* Lead research team across two labs on software development of hardware control and data acquisition devices* Develop long-term maintenance and development processes

* Developed and implemented machine learning models for signal classification on Gridware devices* Built an end-to-end pipeline incorporating signal processing, feature engineering, data augmentation, and various machine learning algorithms (random forest, MLP, CNN, Hidden Markov Models)* Successfully classified a specific type of signal in vibrational time-series data through effective evaluation and visualization techniques

* Pursued development goal of a multi-qubit processor based on single nitrogen-vacancy (NV) centers In diamond* Constructed data acquisition and experimental software for characterization and control of electron spin states (ODMR, Rabi, Ramsey, Dynamical Decoupling)* Developed scanning GUI application for confocal microscope (NIDAQ, Python)* Developed dynamical model of NV- electron state under optical pumping and RF magnetic field * Implementation of projected-Variational Quantum Dynamics algorithm with Pennylane * Advised and assisted students in labAug 2023+: Managing software development for data acquisition systems in QT3 Lab and Fu Lab. Managing student-development of improved functionality and support new experimental apparatus

* Directed product and technical strategy, ensuring alignment between data science team and business objectives for optimal support of products.* Advised VPs and Executive team on advanced statistical analysis and ML tools, contributing to improved product development* Managed ~10 real-time predictive ML models that were responsible for > $1M in monthly auction transactions and $500k MRR* Continuously monitored and improved primary auction bidding model* Developed 4 models to increase efficiency of lead generation for new homebuyer sales team by 50%* Managed data science team members (2 scientists, 2 engineers) activities within different product development projects using Agile methodologies* Developed XGBoost model to predict likelihood of competition in professional contract market over entire continental U.S, driving ~$100k in MRR* Developed an internal Bayes AB testing toolkit to enhancing decision-making capabilities for PMs* Constructed visualization tools to increase understanding of model performance and data drifts* Developed intimate understanding of business operations and engineering stack in order to advise data product development and understand revenue flows: discovered and corrected fatal flaw in data collection issue with sales team* Led "lunch and learn" seminars for data science and data analytics teams* Advised data analytics teams on improved methodologies

* Built a deep-learning model to predict the correct audio file for a given input image.* From a short video clip, used pre-trained VGGish model to compute the latent-space representation of the audio. Then used the pre-trained InceptionV3 model to compute a representation of images from videos. Finally, trained a multi-layer perceptron to predict the audio representation from the image representation.

* Developer Advocate for IBM Watson Data Science Platform, engaging with data scientists and developers through conferences, meetups, workshops and demo machine learning applications built with IBM Watson technologies.* Implemented ML and deep-learning techniques in collaboration with radio-astronomy scientists at SETI Institute to identify anomalous radio signals* Led collaboration between SETI Institute radio-astronomers & IBM to analyze ~2 TB of radio-telescope time-series signals with IBM data science tools (spectrogram and auto-correlation analysis)* Built open-source tools to read, visualize and analyze radio-telescope data* Built open-source tools to simulate radio-telescope data* Conceived and produced a successful open-community workshop and online competition focused on deep-learning techniques for SETI research (managing a team of ~10 individuals at IBM, SETI and Galvanize)* Showcased ML projects at conferences, highlighting the capabilities of IBM Watson technologies* Provided expert guidance to customers in leveraging IBM Watson technologies effectively* Founded and grew Seattle Deep Learning Meetup, which now boasts over 1100 members* Designed and built `ibmos2spark`, a library that connects IBM Apache Spark clusters to four different IBM Object Storage services, written in Python, Java and R, used within IBM Watson Data Platform services* Software Engineer (constructed MVCC and replication queue/state) for Cloudant-Sync Mobile libraries (iOS and android)* Built database performance monitoring tool to detect anomalous changes and regressions in time-series data.

The COHERENT experiment aims to measure coherent neutrino-nucleus cross-section for various nuclei (Cs, Na) by using the intense flux of neutrinos produced by the Spallation Neutron Source at Oak Ridge National Laboratory. I investigated signal processing techniques for event detection and classification.

* Member of Dark Matter detection experiments EDELWEISS and EURECA, and AARM collaborations* Achieved world-leading limits on WIMP dark matter mass and interaction strength through meticulous analysis* Developed a signal processing and analysis toolkit and framework, KData (C++ / Python), to enhance collaboration analysis efficiency and use of our high-performance compute cluster. Package included data structure based on CERN's ROOT framework* Developed data management and data processing utility, based on CouchDB, that tracked signal processing steps from raw data to final analysis format* Mentored graduate students and postdocs from KIT, CEA in Saclay (Paris), and University of Lyon on WIMP detection and interaction limitation analysis.* Constructed tool to monitor experimental setup and internal radioactivity levels, displayed on website available to all collaboration members* Responsible for slow-control data collection, physics-data processing and storage.* Instructor in experimental physics courses and contemporary detector design course. * Principle Investigator for KIT Elementary Particle and Astroparticle Physics Graduate School program.* Presented experimental results at multiple international conferences (Dark Matter 2012, TAUP)* Invited Talks at MIT, University of Washington and University of Dresden

* Instructed multiple courses on fundamental physics topics including mechanics, thermodynamics, and numerical methods with accompanying lab components* Provided guidance to students on course selection and academic planning* Mentored two students in a special-topics course on Introduction to Quantum Mechanics

* KATRIN experiment: using tritium beta-decay to measure the neutrino mass* Developed radiation simulation for experimental design of rear-wall of KATRIN's windowless gaseous tritium source (FORTRAN)* Left position early due to family medical emergency