QInF 2018 US

2018 U.S. 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 partnership. Our goal is to enable students to pursue their futuristic innovative ideas.

Selected Abstracts
Application Process
Participating Universities
Areas of Interest
Timeline
Winners

We received 174 abstracts this year. After a careful selection process, the following have been chosen to proceed to the Proposal phase of the QInF 2018. Congratulations!

School

Innovation Title

Submission #

Caltech

Efficient Near ML Data Recovery in Massive MIMO

S2018-12868

CMU

Active Simultaneous Localization and Mapping

S2018-12941

CMU

Reconfigurable Millimeter-wave Hybrid Beamforming MIMO Transceiver for 5G and Beyond

S2018-12871

CMU

Never-Ending Learner of Sounds

S2018-12927

CMU

Large-Scale Language Grounding and Imitation Learning from Narrated Demonstrations

S2018-12902

CMU

Towards Efficient Hardware-Constrained Deep Learning

S2018-12886

Cornell

Hardware-Algorithm Co-Design for Efficient Embedded Vision

S2018-12805

Georgia Tech

An Artificial-Intelligence (AI) Assisted Mm-Wave Multi-Band Doherty Transmitter with Rapid Mixed-Mode In-Field Performance Optimization and Digital Pre-Distortion Compensation

S2018-12852

Georgia Tech

Additively Manufactured Flexible "Smart Packaging" and Reconfigurable On-Package Antenna Arrays for the Next-Generation 5G/mm-Wave System-on-Package Designs

S2018-12812

Georgia Tech

Enabling the coexistence of autonomous and human-driven vehicles

S2018-12813

Georgia Tech

Scalable Heterogeneous Integration for In-Memory Processors in Non-Von-Neumann Computing

S2018-12830

Georgia Tech

An ultra-low power speech recognition system with self-powered microphone material and analog computing

S2018-12831

Georgia Tech

Robust, Agile Navigation in Perception Space

S2018-12851

Michigan

Towards Compact Reconfigurable RF Front-ends Employing Multifunctional Ferroelectrics

S2018-12845

Michigan

Discovering Cross-Modal Instructional Sequences Through Video and Text

S2018-12856

MIT

Learning to Sense and Act Simultaneously

S2018-12940

MIT

Fully Integrated Data-Driven Perception and Motion Planning for Autonomous Systems with Safety Guarantees

S2018-12897

MIT

Deep Learning Using Radically Less Time, Space, and Data

S2018-12832

Princeton

Automated Neural Network Synthesis

S2018-12935

Princeton

Containment-based Security Architecture

S2018-12839

Princeton

Reinforcement Learning for Program Synthesis

S2018-12857

Rutgers

BigRoad: Scaling Unusual Driving Events Collection for Dependable Self-Driving System

S2018-12849

Rutgers

Digital Computational Nodes Meet Dumb All-analog Transmitting Sensors: Rethinking High-density Sensing in the 21st Century

S2018-12917

Stanford

Safe Multi-Agent Imitation Learning for Self-Driving

S2018-12826

Stanford

Interpretable Algorithms for Intent Inference and Decision-Making on the Road

S2018-12911

Stanford

Engineering a Bidirectional Implantable Neural Prosthesis

S2018-12901

UCB

Performance and Safety Analysis of Systems with Learned Components

S2018-12794

UCB

A 3D-Integrated NEMS-CMOS FPGA

S2018-12919

UCLA

TNT: Trusted Notion of Time for Resilient Autonomous Driving

S2018-12782

UCLA / MIT

Developing Social Robot Learning Companions for Personalized Children’s Education

S2018-12841

UCLA

Communicating to Learn and Compute

S2018-12822

UCLA

Secure Control of Unmanned Aerial Vehicles using Deep Reinforcement Learning

S2018-12840

UCLA

SecSens: Secure State Estimation for Reliable Autonomous Driving

S2018-12854

UCLA

Integration of Atomic Switch Networks with Modern Hardware Architectures for Next-Generation Computing

S2018-12914

UCLA

Edge Node Deep Learning using Ultra-Low Power Stochastic Processing

S2018-12913

UCLA

Wideband Dynamic Power Supply for Next Generation Polar TX

S2018-12883

UCLA

Towards Truly Intelligent Scalable and Explainable Machine Learning

S2018-12888

UCLA

Diverse Neurons and Inhomogeneous Neural Networks

S2018-12876

UCSB

Speeding Up Neural Networks For On-Device Training

S2018-12778

UCSB

Bringing Standard Structure to Discharge Summaries

S2018-12937

UCSB

Unsupervised Learning with Consistency Enforcement for Single-Image Albedo/3D Estimation and Dehazing

S2018-12860

UCSB

Towards Better 3D Scene Understanding and Object Reasoning

S2018-12847

UCSB

Intuitive Diagnostics for Deep Visuomotor Policies

S2018-12850

UCSB

Context-aware solution for fast one-shot one-class 2D and 3D recognition

S2018-12777

UCSD

Coding to Extend the Lifetime of Non-volatile Memories

S2018-12842

UCSD

Monolithic Heterogeneously Integrated High-Power Vertical-Channel GaN Devices with Si CMOS Electronics

