About

Munif is a software engineer and data scientist with 6 years of experience in building solutions for data analytics projects. His exper- tise is in developing large-scale data engineering systems with cloud orchestration, interactive visualizations in web dashboards, monitoring and alerting systems for complex computer hardware, highly automated data pipelines for processing hardware testing datasets, analytics workflows for machine-generated logs, and investigation of noisy time- series datasets from hardware telemetry. His educational training includes a PhD in data science where he studied building complex data products which combine effective ETL techniques with modern machine learning and network science applications. He also has a background in Electrical Engineering, where his areas of focus were digital sensors and signal processing. He posesses state-of-the-art familiarity with data analytics and engineering tools such as Python (Pandas, NumPy, SciPy, Scikit-learn, Plotly, Matplotlib, Seaborn), shell scripting, SQL, Airflow, and Elasticsearch, as well as cloud deployment tools (AWS and GCP).

Contact

• Email: munifishadmujib@gmail.com
• LinkedIn: linkedin.com/in/munifmujib
• Google Scholar: Profile

Skills

• Data Analysis and Scripting: Python (Pandas, NumPy, Airflow, PySpark, NetworkX, SciPy, Scikit-learn, TensorFlow, PyTorch), Unix Shell (bash/zsh).
• Automation and DevOps: Airflow, tmux, cron, Git, AWS, GCP, Jira.
• ETL: PostgreSQL, TimeScaleDB, InfluxDB, MongoDB, Elasticsearch, Logstash.
• Visualization: Plotly/Dash, Matplotlib/Seaborn, Vega.js, d3.js.
• Dashboarding, Reporting, and Documentation: Jupyter, Grafana, Redash, Kibana, rST, Markdown, .
• Other: JavaScript, MATLAB, C, C++, C#

Experience

Cerebras Systems

Sunnyvale, CA
Senior Data Scientist (Data Analytics) Apr 2022 – Present

• Architected and developed ETL data pipelines and automations centered on real-time telemetry and testing data collection, preprocessing, and analysis using Python, Pandas, Airflow, PostgreSQL, TimeScaleDB, InfluxDB, Git, and AWS infrastructure-as-code mechanisms.
• Developed design documents, implemented agile project management on Jira, and documented pipelines on internal Sphinx webserver with rST.
• Executed exploratory and investigative data analysis on noisy telemetry and hardware testing datasets to identify anomalous datapoints and variation patterns with Pandas, SQL, NumPy, and SciPy. Communicated findings visually with Jupyter and Plotly to stakeholders and provided recommendations for adjustment of testing and operation parameters.
• Improved design of deployed data pipelines, improving processing and reporting times by up to 97-99%. Monitored the execution of pipelines with Airflow, AWS CloudWatch, and Slack alerts as designated owner and performed effective debug analysis.
• Deployed statistical outlier detection methods with Pandas and SQL for complex hardware testing datasets and provided data consumers with interactive interfaces for adjustability of detection thresholds.
• Deployed the open-source Elasticsearch-Kibana-Logstash (ELK) stack to provide tools and Kibana QL workflows for collecting, organizing, analyzing, and reporting on machine-generated logs from Linux systems and unstructured text data from tests.
• Developed scalable user-friendly dashboards, interactive visualizations, and reporting utilities for hardware testing and monitoring datasets using Plotly Dash web apps on AWS servers.
• Negotiated stakeholder relationships across the organization to understand data producer and consumer requirements and inculcate a data-driven culture across engineering teams. Led training and communication efforts to improve engineering workflows. Mentored junior professionals through close collaboration.

College of Computing and Informatics, Drexel University

Philadelphia, PA
Data Scientist, Doctoral Researcher (CODED Lab ) Sep 2017 – Mar 2022

• Built a prototype news aggregator that combined graph partitioning algorithms, transformer neural language models (MiniLM/DistilBERT), and clustering techniques (HDBSCAN) to group, summarize, and connect news stories and social conversations with PyTorch, PySpark, and scikit-learn. Built a terabyte-scale real-time ETL system on top of public APIs and constructed a graph dataset of 3M news articles and 1.5B social posts with MongoDB and NetworkX. Published as PhD dissertation and multiple papers, presented at the HICSS conference.
• Developed continuous activity tracking analytics tools to monitor malicious coordinated campaigns and botnets on 3M social accounts with scikit-learn, SciPy, and Plotly. Presented at IC2S2.
• Developed supervised machine learning classifier with scikit-learn and NumPy for bot detection on social platforms using a veracity-annotated news discussion dataset consisting of 1.6M user-generated text records. Published and presented at ICWSM.
• Designed a prototype conversational agent incorporating a neural NLP pipeline (GPT-2 based) for mental health support using PyTorch and DeepSpeech as well as a Raspberry Pi client. Published in JMIR Human Factors and presented at EAI PervasiveHealth.
• Developed a low-cost frequency-based NLP algorithm for automatic expansion of a relational dataset of medical terms using user-generated text corpora with NumPy and SpaCy. Published at ICHI.

