How Justin Jadali Applies Engineering Methodology to Biomaterials and Tissue Engineering Research

Biomaterials research sits at the intersection of material science precision and biological complexity, a pairing that demands researchers who can hold variables constant, interrogate system behavior systematically, and treat inconsistency as data rather than error. Justin Jadali, a graduate student in Mechanical Engineering and Materials Science at Yale University, brings exactly that orientation to his work. Known in academic and research circles as Justin Shayan Jadali, he completed his B.S. in Mechanical Engineering at UCLA at age 20 after earning three Associate of Science degrees in Physics, Mathematics, and Natural Sciences from Irvine Valley College at 18, having graduated high school at 16 following a perfect ACT score. He is now finishing his M.S. in Mechanical Engineering and Materials Science at Yale at 21, alongside a certificate in Physical and Engineering Biology, while conducting research on alginate-based scaffolds for vascular tissue engineering in New Haven.

The methodological habits Justin Jadali carries into the laboratory come from mechanical engineering, a discipline that treats every input affecting a system output as something to be identified, quantified, and controlled. Applied to biomaterials science, this framework reshapes experimental design, imposes discipline on fabrication protocols, and produces biological outcome data that is far more interpretable than what loosely controlled experiments can generate. He also brings a background in team leadership and operational accountability from his experience building and managing teams in a startup environment, a discipline he has transferred directly to the demands of research practice.

Engineering Principles as a Research Foundation

Engineering methodology begins with the problem statement: a precise formulation that defines which variable changes, which variables remain constant, and what measurement will determine whether the change produced a meaningful biological effect. This requirement for clarity before action is a foundational engineering habit, and its value in biomaterials research is substantial.

Tissue engineering experiments are inherently multi-variable systems. Material stiffness, crosslinker chemistry, growth factor concentration, cell type, seeding density, and culture duration each independently influence the biological response being measured. Controlling for all of them simultaneously is the only way to attribute an observed outcome to the variable of interest rather than to an uncontrolled interaction between multiple changing inputs. That is not a biological best practice; it is an engineering requirement, and it produces more defensible data.

How Justin Jadali Structures the Experimental Workflow

Graduate researcher Justin Jadali approaches each experiment as an engineering design process: define the independent variable, specify all other inputs with acceptable tolerances, fabricate the test article according to a documented protocol, verify that the fabricated article matches the design specification, and then introduce it to the biological system. This sequence is not arbitrary. Each step functions as a checkpoint, a gate that the experiment must pass before it advances.

The logic is straightforward. Measuring a biological response produced by an uncharacterized material cannot yield an interpretable result because the material’s state is unknown. The engineering approach insists on knowing the state of every input to the system before trusting the outputs that system produces. This is the central methodological transfer from mechanical engineering to biomaterials science, and it is the lens through which Justin Jadali’s New Haven research is designed and executed.

Material Specification and Process Documentation

Fabricating alginate microparticles with consistent properties requires treating the fabrication process as a controlled manufacturing operation. Polymer concentration, crosslinker identity, crosslinker concentration, mixing conditions, gelation time, and post-fabrication washing steps all affect the mechanical and structural properties of the resulting particle. Specifying each of these parameters as a defined value with an acceptable tolerance, rather than as a general guideline, ensures that two batches prepared under the stated conditions produce particles with equivalent mechanical behavior.

This documentation standard is routine in mechanical and materials engineering, where design drawings specify tolerances and manufacturing records track conformance. Justin Jadali’s work in biomaterials fabrication applies the same principle: the fabrication protocol is a specification document, not a procedure summary, and any deviation from it is recorded and evaluated before the material advances to biological testing.

Characterization as a Quality Gate

Before any fabricated microparticle batch is introduced to a cell culture system, it undergoes mechanical and structural characterization. Elastic modulus, swelling ratio, and particle size distribution are each measured and recorded, producing a material identity record that confirms the batch matches the intended specification. This step is the engineering equivalent of incoming inspection: a verification that the article being tested is what the design document describes.

The consequence of skipping this step is the same in engineering as in biomaterials research: the downstream result cannot be cleanly interpreted. A biological outcome attached to a verified, characterized material is attributable to the material as designed. A biological outcome attached to an unverified batch may reflect the intended design, an undetected fabrication deviation, or a combination of both. Enforcing the characterization gate at every batch eliminates that interpretive ambiguity.

