Josué García-Ávila, a highly accomplished individual from Guerrero, Mexico, boasts a strong educational background, having earned a Bachelor's degree in Mechatronics Engineering from Universidad Anáhuac and a Master’s degree in Manufacturing Systems from Tecnológico de Monterrey. As a graduate student in the Advanced Manufacturing Research Group, Josué excelled and was recognized with an academic scholarship from the National Council of Science and Technology of Mexico (CONACyT).

Josué's expertise in the field of engineering is further highlighted by his successful career in the automotive industry, where he worked as a Sr. Manufacturing Engineer (Machining & Assembly) at Bocar Group for several years. In addition to his professional achievements, Josué also demonstrated his commitment to making a positive impact, having lived in Costa Rica for two years where he dedicated himself to humanitarian work.

His passion for innovation and technology shines through in his current research interests, which include exploring the data-driven mechanics of architected, multifunctional, sustainable, soft, and stretchable materials to create mimetic artificial living matter for biomedical applications and beyond. His impressive research accomplishments are evidenced by his first-author publications.

Josué's dedication to his field and drive for success has not gone unnoticed. He has been awarded the EDGE Doctoral Fellowship, by nomination of the graduate admissions committee and most recently awarded the prestigious Claudio X. Gonzalez Graduate Fellowship to pursue PhD in Mechanical Engineering at the prestigious Stanford School of Engineering.

Honors & Awards

  • Claudio X. Gonzalez Graduate Fellowship in Engineering, Stanford School of Engineering (2023)
  • EDGE Doctoral Fellowship, Stanford University (2022)
  • The Kleiner Perkins, Mayfield, Sequoia Capital Fellowship, Stanford University (2022)
  • Excellence Scholarship for Master's degree, Tecnologico de Monterrey (2020)
  • National Quality Graduate Fellowship for Master's degree, Mexican National Council of Science and Technology (CONACyT) (2020)
  • National Award for Excellence in the Mechatronics Engineering Graduate General Examination, National Center for the Evaluation of Higher Education (CENEVAL) (2017)
  • 15 Global Challenges Program National Award – The Millennium Project, Permanent Mission of Mexico to the United Nations (2014)
  • Merit and Academic Excellence Award, Guerrero State Government (Mexico) (2012)
  • Silver Medal Award, Mathematical Olympiad of Central America and the Caribbean (2012)
  • Silver Medal Award, Asian Pacific Mathematics Olympiad (AMPO) (2012)
  • Gold Medal Award, “Pierre Fermat” National Math Competition, IPN (2011)
  • Silver Medal Award, "A.N. Kolmogorov" XIV National Math Competition, Universidad Anahuac (2011)
  • Weizmann Institute of Science Scholarship, Government of Israel and the Mexican Academy of Sciences (2011)

Education & Certifications

  • Master’s degree, Tecnológico de Monterrey, Manufacturing Systems (2022)
  • Bachelor's degree, Universidad Anáhuac, Mechatronics Engineering (2017)

Lab Affiliations

Work Experience

  • Senior Manufacturing Engineer, Bocar Group (December 1, 2015 - August 1, 2022)

    Oversees the design, development and optimization of high level assembly &machining manufacturing processes. Defines manufacturing process, determines requirements for new processes or equipment, troubleshoots and implements process improvements.



All Publications

  • Predictive Modeling of Soft Stretchable Nanocomposites Using Recurrent Neural Networks. Polymers Garcia-Avila, J., Torres Serrato, D. d., Rodriguez, C. A., Martinez, A. V., Cedillo, E. R., Martinez-Lopez, J. I. 2022; 14 (23)


    Human skin is characterized by rough, elastic, and uneven features that are difficult to recreate using conventional manufacturing technologies and rigid materials. The use of soft materials is a promising alternative to produce devices that mimic the tactile capabilities of biological tissues. Although previous studies have revealed the potential of fillers to modify the properties of composite materials, there is still a gap in modeling the conductivity and mechanical properties of these types of materials. While traditional Finite Element approximations can be used, these methodologies tend to be highly demanding of time and processing power. Instead of this approach, a data-driven learning-based approximation strategy can be used to generate prediction models via neural networks. This paper explores the fabrication of flexible nanocomposites using polydimethylsiloxane (PDMS) with different single-walled carbon nanotubes (SWCNTs) loadings (0.5, 1, and 1.5 wt.%). Simple Recurrent Neural Networks (SRNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) models were formulated, trained, and tested to obtain the predictive sequence data of out-of-plane quasistatic mechanical tests. Finally, the model learned is applied to a dynamic system using the Kelvin-Voight model and the phenomenon known as the bouncing ball. The best predictive results were achieved using a nonlinear activation function in the SRNN model implementing two units and 4000 epochs. These results suggest the feasibility of a hybrid approach of analogy-based learning and data-driven learning for the design and computational analysis of soft and stretchable nanocomposite materials.

    View details for DOI 10.3390/polym14235290

    View details for PubMedID 36501684

  • Novel porous structures with non-cubic symmetry: Synthesis, elastic anisotropy, and fatigue life behavior MATHEMATICS AND MECHANICS OF SOLIDS Garcia-Avila, J., Cuan-Urquizo, E., Ramirez-Cedillo, E., Rodriguez, C. A., Vargas-Martinez, A. 2022
  • E-Skin Development and Prototyping via Soft Tooling and Composites with Silicone Rubber and Carbon Nanotubes MATERIALS Garcia-Avila, J., Rodriguez, C. A., Vargas-Martinez, A., Ramirez-Cedillo, E., Martinez-Lopez, J. 2022; 15 (1)


    The strategy of embedding conductive materials on polymeric matrices has produced functional and wearable artificial electronic skin prototypes capable of transduction signals, such as pressure, force, humidity, or temperature. However, these prototypes are expensive and cover small areas. This study proposes a more affordable manufacturing strategy for manufacturing conductive layers with 6 × 6 matrix micropatterns of RTV-2 silicone rubber and Single-Walled Carbon Nanotubes (SWCNT). A novel mold with two cavities and two different micropatterns was designed and tested as a proof-of-concept using Low-Force Stereolithography-based additive manufacturing (AM). The effect SWCNT concentrations (3 wt.%, 4 wt.%, and 5 wt.%) on the mechanical properties were characterized by quasi-static axial deformation tests, which allowed them to stretch up to ~160%. The elastomeric soft material's hysteresis energy (Mullin's effect) was fitted using the Ogden-Roxburgh model and the Nelder-Mead algorithm. The assessment showed that the resulting multilayer material exhibits high flexibility and high conductivity (surface resistivity ~7.97 × 104 Ω/sq) and that robust soft tooling can be used for other devices.

    View details for DOI 10.3390/ma15010256

    View details for Web of Science ID 000743407000001

    View details for PubMedID 35009402

    View details for PubMedCentralID PMC8746103