Systems and Computer Science

B.S.
Howard University
2018

Computer Science (Artificial Intelligence, Computer Vision, and Human-Computer Interaction)

Ph.D.
Howard University
2021

Applied Artificial Intelligence

AI & ML in Healthcare

Natural Language Processing and Understanding

Computational Biometrics

Human-Computer Interaction

Artificial Inteliigence

Computer Vision

Intro to Machine Learning

This course introduces fundamental topics related to machine learning, including techniques for learning from data and applying these algorithms to application settings; other topics covered include Bayesian methods, linear classifiers such as the perceptron, regression, and non-parametric methods such as k-nearest neighbors. Specifically, we will study how computers can learn from data in this course. We will discuss fundamental concepts of machine learning and practice applying these concepts. The centerpiece of the course is a sequence of interactive learning labs that cover the concepts using real data. After taking this course, students will be familiar with some important approaches and algorithms, including linear models and neural networks for prediction and classification. Students will also learn some of the standard vocabularies of machine learning. The course is offered as a cross-listed course for graduate and undergraduate students. 

Computer Science III

Computer Science III teaches students advanced data structures and algorithmic programming. This course after taking or receiving credits for Computer Science II. While the course was initially taught in C/C++, this variant of the course was revamped and taught in Python. The course mostly focuses on CS majors and minors; however, all majors are welcome.

Computer Science II

Computer Science II teaches students basic data structures and object-oriented programming. This course after taking or receiving credits Computer Science I. While the course was initially taught in C/C++, this variant of the course was revamped and taught in Python. The course is mostly focused on CS majors and minors; however, all majors are welcome.

Computer Science I

Computer Science I teaches students advanced programming concepts. Most students usually take this course after taking or receiving credits for the intro course. While the course was initially taught in C/C++, this variant of the course was revamped and taught in Python. The course is mostly focused on CS majors and minors; however, all majors are welcome.

Grants, Honors, and Awards

2022 – Environmental Impact Data Collaborative/Bezos Earth Fund

2023 - Awarded $500,000 research gift, split into two $250,000 payouts, through Blocpower.io.

- Developing a distributed, realtime open source data platform with a personal ownership model.

- Piloting the developed platform with indoor air quality sensors in the DC-Baltimore area.

2022 – Amazon Sponsored Research Award

2023 - Awarded $80,000 research gift through Amazon to pursue research into healthcare and AI.

- Developing an AI model to understand and map clinical notes to the lab report.

- Detecting and predicting possible side effects from merging clinical notes and biomarkers.

2022 – NIH/AIM-AHEAD Course Development Mini Grant 

2023 - Awarded $20,000 mini-grant for course development through the NIH AIM-AHEAD initiative. 

- Developing a bridge course for social science and natural science majors to learn AI/ML.

- Course will be administered to address issues in diversity and equity in data science fields.

2022 – NVIDIA Jetson Nano Hardware Grant 

2023 - Awarded a grant of 20 Jetson Nano Developer Kits through a competitive application process.

- Designing a collaborative course structure on applying hardware to course content.

2020 – Vertically Integrated Project

2021 - Served as a faculty advisor to a mentorship and industry partnership program for 2 semesters.

- Awarded a grant of $30,000 for two semester: $22,000 funding students and $8000 towards 

   research and development.

2020 – Google Cloud Research Credit Grant

2021 - Awarded a grant to be used towards utilizing cloud computing resources from Google Cloud.

- The award totaled for about $5300 for a duration of one academic year.

2018 – Graduate Assistantship Award

2020 -Awarded ~ $58,000 annually to cover tuition and stipend expenses.

2020 National Security Agency Fund

- Awarded $16,000 toward tuition for dissertation research towards Biometric Identification.

2019 – Edmund & Maria Pioleau Award

2020 - Awarded $1,000 in scholarship from Howard University towards tuition and fees expenses.

2018 Magna Cum Laude Honors

- Graduated B.S. in Systems and Computer Science with honors and GPA > 3.6.

2014 – Founder’s Scholarship Award

2018                - Awarded by Howard University upon acceptance for 4 years of undergraduate study.

                        - Total award valued ~ $150,000 covering full tuition, room, board, and book stipend. 

