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Sony Research Award Program

SONY RESEARCH AWARD PROGRAM 2026 Sony Research Award Program is accepting proposals starting from July 15, 2026, until September 15, 2026. Details of the program, submission guidelines and terms are found in the links below.

OVERVIEW

As part of one of the world's most innovative and recognizable brands, we are committed to support university research and innovation in the U.S., Canada, India, and select European countries, while also fostering partnerships with university faculty and researchers. The Sony Research Award Program provides funding for cutting-edge academic research and helps build a collaborative relationship between faculty and Sony researchers. With awards up to $150,000 USD* per year for each accepted proposal, both the Faculty Innovation Award and Focused Research Award create new opportunities for university faculties and research institutions to engage in pioneering research that could drive new technologies, industries and the future.

Faculty Innovation Award

Up to $100K USD* in funds to conduct cutting-edge research in Sony's general areas of interest

Focused Research Award

Up to $150K USD* in funds to conduct research in the areas of Sony's immediate interest

Submission Guidelines

Eligibility, requirements, submission protocol, and terms are explained in these guidelines.

Application Window

Proposal submission is open from July 15, 2026 to September 15, 2026.

AWARD RECIPIENTS

Congratulations to all award recipients in the Sony Research Award Program! We sincerely look forward to working closely with you.

2024 (more renewals pending)

Professor Gopala Anumanchipalli, University of California, Berkeley, United States
Professor Gedas Bertasius, University of North Carolina at Chapel Hill, United States
Professor Pushpak Bhattacharya, Indian Institute of Technology Bombay, India
Professor Filippo Maria Fazi, University of Southampton, United Kingdom
Professor Ioannis Gkioulekas, Carnegie Mellon University, United States
Professor Boqing Gong, Boston University, United States
Professor Natasha Jaques, University of Washington, United States
Professor Chenfanfu Jiang, University of California, Los Angeles, United States
Professor Vaibhav Katewa, Indian Institute of Science, India
Professor Hun-Seok Kim, University of Michigan, United States
Professor Phillip Koehn, Johns Hopkins University, United States
Professor Karen Liu, Stanford University, United States
University of Texas at Austin, United States
Professor Owen Miller, Yale University, United States
Professor Ramesh Raskar, Massachusetts Institute of Technology, United States
Professor Craig Shultz, University of Illinois Urbana-Champaign, United States
Professor Haoning Tang, University of California, Berkeley, United States
Professor Andrea Vedaldi, University of Oxford, United Kingdom
Professor Lei Wang, Lancaster University, United Kingdom
Professor Ming-Hsuan Yang, University of California, Merced, United States
Professor Kwang Moo Yi, University of British Columbia, Canada
Professor Zhi-Li Zhang, University of Minnesota Twin Cities, United States

2023 (more renewals pending)

Professor Haitham Al Hassanieh, EPFL, Switzerland
Professor Harry Atwater, California Institute of Technology, United States
Professor Changhyun Choi, University of Minnesota Twin Cities, United States
Professor Jia Deng, Princeton University, United States
Professor Rafael Gómez-Bombarelli, Massachusetts Institute of Technology, United States
Professor Laurie Heller, Carnegie Mellon University, United States
Professor Marco Hutter, ETH Zurich, Switzerland
Professor Ranjay Krishna, University of Washington, United States
Professor Johannes Levin, Ludwig-Maximilians-Universität München, Germany
Professor Yunzhu Li, Columbia University, United States
Professor David Lindell, University of Toronto, Canada
Professor David Lindell, University of Toronto, Canada***
Professor Xuezhe Ma, University of Southern California, United States
Professor Alan Marshall, University of Liverpool, United Kingdom
Professor Euan McLeod, University of Arizona, United States
Professor Pratyush Mishra, University of Pennsylvania, United States
Professor Ken Nakagaki, University of Chicago, United States
Professor Sang-Hyun Oh, University of Minnesota Twin Cities, United States
Professor Allison Okamura, Stanford University, United States
Professor Hayder Radha, Michigan State University, United States
Professor Alan Ritter, Georgia Institute of Technology, United States
Professor Qi Sun, New York University, United States
Professor Molei Tao, Georgia Institute of Technology, United States
Professor Chen Wang, University at Buffalo, United States
Professor Nozomu Yachie, University of British Columbia, Canada
Professor Junsong Yuan, University at Buffalo, United States
Professor Yuke Zhu, University of Texas at Austin, United States
Professor Mohit Gupta, University of Wisconsin-Madison, United States**
Professor Chenfanfu Jiang, University of California, Los Angeles, United States**
Professor Ewold Verhagen, NWO Institute AMOLF, The Netherlands**
Professor Bolei Zhou, University of California, Los Angeles, United States**

2022

Professor Mario Barbatti, Aix-Marseille Université, France
Professor Gedas Bertasius, University of North Carolina at Chapel Hill, United States
Professor Debashis Chanda, University of Central Florida, United States
Professor Angel Chang, Simon Fraser University, Canada
Professor Zaijun Chen, University of Southern California, United States
Professor Gregory Durrett, University of Texas at Austin, United States
Professor Jaime Fernandez Fisac, Princeton University, United States
Professor Chuang Gan, University of Massachusetts Amherst, United States
Professor Tatsunori Hashimoto, Stanford University, United States
Professor Chenfanfu Jiang, University of California, Los Angeles, United States
Professor Howard Lee, University of California, Irvine, United States
Professor Yong Jae Lee, University of Wisconsin-Madison, United States
Professor Yaping Liu, Northwestern University, United States
Professor Psyche Loui, Northeastern University, United States
Professor Michele Magno, ETH Zurich, Switzerland
Professor Alan Marshall, University of Liverpool, United Kingdom
Professor Julian McAuley, University of California, San Diego, United States
Professor Prineha Narang, University of California, Los Angeles, United States
Professor Dimitrios Nikolopoulos, Virginia Polytechnic Institute and State University, United States
Professor Sarah Ostadabbas, Northeastern University, United States
Professor Shinsuke Shimojo, California Institute of Technology, United States
Professor Bradley Siwick, McGill University, Canada
Professor Aneta Stefanovska, Lancaster University, United Kingdom
Professor Lili Su, Northeastern University, United States
Professor Ewold Verhagen, NWO Institute AMOLF, The Netherlands
Professor Nandita Vijaykumar, University of Toronto, Canada
Professor Yu Xiang, University of Texas at Dallas, United States
Professor Bolei Zhou, University of California, Los Angeles, United States
Professor Pulkit Agrawal, Massachusetts Institute of Technology, United States**
Professor Federico Capasso, Harvard University, United States**
Professor Aaron Courville, University of Montreal, Canada**
Professor Mohit Gupta, University of Wisconsin-Madison, United States**
Professor Aiichiro Nakano, University of Southern California, United States**
Professor Jose Principe, University of Florida, United States**
Professor Zhou Yu, Columbia University, United States**

