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.

Winner’s Day Instructions
Winners
Finalists
Finalist Instructions
Selected Abstracts
Participating Universities
Areas of Interest
Timeline

The QInF 2018 Winners’ day will be held at the Qualcomm headquarters in San Diego on October 3, 2018.

All winning teams will participate in the following activities:

  • 2017 winning teams will present a summary of their accomplishments in a 12-minute presentation (plus 2 minutes for Q&A)
  • 2018 winning teams will present a summary of the proposal in a short 5-minute presentation
  • All teams will participate in the poster session

Submission sites are open for teams to submit presentation slides and posters for the Winner’s Day event. Submissions are due by 9/27/2018.

2017 Winning Teams - Submission Site
2018 Winning Teams - Submission Site

Submissions should be a single ZIP file with extension name *2018WinnersDay and containing the following:

  • Presentation slides in PDF or PowerPoint format. The projector supports both 16:9 and 4:3 formats
  • Up to two posters (24”X36”) in PowerPoint, or PDF format

Due to the presentation setup and to minimize the switching time between teams, you will not be able to use your own laptop for the presentation.

2018 U.S. Winners

This year 70 proposals were selected from 174 abstracts submitted by teams from 20 QInF schools. Of the 30 finalists chosen (acceptance rate: 17.24%) we selected 8 winning teams (acceptance rate: 4.60%). Each winning team will be awarded a $100,000 fellowship and receive mentorship from Qualcomm engineers.

School

Students

Recommender(s)

Title

UCSD

Jiun-Ting Huang, Alankrita Bhatt

Young-Han Kim

Monte Carlo Decoding of Error-Correcting Codes

Stanford

Jiaming Song, Shengjia Zhao

Stefano Ermon

Safe Multi-Agent Imitation Learning for Self-Driving

UCSD

Hardik Sharma, Mohammad Ghasemzadeh

Hadi Esmaeilzadeh, Farinaz Koushanfar

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

MIT

Davis Blalock, Jose Javier Gonzalez

John Guttag

Deep Learning Using Radically Less Time, Space, and Data

Georgia Tech

Fei Wang, Kyle Xu

Hua Wang, Justin Romberg

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

Caltech

Ehsan Abbasi, Fariborz Salehi

Babak Hassibi

Efficient Near ML Data Recovery in Massive MIMO

CMU

Dimitrios Stamoulis, Zhuo Chen

Diana Marculescu

Towards Efficient Hardware-Constrained Deep Learning

Stanford

Karen Leung, Sumeet Singh

Marco Pavone, Dorsa Sadigh

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

 

Congratulations! The following 30 proposals have been chosen as Finalists. Please see the Finalist Instructions tab for details on next steps.

Submission #

Innovation Title

Students

Recommendor(s)

University

S2018-12779

Machine Learning for Millimeter-wave V2X with Situational Awareness

Yuyang Wang, Monica Ribero

Robert W. Heath Jr.

UT Austin

S2018-12782

TNT: Trusted Notion of Time for Resilient Autonomous Driving

Fatima Anwar, Amr Alanwar

Mani Srivastava

UCLA

S2018-12798

Monte Carlo Decoding of Error-Correcting Codes

Jiun-Ting Huang, Alankrita Bhatt

Young-Han Kim

UCSD

S2018-12802

Robustifying Deep Networks Against Adversarial Examples via Orthogonality and Runtime Randomness

Harrison Rosenberg, Kabir Chandrasekher

Dimitris Papailiopoulos, Kannan Ramchandran

Wisconsin-Madison / Stanford

S2018-12803

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

Ameya Patil, Sujan Gonugondla

Naresh Shanbhag, Alexander Schwing

UIUC

S2018-12806

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

Mohammad Samragh, Shubhanshu Shekhar

Farinaz Koushanfar, Tara Javidi

UCSD

S2018-12815

Fusion of RADAR, Motion, and Imaging for Autonomous Vehicles

Kirk Busche, Teck Yian Lim

Minh Do

UIUC

S2018-12821

Private Information Retrieval in Networks: Fundamental Limits and Practical Schemes

Karim Banawan, Yi-Peng Wei

Sennur Ulukus

UMD

S2018-12826

Safe Multi-Agent Imitation Learning for Self-Driving

Jiaming Song, Shengjia Zhao

Stefano Ermon

Stanford

S2018-12828

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

Hardik Sharma, Mohammad Ghasemzadeh

Hadi Esmaeilzadeh, Farinaz Koushanfar

UCSD

S2018-12832

Deep Learning Using Radically Less Time, Space, and Data

Davis Blalock, Jose Javier Gonzalez

John Guttag

MIT

S2018-12839

Containment-based Security Architecture

Hansen Zhang, Sotiris Apostolakis

David August

Princeton

S2018-12845

Towards Compact Reconfigurable RF Front-ends Employing Multifunctional Ferroelectrics