Professional service and organizational memberships

2023 Program Committee Member, International conference on Innovations in Computing Research

2023 Reviewer, Workshop on the Social Impact of AI for Africa @ AAAI

2022 Program Committee Member, ACM Special Interest Group on Computer Science Education

2022 Session Chair, International Conference on Bioinformatics and Biomedical Science 

2022 – Editorial Board. American Journal of Artificial Intelligence

Present              

2022 – Review Board. American Journal of Medical Imaging

Present

2022 – InSTEM Mentor.  National Science and Technology Medals Foundation

Present

2021, Program Committee Member, IEEE International Conference On Healthcare Informatics

2022  

2020 – Professional Member, Association for Computing Machinery (ACM)

Present

2022 – Professional Member, Institute of Electrical and Electronics Engineers (IEEE)

Present

2014 – Student Member, National Society of Collegiate Scholars (NSCS)

2018

Peer-reviewed Publications (Conferences and Journals)

Prioleau, H., Aryal, S. K., & Blackstone, J. (2026). Leveraging large language models for adverse drug event detection: A comparative study of token and span-based named entity recognition. Biocomputing 2026: Proceedings of the Pacific Symposium, 205–218.

Johnson, J., Williams, L., Nias, J., Aryal, S. K., & Washington, G. (2025). Centering Black voices: Lessons learned and reflections from a large-scale AAVE data collection at a historically Black university. Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, 1–7.

Ince, A., Aryal, S. K., & Prioleau, H. (2025). Example-guided prompt tuning for sentiment analysis of code-switched Hindi and Dravidian languages. Proceedings of the Tenth International Congress on Information and Communication Technology (ICICT 2025), 8, 229. Springer Nature.

Ngueajio, M., Aryal, S., Atemkeng, M., Washington, G., & Rawat, D. (2025). Decoding fake news and hate speech: A survey of explainable AI techniques. ACM Computing Surveys, 57(7), 1–37.

Acharya, S., Aryal, S. K., & Burge, L. (2025). Unlocking the road ahead: Predicting the destination of a car within a small time interval. Proceedings of the International Conference on Information and Communication Technology for Intelligent Systems, 525–537. Springer Nature Singapore.

Hagos, D. H., Aryal, S., Ymele-Leki, P., & Burge, L. (2025). AI-driven multimodal colorimetric analytics for biomedical and behavioral health diagnostics. Computational and Structural Biotechnology Journal.

Prioleau, H., & Aryal, S. (2025). 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, 39(28), 29469–29471.

Prioleau, H., Aryal, S., & Burge, L. (2025). Evaluating Llama-3.1 for adverse drug event entity and relationship extraction across prompting techniques. In International Conference on Advances in Computing Research (pp. 136–147). Springer, Cham.

Johnson, J., Williams, L., Nias, J., Aryal, S., & Washington, G. (2025). Centering Black voices: Lessons learned and reflections from a large-scale AAVE data collection at a historically Black university. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (pp. 1–7).

Rijal, S., & Aryal, S. (2025). Howard University-AI4PC at SemEval-2025 Task 7: Crosslingual fact-checked claim retrieval—Combining zero-shot claim extraction and KNN-based classification. In Proceedings of the 19th International Workshop on Semantic Evaluation (pp. 1777–1782).

Tiwari, S., Aryal, S. K., & Burge, L. (2025). Enhancing geospatial reasoning in large language models: An optimized retriever approach using R-tree-based point-in-polygon and nearest neighbor search. Proceedings of the International Conference on Information and Communication Technology for Intelligent Systems, 509–523. Springer Nature Singapore.

Aryal, S., & Pant, K. (2025). Howard University-AI4PC at SemEval-2025 Task 9: Using open-weight BART-MNLI for zero-shot classification of food recall documents. In Proceedings of the 19th International Workshop on Semantic Evaluation (pp. 1919–1923).

Prioleau, H., Aryal, S. K., & Blackstone, J. (2025). Chicken disease image classification using modern CNNs and vision transformers. World Conference on Information Systems for Business Management, 49–59. Springer Nature Switzerland.

Aryal, S., & Agyemang-Prempeh, J. (2025). Howard University-AI4PC at SemEval-2025 Task 2: Improving machine translation with context-aware entity-only pre-translations with GPT-4o. In Proceedings of the 19th International Workshop on Semantic Evaluation (pp. 1885–1889).

Ince, A., & Aryal, S. (2025). Howard University-AI4PC at SemEval-2025 Task 11: Combining expert personas via prompting for enhanced multilingual emotion analysis. In Proceedings of the 19th International Workshop on Semantic Evaluation (pp. 1645–1655).