2021

Professor Michal Bajcsy, University of Waterloo, Canada
Professor Gedas Bertasius, University of North Carolina at Chapel Hill, United States
Professor Federico Capasso, Harvard University, United States
Professor Li-Jing Cheng, Oregon State University, United States
Professor Aaron Courville, University of Montreal, Canada
Professor Jia Deng, Princeton University, United States
Professor Guillermo Gallego, Technische Universität Berlin, Germany
Professor Aditya Grover, University of California, Los Angeles, United States
Professor Charles Hages, University of Florida, United States
Professor Cho-Jui Hsieh, University of California, Los Angeles, United States
Professor Dongyeop Kang, University of Minnesota Twin Cities, United States
Professor Pan Li, Georgia Institute of Technology, United States
Professor Jun Liu, University at Buffalo, United States
Professor Saturnino Luz, The University of Edinburgh, United Kingdom
Professor Arka Majumdar, University of Washington, United States
Professor Stephen Morris, University of Oxford, United Kingdom
Professor Aiichiro Nakano, University of Southern California, United States
Professor Andrew Owens, University of Michigan, United States
Professor Srijith P.K, Indian Institute of Technology Hyderabad, India
Professor Bryan Pardo, Northwestern University, United States
Professor Arpita Patra, Indian Institute of Science, India
Professor Jose Principe, University of Florida, United States
Professor Ravi Ramamoorthi, University of California, San Diego, United States
Professor Arindam Sanyal, Arizona State University, United States
Professor Sebastian Scherer, Carnegie Mellon University, United States
Professor Alireza Vahid, University of Colorado Denver, United States
Professor Sheng Wang, University of Washington, United States
Professor Xiaolong Wang, University of California, San Diego, United States
Professor Benjamin Williams, University of California, Los Angeles, United States
Professor Lei Zhou, University of Texas at Austin, United States
Professor Pulkit Agrawal, Massachusetts Institute of Technology, United States**
Professor Mohit Gupta, University of Wisconsin-Madison, United States**
Professor Chia Wei Hsu, University of Southern California, United States**
Professor Deepak Pathak, Carnegie Mellon University, United States**
Professor Zhou Yu, Columbia University, United States**

2020

Professor Jacob Andreas, Massachusetts Institute of Technology, United States
Professor Veselka Boeva, Blekinge Institute of Technology, Sweden
Professor Changhyun Choi, University of Minnesota Twin Cities, United States
Professor Steven Cummer, Duke University, United States
Professor Guido de Croon, Delft University of Technology, The Netherlands
Professor Krzysztof Gajos, Harvard University, United States
Professor Rafael Gómez-Bombarelli, Massachusetts Institute of Technology, United States
Professor Aman Haque, The Pennsylvania State University, United States
Professor Felix Heide, Princeton University, United States
Professor Chia Wei Hsu, University of Southern California, United States
Professor Kyle Jamieson, Princeton University, United States
Professor Evangelos Kalogerakis, University of Massachusetts Amherst, United States
Professor Patanjali Kambhampati, McGill University, Canada
Professor Bhaskar Krishnamachari, University of Southern California, United States
Professor Fenglong Ma, The Pennsylvania State University, United States
Professor Shuji Nakamura, University of California Santa Barbara, United States
Professor VP Nguyen, University of Texas at Arlington, United States
Professor Deepak Pathak, Carnegie Mellon University, United States
Professor Marc Pollefeys, ETH Zurich, Switzerland
Professor Fang Song, Portland State University, United States
Professor Natalie Stingelin, Georgia Institute of Technology, United States
Professor Gordon Wetzstein, Stanford University, United States
Professor Joerg Wrachtrup, University of Stuttgart, Germany
Professor Jun Yao, University of Massachusetts Amherst, United States
Professor Junming Yin, Carnegie Mellon University, United States
Professor Pulkit Agrawal, Massachusetts Institute of Technology, United States**
Professor David Bishop, Boston University, United States**
Professor Mark Brongersma, Stanford University, United States**
Professor Siyang Cao, University of Arizona, United States**
Professor Virginia de Sa, University of California, San Diego, United States**
Professor Mohit Gupta, University of Wisconsin-Madison, United States**
Professor Po-Chun Hsu, Duke University, United States**
Professor Pedro Lopes, University of Chicago, United States**
Professor Dimitris Papailiopoulos, University of Wisconsin-Madison, United States**
Professor Daniel Sanchez, Massachusetts Institute of Technology, United States**
Professor Aswin Sankaranarayanan, Carnegie Mellon University, United States**
Professor Zhou Yu, Columbia University, United States**
Professor Jun-Yan Zhu, Carnegie Mellon University, United States**

2019

Professor Alan Aspuru-Guzik, University of Toronto
Professor Farrokh Ayazi, Georgia Institute of Technology
Professor Yoshua Bengio, University of Montreal
Professor Mark Brongersma, Stanford University
Professor Siyang Cao, University of Arizona
Professor Virginia de Sa, University of California, San Diego
Professor Jonathan Fan, Stanford University
Professor Kenan Gundogdu, North Carolina State University
Professor Mohit Gupta, University of Wisconsin-Madison
Professor Po-Chun Hsu, Duke University
Professor Yong Jae Lee, University of California, Davis
Professor Jian Lin, University of Missouri
Professor Pedro Lopes, University of Chicago
Professor Xiaoping Qian, University of Wisconsin-Madison
Professor Xiang Ren, University of Southern California
Professor Aswin Sankaranarayanan, Carnegie Mellon University
Professor Akane Sano, Rice University
Professor Randy Sweis, University of Chicago Medicine
Professor Zhou Yu, University of California, Davis
Professor Xinyu Zhang, University of California, San Diego
Professor Jun-Yan Zhu, Carnegie Mellon University
Professor Pulkit Agrawal, Massachusetts Institute of Technology**
Professor David Bishop, Boston University**
Professor Oliver Cossairt, Northwestern University**
Professor Song Han, Massachusetts Institute of Technology**
Professor Xingjie Ni, The Pennsylvania State University**
Professor Dimitris Papailiopoulos, University of Wisconsin-Madison**
Professor Daniel Sanchez, Massachusetts Institute of Technology**
Professor Ted Sargent, University of Toronto**

