MD Tanim Hasan

+1-832-***-**** ad4kfc@r.postjobfree.com Houston, TX

github.com/Tanim419 linkedin.com/in/tanim-hasan

Experienced R&D professional in Data Science and Data Engineering focusing on physiological data analysis using advanced statistical associations & AI/ML techniques. Proficient in R, Python, and SQL. Specialized in architecting and managing data-driven analytic pipelines for research centered on multimodal data analysis. PROFESSIONAL EXPERIENCE

Research Assistant Affective & Data Computing Lab SEP 2019 – DEC 2023

● Directed a diverse team in naturalistic driving and deadline stress research, showcasing robust leadership and project management skills. Involved throughout the project life cycle, from design and implementation to data collection, ETL and analysis.

● Developed data collection pipelines & analytical models, employing supervised algorithms for classification and regression, as well as unsupervised machine learning techniques like clustering and principal component analysis to proficiently analyze datasets.

● Designed and tailored survey instruments employing REDCap and Qualtrics, optimizing the data collection workflow for a nationwide academic survey.

● Conducted qualitative and quantitative analysis, generating visualizations and reports through Power BI. Derived quality insights using NLP resulting in publications and presentations of detailed findings. Data Scientist Intern Texas A&M Transportation Institute JUN 2021 – AUG 2021

● Conceptualized and developed a data acquisition system for large-scale naturalistic driving study NUBI-DRIVE.

● Partnered with cross-functional project team members to identify data-gathering challenges, requirements and establish deliverables.

● Compiled data from multiple sources, pipelined for visualizations, and models portraying the cardiovascular activation in driving. Teaching Assistant University of Houston JAN 2019 – AUG 2019

● Assisted in planning and delivering Algorithm and Data Structures courses while acting as a support resource for students. Software Engineer (QA) Nilavo Technologies Ltd. MAY 2011 – AUG 2017

● Spearheaded QA efforts, overseeing the testing team in an agile environment to support high-end software and system development.

● Implemented and maintained an automated testing environment, reducing manual testing and increasing efficiency using tools such as Selenium WebDriver, Ruby, Java, Python, and Sikuli.

● Introduced new tools and methodologies, fostering continuous improvement throughout the Software Development Life Cycle. PROJECTS

● Aggressiveness of Driving (2023) - Employed different Machine Learning algorithms Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), utilizing a 5-minute time window to precisely forecast driving aggressiveness within the subsequent 1 minute. LSTM demonstrated superior performance compared to traditional ML algorithms and the CNN.

● Predicting Driver’s Physiological State (2023): Developed a Deep Learning model using a multi-headed attention layer Transformer model to predict driver arousal levels (Hypo, Normal, or Hyper). Utilized 5 minutes of multisensory data to forecast the 6th-minute physiological state, highlighting the direct link between driver well-being and predictive analytics.

● Driving Risks and Attitudes Towards Automated Vehicles (2022-2023) - Identified the factors associated with risks

(acceleration, traffic citations, crashes) in driving and the attitudes towards automated vehicle via a state wide survey in Texas. Exploiting statistical analysis with multivariate logistic regression.

● Naturalistic Ubiquitous Driving Study (2021- 2023) - Studied driver Heart Rate (HR) and Heart Rate Variability (HRV) during routine driving, revealing elevated HR associated with speed predisposition and anxiety-crucial indicators in cardiovascular health data. Utilized multivariate linear regression analysis and multivariate logistic regression for comprehensive insights.

● Sympathetic Activation in Deadlines of Deskbound Research (2019-2022) - Explored factors influencing sympathetic activation in researchers with deadlines, using stress as proxy for healthcare data. Identified correlations with knowledge work, break frequency, and smartphone usage. Exploited mixed multiple linear statistical modeling with participants centered random effects.

● Predictors of grantsmanship and funding success for U.S researchers (2020-2021)- Using statistical logistic regression model, analyzed a large-scale survey completed by academic faculties in the United States, revealing the influence of research tactics, scholar profile, and personality.

LANGUAGES & TECHNOLOGIES

● Advanced: R, Python Intermediate: SQL, Swift, Django, Ruby Basic: Java, C++

● ML & Visualization Tools: PyTorch, Keras, scikit-learn, nltk, NumPy, TensorFlow, Plotly, PowerBI, Matplotlib, ggplot2

● Cloud: AWS, SageMaker, S3

● Other Tools: RStudio, IntelliJ, Jupyter Notebook, PostGreSQL, Acuneticx, Jmeter, Git

● Survey Tools: Qualtrics, REDCap

EDUCATION

● Doctor of Philosophy (Ph.D.) − Computer Science, University of Houston. CGPA: 3.8 DEC 2023

● Master of Science (MS) − Computer Science, University of Houston. CGPA: 3.8 MAY 2023

● Bachelor of Science (BS) − Computer Science, Islamic University of Technology. CGPA: 3.7 OCT 2010 PUBLICATIONS

● Hasan, MD T., Alghamdi, H., Taamneh, S., Manser, M., Wunderlich, R., Tsiamyrtzis, P., & Pavlidis, I. (2023). Investigating cardiovascular activation of young adults in routine driving. IEEE Transactions on Affective Computing. https://doi.org/10.1109/TAFFC.2023.3291330 [Journal Impact Factor = 13.99]

● Hasan, MD T., Zaman, S., Wesley, A., Tsiamyrtzis, P., & Pavlidis, I. (2023). Sympathetic activation in deadlines of deskbound research

- A study in the wild. Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3544549.3585585 [CORE Rank = A*]

● Akleman, E., Hasan, MD T., & Pavlidis, I. (2021). Under the spell of deadlines. Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3411763.3450366 [CORE Rank = A*] OPEN CODE @ GitHub

● Hasan, MD T. & Pavlidis, I. (2023). Open code for Investigating Cardiovascular Activation in Routine Driving project: https://github.com/UH-CPL/NUBI-DRIVE-1

● Hasan, MD T. & Pavlidis, I. (2023). Open code for Sympathetic Activation in Deadlines project: https://github.com/UH- CPL/Sympathetic-Activation-in-Deadlines

● Hasan, MD T. & Pavlidis, I. (2022). Open code for Predictors of Grantsmanship & Funding Success project: https://github.com/UH-CPL/Predictors-Grantsmanship-Funding-Success-Code OPEN DATASETS @ OSF

● Hasan, MD T. & Pavlidis, I. (2023). Sympathetic Activation in Deadlines dataset. Open Science Framework. https://osf.io/46x7w/ SERVICE & AWARDS

● Award: Outstanding Ph.D. dissertation award from Computer Science Department Fall 2023.

● Mentorship: Mentored the undergraduate research students in REU program in 2021, 2022 & 2023.

● Scholarship: NSM Alumni Scholarship for achievements in research, academics, co-curricular and extracurricular activities - 2020 & 2022.

● Volunteer roles: Student volunteer at Computer Supported Collaborative Works (CSCW) Conference at Austin, 2019.

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