Mikel K. Ngueajio 🙋🏽‍♀️

I am a fourth-year Ph.D. student in Computer Science at Howard University in Washington, DC, with a strong passion for leveraging Machine Learning to better understand human language and enhance communication. My doctoral research focuses on the relationship between hate speech and fake news, with the aim of designing robust and explainable AI systems for detecting and mitigating these societal challenges effectively and transparently. Throughout my academic journey, I have collaborated on projects with leading organizations, including Amazon, Google, the Us national Geospcail Agency, and Virginia Tech with work published at top AI venues. I have served as conference reviewer for the Black in AI 2024; Women in Machine Learning 2024, and Currently serve as Progam committee member for Towards a Safer Web for Women at WebConf 2025 workshop. I am currently a Graduate Research Assistant for the Center of Excellence in AIML CoE-AIML advised by Dr. Danda Rawat.

Resume

Accomplishments 🎯

First Time Journal publication at the ACM Csur journal. Access here

Selected as Machine Learning ad Systems Rising Star by ML Commons 2024. Presented research at Nvidia HQ in Santa Clara, CA.

Amazon AWS PHD Research Fellowship recipient 2023-2024; Awarded 69,000 for PHD research advancement.

APPLE TMCF Scholarship Recipient 2023-2024: Awarded $15000 for PHD research advancement.

Third Place poster Winner at Richard Tapia Conference 2023. Poster titled: Towards Improving Chatbots for Multilingual conversations

BlackComputerHer Fellow 2022-2023

First Place Poster winner at ADMI Conference 2022

First Place Innovator Award Recipient at the 2021 CMD-IT Innovation Challenge

First place Research Poster Winner at the 2021 CCSC-Eastern Regional Conference

Experience 🎒

Graduate Research Assistant - CoEAIML Lab
Aug 2024 - Preset
Featured Link
  • Ph.D. dissertation topic: Centers around Assessing the relationship between hate speech and fake news to build a novel explainable system for detecting them from a multimodal text and image perspective. .
  • Skills Acquired: Statistical, sentiment, and correlation analysis and inferencing, Machine and deep learning for Image and Text processing; Explainable AI, Adversarial ML.
Graduate Research Assistant - ABLab
May 2020 - May 2024
Featured Link
  • Created a Microaggression dataset by extracting relevant content from TV shows, Blog posts, and social media.
  • Mentored 8 student in Research and development. Won Research prizes and competitions.
Machine Learning Engineering Intern - Apple
June 2023 - August 2023
  • Build a system for evaluating Siri dialogues integration with LLM.
  • Skills acquired: Python; data extraction and preprocessing, Software creation, Testing, Error Handling, and debugging; Version control and code management; Data Serialization and Parsing using NLP; API integration, request and response handling, Software Documentation, Prompt Engineering, Generative AI and LLM.
Web development Apprentice - Bloomberg
June 2021 - August 2021
Featured Link
  • Designed and built a website, “HBCU Tech Journeys” to help HBCU students network, and collaborate.
  • Skills acquired: CSS, HTML, Web Design, and Hosting: WordPress and GitHub Pages.
Data Scientist Intern - Amazon
August 2019 - November 2020
  • Build a customer subscription database using DataGrip to decide on subscription services available to customers, thus reducing inquiry and operation time by 20% as compared to standard web scraping techniques
  • Annotated data used to create the multiclass dataset and Used it to build model for assessing customer sentiment thus facilitating user satisfaction and retention
Kaggle conpetitor and content creator - Kaggle
June 2020 - present
Featured Link
  • Women in Data Science Datathon (2024): Equity in HealthCare: Task to predict if the patients received metastatic cancer diagnosis within 90 days of screening. Used ML-based data cleaning and supervised ML -CatBoosting Model. (Rank: top 35%).
  • Santander Customer Transaction Prediction (2019): for predicting if a customer will make a transaction. Used the Light gradient boosting method with 15-fold cross-validation and achieved a score of 90.07%. (Rank: Top 37%).
  • Women in Data Science Datathon (2019): An image classification problem to determining the chances of deforestation due to oil palm fields. Used Deep and Transfer learning techniques yield 79% prediction accuracy. (Rank: top 87%).

Education 🎓

Ungoing
PHD Computer Science
Howard University
GPA: 3.7/4.0 (currently)

Research Focus

  • Human Centered AI
  • Machine Learning
  • Natural Language Processing
  • AI for Social good
Masters of Science in Computer Science
Howard University
GPA: 3.6/ 4.0
I Published One papers in the Association for the Advancement of AI titled " ABL-Micro: Opportunities for Affective AI Built Using a Multimodal Microaggression Dataset"
Bachelor of Science in Computer Science
University Of Buea
GPA: Second Class Upper

Contact 📧

My inbox is always open. Have a question?, Wanna collaborate? or Wanna say hi?!
Email me