2018

Professor Pulkit Agrawal, Massachusetts Institute of Technology
Professor David Bishop, Boston University
Professor Oliver Cossairt, Northwestern University
Professor Jonathan Fan, Stanford University
Professor Katerina Fragkiadaki, Carnegie Mellon University
Professor Bolin Liao, University of California Santa Barbara
Professor Patrick Lin, Cal Poly, San Luis Obispo
Professor Wojciech Matusik, Massachusetts Institute of Technology
Professor Florian Metze, Carnegie Mellon University
Professor Xingjie Ni, The Pennsylvania State University
Professor Dimitris Papailiopoulos, University of Wisconsin-Madison
Professor Alexander Rush, Cornell University
Professor Ted Sargent, University of Toronto
Professor Sebastian Scherer, Carnegie Mellon University
Professor Muhammad Shahzad, North Carolina State University
Professor Todd Sulchek, Georgia Institute of Technology
Professor Shimeng Yu, Georgia Institute of Technology
Professor John Zhang, Dartmouth College
Professor Song Han, Massachusetts Institute of Technology**
Professor Aggelos Katsaggelos, Northwestern University**
Professor Daniel Sanchez, Massachusetts Institute of Technology**

2017

Professor Faramarz Fekri, Georgia Institute of Technology
Professor Kristen Grauman, University of Texas at Austin
Professor Song Han, Massachusetts Institute of Technology
Professor Daniel Sanchez, Massachusetts Institute of Technology
Professor Xinyu Zhang, University of California, San Diego
Professor Stefano Ermon, Stanford University**
Professor Aggelos Katsaggelos, Northwestern University**
Professor Shivendra Panwar, New York University**

2016

Professor Dirk Bernhardt-Walther, University of Toronto
Professor Stefano Ermon, Stanford University
Professor Aggelos Katsaggelos, Northwestern University
Professor Scott Kuindersma, Harvard University
Professor Bruno Olshausen, University of California, Berkeley
Professor Shivendra Panwar, New York University

*Awards will be paid in USD or the respective currency depending on the country of the participating university/institution.
**Renewed Research Collaboration
***Principal investigator won an award for each of different research topics.

FACULTY INNOVATION AWARD

Global research and development at Sony enables us to foster innovative ideas, which could ultimately lead to future technology advancements and company growth. In order to speed up and expand the creation of new ideas, we would like to partner with universities and research institutes. This partnership will help cultivate advanced concepts and fertilize our own research and development. The Sony Faculty Innovation Award provides up to $100K USD* in funds to conduct pioneering research in the areas listed below. Please select the single most relevant keyword to your submission. In an effort to further connect with users and creators alike, for many of the keywords listed below, creator tools or creator technologies are often one of the envisioned use-cases.

Information Technology

Keywords are bulleted under each category title

Affective Computing

Affective/Cognitive State Estimation with Context
Emotion Regulation with AI Agent
Motion-robust Remote Vital Sensing
Neural Mechanism of Motion Sickness/Cybersickness

Audio, Music, Speech, and Language Processing

Multimodal Approaches for Advanced Spatial Audio

Cloud Edge System

Distributed System Infrastructure Framework
Real-time Multi-agent (LLM) Vision System
Software Development Processes in LLM Era
Terascale LLM Software Development/QA

Communication

AI-assisted QoE Enhancement for Wi-Fi
AI-based Ray-tracing for Channel Modeling
Deterministic Wi-Fi with Multi-AP Coordination
Non-terrestrial Networks
Use-case-driven Network Optimization via Open RAN
Wi-Fi Systems for Space Environments
Wi-Fi Transformation for AI Traffic

Computer Vision

4D Representations for Physical AI
Co-design of Sensor and Physical AI
Culture-dependent Reasoning in VLMs
Data-efficient RGB-X Learning
Embodied World Model with Multimodal Sensing
Lightweight Performance Predictors for Model Optimization
Sensor-aware Minimal-footprint Neural Imaging
Ultra-low-power/Low-bandwidth Sensing
Vision Model Design with LLM
World Models for Humanoid

Human Sensing and Interaction

Haptic Sensing and Interaction Technologies
Human-object Interaction, Hand-object Interaction
Interactive Content Creation

Linux System

Cyber Resilience Technology for Embedded Linux
Embedded Linux Security Assessment Method
Hardening Linux Using External Technologies
OSS License Verification Techniques and Methods

Machine Learning

Controllable Character Representation for Cross-domain Animation
Coordinated Motion Generation for Multi-character Interaction
Dexterous Object Interaction for Virtual Character
Monocular 3D Human Pose and Mesh Recovery
Physics-based Character Interaction with Perception
Style-agnostic Representation for Avatar Control

RF Sensing

AI-based Indoor-propagation Analysis for RF Sensing
Radar-centric In-cabin Occupant Condition Sensing
Self-supervised Learning for RF Sensing

Robotics

Vision-language-action for Physical and Virtual Agents

Security

Anti-piracy, Anti-leakage and Its Economics
Efficient Graph Structures in Web3

Software Development Technology

Software Architecture Analysis in Large Projects

Entertainment Technology

Keywords are bulleted under each category title

Audio, Music, Speech, and Language Processing

Automatic Evaluation for Multilingual Expressive TTS
Cross-lingual Expressive Voice Cloning with Natural Prosody
Video-grounded Long-context LLM Agents for Translation

RF Sensing

Emotion and Behavior Prediction Using Radar
RF-based Interaction Sensing for Entertainment Experiences

Robotics

Digital Twins for Expressive Interactive Characters

Sports Technology

Sports Technology for Injury Prevention
Sports Technology for Physical/Cognitive Performance

Visual/Visualization

Automated Content Generation
Digital Human (Capture, Modeling, Rigging, Animation, Rendering, Hair Modeling, Cloth Simulation)
Generative AI for Content Creation
Interactive Material Visualization (Multi-physics)
Light Field Processing
Neural Rendering/Reconstruction
Physical AI for Content Creation
Volumetric Video Processing

Device Technology

Keywords are bulleted under each category title

Micro LED Devices

High-efficiency Red Micro LED
MicroLED-based Optical Interconnect
Monolithic RGB/Single-chip Architecture
Optical Extraction/Resonant Structure

Optical Metasurface Design Theory/Framework

Design Enhancement by Physics-informed AI/DL
Image Quality Centric Design Optimization
Metasurface Spaceplate for Compact Lens System

RF Sensing

Energy-efficient RF Transceiver/Receiver Design
Joint Optimization of Radar-waveform and Signal-processing

Spatial Light Modulator

2D Array Tunable Metasurface Light Modulator

*Awards will be paid in USD or the respective currency depending on the country of the participating university/institution.

FOCUSED RESEARCH AWARD

Solid research is the underlying driving force to crystallize fearless creativity and innovation. While we are committed to run in-house research and engineering, we are also excited to collaborate with academic partners to facilitate exploration of new and promising research. The Sony Focused Research Award provides an opportunity for university faculty, research institutes, and Sony to conduct this type of collaborative, focused research. The award provides up to $150K USD* in funds, and may be renewed for subsequent year(s). A list of candidate research topics appears below. Please select the Focused Research Theme for which your submission is written.