Justin Jadali’s Approach to Crosslinking Chemistry

The engineering methodology applied by Justin Jadali is most directly demonstrated in the core experimental comparison of his Yale research: calcium-crosslinked versus zinc-crosslinked alginate microparticles as delivery vehicles for vascular endothelial growth factor. The study is built around a single, controlled independent variable, crosslinking ion identity, with all other fabrication and culture parameters held constant across both conditions.

Crosslinking chemistry determines gel network architecture, junction density, degradation rate, and protein release kinetics simultaneously. Rather than selecting one of these properties as a proxy for the others, the research measures each independently across both crosslinking conditions, producing a multi-property characterization that maps how the two chemistries differ along every relevant performance dimension. This parametric approach, measuring multiple dependent variables against a single, well-controlled independent variable, is standard engineering practice applied to a materials science problem.

The output is a quantitative dataset: elastic modulus values, equilibrium swelling ratios, particle size distributions, and time-resolved growth factor release profiles for each crosslinking condition, forming the interpretive foundation for the biological experiments that follow.

Co-Culture Systems and Quantitative Biological Readouts

The co-culture model incorporates human umbilical vein endothelial cells and pericytes, embedded in three-dimensional alginate scaffolds alongside growth factor-loaded microparticles. Standardizing the cellular input requires the same process discipline applied to the material side: defined passage number ranges, defined seeding densities, and defined culture medium compositions held constant across all experimental conditions.

Vascular network formation is quantified through fluorescence microscopy and computational image analysis. Analysis pipelines extract total tube length, branching point density, average branch length, and lumen diameter from fluorescence micrographs using segmentation parameters that are defined before data collection begins and applied uniformly across every image in the dataset. The result is a numerical record with defined measurement precision, the type of output that supports rigorous statistical comparison and that makes cross-condition differences objectively verifiable rather than visually assessed.

Bioprinting and Additive Manufacturing in the Research Toolkit

Beyond alginate microparticle fabrication, Justin Jadali’s research interests extend to bioprinting and additive manufacturing for constructing three-dimensional tissue constructs with defined architecture. Investigation of microvessel self-assembly in bioprinted skin models extends the vascularization work: the same questions about scaffold mechanics, growth factor delivery, and endothelial cell behavior apply, with the added dimension of spatially controlled deposition.

Additive manufacturing and rapid prototyping enable fabrication of test geometries that conventional casting cannot produce, expanding the design space for tissue engineering scaffolds. The same engineering mindset governs both: treating fabrication as a specification-driven operation rather than an improvised procedure, requiring tight control over print parameters, bioink rheology, and post-processing conditions.

The Significance of Engineering Discipline in Biomaterials Science

The approach Justin Jadali brings to tissue engineering research addresses a reproducibility challenge that has affected the biomaterials field broadly: experimental results that are difficult to replicate or compare across laboratories because fabrication conditions, material characterization procedures, and biological assay parameters are documented too loosely to reconstruct with fidelity. Applying engineering-level process documentation and material verification standards to biomaterials research produces a body of work that can be meaningfully built upon, by other laboratories, by translational researchers, and by the broader scientific community evaluating the field’s progress.

The methodology applied in New Haven is the logical expression of that training: rigorous, systematic, and grounded in the principle that well-defined experiments produce knowledge that loosely defined experiments cannot.

About Justin Jadali

Justin Jadali is a graduate student in Mechanical Engineering and Materials Science at Yale University in New Haven, Connecticut. He holds a B.S. in Mechanical Engineering from UCLA and three Associate of Science degrees in Physics, Mathematics, and Natural Sciences from Irvine Valley College, and is completing a certificate in Physical and Engineering Biology at Yale alongside his M.S. degree. His research focuses on alginate biomaterial design, microparticle fabrication, and vascularization strategies for three-dimensional tissue constructs, with expertise in scaffold mechanical characterization, co-culture experimental systems, bioprinting, and computational image analysis of vascular network formation. He also serves as a teaching assistant for Yale’s mechanical engineering capstone design program. To learn more about his research and academic work, visit Justin Jadali’s academic profile and research portfolio.