A way for the people who believe in a startup first to discover it and back it.
EVIDENCE→VALUATION→ROUND→RECEIPTS
The project
Built with a team at HackNC 2026, Prospecto lets founders submit their website, GitHub repository, and pitch, then explore a transparent valuation and open a shareable, fixed-price funding round. The demo brings together company discovery, evidence research, a market simulator, and portfolio tracking.
The stack combines Next.js, React, TypeScript, Gemini, Hono, and Tiger Data PostgreSQL, with Solana Devnet and Capital One Nessie for the mock participation flow.
My contribution
Co-proposed Prospecto and its approach to discovering and backing early-stage startups.
Developed algorithms to estimate early-stage startup valuations using deep learning.
Built the Solana tokenization mechanism and Capital One Nessie purchase flow.
Prospecto uses simulated funds and participation units. The tokenization and mock-banking flows are built for a sandbox demo; participation does not confer real equity or ownership.
Reliable autonomous landing where controlled conditions disappear.
The project
This project uses residual learning to enable accurate quadrotor landing under strong disturbances and robust disturbance rejection in real-world environments. The integrated drone-rover system is designed to collect ice sheets in Greenland’s extreme conditions.
My work
Engineer hardware for the autonomous drone-docking system
Optimize and test landing algorithms for accuracy, reliability, and energy efficiency
Build a DIY wind tunnel for disturbance and wind-resistance testing
02 / RESEARCH · 2024—2026
Graphene Pollen Biosensor
Can an invisible allergen become an electrical signal?
The graphene four-electrode sensing devicePine pollen under SEM · 600×Poplar pollen under SEM · 4,000×
What I built
Fabricated, chemically modified, and characterized graphene-based electrochemical sensors. I validated the results against hospital and weather-station field data and documented the methodology across an 18,500-word paper and 40,000-word experimental log.
Outcome
Regeneron ISEF 2026 Grand Award
Published in Advances in Materials Physics and Chemistry
Became the sensing foundation for four live stations
The deployed station in BeijingAn early field prototypeField deployment
The system
I built and deployed four monitoring stations, designed the station-to-app pipeline, added Wi-Fi for automatic updates, and improved accuracy in wind and rain.
The IRIS hardware prototypeIRIS at the ENIAC XI HackathonLOOK→IDENTIFY→SPEAK
My contribution
I designed the gaze-to-language pipeline, turning a stable gaze fixation and scene capture into a focused prompt and a spoken suggestion for a caregiver.
Translating environmental evidence into a civic argument.
The work
I authored a 9,800-word proposal for the Beijing municipal government, connecting pollen-allergy evidence with urban-tree management and prevention.
Impact
Contributed to passage of a new local policy
Regulation of pollen-producing trees
Connected technical research to municipal action
06 / WEARABLE SENSING · 2025
Scent Sensor + ML Classifier
Can a compact sensor learn the signature of gases and scents?
Project Sentrix at UPenn EngineeringThe three-person project teamMQ SENSORS+BME688→ML
What I led
I led a three-member team across the dual-sensor build, raw-data collection, calibration, and machine-learning pipeline. We trained the BME688 using clean-air and coffee scent measurements and tracked gas concentration with MQ sensors.
I led a four-person team building the platform and its AI-agent workflow from document input to generated interpretation and prompt prescription suggestions.
Status
Four-week development cycle
Prototype entered trial at Beijing SJS Hospital
Designed as decision support, not a replacement for clinical judgment
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FIG. 03 — ABOUTPROFILE / BACKGROUND
TENNE TIAN — DUKE UNIVERSITY
ENGINEERING, WITH A SECOND LENS
Tenne Tian
I’m a first-year Electrical & Computer Engineering student at Duke, considering Philosophy as a second major. My work moves between nanotechnology, sensor systems, machine learning, and robotics — I love the magical combinations of AI and Hardware!
Technical path
01
Hardware Engineer — Duke General Robotics LabSEP 2026 — PRESENT
02
Researcher — Chinese Academy of SciencesDEC 2024 — JUN 2026
FIG. 04 — COMPETITION FIELD NOTESPHOTO ARCHIVE / 33 MOMENTS
BEHIND THE RESULTS
Competition field notes
A growing archive of the judging floor, project demonstrations, and the people around the work.
01 / TEAM IN THE CLEANROOM02 / ISEF GRAND AWARDS · PHOENIX03 / PRESENTING THE WORK04 / GRAPHENE SENSOR · JUDGING SESSION05 / ISEF · PHOENIX06 / POLLEN UNDER SEM · 2,490×07 / EARLY FIELD PROTOTYPE08 / SCIENCE COMPETITION · WITH MENTORS09 / ISEF · TEAM AT THE VENUE10 / ISEF · PUBLIC DEMONSTRATION11 / GRADUATION PORTRAIT12 / MEDIA FORUM · INTERVIEW13 / ISEF · FRIENDS AND FINALISTS14 / ISEF · PHOENIX MEDAL15 / FIELD NOTES · MOUNTAIN TRAIL16 / BUILD PROCESS · SOFTWARE TESTING17 / NANOTECH · LAB TEAM18 / SCHOOL SCIENCE PRESENTATION19 / TECHNICAL TALK20 / INTERNATIONAL OPEN-MINDED21 / RESEARCH POSTER · RECOGNITION22 / BETWEEN EVENTS · CITY NOTES23 / YOUTH AI FORUM · PRESENTATION24 / YOUTH SCIENCE AWARD25 / POSTER SESSION · LIVE QUESTIONS26 / TRAVEL FIELD NOTE27 / COMPETITION TRAVEL · DEPARTURE28 / DUKE UNIVERSITY29 / NANOTECH · SAMPLE SERIES30 / LAB WORK · INSTRUMENTATION31 / RESEARCH DISPLAY · FINALIST32 / BACKSTAGE · TEAM PORTRAIT33 / BEYOND THE LAB · FINISH LINE
