satellite signals. Theoretical and Applied Climatology, 138(1), 347-362. (https://link.springer.com/article/10.1007/s00704-019-02833-9)
- Zaji, A., Bonakdari, H., & Gharabaghi, B. (2019). Advancing freshwater lake level forecast using King’s castle optimization with training sample adaption and adaptive neuro-fuzzy inference system. Water Resources Management, 33(12), 4215-4230. (https://link.springer.com/article/10.1007/s11269-019-02356-y)
- Zaji, A., & Bonakdari, H. (2019). Robustness lake water level prediction using the search heuristic- based artificial intelligence methods. ISH Journal of Hydraulic Engineering, 25(3), 316-324. (https://www.tandfonline.com/doi/abs/10.1080/09715010.2018.1424568)
- Ebtehaj, I., Bonakdari, H., Zaji, A., & Sharafi, H. (2019). Sensitivity analysis of parameters affecting scour depth around bridge piers based on the non-tuned, rapid extreme learning machine method. Neural Computing and Applications, 31(12), 9145-9156. (https://link.springer.com/article/10.1007/s00521-018-3696-6)
- Ebtehaj, I., Bonakdari, H., & Zaji, A. (2018). A new hybrid decision tree method based on two artificial neural networks for predicting sediment transport in clean pipes. Alexandria engineering journal, 57(3), 1783-1795. (https://www.sciencedirect.com/science/article/pii/S1110016817301850)
- Zaji, A., Bonakdari, H., & Gharabaghi, B. (2018). Remote sensing satellite data preparation for simulating and forecasting river discharge. IEEE Transactions on Geoscience and Remote Sensing, 56(6), 3432-3441. (https://ieeexplore.ieee.org/abstract/document/8291504)
- Zaji, A., Bonakdari, H., & Gharabaghi, B. (2018). Applying upstream satellite signals and a 2-D error minimization algorithm to advance early warning and management of flood water levels and river discharge. IEEE Transactions on Geoscience and Remote Sensing, 57(2), 902-910. (https://ieeexplore.ieee.org/document/8447429)
- Gholami, A., Bonakdari, H., Zaji, A., Fenjan, S. A., & Akhtari, A. A. (2018). New radial ba- sis function network method based on decision trees to predict flow variables in a curved channel. Neural Computing and Applications, 30(9), 2771-2785. (https://link.springer.com/article/10.1007/s00521-017-2875-1)
- Khozani, Z. S., Bonakdari, H., & Zaji, A. (2018). Estimating shear stress in a rectangular channel with rough boundaries using an optimized SVM method. Neural Computing and Applications, 30(8), 2555-2567. (https://link.springer.com/article/10.1007/s00521-016-2792-8)
- Bonakdari, H., & Zaji, A. (2018). New type side weir discharge coefficient simulation using three novel hybrid adaptive neuro-fuzzy inference systems. Applied water science, 8(1), 1-15. (https://link.springer.com/article/10.1007/s13201-018-0669-y)
- Zaji, A., Bonakdari, H., & Gharabaghi, B. (2018). Reservoir water level forecasting using group method of data handling. Acta Geophysica, 66(4), 717-730. (https://link.springer.com/article/10.1007/s11600-018-0168-4)
- Sharifipour, M., Bonakdari, H., & Zaji, A. (2018). Comparison of genetic programming and radial basis function neural network for open-channel junction velocity field prediction. Neural Computing and Applications, 30(3), 855-864. (https://link.springer.com/article/10.1007/s00521-016-2713-x)
- Bonakdari, H., Khozani, Z. S., Zaji, A., & Asadpour, N. (2018). Evaluating the apparent shear stress in prismatic compound channels using the Genetic Algorithm based on Multi-Layer Perceptron: A comparative study. Applied Mathematics and Computation, 338, 400-411. (https://www.sciencedirect.com/science/article/abs/pii/S0096300318305046)
- Khozani, Z. S., Bonakdari, H., & Zaji, A. (2017). Estimating the shear stress distribution in circular channels based on the randomized neural network technique. Applied Soft Computing, 58, 441-448. (https://www.sciencedirect.com/science/article/abs/pii/S1568494617302776)
- Khozani, Z. S., Bonakdari, H., & Zaji, A. (2017). Using two soft computing methods to predict wall and bed shear stress in smooth rectangular channels. Applied Water Science, 7(7), 3973-3983. (https://link.springer.com/article/10.1007/s13201-017-0548-y)
