I am an applied data science researcher with prior industry experience eager to apply my knowledge of machine learning, statistical analysis, and other data science techniques to challenging and high impact business problems. I have a strong track record of leading teams of junior and senior colleagues, working on projects I curated and creating business value.

Below are several data science projects, primarily focused on improving the performance (speed of response, stability, and sensitivity) of biosensors (sensors able to detect harmful biological molecules) for more effective medical diagnostics 🩺💉💊, food safety 🍇🍅🍌 and environmental monitoring 🌱🐟💧, to keep people around the world healthier and safer.

⏱️📈⏩ Faster Sensor Response using Time Series Forecasting	👅🧠🔥 Increased Biosensor Stability using Machine Learning

🔍🎯📏 Enhanced Sensor Detection Limits with Signal Processing	💡🔀📚 Inverse Design of Optical Structures using Deep Learning

For further details of my skills and experience, review the sections below, or see my resume: 📄 Simon-Ward-Resume

 Vanderbilt University (Nashville, TN)                       2019 - 2024 (February)

   PhD in Electrical and Computer Engineering

 Durham University (Durham, United Kingdom)                   2014 - 2015

  Masters of Physics

 Durham University (Durham, UK)                         2011 - 2014

  Bachelors of Physics

Consultant Research Scientist                           2024 - present

 Stealth Startup (remote)

• Building a generative AI-powered chatbot using RAG and fine-tuning open source LLMs, to enhance companies’ competitive edge.

Research Associate                                2019 - present

 Vanderbilt University (Nashville, TN)

Investigating the application of AI, machine learning, and statistics to democratize healthcare, by improving performance of sensors for medical diagnostic testing.

• Devised deep learning-based approach to reduce sensor response time by > 5x, using ensembles of LSTM neural networks (Python, TensorFlow) for time series forecasting, uncertainty estimation, and transfer learning with a large-scale simulated dataset, enabling rapid testing of harmful molecules.
• Designed a new biosensing paradigm by applying data visualization, classification and pattern recognition algorithms (Python, SciKit-Learn) to sensor array data, a step towards unprecedented robust, scalable, and low-cost biosensors.
• Invented algorithm using Morlet wavelet filtering and Fourier analysis (Matlab) which improved detection limits of thin film sensors by 10x, and released user-centric open-source app.
• Built software (Python) and hardware to automate data collection, improving accuracy by 48% and increasing experimental throughput by 100x, enabling larger datasets for more generalizable models.

Embedded Systems Engineer                                2015 - 2019

 Crowcon Detection Instruments Ltd. (Abingdon, UK)

• Developed production-ready firmware (C) and hardware, collaborating with global cross-functional teams, driving the companies push towards IoT and expansion into a previously untapped market.
• Solved design flaws in products by troubleshooting customer issues under pressure and finding the root cause (temperature drift, static electricity), rescuing orders ($70,000+).
• Designed new test procedures, using software (Python) and hardware to raise production yields by 5%.

Research and Development Intern              (two summer internships) 2014 and 2015

 Crowcon Detection Instruments Ltd. (Abingdon, UK)

• Designed, implemented, and analyzed experiments to test software and hardware of a gas detecting camera and designed intelligent junction box, collaborating with a multi-functional global team.

Research Associate                                 2012 - 2015

 Durham University (Durham, UK)

• Engineered eddy current pipeline defect testing solution and data analytics (Python), potentially reducing operating costs by >20%, and communicated findings to partners and stakeholders at GE.
• Probed molecular behaviour of surfactants using dual polarization interferometry, providing valuable insights informing Procter and Gamble product development, and presented to P&G stakeholders.
• Modelled physics of sending a rocket to the moon (Python), adding novel functionality.

Research and Development Intern                 (summer internship) 2011

 Oxford Instruments (Abingdon, UK)

• Quantified vibration in cryogen-free superconducting magnet system with laser Doppler measurements.

Experience using the following machine learning models:

• Linear Regression
• Logistic Regression
• Support Vector Machines (SVM)
• Random Forests (RF)
• K-Nearest Neighbors (KNN)
• Principal Components Analysis (PCA)
• Linear Discriminant Anlysis (LDA)
• Artificial Neural Networks (ANN)
• Recurrent Neural Networks (RNN)
• Gated Recurrent Unit (GRU)
• Long Short-Term Memory (LSTM)
• Large Language Models (LLM)

Research Mentor                                  2019 - 2024

 Vanderbilt University (Nashville, TN)

• Led interdisciplinary team of junior and senior researchers working on projects I curated. The 10 mentees over 4 years went on to be co-authors on publications, presenters at national conferences, and graduate students embarking on PhD degrees of their own.

Teaching Assistant                                 2019 - 2020

 Vanderbilt University (Nashville, TN)

• Instructed undergraduate course focused on Python and digital systems, creating 30% of lab content.

Apprentice Advisor                                 2018

 Crowcon Detection Instruments Ltd. (Abingdon, UK)

• Mentored 3 junior employees during 3-month rotations within the R&D department, resulting in one apprentice taking a permanent position on the team.

