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Ph.D. in Computer Science

Howard University

B.S. in Computer Science

Howard University

Machine Learning Researcher

Artificial Intelligence for Positive Change Lab @ Howard University

As a PhD researcher, I lead interdisciplinary projects and mentor both undergraduate and graduate students. My research focuses on Adverse Drug Events and optimizing system performance through advanced scheduling algorithms, resulting in multiple publications presented at top-tier conferences.

• Led multiple interdisciplinary research projects, mentoring both undergraduate and graduate students.
• Focused on Adverse Drug Events and optimization of system performance using advanced scheduling algorithms.
• Published research in top-tier conferences, contributing to the broader machine learning and healthcare research communities.

Machine Learning Engineer Intern

Reddit

Developed a time-aware Transformer and productionized a low-latency, batched-inference pipeline to forecast user engagement, achieving ~600× speedup and enabling proactive ranking and personalized recommendations.

• Built a time-aware Transformer encoding temporal dynamics as positional embeddings with contextual features.
• Productionized a low-latency inference pipeline with batched inference for full user coverage.
• Delivered ~600× speedup over a non-optimized baseline, unlocking proactive ranking and personalized recommendations

Machine Learning Engineer Intern

Reddit

Worked as a Machine Learning Engineer Intern at Reddit, focusing on enhancing user engagement through AI-driven personalization. Developed a novel LLM-based Guided Onboarding system to improve subreddit recommendations, facilitating deeper user interaction and improved platform navigation.

• Designed and implemented an LLM-based Guided Onboarding system to enhance subreddit recommendations for new users.
• Developed an API enabling dynamic, personalized content suggestions using advanced prompt strategies.
• Improved user onboarding experience and platform navigation through AI-driven engagement techniques.

Software Engineering Intern

Reddit

Worked as an integral part of the Developer Platform team, focusing on building and maintaining apps to enhance user experience and platform functionality. Spearheaded the development of the consumer side of the developer platform, introducing feedback-driven improvements that catered to the needs of Reddit users.

• Integral part of the Developer Platform team, building and maintaining user-centric apps.
• Collaborated closely with cross-functional teams to ensure alignment with strategic goals.
• Employed advanced technologies for improved stability, scalability, and responsiveness of platform applications.

Research Assistant

AIM-AHEAD Training Practicum Pilot Program (PRIME)

Engaged in AI/ML development with a focus on health disparities, Big Data, and EHR, participating in pivotal AIM-AHEAD meetings and presenting vital AI research findings.

• Explored AI/ML applications across various health domains, including imaging and telehealth.
• Presented AI research on racial disparities in prostate cancer at AIM-AHEAD Annual Meeting.

Undergraduate Machine Learning Researcher

Howard University

As a senior member of the research team, I took on leadership roles across multiple projects spanning NLP, Computer Vision, and Acoustic Analysis. My contributions led to 8+ publications in world-class conferences and workshops, with several more under review.

• Led and contributed to research projects in NLP, Computer Vision, and Acoustic Analysis.
• Published 8+ papers in top-tier conferences and workshops, with more under review.
• Developed state-of-the-art models for code-switched sentiment analysis, language identification tasks, and dementia MMSE prediction/classification.
• Utilized a wide range of ML/DL libraries, including Keras, Scikit-Learn, and Hugging Face Transformers.

Machine Learning Engineer Intern

Excella

Contributed to cutting-edge projects like the Synthetic Doctor's Notes Generation using GPT2 and the VoiceFAQ Project, focusing on streamlining ML model deployment and managing AI scalability.

• Streamlined deployment of ML models to the cloud, enhancing AI environments.
• Took a lead role in synthetic data generation leveraging deep learning and NLP models.
• Focused on ML-Ops, AI scalability management, and the interpretation of AI/ML results.

Google PhD Fellowship Recipient

Google

The Google PhD Fellowship recognizes exceptional graduate students conducting innovative and impactful research in computer science and related fields, with the NLP Fellowship specifically awarded for outstanding contributions to Natural Language Processing.

NSF Fellowship Honorable Mention

National Science Foundation

The NSF Fellowship Honorable Mention recognizes promising graduate students who are pursuing research-based master's and doctoral degrees in NSF-supported science, technology, engineering, and mathematics disciplines.

AIM-AHEAD Research Fellowship Recipient

AIM-AHEAD

The AIM-AHEAD Research Fellowship supports researchers leveraging AI/ML to advance health equity, fostering innovation, diversity, and collaboration in addressing healthcare disparities.

Entity Only vs. Inline Approaches: Evaluating LLMs for Adverse Drug Event Detection in Clinical Text (Student Abstract)

Proceedings of the AAAI Conference on Artificial Intelligence

This evaluates prompting strategies for ADE detection on n2c2 clinical text; shows an entity-only extraction approach outperforms inline in precision, recall, and token efficiency.

Evaluating Llama-3.1 for Adverse Drug Event Entity and Relationship Extraction Across Prompting Techniques

International Conference on Advances in Computing Research

Assesses Llama-3.1 for ADE detection, NER, and relation extraction using zero-shot, few-shot, and tree-of-thought prompts; proposes a consistency + thought-merging variant and compares methods via F1, precision, and recall.

