Junaid S Merchant, PhD

Junaid S Merchant, PhD

Data Analyst | Research Scientist | Visual Design

Hi! I’m Junaid.

      

About Me

With over a decade of experience, I have a proven track record of providing data-driven insights through cutting-edge analytics. I specialize in applying data science tools to behavioral research, public health, and social sciences. Currently, I am investigating health inequities using advanced multilevel modeling and novel machine learning applications. Fueled by an unrelenting curiosity, I strive to deepen our understanding of how social environments shape us and am passionate about driving meaningful change that positively impacts the world.

My background is in cognitive neuroscience, and I possess a diverse skill set in research design, data collection, project management, high-performance computing, and multiple programming languages. I have employed a variety of analytical techniques, including network analysis, Bayesian statistics, and artificial vision models. Throughout my professional experiences, I have worked with various types of large-scale data, including public health data from the CDC and NIH, social media data from multiple platforms, private health records, and human neuroimaging data. Driven by my enthusiasm for innovation, I am deeply committed to pioneering evidence-based approaches to uncover new knowledge.

Strengths: Data Science and Visualizations, Analytic Pipeline Development, Multi-Team Collaboration, Multilevel Modeling, Project Management, Machine Learning, Scientific/Technical Writing, fMRI, Social Epidemiology

Professional Highlights

Data Analyst at Big Data for Health Equity (BD4HE)
 • Advancing multilevel modeling techniques for health inequities
 • Developed social media machine learning models for public sentiment
 • Large-scale data management and analytics on public health datasets

Neuroscience of Social Interaction at University of Maryland
 • Supercomputing implementation for neuroimaging data
 • Network-based and Bayesian analyses of fMRI data

Pediatric Psychiatry at Georgetown University / Children’s National
 • End-to-end pipeline development for large-scale clinical data collection
 • Trained graduate students and organized hands-on analysis workshops

Education

 • Ph.D. Neuroscience and Cognitive Science | University of Maryland
 • M.S. in Experimental Psychology | Seton Hall University
 • B.A. in Psychology and Studio Art | University of North Carolina, Asheville

Awards

 • 2nd Place Data Competition | Computational Social & Affective Neuroscience
 • Outstanding Graduate Assistant Award | Top 2% of UMD Graduate Students
 • Network Science Graduate Fellowship | COMBINE
 • Broadening Participation Grant | Open Science Training
 • Diversity Award | Social & Affective Neuroscience Society

Technical Skills

  • Programming Environments: R, Python, MATLAB, Bash/Shell, Linux, SLURM, SGE, Docker, Singularity
  • Data Visualization: Adobe Photoshop, R - ggplot, ShinyR, sjPlot; Python - matplotlib, Seaborn; NetworkX
  • Analysis Software: R - glmmTMB, lmer, glm, ggeffects; Python - scikit-learn, gspread, pandas, numpy scipy, statsmodels, networkX; JAMOVI/JASP, Stata, SPSS
  • Web Tools: Google Colab, AWS, GitHub
  • MRI Software: SPM, AFNI, fMRIprep, NiMARE, FSL, MRIcroGL, CoSMoMVPA, Brain Connectivity Toolbox, Decoding Toolbox, Nilearn, NLtools, NiBabel, Connectome Workbench, Freesurfer, Network Based Statistics