🏆 C.F. Chen 2022 Graduate Student Paper Award for “Best Paper in Electrical Engineering”

🏆 2022 SPIE Optics and Photonics Education Scholarship

🏆 Vanderbilt Graduate Student Council 2024 Leadership Award for Mentorship Excellence

🏆 Vanderbilt Graduate Leadership Institute Fall 2022 Dissertation Enhancement Grant

🏆 Vanderbilt Institute of Nanoscience and Engineering (VINSE) 2023 Summer Image Competition Winner

I value and prioritise model fairness, understanding the data collection process and empathy for end users, and diversity (in all areas, but particularly data science teams).

1. Ward, S. J., Baljevic, M., & Weiss, S. M. (2024). Detection of Proteins in Human Serum, using Machine Learning Applied to Arrays of Porous Silicon Biosensors. Manuscript in Preparation.
2. Ward, S. J., Baljevic, M., & Weiss, S. M. (2024). Sensor Response-Time Reduction using Long-Short Term Memory Network Forecasting. arXiv 2404.17144. doi: 10.48550/arXiv.2404.17144
3. Ward, S. J., Cao, T., Zhou, X., Chang, C., & Weiss, S. M. (2023). Protein Identification and Quantification Using Porous Silicon Arrays, Optical Measurements, and Machine Learning. biosensors 13(9), 879, 1–12. doi: 10.3390/bios13090879
4. Ward, S. J., Layouni, R., Arshavsky-Graham, S., Segal, E., & Weiss, S. M. (2021). Morlet Wavelet Filtering and Phase Analysis to Reduce the Limit of Detection for Thin Film Optical Biosensors. ACS Sensors, 6(8), 2967–2978. doi: 10.1021/acssensors.1c00787
5. Arshavsky-Graham, S., Ward, S. J., Massad-Ivanir, N., Scheper, T., Weiss, S. M., & Segal, E. (2021). Porous Silicon-Based Aptasensors: Toward Cancer Protein Biomarker Detection. ACS Measurement Science Au, 1(2), 82–94. doi: 10.1021/acsmeasuresciau.1c00019

1. Ward, S. J., & Weiss, S. M. (2023). Reduction in sensor response time using long short-term memory network forecasting. Proc. SPIE, 12675(126750E). doi: 10.1117/12.2676836
2. Ward, S. J., Cao, T., Chang, C., & Weiss, S. M. (2022). Analysis of machine learning techniques for capture agent free biosensing with porous silicon arrays. Proc. SPIE, 11979(1197907). doi: 10.1117/12.2614697
3. Ward, S. J., & Weiss, S. M. (2021). Reducing detection limits of porous silicon thin film optical sensors using signal processing. Proc. SPIE, 11662(116620J). 10.1117/12.2579361

1. “Reduction in sensor response time using long short-term memory network forecasting” Ward, S. J., & Weiss, S. M. SPIE Optics and Photonics, San Diego, CA, Aug. 2023.
2. “Using Machine Learning with Porous Silicon to Determine IgG Concentrations in Human Serum” Paier, G., Ward, S. J., & Weiss, S. M. BMES, San Antonio, TX, Oct. 2022.
3. “Reducing Detection Limits of Porous Silicon Thin Film Sensors using Signal Processing” Ward, S. J., Layouni R., Arshavsky-Graham S., Segal E., and Weiss S. M. PSST, Lido di Camaiore, Italy, March. 2022.
4. “Analysis of Machine Learning Techniques for Capture Agent Free Biosensing with Porous Silicon Arrays” Ward, S. J., Cao, T., Chang, C., & Weiss, S. M. SPIE Photonics West, San Francisco, CA, Jan. 2022.
5. “Reducing Detection Limits of Optical Thin Film Sensors using Signal Processing” Ward, S. J., & Weiss, S. M. SPIE Photonics West, Online, March. 2021.

Vanderbilt University Engineering School Ambassador                                   2019 - 2023

 Vanderbilt University (Nashville, TN)

• Represented Vanderbilt School of Engineering to external stakeholders in public online information sessions and several in-person events, sharing research and experiences at Vanderbilt.
• Ran 3 outreach events for summer academy high school students to encourage STEM participation.

Food Packing Volunteer                              2022 - 2023

 Second Harvest Food Bank (Nashville, TN)

• Sorted and packed food bags for children experiencing hunger across middle and west Tennessee.

Assistant Foster Carer/Mentor                           2015 - 2019

 Oxfordshire County Council Social and Health Care (Oxford, UK)

• Cared for disadvantaged foster children from newborn to twelve years old, aiding my parents who are full-time caregivers. These children faced a range of difficulties, requiring specialized care.

Church Volunteer                                 2016 - 2018

 St Aldates Church (Oxford, UK)

• Prepared and served meals to the homeless population of Oxford.

Below are some of the ways to connect, please feel free to reach out with any questions, comments, or opportunities.

🖇 LinkedIn: linkedin.com/in/simon-j-ward/

📫 Email: Simon.J.Ward@outlook.com