Qualcomm Innovation Fellowship Finalists

Congratulations to all of our Fellowship finalists!


2020 North America Finalists

University Innovation Title Students
CMU Hardware-Aware Multimodal 3D Object Detection for On-Device Augmented Reality Applications Ting-Wu Chin, Ahmet Inci
CMU Safe Adaptive Learning and Control for Dynamical Systems Charles Noren, Weiye Zhao
CMU 3D Multi-Agent Social Interaction Understanding and Diverse Future Behavior Forecasting for Next General AI Systems Xinshuo Weng, Ye Yuan
CMU Ad Hoc Spatially-Anchored Augmented Reality Interfaces Karan Ahuja, Sujeath Pareddy
Columbia Protecting Heterogeneous SoCs with Security Sockets Davide Giri, Luca Piccolboni
Columbia Practical Software Security on Heterogeneous System on Chips Mohamed Hassan, Evgeny Manzhosov
Cornell Synthesis-Driven ISA Extensions for DSPs Alexa VanHattum, Rachit Nigam
Georgia Tech Towards Building Machine-Learning Powered EDA Flow and Methodologies Anthony Agnesina, Yi-Chen Lu
MIT Side-Channel Security Analysis of Embedded Machine Learning Implementations and Efficient Software / Hardware Countermeasures Saurav Maji, Utsav Banerjee
MIT Aerial Drone Sensing and Intelligence Favyen Bastani, Songtao He
MIT & UCLA Privacy-Preserving Video Analytics with Untrusted Queries Francis Cangialosi, Neil Agarwal
Princeton Exploring In-Memory Computing for Architectural and Technology Scaling Peter Deaville, Rakshit Pathak
Purdue University EM/Power Statistical and Machine-Learning Side-Channel Attacks & Generic Low-Overhead Synthesizable Circuit-Level Countermeasures Debayan Das, Baibhab Chatterjee
Purdue University Enabling Edge Intelligence with In-Memory Accelerators for Ultra-low Precision Deep Neural Networks Niharika Thakuria, Sourjya Roy
Purdue University Toward energy-efficient and accurate in-memory analog computing systems for machine learning workloads Indranil Chakraborty, Mustafa Fayez Ahmed Ali
Stanford Peer Pressure: On-Device Learning from Soft Decisions Ilai Bistritz, Ariana Mann
Stanford Autoregressive Generation that Adapts to Computational Constraints Yang Song, Rui Shu
UCB Designing Analog Mixed Signal Circuits with Machine Learning Keertana Settaluri, Kourosh Hakhamaneshi
UCLA Robust Crossbar Persistent Memories for In-Memory Learning Acceleration Zehui Chen, Siyi Yang
UCLA Autonomous Driving with Smartphones using Online Expectation-Maximization and Controllable Stereoscopic Vision Tsang-Kai Chang, Kenny Chen
UCSB N-face InGaN/AlGaN RF power HEMTs with a relaxed InGaN channel for mm-wave power amplifier Shubhra Pasayat, Weiyi Li
UCSB & Stanford Learning to Encourage Cooperative Behavior in Multi-agent Systems Daniel Lazar, Erdem Biyik
UCSB Practical algorithms for near-capacity massive MAC: Iterative schemes based on principled convex-relaxations Ganesh Ramachandra Kini, Orestis Paraskevas
UCSD LEGOS: AI for Cross-Domain Multi-Tenant Acceleration of Autonomous Systems Byung Hoon Ahn, Soroush Ghodrati
UCSD Physically-Motivated Deep Inverse Rendering from Sparse Inputs Sai Bi, Zhengqin Li
UCSD Vertical Hybrid Power Delivery for High-Performance Processors and Digital Systems Casey Hardy, Abdullah Abdulslam
UCSD Finding and Eliminating Timing Side-Channels in Crypto Code with Pitchfork Sunjay Cauligi, Craig Disselkoen
UCSD Temporospatial Fusion of Radar, Vision and LIDAR data for Autonomous Driving Dominique Meyer, Hengyuang Zhang
UCSD Doing More with Less:Sparse Sensing for Millimeter Wave Channel Estimation Pulak Sarangi, Rohan Ramchandra Pote
UCSD Learning-Based 3D Mesh Reconstruction Minghua Liu, Xiaoshuai Zhang
UCSD Toward Personalized and Multimodal Conversational Recommender Systems Shuyang Li, Bodhisattwa Prasad Majumder
UIUC High Resolution Millimeter Wave Imaging Using Deep Adversarial Learning Suraj Jog, Junfeng Guan
UMD Provably robust neural networks using curvature regularization Sahil Singla, Yogesh Balaji
USC High-Capacity Mode-Division-Multiplexed Wireless Communications Within and Beyond Millimeter-Wave Band Runzhou Zhang, Huibin Zhou
UT Austin mmWave and TeraHz Channel Estimation and Beamforming using Deep Generative Networks Akash Doshi, Ajil Jalal
UT Austin Radar-to-radar interference: System level analysis and solutions Khurram Mazher, Andrew Graff
Virginia Tech Physics-Driven Machine Learning and Data Fusion for Semiconductor Test, Quality and Yield Learning Yinan Wang, Tim Lutz
Virginia Tech A Low-Power Hybrid Neural Processing Architecture for Mobile Edge Intelligent Computing Yibin Liang, Kangjun Bai
Virginia Tech Hybrid Reinforcement Learning for Autonomous Vehicles in Adversarial Environments Ian Garrett, Leila Amanzadeh
Washington Source Separation with Deep Generative Priors John Thickstun, Vivek Jayaram
Wisconsin-Madison Comprehensively Accelerating Sequence-based Neural Networks Preyesh Dalmia, Suchita Pati
Wisconsin-Madison Ultra Low-Power Machine Learning at the Edge Tianen Chen, Setareh Behroozi