Aryal, S., & Dhungana, P. (2025). Howard University-AI4PC at SemEval-2025 Task 10: Ensembling LLMs for multilingual multi-label and multi-class meta-classification. In Proceedings of the 19th International Workshop on Semantic Evaluation (pp. 1585–1592).

Acharya, A., & Aryal, S. (2025). Howard University-AI4PC at SemEval-2025 Task 4: Unlearning sensitive content from large language models using finetuning and distillation. In Proceedings of the 19th International Workshop on Semantic Evaluation (pp. 1772–1776).

Aryal, S., & Akomoize, M. (2025). Howard University-AI4PC at SemEval-2025 Task 3: Logit-based supervised token classification for hallucination span identification with XGBOD. In Proceedings of the 19th International Workshop on Semantic Evaluation (pp. 1790–1794).

Aryal, S., & Abdulmujeeb, L. (2025). Howard University-AI4PC at SemEval-2025 Task 1: Using GPT-4o and CLIP-ViLT to decode figurative language across text and images. In Proceedings of the 19th International Workshop on Semantic Evaluation (pp. 1842–1848).

Tiwari, S., & Aryal, S. (2025). Howard University-AI4PC at SemEval-2025 Task 8: DeepTabCoder—Code-based retrieval and in-context learning for QA over tabular data. In Proceedings of the 19th International Workshop on Semantic Evaluation (pp. 1702–1708).

Aryal, S., & Prioleau, H. (2024). Ad-hoc ensemble approach for detecting adverse drug events in electronic health records. Journal of Computing Sciences in Colleges, 40(3), 238–249.

Prioleau, H., & Aryal, S. (2023, December). Benchmarking transformer models on token-level and pair language ID. In 2023 International Conference on Computational Science and Computational Intelligence (pp. 193–199). IEEE.

Aryal, S., Shah, U., Prioleau, H., & Burge, L. (2023). Ensembling and modeling approaches for Alzheimer’s disease severity assessment. In 2023 International Conference on Computational Science and Computational Intelligence (pp. 1364–1370). IEEE.

Aryal, S., Prioleau, H., Washington, G., & Burge, L. (2023). Evaluating ensembled transformers for multilingual code-switched sentiment analysis. In 2023 International Conference on Computational Science and Computational Intelligence (pp. 165–173). IEEE.

Aryal, S., Prioleau, H., & Burge, L. (2022, publ. 2023). Acoustic-linguistic features for modeling neurological task score in Alzheimer’s. In Pacific Symposium on Biocomputing 2023 (pp. 335–346).

Aryal, S., Shah, U., Burge, L., & Washington, G. (2023). From predicting MMSE scores to classifying Alzheimer’s disease detection & severity. Journal of Computing Sciences in Colleges, 39(3), 317–326.

Aryal, S., Prioleau, H., Aryal, S., & Washington, G. (2023). Baselining performance for multilingual code-switching sentiment classification. Journal of Computing Sciences in Colleges, 39(3), 337–346.

Aryal, S., & Prioleau, H. (2023). Howard University at SemEval-2023 Task 12: A 2-step system for multilingual sentiment classification. In Proceedings of the 17th International Workshop on Semantic Evaluation (pp. 2153–2159). ACL.

Pokhrel, A., Subedi, N., & Aryal, S. (2023). Evaluating factors influencing COVID-19 outcomes (Student abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16302–16303.

Aryal, S., Ngueajio, M., & Washington, G. (2023). Hey Siri, why are you biased against women? (Student abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16154–16155.

Shah, U., & Aryal, S. (2023). Experimenting with multimodal AutoML for Alzheimer’s detection. ICLR Tiny Papers 2023.

Aryal, S., & Adhikari, G. (2023). Evaluating emoticons and preprocessing in African tweet sentiment classification. ICLR Tiny Papers 2023.

Prioleau, H., & Aryal, S. (2023). Feature importance analysis for MMSE prediction. ICLR Tiny Papers 2023.

Aryal, S., Sapkota, H., & Prioleau, H. (2023). Zero-shot classification reveals sentiment bias in African translations. ICLR Tiny Papers 2023.

Aryal, S., & Tiwari, S. (2023). Fast-tracking geospatial queries with RTree point-in-polygon. Howard University Research Symposium.

Aryal, S., & Acharya, S. (2023). Predicting future car locations with rank learning. Howard University Research Symposium.

Aryal, S., & Abdus-Shakoor, A. (2023). Modeling hypothetical detonations with NukeMap & Python. Howard University Research Symposium.

Aryal, S., & Sankah, J. (2023). Addressing datagaps in ear biometrics: Diverse dataset. Howard University Research Symposium.

