David Widemann Email & Phone Number

California, United States

San Francisco Bay Area

At Density, my primary goal is to accurately track and count people moving through office spaces by training models on radar input data with ZED RGBD-assisted labels. I leverage advanced signal processing techniques like beamforming, Capon, and MUSIC for precise direction-of-approach prediction, and employ neural networks for robust object detection.

Mountain View, California, United States

I meet with customers to understand their computational workloads and collect their requirements. Simultaneously, I introduce them to technical details of Lightmatter's photonic chip technology and demonstrate how it can be used to accelerate both their machine learning and signal processing applications. I am responsible for the design, description and.

Mountain View, California, United States

I develop machine learning and signal processing algorithms for photonic cores (photocores). Photocores compute inner-products much faster than standard compute paradigms. The main drawback is their analog noise causes a decrease in numerical precision. I build algorithms that train neural networks to have 99% the accuracy of digital, non-analog models and.

Livermore

I create machine learning and signal processing algorithms for imagery (RGB,LiDAR,SAR), sensor systems (IoT), natural language processing and medical applications. Currently developing deep reinforcement learning models on cancer and sepsis simulations to optimize treatment policies. Also applying GANs to physics simulations to learn better reduced order.

Menlo Park, CA

I create mathematical models and develop algorithms to perform detection and classification for sensor data. Sensors and data include harmonic radar, synthetic aperture radar, LiDAR, RGB images, gas chromatography-mass spectrometry, acoustic and seismic data. Many of my detection algorithms use statistical signal processing methods such as maximum.

Davis, CA

I used diffusion magnetic resonance imaging data to construct 3-dimensional models, tractography, for white matter in the human brain. The white matter corresponds to axons. Each axon connects its neuron to other neurons. The goal is to be able to model the network of axons for medical purposes. Investigating ideas that allow local diffusion measurements.

Rockville, MD

Project involved modeling quorum sensing in Burkholderia pseudomallei, Burkholderia mallei and Burkholderia thailandensis. Built an SQL database to store microarray results, wrote perl scripts to automatically load microarray data into the database. Performed t-tests and other statistical analysis to determine which genes were significant in regulating.

Univ. Of Maryland At College Park

I worked with the National Geospatial-intelligence Agency on dimensionality reduction for hyperspectral data. Special emphasis given to kernel eigenmap methods such as Locally Linear Embedding, Laplacian Eigenmaps, and diffusion wavelets. Implemented Nystroem methods for large scale problems. Developed new approaches to kernel construction and endmember.

Napa, California

I taught calculus I-II, differential equations, multivariable calculus and linear algebra.

Doctor Of Philosophy (Ph.D.), Computational And Applied Mathematics

Master Of Arts (M.A.), Mathematics

Bachelor Of Arts (B.A.), Mathematics