I am a Machine Learning Researcher in the Foundation Models (FM) team at Apple Inc., the core team training models that power Apple Intelligence and FM Developer API, leading several efforts in pretraining, post-training and evaluation of Large Language Models with a focus on model recipe, data quality and multilinguality.

Previously, I was a research engineer at Facebook AI Research (Meta AI/FAIR), working on AI for Creativity, specifically in human motion modeling and generative modeling for character animation. Previously at Meta AI, I was in the Language and Translation Technologies team, where I co-developed and deployed Neural Machine Translation models for the Facebook family of apps, supporting over 7 billion translations per day.

In the past, I’ve worked as a software engineer at LinkedIn, and as an intern at Facebook AI, Microsoft Research, and Amazon.

I have a master’s degree from Language Technologies Institute, Carnegie Mellon University, where I worked on Multimodal ML with Prof. LP Morency at the MultiComp Lab, and a bachelor’s degree from BITS Pilani, India.

More details about my work experience are in my CV. A full list of my publications can be found here and on Google Scholar.

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Highlights

• [June 2025] Updates to Apple’s On-Device and Server Foundation Language Models [blogpost]

• [May 2025] Paper titled “Interleaved Reasoning for Large Language Models via Reinforcement Learning” significantly reduces time to first token and improves accuracy in reasoning tasks paper

• [2024] Read the Apple Foundation Models technical report and accompanying blogpost

• [2023] Our paper titled “Simulation and Retargeting of Complex Multi-Character Interactions” has been accepted at SIGGRAPH 2023!

• [2023] Our work “CIRCLE: Capture In Rich Contextual Environments” (website) was presented at CVPR 2023!

• [2022] Our paper on “Physics based Character Controllers using Conditional VAEs” has been accepted at SIGGRAPH 2022!

• Two Minute Papers featured our SIGGRAPH 2021 paper on physically simulated AI agents that learn to compete in boxing and fencing in a video that went viral with 2.3+ million views!

• [2021] Our paper titled “A Scalable Approach to Control Diverse Behaviors for Physically Simulated Characters” has been accepted at SIGGRAPH 2021!

• [2020] We open sourced fairmotion, a library that provides AI researchers tools to work with motion capture data, visualizers and operators. [Meta AI tweet]

• [2018] We used multilingual translation models, single model trained on multiple lanugages from the same language family, to expand translation services to Indian and African languages. [Facebook AI blogpost] [Press coverage: Venture Beat]

• [2017] Our work on training and deploying neural network based translation models was featured in the Facebook AI blog and received press coverage [Press coverage: The Verge, Slator]

• [2011] I was part of a small team of undergraduate students that build Acyut, India’s first indigenously built autonomous humanoid robot, at BITS Pilani. [Press coverage: The Hindu , The Times of India]

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Publications

Interleaved Reasoning for Large Language Models via Reinforcement Learning
R Xie, D Qiu, D Gopinath, D Lin, Y Sun, C Wang, S Potdar, B Dhingra
[PDF]

Apple Intelligence Foundation Language Models
T Gunter, Z Wang, C Wang, R Pang, A Narayanan, A Zhang, B Zhang, C Chen, C Chiu, D Qiu, D Gopinath, …
[PDF]

Simulation and Retargeting of Complex Multi-Character Interactions
Y Zhang, D Gopinath, Y Ye, J Hodgins, G Turk, J Won
ACM SIGGRAPH Asia Posters, 2022
[PDF]

CIRCLE: Capture In Rich Contextual Environments
JP Araújo, J Li, K Vetrivel, R Agarwal, J Wu, D Gopinath, AW Clegg, K Liu
CVPR, 2023
[PDF] [Project Page]

Motion In-betweening for Physically Simulated Characters
D Gopinath, H Joo, J Won
ACM SIGGRAPH Asia Posters, 2022
[PDF]

Transformer Inertial Poser: Real-time Human Motion Reconstruction from Sparse IMUs with Simultaneous Terrain Generation
Y Jiang, Y Ye, D Gopinath, J Won, AW Winkler, CK Liu
SIGGRAPH Asia 2022, 2022
[PDF]

Leveraging Demonstrations with Latent Space Priors
J Gehring, D Gopinath, J Won, A Krause, G Synnaeve, N Usunier
[PDF]

Physics-based Character Controllers using Conditional VAEs
J Won, D Gopinath, and J Hodgins
ACM SIGGRAPH, 2022
[PDF] [Project Page]

Control Strategies for Physically Simulated Characters Performing Two-player Competitive Sports
J Won, D Gopinath, and J Hodgins
ACM SIGGRAPH, 2021
[PDF] [Project Page]

A Scalable Approach to Control Diverse Behaviors for Physically Simulated Characters
J Won, D Gopinath, and J Hodgins
ACM SIGGRAPH, 2020
[PDF] [Project Page]

Fairmotion - Tools to Load, Process and Visualize Motion Capture Data
D Gopinath and J Won
GitHub, 2020
[Code]

Harnessing Indirect Training Data for End-to-End Automatic Speech Translation: Tricks of the Trade
J Pino, L Puzon, J Gu, X Ma, AD McCarthy, and D Gopinath
IWSLT, 2019
[PDF]

Deep Multimodal Fusion for Persuasiveness Prediction
B Nojavanasghari, D Gopinath, J Koushik, T Baltrušaitis, and LP Morency
ACM ICMI, 2016
[PDF]

Open Domain Video Description Alignment
J Koushik, D Gopinath, CY Li, and LP Morency
submitted at CVPR, 2016
[PDF]