Internal Mechanisms of Multimodal Generative Models for Content Creation

This theme focuses on the internal mechanisms of LLMs, MLLMs, speech/audio models, music models, and video generation systems, motivated by their growing role in multimodal content creation. As these models increasingly shape how text, images, video, speech, sound, and music are generated, edited, transformed, and interpreted, it becomes important to understand not only their outputs, but the internal processes by which they encode sources, bind modalities, reuse learned patterns, represent style and identity, preserve or distort factual signals, and respond to causal interventions. Sony is seeking research that moves beyond artifact-level analysis toward mechanistic, causal, and representation-level accounts of generative behavior, with particular interest in methods that reveal how internal states, circuits, latent variables, modality-fusion pathways, attention, and generation dynamics give rise to multimodal content. In addition, we are particularly interested in technologies that enable the externalization of knowledge, making it easier to support flexible opt-in and opt-out mechanisms for data usage, as well as more explicit and transparent attribution of sources and contributions.

Scope of Proposal:

Novel technologies including but not limited to:

Mechanistic interpretability of multimodal generation: Studies that identify circuits, features, latent variables, attention pathways, denoising dynamics, or other internal mechanisms underlying generative behavior.
Causal attribution inside generative models: Research that traces how inputs, context, retrieval, training data, model components, or generation steps causally influence intermediate representations and final outputs.
Internal representations of source, authorship, and provenance: Work on how models encode the origin, transformation history, epistemic status, or source dependence of information across prompts, context, retrieval, and generation.
Knowledge externalization and controllable data usage: Technologies that enable the externalization of knowledge, making it easier to support flexible opt-in and opt-out mechanisms for data usage, and facilitating more explicit, transparent, and controllable attribution of sources and contributions.
Representations of style, identity, and modality-specific structure: Research on how models encode writing style, visual identity, speaker identity, prosody, timbre, melody, rhythm, genre, motion, sound events, and other structured signals.
Cross-modal binding, grounding, and inconsistency: Work on how models integrate, route, ignore, or misalign information across language, vision, audio, video, speech, music, time, metadata, and external context.
Intervention, editing, and controllability of internal mechanisms: Studies that use activation steering, circuit editing, representation editing, latent-space control, or causal interventions to modify, verify, or explain model behavior.

Anime-Style Expression Exaggeration – Learning Artistic Deformation Priors

Anime and stylized characters rely on artistic exaggeration - eyes widening dramatically in surprise, mouths opening unrealistically large in shock, tears, sweat-drops, and other non-physical visual conventions that convey emotion. These exaggerations are not derivable from photoreal performance capture and are typically authored manually by skilled animators. As anime and stylized content grows central to Sony's IP, automating or assisting this exaggeration process is a significant unmet need. Sony seeks proposals on learning artistic deformation priors directly from anime data, keyframes, animation cels, motion sheets, and applying them to drive expressive, style-faithful animation of stylized characters from real performance input.

Scope of Proposal:

Learning artistic exaggeration priors from 2D anime / stylized animation datasets
Mapping subtle photoreal performance to exaggerated stylized output
Per-style or per-character exaggeration models (different anime styles have different conventions)
Integration with 2D and 3D anime character rigs
Optional: handling of non-physical visual elements (sweat drops, blush marks, expression symbols)
Approaches: style-transfer with deformation priors, diffusion on rig parameters, learned exaggeration operators
Evaluation: artistic faithfulness, style consistency, animator usability studies

Knowledge Representation and Reasoning for Graph-Based Modeling

This research direction investigates how ontologies and knowledge representation methods can support more explainable, reusable, and adaptable graph-based modeling of interaction data. The focus is on user profile generation for personalized recommendation, micro-region segmentation, and explainability, using formal knowledge structures to enrich user, content, and business representations, infer hidden properties and relationships, represent subjective signals such as likes, dislikes, preferences, and engagement intent, and expose reasoning paths across different domains and business scenarios.

Scope of Proposal:

Develop ontology-based methods to enrich graph-based representations of users, content, interactions, and business concepts with inferred properties, semantic relationships, and contextual knowledge.
Investigate how subjective user signals, such as likes, dislikes, preferences, satisfaction, engagement quality, and intent, can be formally represented as part of user profiles and reasoned over.
Explore how ontologies can support graph enhancement by identifying hidden links, suggesting new relationships, validating entity types, and improving the semantic structure of interaction graphs.
Study how ontology-driven reasoning can improve explainability by connecting recommendations, user segments, and personalization decisions to explicit concepts, rules, assumptions, and reasoning paths.
Investigate mechanisms for validating knowledge consistency, detecting hidden or conflicting information, and maintaining reliable graph-based knowledge structures over time.
Explore cross-domain and cross-business mappings through shared or aligned ontologies, enabling knowledge reuse between different recommendation, personalization, and user engagement scenarios.
Evaluate how knowledge representation and reasoning can improve transparency, debugging, maintainability, and adaptability in practical graph-based personalization workflows.

Psychology-Informed User Modeling and Intervention Design for Content Streaming Experiences

In content streaming services, understanding users is fundamental to evaluating content, designing experiences, and informing creative and marketing decisions. Today this understanding relies largely on demographics and behavioral metrics such as watch/listen time, completion, and retention. These signals capture what users do, but not why they engage, what kind of value an experience delivers, or what would actually move them -- two users consuming the same content may seek entirely different things, from sensory pleasure to meaning, mastery, or self-expression, and may respond to the same intervention in opposite ways. AI is expected to bridge this gap by inferring richer user understanding from data and connecting it to action.

However, current approaches face the following challenges:

Beyond Behavioral Signals: It is difficult to infer users' underlying values, needs, and experiential orientation (e.g., hedonic vs. eudaimonic) from behavioral logs alone.
Interpretable, Theory-Grounded Representations: User models are often black-box embeddings that cannot be communicated to non-technical stakeholders such as creators and marketers.
From Understanding to Intervention: Methods are optimized for recommendation accuracy or descriptive prediction, rather than identifying what intervention (e.g., marketing actions, messaging, content offers) would lead a given user to discover content, change behavior, or broaden their tastes.

Scope of Proposal:

To address these challenges, Sony is seeking the development of innovative methods such as, but not limited to the following:

Psychology-Informed User and Content Modeling

Infer latent user constructs -- values, needs, and experiential orientation -- from behavioral and experiential data, grounded in established theory,
Characterize content along the same experiential dimensions, capturing what kind of experience a title delivers and which constituent elements drive value for different users,
Combine first-party behavioral data with publicly available experiential signals (e.g., reviews, social text), without relying on intrusive personal profiling,

Insight Extraction and Intervention Design

Generate interpretable, actionable insight that draws on psychological and sociological knowledge (e.g., theories of values, motivation, taste, and cultural consumption) to support audience understanding, content evaluation, and creative/marketing strategy beyond recommendation accuracy,
Identify which user attributes govern responsiveness to intervention, and what actions (e.g., messaging, creative, content offers, timing) most effectively spark interest, change behavior, or shift preferences for different users -- distinguishing short-term engagement from longer-term taste formation,
Establish evaluation methodology beyond offline accuracy metrics, such as user-centric and intervention-effect (uplift) evaluation.