- Khozani, Z. S., Bonakdari, H., & Zaji, A. (2017). Efficient shear stress distribution detection in circular channels using Extreme Learning Machines and the M5 model tree algorithm. Urban Water Journal, 14(10), 999-1006. (https://www.tandfonline.com/doi/abs/10.1080/1573062X.2017.1325495)
- Safarzadeh, A., Zaji, A., & Bonakdari, H. (2017). Comparative assessment of the hybrid genetic algorithm–artificial neural network and genetic programming methods for the prediction of longitudinal velocity field around a single straight groyne. Applied Soft Computing, 60, 213-228. (https://www.sciencedirect.com/science/article/abs/pii/S1568494617303915)
- Khozani, Z. S., Bonakdari, H., Akhtari, A. A., & Zaji, A. (2017). Estimating the shear force carried by walls in rough rectangular channels using a new approach based on the radial basis function method. International Journal of River Basin Management, 15(3), 309-315. (https://www.tandfonline.com/doi/abs/10.1080/15715124.2017.1307845)
- Akhbari, A., Zaji, A., Azimi, H., & Vafaeifard, M. (2017). Predicting the discharge coefficient of triangular plan form weirs using radian basis function and M5’ methods. Journal of Applied Research in Water and Wastewater, 4(1), 281-289. (https://arww.razi.ac.ir/article_767.html)
- Ebtehaj, I., Sattar, A. M., Bonakdari, H., & Zaji, A. (2017). Prediction of scour depth around bridge piers using self-adaptive extreme learning machine. Journal of Hydroinformatics, 19(2), 207-224. (https://iwaponline.com/jh/article/19/2/207/3408/Prediction-of-scour-depth-around-bridge-piers)
- Karimi, S., Bonakdari, H., Karami, H., Gholami, A., & Zaji, A. (2017). Effects of width ratios and deviation angles on the mean velocity in inlet channels using numerical modeling and artificial neural network modeling. International Journal of Civil Engineering, 15(2), 149-161. (https://link.springer.com/article/10.1007/s40999-016-0075-5)
- Ebtehaj, I., Bonakdari, H., Zaji, A., Bong, C. H. J., & Ab Ghani, A. (2016). Design of a new hybrid artificial neural network method based on decision trees for calculating the Froude number in rigid rectangular channels. Journal of Hydrology and Hydromechanics, 64(3), 252-260. (https://sciendo.com/article/10.1515/johh-2016-0031)
- Sharafi, H., Ebtehaj, I., Bonakdari, H., & Zaji, A. (2016). Design of a support vector machine with different kernel functions to predict scour depth around bridge piers. Natural Hazards, 84(3), 2145-2162. (https://link.springer.com/article/10.1007/s11069-016-2540-5)
- Ebtehaj, I., Bonakdari, H., & Zaji, A. (2016). A nonlinear simulation method based on a combination of multilayer perceptron and decision trees for predicting non-deposition sediment transport. Water Science and Technology: Water Supply, 16(5), 1198-1206. (https://iwaponline.com/ws/article-abstract/16/5/1198/31657/A-nonlinear-simulation-method-based-on-a)
- Ebtehaj, I., Bonakdari, H., & Zaji, A. (2016). An expert system with radial basis function neural network based on decision trees for predicting sediment transport in sewers. Water Science and Technology, 74(1), 176-183. (https://iwaponline.com/wst/article-abstract/74/1/176/19169/An-expert-system-with-radial-basis-function-neural)
- Bonakdari, H., & Zaji, A. (2016). Open channel junction velocity prediction by using a hybrid self-neuron adjustable artificial neural network. Flow Measurement and Instrumentation, 49, 46- 51. (https://www.sciencedirect.com/science/article/abs/pii/S0955598616300309)
- Gholami, A., Bonakdari, H., Zaji, A., Ajeel Fenjan, S., & Akhtari, A. A. (2016). Design of mod- ified structure multi-layer perceptron networks based on decision trees for the prediction of flow parameters in 90 open-channel bends. Engineering Applications of Computational Fluid Mechan- ics, 10(1), 193-208. (https://www.tandfonline.com/doi/full/10.1080/19942060.2015.1128358)
- Gholami, A., Bonakdari, H., Zaji, A., Michelson, D. G., & Akhtari, A. A. (2016). Improving the performance of multi-layer perceptron and radial basis function models with a decision tree model to predict flow variables in a sharp 90 bend. Applied Soft Computing, 48, 563-583. (https://www.sciencedirect.com/science/article/abs/pii/S1568494616303702)
