2026 North America Program Details

We believe that research and development is the key to harnessing the power of imagination and to discovering new possibilities. We are excited to announce a new kind of Fellowship that promotes Qualcomm’s core values of innovationexecution and teamwork. Our goal is to enable students to pursue their futuristic innovative ideas.

Winners
Application & Proposal Phase
Finalist Selections
Finalist Instructions
Timeline
Participating Universities
Selected Abstracts
Areas of Interest
FAQs

2026 North American Winners

Congratulations to the 16 winning teams of Qualcomm Innovation Fellowship North America 2026!

We commend each of the finalists on excellent presentations and quality proposals.

Bing-Yue Wu

Democratizing Chip Design with On-Device Small Language Models as Assistants and Agents

ASU

Atmadip Dey

Democratizing Chip Design with On-Device Small Language Models as Assistants and Agents

ASU

Atharva Raut

Test-Calibrated Thermal Digital Twins for Chiplet 2.5D/3D SoCs

CMU

Je-Wei Chuang

Test-Calibrated Thermal Digital Twins for Chiplet 2.5D/3D SoCs

CMU

Violet (Yinuo) Han

Building Helpful Embodied AI Agents with Long Horizon Understanding

CMU

Weiwei Sun

Building Helpful Embodied AI Agents with Long Horizon Understanding

CMU

Yutong (Kelly) He

Training a Generally Curious Personal Agent: Learning What to Remember and What to Ask

CMU

Fahim Tajwar

Training a Generally Curious Personal Agent: Learning What to Remember and What to Ask

CMU

Kaifeng Zhang

Learning Physics-Informed Visuo-Tactile World Model for Robot Manipulation

Columbia

Binghao Huang

Learning Physics-Informed Visuo-Tactile World Model for Robot Manipulation

Columbia

Jinze Shi

Heterogeneous Electro-optic RF-Optical Front-End for 6G (HERO-6G)

Michigan

Yang Lan

Heterogeneous Electro-optic RF-Optical Front-End for 6G (HERO-6G)

Michigan

Kaiwen He

Making Modern Cryptography Faster and More Energy-Efficient Through Hardware Acceleration of Modular Arithmetic

MIT / CMU

Tianyao Gu

Making Modern Cryptography Faster and More Energy-Efficient Through Hardware Acceleration of Modular Arithmetic

MIT / CMU

Aryaman Gupta

Safety Guardrails for Reasoning in End-to-End Autonomous Driving Foundation Models

Stanford

Sathwik Karnik

Safety Guardrails for Reasoning in End-to-End Autonomous Driving Foundation Models

Stanford

Zizhang Li

Physically Grounded World Modeling through Controllable Visual Generative Models

Stanford

Ziyu Chen

Physically Grounded World Modeling through Controllable Visual Generative Models

Stanford

Muhammad Ahmed Mohsin

State-Space World Models for Intelligent RAN

Stanford

Muhammad Umer

State-Space World Models for Intelligent RAN

Stanford

George Karfakis

Heterogeneous Chiplet-Based Architecture for Fully-Weight-Stationary Compute-in-Memory execution of Billion-Parameter models

UCLA

Samyak Chakrabarty

Heterogeneous Chiplet-Based Architecture for Fully-Weight-Stationary Compute-in-Memory execution of Billion-Parameter models

UCLA

Seth Z. Zhao

Think Deep - Move Fast: Building Vision-Language-Action Model for Generalizable Autonomous Driving with Deep Reasoning and Efficient Action Generation

UCLA

Zewei Zhou

Think Deep - Move Fast: Building Vision-Language-Action Model for Generalizable Autonomous Driving with Deep Reasoning and Efficient Action Generation

UCLA

Sujoy Ghosh

Thermocompression Bonded III-V/Si Integration Platform for Optical, RF/mmWave, and Power Devices

UCLA

Daniel McGovern

Thermocompression Bonded III-V/Si Integration Platform for Optical, RF/mmWave, and Power Devices

UCLA

Annabelle Sujun Tang

Reasoning Compiler: LLM-Guided Optimizations for Efficient Model Serving

UCSD

Christopher Priebe

Reasoning Compiler: LLM-Guided Optimizations for Efficient Model Serving

UCSD

Nilesh Prasad Pandey

K-MEM: Synergizing Agentic Textual Memory with KV-Cache Reuse for Long-Horizon Agents

UCSD

Lanxiang Hu

K-MEM: Synergizing Agentic Textual Memory with KV-Cache Reuse for Long-Horizon Agents

UCSD

Sreevatsank Kadaveru

When Multimodal Sensing Meets the RAN: Sensing-to-Action Autonomy at the Edge

UCSD

Ushasi Ghosh

When Multimodal Sensing Meets the RAN: Sensing-to-Action Autonomy at the Edge

UCSD

Fellowship Winners and Finalists

The Qualcomm Innovation Fellowship began in 2009, and has continued to grow with the addition of more universities, more candidates, and expansion to our research centers internationally. Take a look at a list of all our fellowship winners and finalists from years past:

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Qualcomm Innovation Fellowship Finalists' Day

May 28, 2016 | 1:35

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