Post-Training of SpeechLMs for Anime

Speech LMs are gaining attention for their utility in generating and analyzing voice assets for entertainment content. Sony produces and distributes a vast catalog of Anime, much of which remains un-localized for global audiences, and character-based voice interaction offers a promising avenue to deepen fan engagement with our IPs. However, adapting speech LMs to capture anime-specific stylistic, emotional, and cultural nuances, while remaining controllable, remains an open challenge. Sony seeks proposals addressing these challenges to enhance fan engagement with entertainment IPs.

Scope of Proposal:

Novel technologies for adapting speech LMs toward specific domains using post-training, including but not limited to:

To improve translation that is aware of cultural context;
To localize the content at a production quality;
To realize a controllable conversational system; or
Automated data annotation pipeline for post-training

Innovative Visual Technology Powered by AI

Recent advances in machine learning have created a paradigm shift across a wide range of applications. In particular, foundation models and generative AI have significantly expanded the capabilities of image and video processing, enabling not only recognition and reconstruction, but also high-quality content generation, understanding, and interaction. Sony is seeking innovative research in image and video technologies based on machine learning to significantly advance existing techniques and applications across 2D as well as 3D/4D domains. This includes, but is not limited to, content generation, multimodal understanding, spatial-temporal modeling, and efficient representation. Our goal is to create new value for creators by enabling novel forms of expression, improving creative workflows, and unlocking new content experiences through advanced visual technologies.

Scope of Proposal:

Topics of interest include:

Generative AI such as image to photo-realistic video, video to video, style transfer, modal transfer based on a new approach, e.g. photo-realistic image generation using neural rendering or novel generative model with high controllability,
Multimodal content generation based on vision language model(VLM),
Physical AI(vision language action model: VLA) that connects the virtual and real world,
Novel 3D/4D model generation/reconstruction, e.g. NeRF, 3D/4D gaussian splatting, world model generation,
Perceptual metrics for predicting photo-realistic image quality or 3D image quality based on VLM, and
Explainable and controllable AI for visual content processing and generation.

AI-Based Digital Human Content Creation for Sports Science and Sports Entertainment

Sony is seeking innovative research on AI and machine learning-based techniques for data-driven digital human 3D model creation and animation in the field of sports science and sports entertainment.
Relevant sports science application areas include biomechanics, physiology, coaching and training, performance analysis, and injury prevention and monitoring. The objective is to significantly improve the quality, accuracy, and efficiency of existing sports monitoring and diagnostic workflows, while also enabling the development of new tools that help understand, optimize, and enhance athletes' performance, physical health, and well-being.
Relevant sports entertainment application areas include immersive 3D/4D visualization techniques for players’ replay and fan engagement. The current state-of-the-art approach (3D/4D Gaussian splatting) takes hours to process. The objective is to develop novel photo-realistic content creation techniques based on AI-generated digital human assets in real-time or within a few minutes.

Scope of Proposal:

Topics of interest include, but are not limited to:

High-fidelity biomechanical measurement, analysis, and diagnostics for improving athletic performance and health using digital human CG models and animations generated from 2D images and videos.
High-fidelity immersive sports visualization that delivers emotional engagement and novel experiences beyond conventional 2D images and videos, enabling deeper fan engagement and richer connections with athletes and the sporting events in which they compete.

Provenance Proofs for AI-Generated Derivative Content

AI-generated content is increasingly remixed, transformed, and redistributed, making it difficult to determine origin, ownership, and authenticity. This research will investigate provenance proofs for AI-generated derivative content, enabling verifiable tracking of how digital assets evolve across multiple generations of human and AI modification. The project will develop cryptographic and machine-learning techniques to create tamper-resistant provenance chains that preserve attribution while protecting privacy. By linking derivative outputs to their source content and transformation history, the framework will support copyright compliance, creator compensation, misinformation mitigation, and trustworthy AI ecosystems. The outcome will be scalable provenance infrastructure for future generative media platforms.

Scope of Proposal:

Provenance Representation Models
- Define a standardized provenance graph that captures relationships between original content, AI-generated outputs, and subsequent derivatives.
- Support multimodal content including text, images, audio, video, and 3D assets.
Cryptographic Provenance Infrastructure
- Develop cryptographic mechanisms (hash chaining, digital signatures, Merkle trees, zero-knowledge proofs) to establish tamper-evident provenance records.
- Enable verification without exposing proprietary content.
Derivative Content Attribution
- Design algorithms to identify and quantify contributions from multiple source assets.
- Investigate attribution mechanisms for transformations, style transfer, summarization, editing, and content fusion.
AI-Native Watermarking and Fingerprinting
- Explore robust watermarking techniques that survive common editing operations and model-to-model transformations.
- Evaluate resilience against adversarial removal attacks.
Provenance Across Multi-Step Generation Pipelines
- Track provenance through complex workflows involving multiple AI models, human edits, and external assets.
- Create lineage chains that remain verifiable across generations of derivative content.
Privacy-Preserving Verification
- Research mechanisms that allow provenance validation while protecting confidential training data, prompts, and creator identities.
- Investigate selective disclosure and zero-knowledge approaches.
Scalable Provenance Storage and Retrieval
- Develop efficient architectures for storing and querying provenance metadata at internet scale.
- Assess distributed ledger and decentralized identity technologies where appropriate.
Trust, Authenticity, and Misinformation Detection
- Enable consumers and platforms to verify content origin and modification history.
- Support detection of manipulated, synthetic, or deceptively altered media.
Copyright and Creator Compensation Frameworks
- Explore how provenance proofs can support licensing, royalty distribution, and ownership claims.
- Develop mechanisms for transparent attribution of creators and source materials.
Benchmarking and Evaluation
- Create datasets and benchmarks for provenance tracking across diverse generative AI workflows.
- Measure robustness, scalability, attribution accuracy, privacy guarantees, and usability.

Long-Term Memory for Sequential Decision-Making in Game Video Understanding

Recent advances in foundation models have significantly improved visual understanding and decision-making from video. However, most existing approaches operate on short temporal windows and struggle to reason about events, actions, and state changes that unfold over extended gameplay sessions.

Many important gameplay phenomena require long-term memory. Examples include:

tracking persistent world state,
understanding player progression,
reasoning about inventory and objectives,
detecting delayed effects of gameplay events, and
maintaining coherent agent behaviour over time.

