About me

I'm a machine learning engineer and computational scientist skilled in building scalable data pipelines, training deep learning models, and accelerating analysis on high-performance computing clusters. With expertise in Python, MATLAB, C/C++, and modern ML frameworks, I focus on turning complex data challenges into efficient, reliable solutions that drive real-world impact.

My Expertise

Daniel lewis
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Daniel lewis

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Resume

Technical Expertise

  1. Programming Languages:

      Python MATLAB R C C++ C# PostgreSQL SQL

    Machine/Deep Learning Frameworks:

      MATLAB Deep Learning Toolbox Pytorch TensorFlow/Keras Q-Learning Reinforcement Learning (Un)Supervised Learning Computer Vision

      Data Handling/Visualization:

        Pandas Matplotlib Seaborn

      Other tools:

        Github VS Code Jupyter SLURM Linux High Performance/Distributed Computing

    Experience

    1. Microsoft Research

      Biomedical Machine Learning Intern

      Summer 2022
      • -Used Scikit-Learn’s random forest model to predict the progression of Amyotrophic Lateral Sclerosis
      • -Demonstrated how many models used for drug viability are insufficient
      • -Trained multiple deep learning models using a High-Performance Computing cluster via Microsoft Azure, testing 5000+ hyperparameters for the models
      • -Utilized Python libraries such as Pandas, NumPy, Matplotlib, and Seaborn for data analysis and visualization

    2. Research Assistant with UTEP Brain Computation Lab

      2023— Present
      • Project Github: https://github.com/lddavila/clustering_neuron_spikes_with_deep_learning
      • -Led a 3-person team to design/deploy an ML pipeline, improving processing throughput by 3900%
      • -Designed M.L. models to rate the quality of clusters using image processing with 70-92% accuracy
      • -Enhanced signal processing pipeline reliability by 19%, reducing error rates in neural data analysis

    PUBLICATIONS

    1. PEER REVIEWED PUBLICATIONS

      • 1. Giri A, Heaton CN, Batson SA, Macias AY, Reyes NF, Salcido AA, Davila LD (co-first), et al. Effect of acute alcohol consumption in a novel rodent model of decision-making. Alcohol Alcohol. 2025 Mar 25;60(3):agaf017. doi: 10.1093/alcalc/agaf017. PMID: 40229991.
      • 2. Dirk W. Beck, Cory N. Heaton, Luis D. Davila(co-first), et al. A Decision-Space Model Explains Context-Specific Decision-Making. Will be featured by NSF. doi: https://doi.org/10.1101/2024.07.29.605535
      • 3. Ibáñez Alcalá RJ, Beck DW, Salcido AA, Davila LD (co-first), et al. RECORD, a high-throughput, customizable system that unveils behavioral strategies leveraged by rodents during foraging-like decision-making. Commun Biol. 2024 Jul 6;7(1):822. doi: 10.1038/s42003-024-06489-8. PubMed PMID: 38971889; PubMed Central PMCID: PMC11227549. Featured by NSF https://new.nsf.gov/news/making-strides-understanding-decision-making
      • 4. Ibanez-Alcala, Rodrigo J.*; Luis D. (co-first); et al. Implant for long-term electrophysiological recordings using multiple neuropixel probes. In preparation Communication Biology-invited.

    2. PREPRINTS/UNDER REVIEW

      • 1. Lara I. Rakocevic*, Luis D. Davila (co-first), et al. Computational Primitives for Cost-Benefit Decision-Making. https://www.biorxiv.org/content/10.1101/2024.12.09.627657v1 . Nature under review
      • 2. Salcido, Alexis A.*; Reyes, Neftali F.; Batson, Serina A.*; Macias, Andrea Y.*; Negishi, Kenichiro*; Hossain, Safa B.*; Giri, Atanu*;Luis D. Davila(co-first); et al. Striosome Ghrelin interaction mediates cost-benefit decision-making state. Cell-invited.

    3. IN PREPARATION

      • 1. Luis D. Davila(co-first) et al. Spike sorting with dimension selection-based clustering algorithm. In preparation Communication Biology-invited

    Education

    1. University of Texas at El Paso (U.T.E.P)

      B.S. Computer Science - Data Analytics Concentration

      2018 - 2023

      Courses Included:

      • -Machine Learning
      • -Data Science
      • -Database Management Systems
      • -Data Mining
      • -Algorithms and Data Structures
      • -Software Engineering
      • -Matrix Algebra
      • -Calculus I, II, III

    2. M.S. in Computational Science

      2023 — Present

      Courses Included:

      • -Numerical Analysis
      • -Mathematical and Computational Modeling
      • -Advanced Scientific Computing
      • -Deep Learning for Partial Differential Equations
      • -Statistical Programming

    Portfolio

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