S2018-12818

UCSD

Automated and light-weight defense against adversarial attacks on deep learning models

S2018-12806

UCSD

Monte Carlo Decoding of Error-Correcting Codes

S2018-12798

UCSD

Magneto: Hardware-Algorithm Co-design for Training at the Edge using MRAM

S2018-12828

UCSD

Learning to Map and Navigate in Unknown Dynamic Environments

S2018-12853

UIUC

Fusion of RADAR, Motion, and Imaging for Autonomous Vehicles

S2018-12815

UIUC

Energy-efficient system architecture for real-time video inference on embedded platforms

S2018-12803

UIUC

Enhancing On-chip Communication through Fast On-chip Wireless Transfers

S2018-12800

UIUC

Swift Millimeter-Wave Imaging for Self-Driving Cars

S2018-12915

UIUC

A Testing Framework for Driver Task Analysis in Autonomous Driving Systems

S2018-12906

UIUC

Application-Aware Flexible Congestion Control

S2018-12895

UMD

Private Information Retrieval in Networks: Fundamental Limits and Practical Schemes

S2018-12821

UMD

Progressive Machine Learning, Knowledge Compaction, Human-Robot Collaboration

S2018-12928

USC

Safety Assurance for Autonomous Cars Using Deep Reinforcement Learning and Signal Temporal Logic

S2018-12924

USC

Learning Environment-Aware Acrobatic Flight from Video Demonstrations

S2018-12926

UT Austin

Collaborative Perception and Planning for Networked Autonomous Vehicles

S2018-12909

UT Austin

Machine learning for millimeter-wave V2X with situation awareness

S2018-12779

Washington

Personalizing Gesture Recognition Using Sample-Efficient Transfer Learning

S2018-12844

Washington

MegaIoT: Enabling Thousands of Concurrent Transmissions in Low-Power Networks

S2018-12918

Washington

DNN-Guided Synthesis of Schedules for Deep Learning Workloads

S2018-12932

Washington / UIUC

Discovering Communication Algorithms Via Deep Learning

S2018-12893

Washington

i-FLAVOR:Instruction and Field Programmable Deep Learning Processor with Compiler-Driven Activity-Aware All-Digital Voltage Regulation

S2018-12933

Wisconsin-Madison / Stanford

Robustifying Deep Networks against Adversarial Examples via Runtime Randomness

S2018-12802

Wisconsin-Madison

Configurable Tightly-Coupled FPGA for Fine-Grained Acceleration

S2018-12846

Wisconsin-Madison

Optical Metasurfaces for Imaging and Depth Sensing

S2018-12824

Each team shall submit an application by the specified deadline (see Timeline tab) that must include:

  1. 1. One-page abstract of innovation proposal

    (Single- or double-column with font size no smaller than 11)

  2. 2. Letter from one or two faculty members recommending the innovation
    1. a. Why the proposal is innovative
    2. b. Why the proposal is important
    3. c. Why the current team is likely to succeed in their proposal
  3. 3. Each student's CV
  4. 4. The official rules for the Qualcomm Innovation Fellowship are available for download and set forth the Program’s governing guidelines

     

SUBMISSIONS SHOULD BE IN A ZIP FILE

The submission portal will open two weeks before the deadline.

 

PROPOSAL PHASE
Selected teams will be notified directly to participate in the next phase by submitting a final proposal that must include:

Three-page (Single- or double-column with font size no smaller that 11) innovation proposal (plus one for reference/bibliography) including:

    1. a. Introduction and problem definition
    2. b. Innovation proposal and relation to the state of the art
    3. c. One-year horizon of the project, even if the proposal is a multi-year project
    4. d. Strength of the team to achieve the proposal milestones

SELECTION OF FINALISTS AND WINNERS
Finalist teams will be selected to participate in the final presentation phase of the Program.  Each finalist team must prepare a 12-minute presentation for the judges. Presentations must be in PowerPoint or PDF format. The presentation generally includes:

    1. a. The idea
    2. b. The differentiating factors from state of the art
    3. c. The execution plan and strength of the team

The winning teams will be chosen from these finalists after they present to the judging panel.

ADDITIONAL INFORMATION

  • Applications must be submitted online by the specified deadline (see Timeline tab).  
  • The official rules for the Qualcomm Innovation Fellowship are available for download and set forth the Program’s governing guidelines
  • All QInF-related information will be announced on this webpage. Please check back regularly for updates

See FAQs for more details
Do you need further Information? Please direct your questions to: innovation.fellowship@qualcomm.com

 

Qualcomm Research is a division of Qualcomm Technologies, Inc.

