Aamer Kazi
Research, FEA, AI and ML at Align Technology
Profile

About Me

I work at the intersection of physics-based simulation and applied machine learning. My PhD focused on nonlinear finite element modeling of manufacturing processes — building high-fidelity ABAQUS models with experimental validation under U.S. DOE and Honeywell programs. Today, at Align Technology, I ship production machine learning models for orthodontic treatment planning serving millions of patients annually under FDA regulatory oversight. I'm most interested in problems where domain knowledge changes how a model should be built — physics-informed features, geometric priors, constraint-aware optimization.

Skills & Expertise

Machine Learning & AI
PyTorchTensorFlowTransformers / HuggingFacescikit-learnXGBoost / LightGBMDiffusionNetOptunaLangGraphMLflowClearMLPhysics-Informed ML
Simulation & Computational Mechanics
ABAQUS (Explicit & Standard)Nonlinear FEAConstitutive ModelingDrucker–Prager / Johnson–CookDamage & Fracture MechanicsExperimental Validation & V&VManufacturing Process Simulation
Data & Scientific Computing
PythonMATLABNumPy / SciPyPandasOpen3D / TrimeshGeometric PriorsConstraint-Aware Optimization
Engineering & Infrastructure
GitDockerAWS BedrockVertex AIFDA Regulatory (ML in Production)CI/CD for ML Pipelines

Work Experience

Align Technology
R&D Engineer III, Modeling and Simulation
San Jose, CADec 2023 — Present
ML / Deep Learning

Deep Learning Enhanced Treatment Planning for the Invisalign System

Developed production ML/DL models for orthodontic treatment planning, iterating from multivariable regression to a Transformer architecture (multi-head attention) that contributed to a 20% increase in clinical efficacy for millions of patients annually. Led V&V under FDA regulatory oversight across 13+ demographic and clinical dimensions. Generated surrogate training labels via counterfactual analysis across 30,000+ cases, framing attachment placement as a Learning-to-Rank problem (LightGBM LambdaRank) deployed to production processing 1,000+ cases/week.

PyTorchTransformersLightGBMFDA V&V
Agentic AI

LLM-based Dental Treatment Assistant & Evaluation Framework

Architected a LangGraph ReAct agent integrating LLMs (Claude, Llama, Gemini) over AWS Bedrock for natural-language querying and modification of orthodontic treatment data, with a 3-category evaluation framework. Built a middleware layer with query sanitization and complexity classification for dynamic per-request model routing; currently being tested as a case screening tool for clinical studies.

LangGraphAWS BedrockClaudeLlama
Geometric Deep Learning

3D Shape Representation Learning for Orthodontic Attachments

Built a DiffusionNet-based mesh autoencoder encoding variable-topology 3D meshes into 8-12D latent vectors at sub-millimeter fidelity. Conducted systematic AE vs beta-VAE comparison across reconstruction fidelity, generative specificity, disentanglement, and interpolation smoothness. Designed embeddings as drop-in geometric features for the downstream treatment planning Transformer.

DiffusionNetPyTorchOpen3Dbeta-VAE
Clinical Research

Clinical Study Lead, APAC Region

Led an end-to-end clinical study assembling a cross-functional team of clinical experts, software developers, and CAD designers. Performed statistical power analysis via KNN-based case similarity search for sample size calculation.

KNNStatistical AnalysisJMP
Texas A&M University
Graduate Research Assistant
College Station, TXAug 2018 — Dec 2023
U.S. DOE

Finite Element Modeling of Rock Drilling Processes

Developed a finite element model in ABAQUS for PDC rock cutting simulation using the Drucker-Prager yield criterion with damage evolution to capture failure of granite under varying crack density, orientation, and confining pressure (0-100 MPa). Calibrated constitutive model parameters through uniaxial and diametrical compression testing with experimental validation. Built a MATLAB-based node separation algorithm for parametric crack studies. Constructed a laboratory-scale rock drilling setup demonstrating 30% reduction in cutting forces from plasma-induced pre-cracking.

ABAQUSDrucker-PragerMATLABV&V
Honeywell

Machining of Additively Manufactured Metals

Developed a 2D finite element model in ABAQUS with Johnson-Cook plasticity and damage criteria to simulate serrated chip formation during turning of Ti-6Al-4V. Investigated the effect of fracture energy on chip morphology. Analyzed machinability of additively manufactured Ti-6Al-4V, A205, and 17-4 SS through a full factorial DOE with SEM-EDS microstructural characterization.

ABAQUSJohnson-CookSEM-EDSDOE

Personal Projects

Coming Soon

Personal projects and open-source contributions are on the way. Check back soon.

Education

Texas A&M University
Ph.D., Mechanical Engineering · GPA: 3.74 / 4.0
College Station, USAAug 2018 — Sep 2023
Vellore Institute of Technology
B.Tech., Mechanical Engineering · GPA: 9.17 / 10
Vellore, IndiaAug 2014 — Jun 2018

Contact

Reach out via email or LinkedIn.

Email: aamerk4716@gmail.com