Milad Zolfagharloo Koohi, Suhyun Nam

Amir Mortazawi

Michigan

S2018-12846

Configurable Tightly-Coupled FPGA for Fine-Grained Acceleration

David Schlais, Heng Zhuo

Mikko H. Lipasti

Wisconsin-Madison

S2018-12847

Towards Better 3D Scene Understanding and Object Reasoning

Zhe Fu, Brandon Huynh

Matthew Turk, Tobias Hollerer

UCSB

S2018-12852

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

Fei Wang, Kyle Xu

Hua Wang, Justin Romberg

Georgia Tech

S2018-12868

Efficient Near ML Data Recovery in Massive MIMO

Ehsan Abbasi, Fariborz Salehi

Babak Hassibi

Caltech

S2018-12871

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

Susnata Mondal, Mazen Soliman

Jeyanandh Paramesh

CMU

S2018-12876

Diverse Neurons and Inhomogeneous Neural Networks

Albert Lee, Hao-Yuan Chang

Kang-Lung Wang

UCLA

S2018-12886

Towards Efficient Hardware-Constrained Deep Learning

Dimitrios Stamoulis, Zhuo Chen

Diana Marculescu

CMU

S2018-12893

Discovering Communication Algorithms Via Deep Learning

Yihan Jiang, Ashok Makkuva

Pramod Viswanath, Sreeram Kannan

Washington / UIUC

S2018-12897

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

Lucas Liebenwein, Wilko Schwarting

Daniela Rus, Sertac Karaman

MIT

S2018-12901

Engineering a Bidirectional Implantable Neural Prosthesis

Nandita Bhaskhar, Nishal Shah

E. J. Chichilnisky, Subhasish Mitra

Stanford

S2018-12902

Large-Scale Language Grounding and Imitation Learning from Narrated Demonstrations

Adam Harley, Hsiao-Yu Tung

Katerina Fragkiadaki

CMU

S2018-12911

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

Karen Leung, Sumeet Singh

Marco Pavone, Dorsa Sadigh

Stanford

S2018-12913

Edge Node Deep Learning using Ultra-Low Power Stochastic Processing

Tianmu Li, Jiyue Yang

Puneet Gupta, Sudhakar Pamarti

UCLA

S2018-12919

A 3D-Integrated NEMS-CMOS FPGA

Zhixin (Alice) Ye, Urmita Sikder

Tsu-Jae King Liu, Vladimir Stojanovic

UCB

S2018-12926

Learning Environment-Aware Acrobatic Flight from Video Demonstrations

James Preiss, Eric Heiden

Gaurav Sukhatme

USC

S2018-12927

Never-Ending Learner of Sounds

Benjamin Elizalde, Abelino Jimenez

Bhiksha Raj

CMU

S2018-12928

Progressive Machine Learning, Knowledge "Compaction", Human-Robot Collaboration

Zhenyu Lin, Christos Mavridis

John S. Baras

UMD

The QInF 2018 Finals will be held at Qualcomm headquarters in San Diego on April 17th and 18th, 2018.

The Finals presentations are a major part of the winner selection. Both members of each finalist team are expected to attend and present at the Finals.

About the Finals presentations

Prepare a 12 minute presentation (with an additional 3 min for questions). Please practice to ensure that you adhere to the time limit.

You must submit your presentation slides in PDF or PowerPoint format and poster in PDF only format by 08:00AM PST on Monday April 9th 2018.

The submission site will be open on March 20th, 2018. Details of the format of the slides and poster are below. Use your existing confirmation number for the submission.

SUBMISSION SHOULD BE A SINGLE ZIP FILE CONTAINING THE THREE OR FOUR DOCUMENTS: PRESENTATION SLIDES, PDF OF SLIDES _FORPRINT, FIRST POSTER, AND OPTIONAL SECOND POSTER.

The presentations you submit will be preloaded on a computer provided by us for the Finals. Due to the presentation setup and to minimize the switching time between teams, you cannot use your own laptop for the presentation.

Students must use the submitted PowerPoint or PDF slides for their presentation.

The presentation schedule of the QInF Finals will be provided at registration on the first day.

About the slides

12-slides maximum, including the cover page. The presentation slides generally include:

  • The idea
  • The differentiating factors from state of the art
  • The execution plan and strength of the team

We do not recommend adding slides with references. If you do, they would also count towards the 12-slide limit.

Please do not use animations purely to get around the 12-slide limit.

Please ensure that all external media is embedded in the PDF or PowerPoint file itself, e.g., videos. The PDF/PowerPoint file must be self-contained.

In addition to your presentation, please provide a for-print PDF (8.5”X11” landscape) of your presentation (max. 12 pages). The name of the for-print PDF should end with "_forprint.pdf". It should have the same content (sans animations) as your presentation.

About the poster

Due to the tight program, we offer a poster session, where you have the opportunity for in-depth talks with the judges and Qualcomm engineers.

Each team may submit up to two posters (PDF format required, 24"x36").

We will print the posters for you ahead of time and provide the easels at the poster sessions.

See FAQs for more details

Do you need further Information?
Please direct your questions to: innovation.fellowship@qualcomm.com

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

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 7, 2018

Finalist presentations submission deadline: April 9, 2018

Finalist presentations: April 17-18, 2018

Winners’ announcement: May 2018

QInF Winners’ Day: October 2018

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 14, 2018

1:35