Now available in the App Store: https://apps.apple.com/us/app/aisthetic/id6458485247● Built an iOS app from scratch using Flutter, Dart, and Xcode● Hired a contract designer, worked with an attorney on the patent, and deployed to the App StoreAisthetic is a wardrobe optimization app that allows users to streamline their outfit decisions, reducing decision fatigue while maximizing their closets’ potential. Users simply upload their closet into the app and are able to easily access all their items, filtering by season or occasion as needed. Daily AI-driven outfits are selected for the user, with weather data integration to ensure selected outfits are weather appropriate. Additionally, users are able to use the app to make packing more efficient and optimized. They just input their destination and dates, and AI handles the packing list, displaying an outfit for every day of the trip (including daytime and/or dinner/work/evening outfits, depending on users occasion selections). The app gradually fine-tunes it’s outfit suggestions to a users’s specific preferences.

Google Health is committed to helping everyone live more life every day.• Designed and implemented a human eval labeling task for structured and unstructured medications data in order to extract additional high-quality medications information (i.e. dose, rate, route, etc.)• Built a self-service tool for stakeholders to quickly build a panel of patients using a predefined set of clinical and administration attributes – this helped simplify and promote consistency when finding patient examples or cohorts for various use cases• Supported the Medications summarization project as an embedded analyst

Carbon Health is aiming to make healthcare accessible to all.• Developed COVID-19 Risk Assessment Simulation and Web App (Python and Streamlit) that was used by CSMs for policy planning services for enterprise (https://medium.com/carbon-blog/covid-19-risk-assessment-simulation-model-684fc27d5019)• Ran statistical analysis on clinical data to research COVID-19 testing swab type reliability and extract information on symptoms, vitals, demographics, etc.• Created metrics dashboard reports (utilizing SQL and Periscope tools) for both long term use and ad-hoc needs for product, clinic ops, and medical teams

Afresh is reinventing the food supply chain process to reduce food waste and costs. • Helped launch new customer by developing pipeline to ingest and fully integrate their data with our system — including cleaning, processing, transforming and investigating their data• Developed and implemented algorithm to match same items together that are tracked under different item IDs, resulting in a 6.4% increase in the number of items by total sales that we predict on (~$5.3million in sales dollar amount)• Ran ad-hoc analysis and investigated data to help improve forecasting and recommendations

Radius Intelligence seeks to power data intelligence across all B2B applications, channels, and users. • Developed and defined productionized research and prototype models in Apache Spark and Python to automate data processing and analysis • Replaced rules-based matching system with machine learning algorithm improving the performance from 82% to 96% precision rate – while also refactoring code to make potential future updates easier to implement • Refined phone validation model to make it more scalable – now able to run 42 customers in the time it took to run 1 customer, while also improving accuracy metrics • Helped start up Radius Tech Blog by writing first article for it (https://medium.com/radius-engineering/demystifying-data-science-why-profanity-isnt-always-profane-520434d4c503)

• Intensive data science program covering topics in: statistical analysis, supervised and unsupervised machine learning, data pipelining, data visualization, working with data at scale, etc.• Collaborated on case studies involving detecting fraud, predicting churn rates, and implementing recommenders• For my Capstone project created and implemented a gender prediction app from the stylometry of text by: - Web scraping, cleaning, and structuring data gathered from multiple media site sources - Analyzing data to discover and interpret trends, patterns, and relationships in writing among articles and between media sites by engineering features that extrapolate style from writing - Training Ada Boost Classifier to predict gender of author from engineered features - Deploying Flask app that takes user-inputted text and outputs prediction (www.rismakov.com/app)

• Working with Dori Derdikman, aiming to understand neuronal basis for spatial representation in the brain • Methods used include electrophysiological recordings, chemical-genetic approaches, immunohistochemisty/histology • Utilizing MATLAB for computational and statistical analysis of data set

• Worked under Ehud Gazit on various nanotechnology research projects • Synthesized protein nanospheres for further research on effectiveness of use in drug delivery systems• Improved procedure for formation of peptide nanotubes with wire casting • Operated Scanning Electron Microscope (SEM) for observation of nanoparticles

• Assisted Dr. Jaime A. Pineda on research project focused on mirror neuron system (MNS) and its connection to autism spectrum disorders (ASD)• Trained patients with neurofeedback, technique that allows individuals to control their own brain activity through help of EEG and visual feedback• Reviewed and critiqued scientific journal articles before publication

• Worked with Dr. Doug Nitz on research program aimed at determining neural basis for spatial cognition, decision-making, and episodic memory• Examined multiple hippocampal single-unit recordings made in freely behaving lab rats