Publications

• The future workforce: Exploring the role of artificial intelligence and technology in workforce skilling.

Ph.D. in Interdisciplinary Engineering, Texas A&M University, College Station, TX, USA [May 2023]

• Wearable technology to assess the effectiveness of virtual reality training for drone pilots.

M.S. in Construction Management, Texas A&M University, College Station, TX, USA [December 2019]

• Determination of Absorption Capacity of Jute Geotextile.

B.Sc. in Civil Engineering, Bangladesh University of Engineering & Technology, Dhaka, Bangladesh [October 2014]

• Sakib, M.N., Chaspari, T. and Behzadan, A.H., 2021. A feedforward neural network for drone accident prediction from physiological signals. Smart and Sustainable Built Environment. DOI: https://doi.org/10.1108/SASBE-12-2020-0181

• Hagen, E., Sakib, M.N., Rani, N., Nirjhar, E.H., Nenkova, A.N., Chaspari, T., Chu, S.L., Behzadan, A.H., & Arthur Jr, W. (In Preparation). “Interviewer perceptions of the strengths of veteran workers and weaknesses of veteran interviewees, and interventions to improve their employment interview performance”.

• Sakib, M.N., Behzadan, A.H. (In Preparation). “Human-AI partnership to improve workers’ experience in the construction industry: A systematic review.”

• Nirjhar, E.H., Sakib, M.N., Hagen, E., Rani, N., Chu, S.L., Arthur Jr, W., Behzadan, A.H., & Chaspari, T. (2022). “Investigating the Interplay Between Self-Reported and Bio-Behavioral Measures of Stress: A Pilot Study on U.S. Veterans’ Civilian Job Interviews”. In: 10th International Conference on Affective Computing & Intelligent Interaction (ACII). Nara, Japan. (accepted)

• Sakib, M. N., Chaspari, T., Ahn, C., & Behzadan, A. (2020). “An experimental study of wearable technology and immersive virtual reality for drone operator training”. In: 27th International Workshop on Intelligent Computing in Engineering, Berlin, Germany (pp. 154-163).

• Feng, K., Yadav, M., Sakib, M.N., Behzadan, A., Chaspari, T. (2019). “Estimating Public Speaking Anxiety from Speech Signals Using Unsupervised Transfer Learning”. In: IEEE Global Conference on Signal and Information Processing (GlobalSIP). Ottawa, Canada.

• Yadav, M., Sakib, M.N., Feng, K., Behzadan, A., Chaspari, T. (2019). “Virtual reality interfaces and population-specific models to mitigate public speaking anxiety”. In: 8th International Conference on Affective Computing & Intelligent Interaction (ACII). Cambridge, United Kingdom. [Received best paper award]

• Sakib, M.N., Yadav, M., Feng, K., Behzadan, A., Chaspari, T. (2019). “Coupling virtual reality and physiological markers to improve public speaking performance.” In: 19th International Conference on Construction Applications of Virtual Reality (CONVR). Bangkok, Thailand.

• Sakib, M.N., Khan, A.J., Islam, M.M., Khan, I. (2016). “Determination of Absorption Capacity of Open Mesh Type Jute Geotextile (JGT)”. In: International Geotechnical Engineering Conference on Sustainability in Geotechnical Engineering Practices and Related Urban Issues. Mumbai, India.

• Alizadeh, B., Sakib, M.N., Behzadan, A.H. (In Preparation). “Measuring flood risk perception in immersive virtual reality”.

• Sakib, M.N., Chaspari, T. and Behzadan, A.H. (In Preparation), " Long short-term memory networks to predict drone accidents from physiological signals".

• HOMA Dataset: Data and implemented code for the paper: Sakib, M.N., Chaspari, T. and Behzadan, A.H. (2021), "A feedforward neural network for drone accident prediction from physiological signals", Smart and Sustainable Built Environment. https://doi.org/10.1108/SASBE-12-2020-0181.

This dataset consists of different time-series physiological signals (i.e., electrocardiogram, electrodermal activity, skin temperature, heart rate) collected from 25 participants (19 male, 6 female), during 5-minutes long drone flying experiments in outdoor and virtual reality environments. The dataset is available at: CIBER LAB GitHub

• VerBIO Dataset: Yadav, M., Sakib, M.N., Nirjhar, E.H., Feng, K., Behzadan, A. and Chaspari, T. (2020), "Exploring individual differences of public speaking anxiety in real-life and virtual presentations", IEEE Transactions on Affective Computing. https://doi.org/10.1109/TAFFC.2020.3048299.

It is a multimodal bio-behavioral (e.g., audio, physiological signals, and self-reports) dataset of 344 public speaking sessions from 55 participants.

This data has been collected as part of a research study (EiF grant 18.02) jointly performed by HUBBS Lab and CIBER Lab at Texas A&M University. The dataset is available at: HUBBS LAB @ TAMU.

• Hagen, E., Sakib, M.N., Rani, N., Nirjhar, E.H., Nenkova, A.N., Chaspari, T., Chu, S.L., Behzadan, A.H., and Arthur Jr, W. (2022), “Interviewer Perceptions of Veterans in Civilian Employment Interviews and Suggested Interventions”, In: International Military Testing Association (IMTA) Conference. North Carolina, USA.