- Zaji, A., Bonakdari, H., Khodashenas, S. R., & Shamshirband, S. (2016). Firefly optimization algorithm effect on support vector regression prediction improvement of a modified labyrinth side weir’s discharge coefficient. Applied Mathematics and Computation, 274, 14-19. (https://www.sciencedirect.com/science/article/abs/pii/S0096300315014216)
- Khozani, Z. S., Bonakdari, H., & Zaji, A. (2016). Application of a genetic algorithm in predicting the percentage of shear force carried by walls in smooth rectangular channels. Measurement, 87, 87-98. (https://www.sciencedirect.com/science/article/abs/pii/S0263224116001810)
- Khozani, Z. S., Bonakdari, H., & Zaji, A. (2016). Application of a soft computing technique in predicting the percentage of shear force carried by walls in a rectangular channel with non- homogeneous roughness. Water Science and Technology, 73(1), 124-129. (https://iwaponline.com/wst/article-abstract/73/1/124/18885/Application-of-a-soft-computing-technique-in)
- Khoshbin, F., Bonakdari, H., Ashraf Talesh, S. H., Ebtehaj, I., Zaji, A., & Azimi, H. (2016). Adaptive neuro-fuzzy inference system multi-objective optimization using the genetic algorithm/singular value decomposition method for modelling the discharge coefficient in rectangular sharp-crested side weirs. Engineering Optimization, 48(6), 933-948. (https://www.tandfonline.com/doi/abs/10.1080/0305215X.2015.1071807)
- Zaji, A., Bonakdari, H., & Shamshirband, S. (2016). Support vector regression for modified oblique side weirs discharge coefficient prediction. Flow Measurement and Instrumentation, 51, 1- 7. (https://www.sciencedirect.com/science/article/abs/pii/S095559861630098X)
- Shamshirband, S., Bonakdari, H., Zaji, A., Petkovic, D., & Motamedi, S. (2016). Improved side weir discharge coefficient modeling by adaptive neuro-fuzzy methodology. KSCE Journal of Civil Engineering, 20(7), 2999-3005. (https://link.springer.com/article/10.1007/s12205-016-1723-7)
- Zaji, A., & Bonakdari, H. (2015). Efficient methods for prediction of velocity fields in open channel junctions based on the artificial neural network. Engineering Applications of Computational Fluid Mechanics, 9(1), 220-232. (https://www.tandfonline.com/doi/full/10.1080/19942060.2015.1004821)
- Sharifipour, M., Bonakdari, H., Zaji, A., & Shamshirband, S. (2015). Numerical investigation of flow field and flowmeter accuracy in open-channel junctions. Engineering Applications of Com- putational Fluid Mechanics, 9(1), 280-290. (https://www.tandfonline.com/doi/full/10.1080/19942060.2015.1008963)
- Gholami, A., Bonakdari, H., Zaji, A., & Akhtari, A. A. (2015). Simulation of open channel bend characteristics using computational fluid dynamics and artificial neural networks. Engineering Applications of Computational Fluid Mechanics, 9(1), 355-369. (https://www.tandfonline.com/doi/full/10.1080/19942060.2015.1033808)
- Zaji, A., & Bonakdari, H. (2015). Application of artificial neural network and genetic program- ming models for estimating the longitudinal velocity field in open channel junctions. Flow Measure- ment and Instrumentation, 41, 81-89. (https://www.sciencedirect.com/science/article/abs/pii/S0 955598614001307)
- Zaji, A., & Bonakdari, H. (2015). Impact of the confluence angle on flow field and flowmeter accuracy in open channel junctions. International Journal of Engineering, 28(8), 1145-1153. (https://www.ije.ir/article_72560.html)
- Ebtehaj, I., Bonakdari, H., Zaji, A., Azimi, H., & Sharifi, A. (2015). Gene expression program- ming to predict the discharge coefficient in rectangular side weirs. Applied Soft Computing, 35, 618-628. (https://www.sciencedirect.com/science/article/abs/pii/S1568494615004330)
- Bonakdari, H., Zaji, A., Shamshirband, S., Hashim, R., & Petkovic, D. (2015). Sensitivity anal- ysis of the discharge coefficient of a modified triangular side weir by adaptive neuro-fuzzy method- ology. Measurement, 73, 74-81. (https://www.sciencedirect.com/science/article/abs/pii/S0263224115002808)