2020 Europe Finalists

Name Innovation Title University Supervisor
Chen Liu Neural Architectural Design Resistant Against Adversarial Attacks EPF Lausanne Sabine Süsstrunk,Mathieu Salzmann
Daniel Joseph Ringis Using deep learning for efficient per clip encoding with modern video codecs Trinity College Dublin Anil Kokaram
David Burt Variational Inference in Compositional Models Cambridge Carl Edward Rasmussen,Mark van der Wilk
David W. Romero Equivariant generative Networks Vrije Universiteit Jakub M. Tomczak,Mark Hoogendoorn
Denys Rozumnyi Detection and reconstruction of fast moving objects ETH Zurich Marc Pollefeys, Jiri Matas
Edoardo Remelli Enabling Differentiable Multi-Topology Mesh Representations through Shape Derivative EPF Lausanne Pascal Fua
Erik Daxberger Towards Efficient Deep Uncertainty Quantification Cambridge José Miguel Hernández-Lobato
Fangcheng Zhong Zhong Learn a Path to Perceptual Realism Cambridge Rafal Mantiuk
Georgios Rizos Uncertainty Aware Acquisition Policies for Addressing the Exploration Versus Exploitation Problem in Active Learning ICL Bjoern Schuller, Abbas Edalat
James Meech Efficient Programmable Non-Uniform Random Variate Generation for Sensor Fusion Cambridge Phillip Stanley-Marbell
Miguel Cacho Soblechero Sense-aware System-on-Chip for ISFET diagnostic platforms ICL Pantelis Georgiou,Jesus Rodriguez Manzano
Ning Yu Inclusive GAN: Reducing Bias in Generative Models by Improving Coverage and Equalizing Utility MPI Mario Fritz, Larry Davis
Pietro Frigo Reverse Engineering in-DRAM Rowhammer Mitigations via Automata Learning Vrije Universiteit Cristiano Giuffrida
Vidhi Ramesh Lalchand Probabilistic Kernel Learning in Gaussian Processes Cambridge Carl Edward Rasmussen
Yuki Asano Learning self-supervised learning University of Oxford Andrea Vedaldi