Long-horizon reasoning is critical for gameplay data understanding and gameplay decision-making, because these problems contain rich visual dynamics, structured interactions, persistent state, and clear cause-and-effect relationships that evolve throughout a session. In addition, robust gameplay understanding requires game-specific knowledge, such as rules, mechanics, objectives, and world context. Since such knowledge can vary significantly across titles, it is difficult to assume that all relevant knowledge is already contained within the model. (e.g., new titles)
We invite research proposals on scalable memory architectures and long-context learning methods that enable AI systems to retain, retrieve, and reason over information accumulated across extended gameplay experiences. Such systems may also ground their understanding in relevant external game knowledge, form and maintain hypotheses about gameplay situations, and update their interpretation as new evidence emerges.
Research may explore memory-augmented foundation models, hierarchical memory representations, retrieval-based reasoning, state-space approaches, reinforcement-learning-based memory mechanisms, training-free memory compression strategies, and other techniques for efficient long-horizon understanding. We envision these technologies enabling a new generation of gameplay understanding systems, autonomous agents, and AI-assisted development tools that can reason consistently over complete gameplay sessions rather than isolated video clips.

Scope of Proposal:

Long-term memory representations for gameplay video, multimodal gameplay data, game knowledge, and sequential decision-making.
Mechanisms for memory acquisition, storage, compression, retrieval, and Hierarchical / multi-resolution memory. Short-term temporal tokens compressed into medium- and long-term summaries via learned pooling, gating, or recurrent state. Analogous in spirit to the SAM 3 memory bank, but adapted to the episodic structure of gameplay rather than per-object segmentation tracks.
Attention-based memory with learned read/write policies. Sparse, content-addressable memory where what gets retained, evicted, and retrieved is learned end-to-end under an explicit memory budget, drawing on Memorizing Transformers, Compressive Transformers, RMT, and recent retrieval-augmented sequence models.
Knowledge-grounded and incremental, hypothesis-driven gameplay understanding. Methods that ground scene interpretation in external game knowledge, incrementally build and revise understanding over time, generate plausible hypotheses about gameplay situations, and update or discard those hypotheses based on later observations.
Controllable and contextual memory that retains only relevant information to specific purposes given a condition, e.g., a specific objective for a level to be achieved by a gameplay agent, or a type of glitch to look for in a sequence of gameplay data.
RL-style memory management. Treat “what to keep in memory” as a policy and use a downstream-task reward (anomaly recall, agent return, prediction accuracy) to incentivise the model to retain causally-relevant information within a fixed budget. Underexplored angle that fits naturally with autonomous-agent training.
State-space and linear-attention backbones (e.g., Mamba-style) as cost-efficient long-context substrates, evaluated on the same long-horizon benchmarks.
Action- and state-grounded memory. Exploit the structured signals gameplay already provides (inventory state, score, level transitions, agent actions) as auxiliary supervision for what the memory should encode, without expensive human labelling.
Adaptable strategies to endow small models (non-foundational) with descriptive memory.

Cloud Creation Everywhere

Within our Creative Entertainment Vision is the dream that in the future any part of the world could be seamlessly connected to a virtual space, in which creativity and play are integrated. This ties tightly with the goals of Cloud Native Gaming, and the potential of using cloud technology to expand play beyond the realm of the living room.
Customer research has shown that younger generations interact with games and experiences differently, that they enjoy a broader range of experience, crave novelty, and like to play on their own schedules in ways that they can drop in and out of easily. Being creative is a core part of their identity, and their lives are tightly integrated with social media and social experiences.
Novel ways to enable social experiences, link the real world and virtual world, and support creativity and creatives will help us to reach out to these demographics and give us the ability to pivot into new ways of engaging with our audience.

Scope of Proposal:

The goal of this proposal is around increasing engagement with art and creative experiences while outside the home, and integrating game experiences tightly with social media and social expression.
Can we make people feel connected to others online and part of an online community even while they are not actively looking at their screens?
Can we use cloud technology to transform real-world spaces into virtual cloud spaces? Think Pokemon Go or Geocaching, but you can place digital expressions out into the real world people can see and interact with, or connect to people asynchronously who visited where you are in the past.
Novel peer-to-peer experiences. Can we turn a player's device into a virtual space of their own that exists when they are online, forming a wide interconnected web of explorable experiences that grows and shrinks as the players within join and leave?
Proposals should focus on cloud tools and cloud services, either traditional cloud servers or peer-to-peer clouds formed from disparate user devices spread out either across the world or within a local space
Focus primarily on mobile phones, tablets and possibly new cloud-connected peripherals, as well as AR or VR to a lesser extent
We are also open to research into connecting to other existing social media and application services for the purpose of combining them into a larger cohesive whole, such as development of new universal standards and interoperability

Design and Evaluation of an Intermediate Interaction Layer for Integrating Diverse Input/Output Environments

Recent advances in multimodal sensing, such as gaze, voice, gesture, and biometric signals have significantly changed the way user inputs and intentions are interpreted in generative AI technologies.
In this context, there is a growing need for abstraction-layer technologies capable of inferring a user's operational intent from arbitrary input/output devices and physiological states, and of automatically mapping them to application commands and feedback modalities in an adaptive manner.
Furthermore, context-aware, dynamically generated feedback including visual, auditory, and haptic responses using AI technologies alongside translation and optimization of nonverbal communication in asynchronous settings, has emerged as a critical area of interest.

Scope of Proposal:

Sony seeks proposals for research on high-level abstraction, automatic transformation, and adaptive control technologies that enable seamless interaction, expression, and shared experience across diverse environments, users, and devices, namely;

Research on integrating multimodal inputs (e.g., gaze, voice, gesture, biometric data) to infer user intent and interaction context,
Design and implementation of intermediate abstraction layers or command representations that translate arbitrary input/output devices into unified control schemes,
Development of adaptive feedback mechanisms that dynamically generate and select appropriate responses (visual, auditory, haptic, etc.) based on user state and environmental context,
Techniques for supporting multimodal communication across heterogeneous device environments, including translation of nonverbal cues (facial expression, posture, prosody, etc.), and
Prototyping and evaluation of the integrated technologies in real-world applications such as games, XR environments, and remote communication platforms.

SUBMISSION GUIDELINES

Eligibility

Eligible Applicants

We accept applications from Principal Investigators (PIs) who meet the eligibility criteria below.

PI's institution: PIs must belong to a university, educational institution, or governmental/non-profit research institute (e.g., we accept submissions from research institutes that are categorized as governmental and Non-profit Organization/Non-Governmental Organization (NPO/NGO) in nature index tables).
PI's position: PIs must be a full-time professor (adjunct professors and adjunct researchers are not eligible) or researcher and be eligible to supervise Ph.D. students at the PI's institution. Full professors, associate professors, and assistant professors are eligible to apply. For the United Kingdom, this includes Senior Lecturers, Principal Lecturers, Lecturers, and Readers who are eligible to supervise Ph.D. students at their institutions.
Countries: The PI's institution must be in Austria, Belgium, Bulgaria, Canada, Croatia, Republic of Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, India, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, the United Kingdom, and the United States of America.

