Work with thought leaders and academic experts in Machine learning

Machine Learning Researcher / April, 2023 — Present

Analyzing local Representations of self-supervised vision transformers. Based on this work we published the following paper: https://arxiv.org/pdf/2401.00463.pdf; ◦ Foundation Model for Aerial Imagery; ◦ Aerial Vision-and-Dialog Navigation.

Staff Machine Learning Engineer / November, 2022 — March, 2023

Gesture Recognition: • The goal is to develop a system that can accurately classify and recognize different gestures as they happen, without any delay; • The number of gestures can be very high; • Both static and dynamic gestures should be detected.

Staff Machine Learning Engineer / January, 2022 — November, 2022

Worked on creating real-time state-of-the-art video segmentation/matting technology.

Senior Machine Learning Engineer II / January, 2020 — January, 2022

State of the art image segmentation pipeline

Image Processing Machine Learning Scientist/Engineer / May, 2019 — January, 2020

Audio Dereverberation; Image Segmentation

Research Fellow / 2016 — Present

Head of Research / 2014 — 2016

Robo-Advisor startup applying machine learning and the latest finance research to produce dramatically more profitable portfolios for ordinary investors.

Chief Scientist / 2013 — 2016

Datagami is a machine learning startup, bringing insights about your data to you, in terms you already understand. Under the hood it's cutting edge algorithms and cloud services. At the front end, a disarmingly simple interface you likely already use.

Chief Scientific Officer / January, 2022 — Present

•Leads team's scientific and technical efforts for company focused on identifying mental health risks from speech data •Direct clinical mental health data collection for machine learning model development, validation, and utility assessment •Heads communication of scientific achievements through peer-reviewed publications and conference presentations

Director of Data Science / September, 2020 — January, 2022

•Invented novel quantitative methods to drive product changes and improve end-to-end customer experience (e.g., automatic speaker identification, transcript quality assessments, and on-topic speech detection) •Developed customer facing dashboard and data model in Microsoft PowerBI to communicate sensitive patient data •Published 2 first author articles validating machine learning models and presented findings at international conference •Principal Investigator for Phase I NIH SBIR grant investigating and mitigating machine learning model bias •Oversaw interviewing, onboarding, training, and daily tasks of 5 data scientists

Senior Data Scientist / September, 2019 — September, 2020

Developed and deployed ML models in Python to identify suicidal risk, depression, and anxiety from speech data •Experimented with various ML approaches (e.g., SVM, RF, and ANN) to improve model performance •Utilized advanced NLP (e.g., word embeddings and sentiment analysis) to extract meaningful features and improve accuracy •Designed and implemented ETL pipelines to efficiently extract, transform, and load large volumes of speech data

Data Scientist / January, 2019 — September, 2019

Developed models to predict mouse sleep states (sleep, REM, wake) from brain and muscle signals with 96% accuracy

Research Scientist / August, 2018 — September, 2019

•Lead R&D program for medical countermeasures of directed energy in 711th HPW/Force Health Protection Branch •Principal Investigator: Feasibility of Deployed Medical System Hardening to Directed Energy, In-Vitro Cellular Response to Directed Energy, Rapid Environmental Site Assessment with Readily Available Biomaterials •Characterized emissions from ammunition causing health issues with microscopic, spectroscopic, and statistical analysis

Digital Fellow / August, 2020 — Present

Senior Adviser, National AI Institute / January, 2020 — Present

Adjunct Associate Research Scholar / February, 2022 — Present

Postdoctoral Researcher / November, 2019 — Present

I work in Dr. Trevor Pugh's lab and design cancer diagnosis and treatment strategies by analyze cell-free DNA and single cell sequencing data

Bioinformatics Specialist / September, 2023 — Present

I work in Dr. Tak Mak's lab and study tumor immunology using single cell and TCR sequencing data.

Affiliate Faculty / October, 2023 — Present

Neuroscience

Neuroscientist / July, 2020 — December, 2023

AI/ML Subject Matter Expert

Neuroscience Software Engineer / July, 2004 — July, 2020

AI/ML Software Engineer

Principal Data Scientist / 2002 — July, 2024

Part of a team focused on initiatives in mobility network usage analytics, performance monitoring, and demand forecasting. Plays a pivotal role in directing the company’s multibillion-dollar network capital investments and collaborates with AT&T research scientists on highly complex data science projects, contributing to the company's leadership in telecommunications technology.

Research Associate

Technical Writer/Developer / February, 2022 — June, 2023

Updated the developer guide to inform customers on how to use the QPU's and simulators. Wrote quantum computing code that recreated various quantum algorithms.

Research Assistant / January, 2018 — August, 2022

Constructed simulations of a Floquet topological system known as the Anomalous Floquet-Anderson Insulator as well as simulations of NV-center qubits being used as quantum sensors.

Research Assistant / August, 2016 — May, 2018

Used machine learning along with Landsat data to forecast the pollen levels in Tulsa, Oklahoma.

Machine Learning Researcher / October, 2008 — Present

• Pure AI/ML research and software development. • AI research/development contract with SRAM. • Derived math results (currently 6,500 pages of notes), and designed new learning algorithms involving probabilistic graphical models, Bayesian methods, and information theory. • Built internal software tools in C/C++ and Python to aid research, including interactive visualizations of machine learning and Monte Carlo sampling algorithms. • Designed a novel brain-inspired architecture for artificial general intelligence, and implemented it in in C++ and Python with interactive debugger and test environments. • Developed commercial software to fund research agenda, including iBonsai, a meditative interactive 3D tree simulation in C++ for iOS (120k users). • Graphics engineering contract with Avatree (custom generative 3D tree growth algorithm and glTF exporter in C).

AI/ML Graduate Researcher / August, 2006 — December, 2009

• Performed independent research on topographic maps, maximum entropy learning algorithms, Bayesian networks, reinforcement learning, and systems neuroscience. • Developed open source C++ libraries for unit testing, profiling, and parallel programming.

Computational Neuroscience Research Intern / May, 2006 — August, 2006

• Implemented a novel computational model of the cerebellum. • Demonstrated motor learning and transfer of complex reaching behaviors with a simulated 6-muscle arm. • Participated in discussions of global brain modeling and information theoretic learning rules.

Assistant Professor

Senior Principal, Quantitative Analytics and Data Science / August, 2017 — April, 2020

Director, Quantitative Analytics and Data Science / May, 2020 — July, 2022

Principal, Data Science Lead / July, 2022 — Present

Professor / 2022 — Present

Manning Assistant Professor / 2018 — 2022

Postdoc / 2014 — 2018