The 2025 Qualcomm Innovation Fellowship Submission Portal is now CLOSED
2025 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 innovation, execution and teamwork. Our goal is to enable students to pursue their futuristic innovative ideas.
2025 North American Winners
Congratulations to the 17 winning teams of Qualcomm Innovation Fellowship North America 2025!
We commend each of the finalists on excellent presentations and quality proposals.
Marziyeh Rezaei
Ultra-low power Coherent Front-haul Optical Links to Enable multi-Tb/s Capacity for 6G Massive MIMOs and Edge AI Datacenters
Washington
Pengyu Zeng
Ultra-low power Coherent Front-haul Optical Links to Enable multi-Tb/s Capacity for 6G Massive MIMOs and Edge AI Datacenters
Washington
Jonathan Zhou
AI-Enabled End-to-end Synthesis of Radio Frequency Integrated Circuits and Systems
Princeton
Sherif Ghozzy
AI-Enabled End-to-end Synthesis of Radio Frequency Integrated Circuits and Systems
Princeton
Jiangyifei Zhu
Contactless Cardiovascular Health Monitoring using AI-enabled mmWave Radars
CMU
Kuang Yuan
Contactless Cardiovascular Health Monitoring using AI-enabled mmWave Radars
CMU
Ruiyi Shen
Joint Communication and Sensing Using Beams Carrying Orbital Angular Momentum
Princeton
Poorya Mollahosseini
Joint Communication and Sensing Using Beams Carrying Orbital Angular Momentum
Princeton
Sandro Papais
Spatiotemporal Reasoning for Unified Perception and Prediction World Models
University of Toronto
Letian Wang
Spatiotemporal Reasoning for Unified Perception and Prediction World Models
University of Toronto
Jiawei Yang
Building Next-Generation Foundation Models for Autonomous Driving through Self-Supervised 4D Neural Reconstruction
USC
Cameron Smith
Building Next-Generation Foundation Models for Autonomous Driving through Self-Supervised 4D Neural Reconstruction
USC
Deepak Sridhar
Meta-Prompting for Scalable and Efficient Adaptation, Personalization, and Fine-Grained Control of Foundation Models
UCSD
Alakh Himanshu Desai
Meta-Prompting for Scalable and Efficient Adaptation, Personalization, and Fine-Grained Control of Foundation Models
UCSD
Haoran Geng
Bridging the Real2Sim2Real Gap for Generalizable Robot Learning with Dynamic 3D Modeling
UCB
Junyi Zhang
Bridging the Real2Sim2Real Gap for Generalizable Robot Learning with Dynamic 3D Modeling
UCB
Paschal Amusuo
AutoUP: Unit Proof Generation for Enabling Code-level Security Verification
Purdue University
Dharun Anandayuvaraj
AutoUP: Unit Proof Generation for Enabling Code-level Security Verification
Purdue University
Dhruv Thapar
SMART: Scalable Machine Learning-Assisted Routing-Aware Testing for Many-Chiplet FOWLP
ASU
Partho Bhoumik
SMART: Scalable Machine Learning-Assisted Routing-Aware Testing for Many-Chiplet FOWLP
ASU
Jiho Kim
Network-on-X: Hierarchical Modeling and DSE for 3D Heterogeneous Chiplet Architectures
Georgia Tech
Danish Baig
Network-on-X: Hierarchical Modeling and DSE for 3D Heterogeneous Chiplet Architectures
Georgia Tech
Sujay Pandit
Machine learning based power estimation for reconfigurable SoCs
Purdue University
Aradhana Mohan Parvathy
Machine learning based power estimation for reconfigurable SoCs
Purdue University
Megan Frisella
Productive Programming of Multi-Threaded Hardware Accelerators
Washington
Andrew Alex
Productive Programming of Multi-Threaded Hardware Accelerators
Washington
Shwai He
Less Attention, Much Faster: Toward a Future of Efficiency-Optimized Transformer Architectures
UMD
Guoheng Sun
Less Attention, Much Faster: Toward a Future of Efficiency-Optimized Transformer Architectures
UMD
Shiyu Wang
Fast Manifold Denoising by Learning Traversals
Columbia
Mariam Avagyan
Fast Manifold Denoising by Learning Traversals
Columbia
Zixian Ma
Learning Multi-modal Agents that Can Reason and Act for Complex Multi- modal Tasks with Synthetic Data
Washington
Yushi Hu
Learning Multi-modal Agents that Can Reason and Act for Complex Multi- modal Tasks with Synthetic Data
Washington
Christophe Gyurgyik
Decoupling Algorithms from Data Structures for High-Performance Computing
Stanford
Alexander J Root
Decoupling Algorithms from Data Structures for High-Performance Computing
Stanford
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:
Qualcomm Innovation Fellowship Finalists' Day
May 28, 2016 | 1:35
