About me

I'm a PhD student at the University of Edinburgh, in the Artificial Intelligence and its Applications Institute. I am focusing on using and improving neurosymbolic AI for biomedical applications.

To see my latest publications and preprints, check out my Google Scholar page.

News & Announcements

In a Nutshell: What I'm doing

  • hospital icon

    Neurosymbolic AI for Few Shot Prediction of Rare Side Effects

    As an Enrichment Scheme fellow at the Alan Turing Institute, I am using neurosymbolic approaches for few shot prediction of rare side effects in a clinical dataset.

  • drug icon

    Drug Discovery and Mechanism of Action Deconvolution

    In a partnership with Enveda Biosciences, I developed a neurosymbolic model for deconvoluting drug mechanisms of action.

  • network icon

    Neurosymbolic Methods for Reasoning over Knowledge Graphs

    With colleagues at the University of Edinburgh, we have created a comprehensive survey on neurosymbolic methods for reasoning over graph structures.

  • medical record icon

    Multimorbidity Clustering and Accrual with Depression

    In a collaborative project with clinicians and statisticians at the Usher Institute in Edinburgh, I investigated how groups of physical conditions cluster, and how they accrue alongside depression.

  • brain icon

    Meaningful Data Fusion for Alzheimer's Disease Data

    My most recent project involves the development of a novel method for meaningful multimodal data fusion. This is a joint effort with colleagues at the Fraunhofer Institute for Algorithms and Scientific Computing in Bonn, Germany.

Affiliations

Resume

Education

  1. Ph.D. Candidate in Artificial Intelligence

    Oct. 2021 — present

    University of Edinburgh School of Informatics
    Alan Turing Institute Enrichment Scheme recipient;
    Global Informatics Scholar;
    Under the supervision of Prof. Jacques Fleuriot & Dr. Paola Galdi;
    Thesis topic: Using and improving approaches in neurosymbolic artificial intelligence (hybrid models between deep learning and symbolic methods, such as logic programs) for biomedical applications.

  2. Master (M.Sc.) of Life Science Informatics

    Oct. 2019 — Aug. 2021

    Rheinische Friedrich-Wilhelms-Universität Bonn, B-IT
    Fulbright Research, DAAD, and Deutschlandstipendium Scholar;
    Grade: 1.2 (on a 1.0-4.0 scale, 1.0 being the best);
    Under the supervision of Prof. Holger Froehlich;
    Thesis topic: Network representation learning methods for adverse drug reaction prediction.

  3. Bachelor of Science (B.S.): Biology, Minors in Mathematics and Chemistry

    Aug. 2015 — May 2019

    Salisbury University
    Goldwater Honorable Mention, DAAD RISE Scholar;
    3.98/4.00 GPA, Summa Cum Laude;
    Under the supervision of Dr. Jennifer Nyland;
    Thesis topic: Assessing the effects of inorganic Arsenic on IL-1β and TNFɑ secretion, gene expression, and DNA methylation in murine macrophages to gauge immunotoxic effects.

Internships and Research Fellowships

  1. Alan Turing Enrichment Scheme Fellow

    October 2024 — June 2025

    The Alan Turing Institute, London, UK
    Thanks to the Alan Turing Institute Enrichment Scheme, I will be spending half of my remaining PhD time with the Turing Institute in London! There, I will be using neurosymbolic approaches for few shot prediction of rare side effects. See more here.

  2. Data Science Intern

    June 2023 — Aug. 2023

    Enveda Biosciences, Boulder, CO, USA
    During this recent internship, I designed a neurosymbolic approach, the “Mechanism of Action Retrieval System (MARS),” to deconvolute drug mechanisms-of-action for drug discovery. We use MARS upon our novel knowledge graph, which we coined MoA-net. Preprint out soon!

  3. Fulbright Scholar & DAAD Research Fellow

    Sep. 2019 — Aug. 2021

    Fraunhofer Institute for Algorithms and Scientific Computing, Germany
    After completing a 10-month Fulbright Research Fellowship, I applied for and received a 6-month DAAD short term grant to continue my research. Here, I usedgraph reasoning methods for side effect prediction and drug target identification.

Teaching Experience

  1. Tutor: INF2D Reasoning and Agents

    Jan. 2022 — May. 2022

    University of Edinburgh School of Informatics
    Organized and led weekly sessions outside of lectures to help students understand the course material and prepare for exams. This course discusses the basics of artificial intelligence and its underlying algorithms.

  2. Master's student supervisor

    Nov. 2021 — Aug. 2022

    University of Edinburgh School of Informatics
    I supervised Scott O'Donoghue for his master's thesis on "Applying Machine Learning and Interpretation Techniques to Persistent Critical Illness".

Service

Program Committee Member

  1. NeurIPS 2023: New Frontiers of AI for Drug Discovery and Development

    December 2023

    Workshop Website

  2. ICML 2023: Knowledge and Logical Reasoning in the Era of Data-driven Learning

    July 2023

    Workshop Website

  3. ICLR 2023: Neurosymbolic Generative Models (NeSy GeMs)

    May 2023

    Workshop Website

Peer Review

  1. ICLR 2025 (The Thirteenth International Conference on Learning Representations)

    October 2024

    ICLR Website

  2. Heliyon: A Cell Press Journal

    December 2023

    Journal Website

  3. IEEE Transactions on Neural Networks and Learning Systems

    May 2023

    Journal Website

Volunteer Experience

  1. Student Representative, AI and its Applications Institute (U. of Edinburgh School of Informatics)

    October 2024 - present

    AIAI website

  2. Royal Society Summer Science Exhibition

    July 2022

    Event Website

  3. STEM Ambassador for Informatics Circle

    October 2021 — present

    Organization Website

Blog