Co-PI(s)

We accept applications that have a PI and one or more Co-PIs for the same proposal as long as all Co-PIs are from the same institution as the PI. However, only one award is made to the PI and the PI's university/institution if the proposal is selected. All Co-PIs must meet the same eligibility criteria as that for a PI, and Co-PIs will be required to sign program documents.

Multiple Proposal Submissions

A PI or different PIs from the same university/institution may submit more than one proposal for different research topics. However, we ask each PI to please not submit identical proposals, and for each PI to not submit more than one proposal for each keyword or focused research theme . Sony will gladly accept an unlimited number of proposals from the same university for the same research topic as long as they each have different PIs. Note that at the bottom of your submission confirmation email, there is a link to resubmit your proposal in the event that you discover that your original had an error or an omission. Please use this link for resubmissions. Do not resubmit identical proposals. Also, please do not submit "test" submissions. In the rare event that you do not receive a confirmation email for your submission, and you have confirmed that the submission confirmation email was not blocked or marked for deletion by an automatic email filter, please complete the Inquiry Form available here informing us of your issue.

Requirements

Target Award

Please select one target award between the Focused Research Award and the Faculty Innovation Award when you submit a proposal. Refer to the following comparison chart:

	Award	Research Theme	Period
Focused Research Award	up to $150K USD*	Choose from the Focused Research Theme List	One (1) year, with a possible extension
Faculty Innovation Award	up to $100K USD*	Choose from the list of Keywords	One (1) year, with a possible extension

Target Research Topic

Please select one Focused Research Theme for your proposal if you choose the Focused Research Award. Please select one Keyword (the single most relevant keyword for your proposal) if you choose the Faculty Innovation Award.

Research Agreement

A sponsored research agreement is required that is mutually agreed upon by Sony**, the PI, and his/her institution on all program terms including objectives, milestones, publication, use of research, and patent rights before any award is made or funding is available. This sponsored research agreement is concluded at the award stage and must be between Sony** and the PI's university/institution only.

Term of Proposals

Please submit proposals for a one-year period only. No multi-year proposals will be accepted. An extension of the research may be possible depending upon the results of the research collaboration in year one, but it will require a separate discussion for another award the following year.

Deliverables

Three (3) quarterly reports, a final research summary report, and your responses to a progress questionnaire are required at a minimum. Other deliverables may be specified by Sony based on the nature of the project.

Confidentiality

Proposal authors or universities/institutions must ensure that no confidential or proprietary information is included in submitted proposals. Sony will treat all information submitted in proposals as non-confidential and non-proprietary.

Intellectual Property (IP) Rights

IP rights will be specified in the sponsored research agreement. At a minimum, Sony requires the right to utilize the results of the research that Sony sponsors for noncommercial purposes, including results that describe potentially patentable subject matter. Sony also requires rights to any background IP that is required to utilize the research results sponsored. No submissions are allowed that use background IP to which the PI and his/her institution do not have the full authority to grant noncommercial use rights to Sony.

Proposal Format

Please include the required items listed below in your submission:

Title, abstract, methods, goals/milestones, references, and either one Focused Research Theme (if for the Focused Research Award) or one primary keyword (if for the Faculty Innovation Award).
Please describe your submission's differentiation from the current state-of-the-art.
Please include the best contact email address and phone number (complete with country code) for the PI.

All proposal contents must fit within 11 pages (a ten-page maximum proposal with references and a one-page budget summary). The file format must be a PDF file format and must be under 16 MB in size. Out of consideration to reviewers, please limit your minimum font size to a 10-point font.

Curriculum Vitae (CV)

The CV for the PI must be included when you submit a proposal. The CV file must be a separate file from the proposal file. There is no page limitation for the CV, however the CV file size must be under 16 MB.

Budget Guidelines

The Focused Research Award is limited to a maximum of $150K USD* per proposal. The Faculty Innovation Award is limited to a maximum of $100K USD* per proposal. This funding is a sponsored research grant that is to be used to conduct the research described in the proposal, and includes any overhead related to this research and any and all other fees or charges needed to carry out the research. There is a single payment for the award, and it is all-inclusive of all associated expenses and fees. Sony will not specify a maximum amount or percentage of the budget that may be allocated to overhead.

Submission

Submission Terms and Conditions

The submitter must agree to the 2026 Sony Research Award Program Submission Terms and Conditions (click here to see) before submitting a proposal. Please obtain a prior review of your submission by your institution's sponsored research office if they require a review of research proposals prior to their submission.

Submission Protocol

Submissions must be done through the online submission form.

Click here to open the submission form

Submissions made by email will not be accepted. Submissions must be in the English language or they will not be accepted. Submitters will automatically receive a confirmation email once they complete the online submission form and process. Please keep the confirmation email as proof of each submission.

Deadline

Submissions must be completed by 11:59 pm PDT (Pacific Daylight Time; UTC-7) on September 15, 2026 / 8:59 am CEST (Central European Summer Time; UTC+2) September 16, 2026 / 12:29 pm IST (Indian Standard Time; UTC+05:30) September 16, 2026.

Duplicate Submissions and Resubmissions

Duplicate submissions will not be accepted for the same proposal title from the same PI. Please do not make duplicate submissions. Use the resubmission link at the bottom of your confirmation email if you discover that your submission has an error or an omission.

Required Information for the Online Submission Form

The following information will be required in order to complete the online submission form for each proposal submitted.

Proposal file:
PI's Information: PI's full name, PI's title, PI's CV file, PI's email address, and PI's department name.
Only for the US and Canada, please enter your SAT International Code for PI's university/institute (please find PI's SAT International code here). Please be careful to use the correct code because some schools have several codes to designate different programs or campuses. Also note that some school names use common abbreviations (e.g., INST for INSTITUTE) or simply truncate some words in the school's name when searching for a school code. Please enter "0000" if you cannot find the four-digit SAT International Code for your school.
Submitter's Information, if not the PI (optional): Submitter's full name and email address.

Award Candidates

Announcement of Review Results

Award winners will be notified around March 2027. Individual feedback will not be provided for all proposals.

Funding

Program details will be negotiated with a PI and their university/institution if their proposal is selected. Funding will be available only after we have agreed to the terms of and signed a sponsored research agreement.

Inquiries

Inquiries Related to the 2026 Sony Research Award Program

Please contact us via the Inquiry Form if you have a question regarding the 2026 Sony Research Award Program. The Research Award Program Administration Office is the only resource that can officially answer your question(s). Sony will only respond to those inquiries whose question has not already been answered on this page, the Frequently Asked Questions (FAQs) page or the Submission Terms & Conditions page.

Inquiries Outside of the 2026 Sony Research Award Program

Please visit www.sony.com and contact the appropriate channel listed at the bottom of the web page if you have questions outside of the Sony Research Award Program (such as business proposals, joint venture proposals, research proposals that are not related to any of Focused Research Themes or keywords for the Faculty Innovation Award), or any other inquiry or proposal.