Ad-hoc Ensemble Approach for Detecting Adverse Drug Events in Electronic Health Records

Consortium For Computing Sciences In Colleges Eastern Regional 2024

Uses zero-shot LLMs combined with ensemble strategies to identify and classify ADEs in EHR clinical notes; experiments compare ensemble techniques and model performance.

Benchmarking Current State-of-the-Art Transformer Models on Token Level Language Identification and Language Pair Identification

International Conference on Computational Science and Computational Intelligence (CSCI)

This study benchmarks state-of-the-art transformer models for token-level language identification (LID) and introduces a novel Language Pair Identification (LPI) task. The results highlight the effectiveness of multilingual transformers in analyzing code-switched data, establishing new baselines for language identification in mixed-language corpora.

Ensembling and Modeling Approaches for Enhancing Alzheimer's Disease Scoring and Severity Assessment

International Conference on Computational Science and Computational Intelligence (CSCI)

This work investigates ensemble modeling techniques to improve the scoring and severity assessment of Alzheimer's Disease. Utilizing computational paralinguistics and predictive models, the research enhances reliability in assessing cognitive decline through machine learning approaches.

Evaluating Ensembled Transformers for Multilingual Code-Switched Sentiment Analysis

International Conference on Computational Science and Computational Intelligence (CSCI)

This research explores the effectiveness of ensembled transformer models in multilingual code-switched sentiment analysis. By combining multiple transformer-based architectures, the study evaluates performance improvements and robustness in handling mixed-language text, showcasing advancements in language modeling for code-switching tasks.

Baselining Performance for Multilingual Code Switching Sentiment Classification

Consortium For Computing Sciences In Colleges Eastern Regional 2023

In multilingual communities, significant social media content contains code-switched data; our research fine-tunes models across five datasets with English code-switched with other languages, outperforming current multi-language pair models on binary sentiment classification and showing comparable results on ternary tasks.

Term Frequency Features vs Transformers: A Comparison for Sentiment Classification of African Languages

Consortium For Computing Sciences In Colleges Eastern Regional 2023

Given the underrepresentation of over 2000 African languages in NLP, the recent release of AfriSenti-SemEval Shared Task 12 provides essential sentiment analysis datasets; our Delta TF-IDF approach shows promise in this low-resource setting, outperforming data-heavy transformer models.

Zero-Shot Classification Reveals Potential Positive Sentiment Bias in African Languages Translations

International Conference on Learning Representations 2023

Using the AfriSenti-SemEval dataset for sentiment analysis on 13 African languages, we translate each to English and employ a BART model for zero-shot classification, finding a potential translation bias towards positive sentiments in African languages.

Feature Importance Analysis for Mini Mental Status Score Prediction in Alzheimer Disease

International Conference on Learning Representations 2023

Using the SHapley Additive exPlanations (SHAP) method, this study proposes predictive models to forecast MMSE scores with 54 key features from a leading model, highlighting the Automated Readability Index (ARI) as the most influential feature for capturing language impairments in dementia patients.

Howard University Computer Science at SemEval-2023 Task 12: A 2-Step System Design for Multilingual Sentiment Classification with Language Identification

The 17th International Workshop on Semantic Evaluation (SemEval-2023) at ACL

The release of AfriSenti-SemEval Task 12 introduced 14 datasets for African languages sentiment analysis; our two proposed approaches, Delta TF-IDF and Language-Specific Model Fusion, showed comparable or superior performance to state-of-the-art models like AfriBERTa, AfroXLMR, and AfroLM.

Sentiment Analysis for Multiple African Languages: A Current Benchmark

Social Impact of AI for Africa (SIAIA-23) at AAAI-23

Despite the growth in sentiment analysis research, African languages remained underexplored until the release of AfriSenti-SemEval Shared Task 12. Our benchmarks across 12 languages revealed that while more data improves per-language model performance, models tailored for African languages excel, and a one-size-fits-all model approach isn't ideal, especially for languages with fewer samples.

Acoustic-Linguistic Features for Modeling Neurological Task Score in Alzheimer’s

Pacific Symposium on Biocomputing 2022 (PSB)

With global life expectancy rising due to medical advancements, there's an urgent need for technologies that detect aging-related cognitive diseases, especially Alzheimer's. Using natural language processing and machine learning, we analyzed speech patterns in Alzheimer's patients and, by comparing ten regression models and over 13,000 features, we outperformed existing baselines in predicting Mini-Mental Status Exam scores, emphasizing the importance of handcrafted linguistic features over acoustic ones.

Sentiment Classification of Code-Switched Text using Pre-Trained Multilingual Embeddings and Segmentation

8th International Conference on Natural Language Computing (NATL 2022)

In our increasingly bilingual world, most natural language processing research remains focused on singular languages. We've developed a sentiment analysis algorithm for mixed-language texts that outperforms baselines by over 11% in accuracy, using semantic similarity from pre-trained models and a tailored word set, offering potential for broader multi-language application.