Vikas Chandra

@vikasc

Senior Director of #AI Research @Meta | CMU Ph.D. | Ex visiting faculty at Stanford

Menlo Park, CA

v-chandra.github.io

Joined April 2009

Vikas Chandra
@vikasc
Jun 1
Vision Language Models are native 3D learners!
Zhipeng Cai
@cai_zhipeng
Jun 1
🎇Thrilled to release VLM^3! Most 3D vision papers nowadays still spend months/years designing complex archs/losses/augmentations for different tasks. Are they necessary? VLM^3 shows that most designs that you think are important for 3D vision are [not] important at all!
Vikas Chandra
@vikasc
May 29
We just released MobileMoE, first sub-B-active-parameter MoE language model family. MoE isn't just for 100B+ parameter models on servers. At sub-B scale, sparse expert routing lets you match dense models at 2-4x fewer FLOPs while fitting in mobile DRAM. arxiv.org/pdf/2605.27358
Vikas Chandra
@vikasc
May 20
Grateful to @sallywf and @eetimes for the thoughtful writeup of my Embedded Vision Summit keynote. The thesis in one line: the next decade of AI won't be won by the biggest model, but by the smartest, most efficient one that lives on the devices you wear!
Meta: Scaling Agentic AI on Resource-Constrained Devices- EE Times
From eetimes.com
Vikas Chandra
@vikasc
May 2
Audio is the most ignored perception modality in on-device AI. Every smart glass, robot, and drone has a mic. Almost none fuse audio + vision at perception time. Vision-only is the vibe-coded version of multimodal perception.
Vikas Chandra
@vikasc
Apr 29
1/ New @ieeeICASSP 2026 (Oral): "Exploring Audio Hallucination in Egocentric Video Understanding." Audio-visual LLMs often "hear" things they didn't, inferring sounds from visual cues alone. We built a benchmark to quantify it.