A Little About Me:

Working directly with a partner at HP to recommend startups for due dilligence. Researching startup metrics, industry data, and business models. Utilizing SQL and Excel to clean 1000s of rows of data and visualizing findings through Tableau.

Validate business models for the monetization of 5G in emerging AR/VR retail use cases by conducting 30+ interviews with executives and ground-level staff, open innovation research, and extensive data analysis under the Head of Innovation.

Designed and implemented a deep learning recommender system through Neural Collaborative Filtering using Deep Neural Networks, utilizing Tensorflow, Keras, Surprise, and PyTorch to accurately predict user lending rates from pool of 40+ options. Leveraged embedding similarities between users and calculated probabilities through a SoftMax layer to improve accuracy.

Sole intern for IPP startups under the Batch 14 cohort.

Keywords: AR/VR, BioTech, NeuroTech

Conducted extensive user interface testing and market research for three global CEOs, utilizing data analysis skills and qualitative customer insights, resulting in an increase of platform efficiency by 30% and improved user experience. Secured a successful fundraising round by revising 50+ slides and overall product messaging for different verticals.

Formulated a SMART plan for market research and developed a go-to-market strategy: identifying customer needs, ideating sales strategy, and optimizing product-market-fit to generate product demand via quantitative/qualitative Excel analysis. Prepared a presentation summarizing key findings from consumer surveys and external research related to pricing, target market differentiators, and marketing approaches to display a plan for product implementation to the CEO & Founder.

Received the Market Research Excellence Award.

Built a logistic regression model for the classification of spam emails, achieving a 96.2% accuracy on 8000+ emails. Conducted feature engineering on text data through Pandas, NumPy, Seaborn and Matplotlib. Utilized the scikit-learn machine learning library to fit models; ROC curve analysis for threshold selection; cross-validation, and regularization to avoid overfitting & improve performance. Placed in the Top 30 out of 1100 participants.

Built a logistic regression model to classify housing affordability categories to ensure fair house price valuations. Conducted exploratory data analysis, feature engineering, and cross validation via NumPy, Pandas, Matplotlib, and Seaborn. Gained insight into socioeconomic and ethical issues as related to the implementation of algorithms & models in real life. Achieved a testing RMSE of less than 210k and a 90% testing accuracy across 500,000+ housing records in Cook County, Illinois. Placed in the Top 25% of the competition based on my classifications.

Built backend packages (storage, processing input, generating output) for a browser-based tool displaying words’ hyponyms and popularities in English texts over specified time periods based on Google's NGram Viewer. Utilized a graph-based implementation of WordNet (large lexical database of English words) and implemented Depth-First-Search (DFS).

Applied advanced machine learning techniques, including exploratory data analysis, feature engineering, and utilized a random forest model on 12k data points to predict STEM salaries from job characteristics (Top 5 placement).

Developed a machine learning regression model to predict traffic collision injuries and improve disaster response, working with 200 features and 10 million rows of data in collaboration with the Disaster Lab at Berkeley (Top 5 placement).

Data Consulting: Industry projects in Machine Learning: Time Series, Computer Vision, and NLP.

External Affairs: Hosting the first ever interdisciplinary data science focused industry & research networking night for 80+. Hosted a Q&A panel with speakers from Google, Accenture, Adobe, Berkeley AI Research, and AI startups.

Insights & Analytics: Application of various machine learning techniques into a client-centered data consulting project: clustering, neural networks,

Career Exploration: Explored a diverse range of concepts in data science such as decision trees, linear regression, logistic regression, linear algebra, classification, data cleaning, machine learning, and deep learning.

1 of 50 fellows in the Fung Institute's Health + Technology track engaging in coursework, research, and human-centered design projects with an emphasis on the intersection between public health and tech innovation.

Working with Professor Van Dusen, instructional designers, graduate students and other campus thought leaders on online course development and launch as part of a broader professional certificate in Data Science to democratize access to education. [specific details redacted]

Gold-Tier Ranked Fellow engaging in a gamified initiative to help students grow their entrepreneurial mindset, participate in projects focused on learning cutting-edge technologies, gain insight into diverse and personal entrepreneurial journeys, and make connections with students, alumni and industry leaders.

A data analytics training program that allows students to explore by analyzing the latest consumer trends for consumer-packaged goods (CPG) manufacturers and retailers. NielsenIQ Scholars work to solve a real marketing challenge for a major brand using syndicated data. Program Scholars also receive mentoring, professional development opportunities, and direct access to internships in the data analytics, CPG and retail industries. NielsenIQ is the #1 global data and measurement (consumer intelligence) company in the world. Working on a data analytics challenge to serve our client, Procter and Gamble (P&G).

Externs participate in learning sessions, networking, and engagement activities focused on how technology can solve pressing business challenges. They develop industry acumen, build long-lasting relationships, develop in-demand tech skills and walk away with a better understanding of Accenture. Learnt a lot about Diversity, Equity, and Inclusion. Participated in specialized AWS, SAP, and Tableau (Data & AI) trainings.

Finalist selected out of 10,000 applicants to participate in an exclusive program. The immersive four-week program gives participants the opportunity to learn more about the firm's divisions, network with managing directors, and gain insights on preparation for summer analyst programs.

Participants have a unique opportunity to connect with current BCGers as they share firsthand knowledge about their impactful work across roughly 30 sectors and industries, and transformative digital capabilities. Gained insight into the recruitment process.

An immersive experience introducing students to internship opportunities and insight into the recruiting process. Completed coursework and learnt about specific lines of business. Received tips on interview preparation specific to the Data & AI roles.

One of only 100 student fellows across the nation chosen to strengthen my capacity to create and deploy high quality projects that have a sustainable impact on the world. Focused on competencies of entrepreneurship, leadership, career readiness, and project management. Received national recognition.

Series of weekly workshops featuring founders, venture capitalists, and prominent voices at Berkeley and the greater Silicon Valley, as well as office hours with SkyDeck staff and ACE sponsors.