 

Qualcomm is inviting applications for the Qualcomm Innovation Fellowship 2018 from PhD students in the Electrical Engineering and Computer Science (and related) departments at:

  • California Institute of Technology
  • Carnegie Mellon University (CMU)
  • Columbia University
  • Cornell University
  • Georgia Institute of Technology
  • Massachusetts Institute of Technology (MIT)
  • Princeton University
  • Rutgers University
  • Stanford University
  • UC Berkeley (UCB)
  • UC Los Angeles (UCLA)
  • UC San Diego (UCSD)
  • UC Santa Barbara (UCSB)
  • University of Illinois at Urbana-Champaign (UIUC)
  • University of Maryland, College Park
  • University of Michigan
  • University of Southern California (USC)
  • University of Texas at Austin
  • University of Washington
  • University of Wisconsin-Madison

We invite teams to submit proposals in the following areas. We also welcome proposals outside of the sub-areas listed below:

Advanced Semiconductor Electronics

  • Ultra-low (uW) power embedded platform for edge computing (ULP architectures and designs, HW accelerators, power generation and management, novel memories, security)
  • Novel materials and heterogeneous integration (2D semiconductors, GaAs, GaN, etc.)
  • CMOS (3D IC, thermal-aware designs, circuits, advanced packaging techniques, etc.)
  • RF / analog ASICs and architectures (Sub-6GHz 5G power amplifiers, mmWave RFIC for 5G NR, adaptive RF signal processing algorithms, etc.)
  • Advanced antenna (millimeter-wave and phase-array antennas), novel antenna materials, structures and implementations

Processor Architecture and Implementation

  • Novel processor architectures, microarchitectures, extensions, and accelerators
  • Multimedia and gaming architectures (not limited to GPU, GPGPU, VLIW, DSP, etc.)
  • Novel architectures for artificial intelligence, edge training and inference
  • Security features of CPUs and accelerators at the instruction set, memory system, and SOC levels

Autonomous Driving

  • Advanced sensors and sensor fusion
  • Imaging radar
  • Deep learning with guarantees
  • Safe and reliable path planning

Machine Learning

  • Natural language processing
  • Computer vision
  • Reinforcement and continual learning
  • On-device training
  • Intermediate representation for machine learning workloads/compilers

Advances in Communication Techniques and Theory

  • Ultra reliable and low latency communications
  • Wide-area wireless networks using high-frequency and mmWave spectrum
  • Massive MIMO, network MIMO, and coordinated multipoint processing
  • Wireless systems for unlicensed/shared spectrum
  • Low energy networks (Bluetooth LE, 802.15.4, Zigbee, Wi-Fi, etc.)

Submission site opens: November 3, 2017

Application submission deadline: November 12, 2017 (23:59h PST)

Selection announcement: December 12, 2017

Proposal submission deadline: January 15, 2018 (23:59h PST)

Finalists’ announcement: March 2018

Finalist presentations submission deadline: April 2018

Finalist presentations: April/May 2018

Winners’ announcement: May 2018

QInF Winners’ Day: October 2018

2017 U.S. Winners

We received 116 proposals from 18 schools this year, from which, 33 finalists were selected (acceptance rate: 26.44%). From the finalists, we selected 8 winning teams (acceptance rate: 6.9%). Each winning team will be awarded a $100,000 fellowship and receive mentorship from Qualcomm engineers.

 

School

Students Recommender(s) Title
Washington

Max Willsey, Vincent Lee

Luis Ceze, Rastislav Bodik, Alvin Cheung 

Program Synthesis for Domain Specific Reconfigurable Accelerators

CMU

Yang Zhang, Abdelkareem Bedri

Chris Harrison, Thad Starner

Towards General-Purpose Sensing with Synthetic Sensors

Columbia

Thomas Repetti, Joao Cerqueira

Martha Kim, Mingoo Seok

A Programmable Spatial Architecture with Temporally/Spatially Fine-Grained Active Leakage Management for Energy-Efficient Near-Threshold-Voltage Computing

Princeton

Xue Wu, Chandrakanth Chappidi

Kaushik Sengupta

Universally Reconfigurable mm-Wave Transmitter Architectures and Antenna Interfaces for the Next-generation of Heterogeneous mm-Wave Wireless Networks

MIT

Hongzi Mao, Ravi Netravali

Mohammad Alizadeh, Hari Balakrishnan

Adaptive Bitrate Streaming with Deep Reinforcement Learning    

Georgia Tech

Grady Williams, Paul Drews

Evangelos Theodorou, James Rehg

Autonomous Racing Using Deep Learning and Game Theoretic Optimization

UT Austin

Lakshay Narula, Matthew Murrian

Todd Humphreys

Robust and Secure Precise Location for Connected Vehicles and Pedestrians

CMU

Minh Vo, Aayush Bansal

Deva Ramanan, Srinivasa Narasimhan, Yaser Sheikh

Time Travel: Automatically Organizing and Browsing Massive Visual Data

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 27, 2016

1:35