DANIEL
HARIYANTO

Expanded platform coverage beyond Windows OS, Store, and Drivers to now include all Microsoft 1st-party products—growing total addressable market 15× and establishing it as a company-wide delivery foundation.

Cemented the platform as the backbone of Microsoft's software delivery by securing buy-in from its crown-jewel product groups—Office, Teams, OneDrive, Defender, SQL Server, Visual Studio, Exchange, and 14 others.

Enabled 7x publisher capacity in year 1 alone (3 → 21 publishers). Drove the org-wide shift from reactive support to a product-led platform by launching an AI-powered publisher-facing web platform that centralized end-to-end publisher workflows—unlocking platform scale to virtually unlimited publishers.

Cut publisher onboarding lead-time by 94% (32 weeks → 2 weeks). Redesigned onboarding as a self-serve web wizard with built-in integration, abstracting complex distribution setup into intuitive UI flows that save ~500 engineer-hours per onboarding. Leveraged AI to generate hyper-realistic UI prototypes, accelerating design iteration and stakeholder alignment.

Protected 1.5B devices by isolating security breaches to the affected publisher, averting >$150B in potential losses. Designed and rolled out infra-wide security guardrails adopted by 20 partner teams, ensuring a compromised publisher cannot impact others—a 15,000× threat exposure reduction.

Drove 10× PM efficiency through AI-powered workflows. Built and deployed agentic tools for specs, prototypes, and cross-team communication—reducing cycle time and manual effort—and showcased in two main-stage sessions at a Microsoft PM summit with >600 PM leaders.

Enabled 90× faster detection of riot formations (30 min → 20 sec). Delivered real-time aerial analytics—high-altitude reconnaissance, crowd density mapping, vehicle tracking, facial recognition, live annotation tooling, and automated alerts—to support rapid tactical coordination.

Achieved full pilot adoption in <2 months. Embedded in 3 field missions and led >10 iterations with commanders to translate workflows into features.

Increased riot containment success rate from 25% to 90% by enabling commanders to preemptively re-position forces based on real-time riot movement.

Reduced officer casualty incidents by 50% by replacing manual reconnaissance in high-risk zones with drone-based intelligence.

Recorded 100% retention among field users, with officers citing tool as “critical” for situational awareness and tactical response in debriefing interviews.

Achieved 87% detection accuracy for early-onset Alzheimer's using voice alone. Built cloud pipeline and trained ML models on real patient speech from DementiaBank to screen for cognitive decline.

Secured 6-figure non-dilutive funding and 8 top awards. Awards include 1st at Babson $100K Pitch Competition, 1st at Brandeis SPARKTank, 2nd at AI@MIT, Harvard Capital Partners Fund Winner, Harvard iLab Spark Grant, Dove InstaGrants Winner (1 of 3), Rothberg Catalyzer, and Harvard CBE Venture.

Undergoing clinical validation in the Swedish BioFINDER study, one of the advanced programs on early Alzheimer's detection. Positioned product for FDA De Novo pathway with top-tier neuroscience partnerships like Carney Institute for Brain Sciences and Butler Hospital.

Stepped away due to elongated clinical timelines and slow go-to-market momentum, but still rooting for the team and firmly believing in the tech's impact.

Spearheaded independent machine learning research with Dr. Pengyu Hong, investigating novel dataset pruning strategies that enabled accelerated training convergence while maintaining remarkable accuracy; novelty: uniquely targeted samples with similar difficulty levels for removal

Successfully achieved a notable >30% reduction in training time with a mere 0.3% accuracy compromise, validated through rigorous trials with ResNet-18 on 1.3M ImageNet samples

Impact: more time-efficient training processes, reduction in computational resource requirements, and enhanced accessibility to previously large datasets

1 of 20 Fellows selected out of >2,000 applicants across the US to be part of the Bessemer VC network

Interned as a software engineer at Bevi, a Bessemer portfolio company, building Alexa smart speaker functionality for Bevi's IoT flagship product

Built an interface based in Java Dropwizard that enables a custom Alexa skill to connect to Bevi's IoT product and recreate all functionality via voice commands