Aryal, S., & Eshun, F. (2023). Evaluating robustness of open-source face verification models. Poster, Howard University Research Symposium.

Aryal, S., Prioleau, H., & Aryal, S. (2023). Sentiment analysis across multiple African languages: A current benchmark. In Social Impact of AI in Africa Workshop @ AAAI.

Aryal, S., Barrett, T., & Washington, G. (2022). Comparative analysis of Mask RCNN variants for ear mask segmentation. In International Conference on Bioinformatics and Biomedical Science (pp. 46–53).

Aryal, S., Prioleau, H., & Washington, G. (2022). Sentiment classification of code-switched text using semantic similarity. In Proceedings of the 8th International Conference on Natural Language Computing.

Aryal, S., & Keiller, P. (2022). Associating eHealth policies and national data privacy regulations. arXiv preprint arXiv:2203.04089.

Washington, G., Mejias, M., Aryal, S., Shurn, T., & Burge, L. (2021). Using HBCU culture to teach data structures. Journal of Computing Sciences in Colleges, 37(3), 63–73.

Washington, G., Mance, G., Aryal, S., & Kengni, M. (2021). ABL-MICRO: Affective AI using a multimodal microaggression dataset. In Proceedings of the 4th Workshop on Affective Content Analysis @ AAAI (pp. 23–29).

Aryal, S., Williams, C., Rosemond, K., Salaam, C., & Washington, G. (2020). Self-efficacy Sydney: An EHWLT for young children with chronic illness. In Extended Abstracts of the 2020 ACM Symposium on Computer-Human Interaction in Play (pp. 178–181).

Books and Chapters

Aryal, S., Okunji, P., Shara, N., Libin, A., Jackson, A. D., Laurence, B., Symonette Ferguson, E., Washington, G., & Burge, L. (2023). Bias in Data, Artificial Intelligence and Machine Learning: Community Case Studies. Beech, B. M., & Heitman, E. (Eds.), Race and Research: Perspectives on Minority Participation in Health Studies 2nd ed., Vol.2) American Public Health Association. (in press)

Peer-reviewed Publications (Conferences and Journals)

Aryal, S. K., Thomas, Shondace., Johan Greene, & Gloria Washington (2019). EmotiColl: Towards Understanding and Prediction of Harmful Acts by Depressed Students. Poster presented at the Howard University Research Symposium.

Aryal, S. K., Prioleau, H., & Burge, L. (2022). Acoustic-Linguistic Features for Modeling Neurological 

Task Score in Alzheimer’s. Poster presented at the PACIFIC SYMPOSIUM ON BIOCOMPUTING, Kohala Coast, Hawaii, USA, 3–7 January 2023.

Aryal, S., Aryal, S.K., Washington, G. (2023) Skin Tone Detection using K-means Clustering. Poster presented at the Howard University Research Symposium.

Shah, U., & Aryal, S. K. (2023). Experimenting with Multimodal AutoML: Detection and Evaluation of 

Alzheimer's Disease. Poster presented at the Howard University Research Symposium.

Aryal, S. K., & Adhikari, G. (2023). Evaluating Impact of Emoticons and Pre-processing on Sentiment 

Classification of Translated African Tweets. Poster presented at the Howard University Research Symposium.

Prioleau, H., & Aryal, S. K. (2023). Feature Importance Analysis for Mini Mental Status Score Prediction in 

Alzheimer’s Disease. Poster presented at the Howard University Research Symposium.

Aryal, S. K., Sapkota, H., & Prioleau, H. (2023). Zero-Shot Classification Reveals Potential Positive Sentiment 

Bias in African Languages Translations. Poster presented at the Howard University Research Symposium.

Aryal, S. K.  & Tiwari, S. (2023), Fast-Tracking Your Geospatial Queries with RTree-based Point-in-Polygon and Nearest Neighbor Search. Poster presented at the Howard University Research Symposium., Washington, DC.

Aryal, S. K. & Acharya, S (2023). Unlocking the Road Ahead: Predicting Future Car Locations with Rank Learning. Poster presented at the Howard University Research Symposium.

Aryal, S. K. & Sankah, J. (2023), Addressing Datagaps in Ear Biometrics Research: Diverse Ear Dataset. Poster presented at the Howard University Research Symposium.

Aryal, S. K. & Eshun, F. (2023), Evaluating the Robustness of Open-Source Face Verification Models. Poster presented at the Howard University Research Symposium.