- Ebtehaj, I., Bonakdari, H., Zaji, A., Azimi, H., & Khoshbin, F. (2015). GMDH-type neural network approach for modeling the discharge coefficient of rectangular sharp-crested side weirs. Engineering Science and Technology, an International Journal, 18(4), 746-757. (https://www.sciencedirect.com/science/article/pii/S2215098615000774)
- Zaji, A., Bonakdari, H., Shamshirband, S., & Qasem, S. N. (2015). Potential of particle swarm optimization based radial basis function network to predict the discharge coefficient of a modified triangular side weir. Flow Measurement and Instrumentation, 45, 404-407. (https://www.sciencedirect.com/science/article/abs/pii/S0955598615000618)
- Gholami, A., Bonakdari, H., Zaji, A., Akhtari, A. A., & Khodashenas, S. R. (2015). Predicting the velocity field in a 90 open channel bend using a gene expression programming model. Flow Measurement and Instrumentation, 46, 189-192. (https://www.sciencedirect.com/science/article/abs/pii/S0955598615300285)
- Zaji, A., Bonakdari, H., & Karimi, S. (2015). Radial basis neural network and particle swarm optimization-based equations for predicting the discharge capacity of triangular labyrinth weirs. Flow Measurement and Instrumentation, 45, 341-347. (https://www.sciencedirect.com/science/article/abs/pii/S0955598615300029)
- Karimi, S., Bonakdari, H., & Zaji, A. (2015). Numerical Examination of the Relative Effect of the Channel Width in the Intakes on the Velocity Distribution Curves in the Flow Deviation Location. Journal of Civil and Environmental Engineering, 45(78), 93-102. (https://ceej.tabrizu.ac.ir/article_3681_0.html?lang=en)
- Zaji, A., & Bonakdari, H. (2014). Performance evaluation of two different neural network and particle swarm optimization methods for prediction of discharge capacity of modified triangular side weirs. Flow Measurement and Instrumentation, 40, 149-156. (https://www.sciencedirect.com/science/article/abs/pii/S0955598614001204)
Awards
- International Four-Year Doctoral Partial Tuition Award - School of Engineering, The University of British Columbia - British Columbia, Canada - 2022
- University Graduate Fellowship - School of Engineering, The University of British Columbia - British Columbia, Canada - 2022
- University Graduate Fellowship - School of Engineering, The University of British Columbia - British Columbia, Canada - 2021
- Special University of BC Okanagan Graduate Award - School of Engineering, The University of British Columbia - British Columbia, Canada - 2021
- International Four-Year Doctoral Partial Tuition Award - School of Engineering, The University of British Columbia - British Columbia, Canada - 2021
- University Graduate Fellowship - School of Engineering, The University of British Columbia - British Columbia, Canada - 2020
- Golden Associates Graduate Award in Civil Engineering - School of Engineering, The University of British Columbia - British Columbia, Canada - 2019
- Top Researcher amongst all candidates - Razi University – Kermanshah, Iran - 2016
- Ranked as the 2nd instructor in the department (out of 49) based on student evaluations - Razi University – Kermanshah, Iran – 2nd semester of 2015/2016
- Ranked as the 1st instructor in the department (out of 53) based on student evaluations - Razi University – Kermanshah, Iran – 1st semester of 2015/2016
- Ranked as the 1st instructor in the department (out of 39) based on student evaluations - Razi University – Kermanshah, Iran – 3rd semester of 2014/2015
- Ranked as the 2nd instructor in the department (out of 42) based on student evaluations - Razi University – Kermanshah, Iran – 1st semester of 2014/2015
- Ranked as the 1st instructor in the department (out of 30) based on student evaluations - Razi University – Kermanshah, Iran – 3rd semester of 2013/2014
- Ranked as the 2nd instructor in the department (out of 26) based on student evaluations - Razi University – Kermanshah, Iran – 2nd semester of 2013/2014
- Ranked as the 1st instructor in the department (out of 30) based on student evaluations - Razi University – Kermanshah, Iran – 1st semester of 2013/2014
Contact him through LinkedIn.