Machine Learning Engineer
2019 - 2021Twosense- Created models for behavioral biometrics and continuous multi-factor authentication in the field of cybersecurity.
- Designed and implemented a typing signature machine learning model used in production.
- Built deep learning models using accelerometer, GPS, and other mobile data to create multiple behavioral biometrics for a Department of Defense contract.
Technologies: Python, Amazon Web Services (AWS), Data Science, Machine Learning, Agile, SQL, Git, PyCharmProgram Director of Research Science
2018 - 2019New York-Presbyterian Hospital- Built predictive models using historical data to predict the number of patients in the emergency departments at the different NYP hospitals.
- Cleaned and parsed millions of electronic health records and determined hospital-acquired VTE (Venous thromboembolism) rates and metrics of how it's addressed by different hospitals and departments.
- Developed analytics for oncology rates of the different departments and different cancer types throughout NYPs ambulatory care network.
- Taught Python programming and data analysis courses to over 50 NYP employees.
Technologies: SQL, Python, Machine Learning, Data ScienceDeep Learning Research Scientist
2017 - 2018National Energy Research Scientific Computing Center (NERSC)- Implemented deep learning architectures on cosmology simulations to understand and predict the parameters that govern the evolution of the universe.
- Collaborated with a diverse team from Lawrence Berkeley National Lab, Intel, and Cray, to run these models at state-of-the-art performance on the world's eighth-largest supercomputer.
- Published in SC18 (The International Conference for High-Performance Computing, Networking, Storage, and Analysis).
Technologies: TensorFlow, PythonGraduate Student Researcher
2013 - 2018UC Berkeley- Led multiple computational projects and developed novel algorithms in machine learning.
- Developed a novel exploration algorithm using Bayesian non-parametric statistical analysis and information theory (accepted to NIPS 2014).
- Trained an autoencoder neural network to learn temporal dynamics of cellular automata evolution.
- Classified hand-written digits with an unsupervised neural network algorithm using only local learning rules.
- Mentored research assistants to take on original research projects.
Technologies: TensorFlow, Python, Deep Learning, Artificial Intelligence (AI), Machine Learning, Data ScienceData Science Intern (Artificial Intelligence Group)
2017 - 2017Intel- Implemented Neural Style Transfer with VGG-19 (Convolutional Neural Network).
- Reconstructed audio spectrograms from hidden layer activations of Deep Speech 2 (many-layered Bidirectional Recurrent Neural Network model for speech to text).
- Developed a novel approach for style transfer applied to audio signals.
Technologies: TensorFlow, Python