Investigated new business areas to expand Microsoft's content delivery optimization technology, which is responsible for reliably and performantly distributing 10+ exabytes of Microsoft content per month to 1.5B+ Windows devices globally

Designed a go-to-market plan that evaluates business opportunities and risks with 3rd party content support for Microsoft's peer-to-peer and caching services, addressing highly expressed customer pain points (88% customer approval via surveys) and providing value-add to Microsoft's Intune and Azure CDN services simultaneously

Conducted usage and revenue modeling based on large internal datasets across 3 teams to evaluate growth opportunities in the next 5 years

Created feature spec and UX wireframes using Figma, coordinated feature implementation with the development team, and presented project to CVP

Implemented features in web application of PayPal's Business Debit Mastercard, the #1 largest US Mastercard small business debit portfolio; built multiple secondary cards functionality and recurrent payments dashboard with React.js & Node.js (Kraken framework) used by >1.8 million cardholders

Designed and led isolated component testing in Java Spring application of PayPal's Shipping & Tracking, which reduced dependency on shared staging environments and enabled developers to have complete control over their test suite; increased development productivity by >400%

Developed NLP clustering model for PayPal's Innovation Lab that classifies ideas based on their content; after receiving positive feedback from leadership, implemented model in their Idea Portal now used to classify >4,000 yearly instances of employee ideas to inform better decision-making for key stakeholders

Developed an end-to-end data pipeline that transforms Zoom class recording data into useful data abstractions and visualizations; this is a university-funded research project exploring how pedagogy affects student engagement in online courses and, in particular, how it can enhance equity in the classroom

Built PostgreSQL database within on-prem Apache server from scratch that models complex interactions among courses, instructors, Zoom meetings, participants, and transcripts

Designed efficient algorithms capable of matching >80,000 Zoom meetings to classes and >60,000 encrypted Zoom participants to registered students

Leveraged advanced SQL join queries and analytical algorithms to transmute raw Zoom data into insightful statistics on student engagement

Led a 5-person team to build book collection APIs using BeautifulSoup, Flask, and Heroku for Mappo's literature-based content

Designed clever CAPTCHA-bypassing web scrapers for Amazon, Goodreads, and Project Gutendex that return cleaned data for >428 million books

Built Mappo's book data insertion pipeline, which will be responsible for providing >70% of Mappo's cultural content in their app

Led a 3-person team to build an iFrame widget implementation of SOLO's flagship product based in React.js frontend and Node.js backend

Enabled SOLO's product to exist as an isolated app widget on external websites, which is currently used by one of SOLO's clients with >200k users

Led a team of 4 student engineers to build monetization strategy of TapTapes, a podcast-sharing social network mobile application

Designed digital tokenization mechanism in React Native frontend and Node.js backend with MongoDB database and S3 storage, to become TapTapes's first revenue stream

Led a team of 2 student engineers to create a song trends analysis platform on Vibo, a music management tool for DJs and event planners, based in React.js frontend and Node.js & GraphQL backend that queries MongoDB database; increased screen-time and user engagement by >40%

Headed software testing: internal testing using generated fake data via Faker.js & GraphQL integration tests via Jest

Developed data visualization dashboards using React and D3.js for agricultural analytics and crop monitoring systems

Built IoT sensor data integration pipelines that processed real-time environmental data from farm sensors

Implemented machine learning models for crop yield prediction and irrigation optimization recommendations

Created responsive farmer-facing interface that simplified complex agricultural data into actionable insights

1 of 7 to acquire two highly competitive undergraduate research fellowships, funded by the NSF and by Brandeis University, for two consecutive years

Organized independent nanotechnology research with Dr. Ben Rogers and Dr. Melissa Rinaldin, analyzing the liquid-liquid phase separation (LLPS) behavior of DNA nanostars

Investigated unexpected LLPS behavior of DNA nanostars at high temperatures using a variety of experimental techniques including confocal, brightfield, & DIC microscopy (using Leica Application Suite X), FRAP, and NUPACK

Conducted extensive data analysis using a variety of NumPy, Pandas, Matplotlib, Scipy, and image-analysis Python libraries to investigate the biophysical properties of DNA nanostars