2020 South Korea Finalists

Application Number University Student(s)
QIFK-2020-3 Seoul National University Byeongchang Kim
QIFK-2020-4 Seoul National University Soochan Lee
QIFK-2020-6 KAIST Janghyeon Lee
QIFK-2020-7 KAIST Janghyeon Lee
QIFK-2020-14 Korea University Bumsoo Kim, Taeho Choi
QIFK-2020-16 Seoul National University Saehyung-Lee
QIFK-2020-21 KAIST Jihoon Tack, Sangwoo Mo
QIFK-2020-32 Yonsei University Min Soo Sim
QIFK-2020-33 KAIST Yuji Roh
QIFK-2020-35 Seoul National University Jaemin Yoo
QIFK-2020-39 Seoul National University Jaemin Yoo
QIFK-2020-41 Seoul National University Inseop-Chung
QIFK-2020-44 KAIST Woobin Im
QIFK-2020-45 Seoul National University Chris Dongjoo Kim
QIFK-2020-48 Seoul National University Jang-Hyun Kim
QIFK-2020-55 Seoul National University Yeonwoo Jeong
QIFK-2020-56 Seoul National University Yeonwoo Jeong
QIFK-2020-58 POSTECH Sungyeon-Kim
QIFK-2020-59 POSTECH Sungyeon-Kim
QIFK-2020-61 Seoul National University Seungyong Moon, Gaon An
QIFK-2020-64 Seoul National University Jungbeom Lee
QIFK-2020-77 KAIST Jongheon Jeong
QIFK-2020-81 Seoul National University Gyeongsik Moon
QIFK-2020-85 KAIST Hyeokjea-Kwon
QIFK-2020-86 Seoul National University Jaekyeom Kim
QIFK-2020-87 POSTECH Juhong-Min
QIFK-2020-92 Seoul National University Hongsuk Choi
QIFK-2020-94 KAIST Dong-Jin Kim
QIFK-2020-95 Sungkyunkwan University Sangwon-Jung, Hongjun Ahn
QIFK-2020-100 Seoul National University Sungyong Baik
QIFK-2020-101 KAIST Woojun Kim
QIFK-2020-111 KAIST Inkyu-Shin
QIFK-2020-114 KAIST Jinsun-Park
QIFK-2020-122 KAIST Insu Han
QIFK-2020-123 KAIST Seokju Lee
QIFK-2020-128 KAIST Philipp Benz, Chaoning Zhang
QIFK-2020-137 KAIST Juseung Yun
QIFK-2020-140 Sungkyunkwan University Sungmin Cha
QIFK-2020-141 Sungkyunkwan University Sungmin Cha
QIFK-2020-142 Sungkyunkwan University Sungmin Cha
QIFK-2020-143 KAIST Pan Fei

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:

©2021 Qualcomm Technologies, Inc. and/or its affiliated companies.

References to "Qualcomm" may mean Qualcomm Incorporated, or subsidiaries or business units within the Qualcomm corporate structure, as applicable.

Qualcomm Incorporated includes Qualcomm's licensing business, QTL, and the vast majority of its patent portfolio. Qualcomm Technologies, Inc., a wholly-owned subsidiary of Qualcomm Incorporated, operates, along with its subsidiaries, substantially all of Qualcomm's engineering, research and development functions, and substantially all of its products and services businesses. Qualcomm products referenced on this page are products of Qualcomm Technologies, Inc. and/or its subsidiaries.

Materials that are as of a specific date, including but not limited to press releases, presentations, blog posts and webcasts, may have been superseded by subsequent events or disclosures.

Nothing in these materials is an offer to sell any of the components or devices referenced herein.