Work with thought leaders and academic experts in deep learning

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.

PhD Candidate / August, 2018 — Present

AI Consultant / 2021 — Present

Senior Data Scientist / July, 2022 — Present

Led collaborations with industry giants like Amazon, Microsoft, Nvidia, and HuggingFace to enhance sales team efficiency and BOI. Implemented cutting-edge techniques from top publications in AI, focusing on transformer models and RLHF. Directed a team to optimize LLM models for GenAI project, enabling solutions like content tagging and workflow automation. Developed RAG pipelines and a vector database to bolster generative AI accuracy. Implemented RLHF pipeline and data preprocessing for sensitive content filtering. Evaluated tokenizers' impact on model performance and devised Bayesian MMM, optimizing promotional budgets by 3% and boosting revenue by ~2%. Utilized microsegment enrichment for personalized HCP engagement and developed A/B testing and causal inference tools for model monitoring across commercial AI projects.

Machine Learning Engineer / December, 2021 — June, 2022

ReiPower, a government-funded startup, targets carbon footprint reduction in buildings. Leveraged BERT to create a conversational agent, offering stakeholders insights on energy usage. Employed multi-modal deep learning and transformer-based embeddings to predict plant failures and optimize energy consumption, yielding a remarkable ~60% reduction in energy usage during pilot phase. Implemented a real-time ETL solution to handle streaming and storage of multimedia and sensor data from energy plant actuators.

senior system engineer / July, 2015 — July, 2018

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

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.

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 ASSISTANT / August, 2022 — Present

My research experience includes semantic segmentation of aerial imagery, synthetic image generation, multispectral image analysis, audio classification using sound event detection and optimizing ML workflows with GPU clusters.

Data Science Intern / June, 2021 — August, 2021

Set up a PostGRES server and SQL database for a financial trading system. Used this data to create a pipeline for an algorithmic trading toolkit.Developed a module to assess portfolio performance and associated risks. Performed backtesting to evaluate different trading strategies.

Assistant Professor / August, 2018 — Present

Graduate Research Assistant / January, 2023 — Present

Graduate Teaching Assistant / January, 2022 — January, 2023

Associate Member of Technical Staff / — June, 2019

Founder & CEO / June, 2018 — Present

World's smallest machine learning inference engine for tabular and time-series data

Senior Research Scientist / August, 2020 — August, 2023

 Designed an automatic system to recognize and extract aortic tissues in abdominal aortic aneurysms (AAAs) using deep learning models. A patent application was filed for this study in February 2020.  Developed an automated system for pre-interventional planning and post-interventional monitoring of endovascular aortic repair using machine learning and deep learning models. A patent application was submitted for this study in October 2021.  Designed a machine learning-based model to incorporate image information into AAA growth prediction. A patent application was filed for this study in October 2022.

Research Assistant / June, 2019 — August, 2020

 Analyzed intraluminal coronary arteries, including the automatic detection of red thrombus, white thrombus, and residual blood.  Quantified the coronary arterial wall by automatically detecting pathological tissues, particularly calcification, macrophage accumulation, neovascularization, atheroma, lipid, and endothelial fibrosis, to prevent cardiac adverse events.

Postdoctoral fellow / June, 2018 — June, 2019

 Created a computer-aided diagnostic framework that provides clinicians with operator-independent diagnoses of histological coronary lesions.  Conducted volumetric assessments of different coronary artery tissues to evaluate their dynamics and functionality.  Estimated the elasticity of coronary artery tissues affected by coronary artery disease (CAD) to assess the dynamics and functionality of the arterial wall.

Postdoctoral Researcher / November, 2024 — December

Leading research on multimodal learning combining speech vision and language for scalable AI systems. Designing and evaluating methods to improve fine-grained reasoning in large language and vision-language models.

Computer Vision Project Assistant / January, 2021 — October, 2024

Developed self-supervised and unsupervised learning techniques to improve neural network robustness to distribution shifts at test time. Conducted extensive research on LLMs and multimodal VLMs resulting in multiple publications at NeurIPS ICCV and CVPR.

Research Scientist Intern / May, 2024 — August, 2024

Designed multimodal learning methods integrating vision audio and language signals. Prototyped and evaluated models for cross-modal understanding in real-world scenarios.