Semion Inc.

Dhaka, Bangladesh / Berkeley, CA
Machine Learning Engineer, NLP Jul 2016 – Jul 2017

Built business applications with state-of-the-art deep learning technologies. Developed a sequential CNN-based system for legal document discovery. Prototyped named entity recognition for financial documents utilizing LSTM and CRF models. Developed NLP and speech recognition components of voice-activated electronic health records software for medical professionals.

Education

• Drexel University, Philadelphia, PA
  Ph.D., Data Science 2022
  Combining software engineering, statistical analysis, and computational modeling with sociotechnical systems theory from complex systems, computational social science, network science, and communication literature to study the design and application of social data products leveraging large datasets, machine learning, and network analysis. Focus on data science for social good: analysis of malicious activity in social networks such as bots, misinformation, and coordinated campaigns as well as development of software tools for user empowerment.

• Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
  B.Sc., Electrical and Electronic Engineering 2016
  Developed a support vector machine (SVM) based machine learning system for forensic location classification from electrical network frequency data that won the IEEE Signal Processing Cup at ICASSP 2016. Developed an automatic household electrical load monitoring system utilizing power signature classification techniques with real-time user notifications and analytics.

Publications

• Mujib, M. I., Zelenkauskaite, A., & Williams, J. R. (2023). Which tweets ‘deserve’ to be included in news stories? Chronemics of tweet embedding. In Proceedings of the 56th Annual Hawaii International Conference on System Sciences (HICSS56).
• Smtriti, D., Kao, T. S., Rathod, R., Shin, J. Y., Peng, W., Williams, J. R., Mujib, M. I., Colosimo, M., & Huh-Yoo, J. (2022) MICA: Motivational Interviewing Conversational Agent for Parents as Proxies for Their Children in Healthy Eating. JMIR Human Factors, 06/08/2022:38908.
• Mujib, M. I. (2022). Modeling Emerging News Stories Across Digital Publications and Social Media. Drexel University, Philadephia. Doctoral dissertation.
• Wang, L., Mujib, M. I., Williams, J., Demiris, G., & Huh-Yoo, J. (2021). An Evaluation of Generative Pre-Training Model-based Therapy Chatbot for Caregivers. arXiv preprint, arXiv:2107.13115.
  
• Mujib, M. I., Heidenreich, H. S., Murphy, C. J., Santia, G. C., Zelenkauskaite, A., & Williams, J. R. (2020, August). NewsTweet: A Dataset of Social Media Embedding in Online Journalism. arXiv preprint, arXiv:2008.02870.
  
• Heidenreich, H. S., Mujib, M. I., & Williams, J. R. (2020, July) Investigating Coordinated ‘Social’ Targeting of High-Profile Twitter Accounts. arXiv preprint, arXiv:2008.02874. Presented at the 6th International Conference on Computational Social Science (IC2S2).
  
• Smriti, D., Shin, J. Y., Mujib, M. I., Colosimo, M., Kao, T. S., Williams, J., & Huh-Yoo, J. (2020, May). TAMICA: Tailorable Autonomous Motivational Interviewing Conversational Agent. In Proceedings of the 14th EAI International Conference on Pervasive Computing Technologies for Healthcare (pp. 411-414).
  
• Santia, G. C., Mujib, M. I., & Willams, J. R. (2019, June). Detecting Social Bots on Facebook in an Information Veracity Context. In Proceedings of the International AAAI Conference on Web and Social Media (ICWSM) Vol. 13 (pp. 463-472). AAAI.
  
• Mujib, M. I., Yang, C. C., Zhao, M., & Williams, J. R. (2018, June). Expanding Consumer Health Vocabularies with Frequency-Conserving Internal Context Models. In 2018 IEEE International Conference on Healthcare Informatics (ICHI) (pp. 241-246). IEEE.