David Villarreal Email & Phone Number

San Francisco, Ca, Us

San Francisco, Ca, Us

Malta, Ny, Us

• Working on all product lines for the integrated circuits manufacturer, ranging from 130 nm nodes used for things like Internet of Things (IoT) and wearables, to 12 nm nodes developed by Intel and United Microelectronics Corporation (UMC), to be used for mobile and infrastructure markets. Focusing on FDX™ FD-SOI product line for System-on-Chip (SoC) integration for ultra-low power and performance; BCD® series for power management solutions with embedded memory options; 9SW RFSOI Technology for Next-Generation Mobile and 5G Applications for advanced RF solution focused on front-end modules (FEMs) for 5G frequencies, Sub-8 GHz, mmWave and FR3; and Manufacturing Analysis and Scoring (MAS) for code translation and violations monitoring to prevent Integrated circuits (ICs) failure. • Responsible for manufacturing optimization through pattern matching to check for lithographic hotspots in an integrated circuit. Created Machine Learning workflows, using Neural Networks, along with own data extraction pipelines to accelerate computations. Architected and developed automation software in Python, including the Quality Assurance process to support Cadence, Synopsys and Siemens software. Offered Yield Improvement solutions and created Machine Learning algorithms, inventing a new subsampling method to calculate the IC score based on a sub-selected population, drastically reducing compute time.• Built a knowledge graph chatbots through creating ontology to be extracted by LLMs to be converted to Python lists, readable in Neo4j as a CSV. Embedded conversations with Neo4j Cypher Declarative Query Language and Python, to be retrieved by GraphRAG.

Malta, Ny, Us

• Created AutoML workflows, combining annealing and genetic algorithm workflows to create and select optimal neural network to avoid hyperparameter tuning. Working on 3DIC pin placement algorithm development off of Verilog code; created data algorithms for the Process Design Kit (PDK) chatbot to streamline Salesforce data; tested Retrieval-Augmented Generation (RAG) architecture, incorporating GraphRAG for LLM optimization and Neo4j for graph analysis. Ongoing use of LLMs to extract Personal Identifying Information (PII) in conversations. • Set up and used a variety of AWS Services for the PDK and DFM Chatbots, including EC2, Lambda, Amazon Simple Queue Service (SQS), OpenSearch, Kendra, Bedrock, SageMaker, DynamoDB, and Amazon Elastic Container Registry (Amazon ECR• As the acting manager for the Job Enrichment Project for research projects development trained staff on AWS and created code for outlier detection, using multimodal capabilities for Virtual Assistants (VAs) and code summarization, using packages like OpenCV for data extraction from videos and documents. Ensured GenAI technology integration into the daily routines for the project. • Performing extensive work with Deep Learning, developing neural networks for regression and classification problems. Created testing algorithms to interpret Neural Network learning, and Recurrent Neural Network (RNN) for outlier and anomaly detection of Data Fabrication for issues, like dielectric breakdown, transistor characteristics, and others. Retrieved and organized large data sets of customer raw unstructured Salesforce data, unfit for RDBMS storage, into a Pandas DataFrame, categorically organizing it and feeding into the Chatbot. • Created Python Master Code to simplify usage of available tools. Used all machine Learning packages, such as NumPy, SciPy, TensorFlow, Keras, PyTorch, Pandas, and Scikit-learn.

• Founding Engineer for the firm specializing in predicting valence of popular songs. Working as a Full Stack Engineer and mobile app developer. • Developed data analytics algorithms. Using Django and GraphQL to create the statistical output to determine customer viability and increase ROI; Selenium for web scraping; PyTorch to create Neural Network regression model for DJ mixes and to determine transition types; and Neo4j to convert user behavior into a graph database.

Berlin, De

• Utilized the code from Harvard professor Boriz Kozinsky’s GitHub repository (NequIP), meant for building E(3)-equivariant interatomic potentials. Created equivariant neural networks for SrTiO3 (Strontium Titnate) molecular dynamics and liquid silicon. Engaged molecular dynamics to predict material behavior, complimenting it with Machine Learning techniques to confirm accuracy. • Created SrTiO3 crystal in LAMMPS (Molecular Dynamics Language) using object orientation and ran structure relaxation for molecular dynamics; and generated neural networks to be trained on data from FHI-aims, a C++-based electronic structure theory with numeric atom-centered orbitals. Engaged python to analyze molecular dynamics structures and geometry for octahedral tilting, including producing related statistics; and NumPy and Pandas libraries for the geometrical computations. Performed QA and analysis, using seaborn’s data visualization, graphing, and analytics.

Austin, Tx, Us

Austin, Tx, Us

First project:Low power two stage fully differential OTA using 180 nm technology in Cadence. First stage was telescopic, second stage common source. The circuit has common mode and differential mode feedback circuits along with frequency compensation techniques. The whole design was based on a single ideal current source.Second projectLow power, low noise receiver stage using 180nm Cadence simulation. Topology was a 30 dB Cascode LNA, with a 4 path (N Path) downconversion filter with < 3 dB NF.

Champaign, Il, Us

Worked on implementing machine learning algorithms to the problem of many body localization. Research available at https://www.ideals.illinois.edu/bitstream/handle/2142/98888/SROP_report.pdf?sequence=2&isAllowed=y

Champaign, Il, Us

• Highly parallelized C program for numerical fluid dynamics NumericalFluidDynamics: https://github.com/davidleocadio94/NumericalFluidDynamics• Earthquake data analysis in the context of modern statistical physics (renormalization group and scaling theories).The Renormalization Group: https://courses.physics.illinois.edu/phys498cmp/sp2022/Ising/RG.html• Worked on implementing machine learning algorithms to the problem of many body localization.Summer Research Opportunities Program (SROP): https://grad.illinois.edu/diversity/srop

Masters, Electrical And Computer Engineering

Exchange Student, Physics

Bachelor Of Science - Bs, Engineering Physics