*Awards will be paid in USD or respective currency depending on the country of the participating university/institution.
**For European institutions, the contracting party will be the Sony entity/ branch of the country of the participating university/institution.

Frequently Asked Questions (FAQ)

2026 Sony Research Award Program

ELIGIBILITY

1. Does the program require a prior collaboration with Sony?: No. The Sony Research Award Program does not require a prior collaboration with Sony. The program is designed to establish new research collaborations between university faculty members and Sony scientists.
2. I previously received a Sony Research Award. Am I eligible to apply again?: Yes. Principal Investigators who have previously received funding under the Sony Research Award Program are eligible to apply again in subsequent program years.
3. Is Professor Emeritus eligible to apply as a Principal Investigator?: Professor Emeritus status, which is not a full-time appointment, does not meet the eligibility requirements for a Principal Investigator.
4. My position at my institute meets all requirements for Principal Investigator except for supervising Doctor of Philosophy ("Ph.D.") students. Am I still eligible to apply?: The ability to supervise Ph.D. students is required because the program recognizes that it is typically the Ph.D. students who conduct the research activities under the supervision of the Principal Investigator.
5. Can I forward the program invitation to my colleagues?: Yes. You are welcome to share program information with any colleagues who may be interested in submitting a proposal.

AWARD TYPES AND RESEARCH TOPICS

6. My research does not exactly match the listed keywords. Can I still submit a proposal?: The keywords in the Faculty Innovation Award category are intentionally broad and open-ended to encourage creativity in proposal submissions. Please select the single keyword that best fits your research proposal. The submission form also provides the option to enter up to ten additional keywords to further describe the submission.
7. Could you please confirm which keyword, or research theme would best align with my research topic or research area?: Please choose the keyword or theme which you determine is the best fit for your research proposal. The submission form also provides the option to enter up to ten additional keywords to further describe the submission.
8. Is there a limit to the number of proposals submitted by one Principal Investigator or by one university/institution?: There is no limit to the total number of proposals from either one PI or from one university/institution. Please note, however, that only one proposal is allowed per PI for each keyword or research theme to prevent duplicate submissions.

CO-PRINCIPAL INVESTIGATORS ("Co-PIs")

9. Can I include a Co-PI from a different university or institution?: No.
10. Can I hire my colleague from another university/institution as an advisor or consultant on my project?: No, no third parties are allowed in the sponsored research agreement between Sony and the Principal Investigator's university/institution.
11. How do I include my Co- Principal Investigator's Curriculum Vitae ("CV") in the submission?: Please consolidate the Principal Investigator's and Co- Principal Investigator's CVs into a single PDF file, with the Principal Investigator's CV at the front, and upload it as a combined CV file.

PROPOSAL FORMAT AND CONTENT

12. Does the 11-page limit apply to both the Faculty Innovation Award and the Focused Research Award?: Yes. The eleven-page limit applies to both award categories. This consists of a ten-page maximum proposal (including all references) plus a one-page budget summary.
13. Is a letter of support or recommendation from the Principal Investigator's department head required?: No. There is no requirement for a letter of support or recommendation from the PI's department head or other university personnel.
14. Is it necessary to include the names of potential collaborators at Sony in my proposal?: No, it is not necessary and not recommended because your existing contact may not be the reviewers for your research proposal.
15. What can help strengthen my submission?: Two commonly noted areas for improvement are:

Differentiation from the state-of-the-art: The most commonly omitted element in submissions is a clear description of how the proposed research is differentiated from existing technologies. This is requested in the Submission Guidelines.

Use cases: Including one or two potential use cases for the proposed technology is beneficial, particularly if the applications may not be immediately apparent. Reviewing Sony's product and business pages at www.Sony.com may provide insight into use cases of relevance to Sony.

BUDGET AND FUNDING

16. Are indirect costs (overhead/Facilities & Administration) allowed within the budget?: Yes. Indirect costs must be included within the total award amount and should be indicated as line items within the one-page budget summary. Sony does not specify any maximum rates for such costs.
17. Is the award disbursed as a grant or a gift?: The award is disbursed under a sponsored research agreement which must be ratified by Sony and the Principal Investigator's institution before any funding is made available.
18. My university/institution cannot agree to the Intellectual Property Rights sections of the Submission Guidelines. Is there a process to grant an exception?: There is no process to grant such an exception. The program has consistently reached consensus with universities/institutions on such terms and conditions, many of whom are listed as awardees on the program's homepage.

SUBMISSION PROCESS

19. Can a university's research administration office submit the proposal on behalf of the Principal Investigator?: Yes. Proposals may be submitted by the PI's representative at the university's office of research administration. The submission form includes a field to indicate the submitter's details separate from the PI's details.
20. I submitted my proposal but did not receive a confirmation email. Was my submission successful?: Sony will always send a confirmation e-mail to each submitter after completion of the Submission Form. In most cases, the confirmation email has been filtered into the submitter's Trash, Junk, or Spam folder. Please check those folders first. If the confirmation email cannot be located, please complete the Inquiry Form and the program administration will verify submission status and resend the confirmation. Please do not submit the proposal again, as duplicate proposals are not accepted.
21. I submitted a proposal and would like to make edits before the deadline. Can I submit another proposal?: Please use the link provided at the bottom of your submission confirmation email to access, edit, and resubmit your proposal. Do not create a new submission, as duplicate proposals are not accepted.
22. Can I make changes to my proposal after the submission deadline?: No. In the interest of fairness to all participants, no changes to submitted materials are accepted after the submission deadline. If a proposal is selected for an award, any necessary adjustments may be discussed at that stage.

REVIEW PROCESS AND FEEDBACK

23. Can I meet or speak with Sony researchers or program personnel before submitting my proposal?: No. Due to the volume of requests and limited resources, the program is unable to accommodate such requests.
24. Would you provide feedback on proposal submissions?: Due to the large number of submissions each year, Sony is unable to provide feedback on all proposal submissions.
25. When will I be notified of review results?: Award nominees will be notified of review results around March of the year following the submission deadline.

TIMELINE AND AWARD LOGISTICS

26. Are there any upcoming information sessions, webinars, or key dates beyond those posted online?: There are no information sessions or key dates beyond those posted on the program website at https://www.sony.com/en/SonyInfo/research-award-program.

For additional inquiries regarding the 2026 Sony Research Award Program, please confirm that your question is not already answered within the Submission Guidelines or the Submission Terms and Conditions and then use the Inquiry Form. This website, including the Inquiry Form, is the sole official source for program-related responses.

STORIES

Collaborative research with universities through the Sony Research Award Program continues to drive meaningful progress. The articles below highlight representative projects that are moving the field forward at various stages of development.