| CARVIEW |
AI/ML Engineer specializing in Computer Vision & Generative AI
Solving complex engineering problems through ML, computer vision, and generative AI — NYU & IIT Madras alum | Intel award-winning intern | Research Assistant at NYU
About Me
👋 Hello! I'm Manoj, an AI/ML engineer who thrives on solving complex challenges at the intersection of computer vision, generative AI, and 3D rendering. My passion isn't just research — it's turning cutting-edge ideas into practical, impactful solutions.
I’m a recent Master’s in Computer Engineering graduate from New York University and an alumnus of IIT Madras. Currently, I’m a research assistant at the NYU Video Lab (advised by Prof. Yao Wang), developing diffusion-based solutions for 3D scene refinement and reconstruction.
I love bringing applied AI projects to life. During my internship at Intel, I received an Excellency Award for developing novel vision-based automation tools for integrated chip design validation. I’ve also contributed to fast-growing startups like Preimage and GalaxEye Space, where I worked on 3D reconstruction pipelines and satellite image super-resolution, respectively.
On the academic side, I worked on "U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields" as part of my master’s thesis, focusing on 3D-consistent unsupervised image restoration for underwater scenes. This work was later published at the ICLR ’24 Tiny Papers conference, held in Vienna.
🚀 I’m always excited to connect with teams pushing the boundaries of AI for engineering, robotics, and visual intelligence. If that sounds like your world, I’d love to connect and explore opportunities to build something impactful together.
Work Experience
A timeline of my professional roles and contributions.
Graduate Research Assistant, NYU Video Lab
Aug 2025 – Present (New York City, NY)
- Implemented a pipeline integrating 2D video diffusion priors with 3D Gaussian Splatting (3DGS), achieving a 19% LPIPS improvement in novel-view synthesis under sparse input conditions.
- Enhanced 3D view consistency by formulating view generation as a temporal continuity task, integrating camera-pose embeddings with diffusion-guided latent features across viewpoints.
- Exploring mesh registration for human poses using learned skinning methods such as SMPL, to improve 3D consistency.
Graduate Engineering Intern, Intel Corporation
Jun 2024 – Aug 2024 (Santa Clara, CA)
- Developed a computer vision framework for automated detection of IC package design violations, emulating manually-performed inspection heuristics.
- Optimized computational efficiency using segmentation models and OpenCV's morphological algorithms, reducing detection pipeline runtime by 85% (from >4 hours to <30 minutes).
- Honored with Intel’s Impact Award for strong productivity and delivering high-quality solutions in a short timeframe.
Graduate Research Student, Indian Institute of Technology, Madras
Aug 2022 – May 2023 (Chennai, India)
- Proposed U2NeRF, a fully self-supervised transformer-based framework for joint underwater image restoration and neural 3D reconstruction, embedding physics-informed light modeling into the NeRF pipeline.
- Leveraged a disentangled representation of underwater degradations, which includes scene radiance, global illumination, and scattering maps, to enable accurate color and structure recovery in the absence of ground-truth supervision.
- Introduced patch-level rendering to address limitations of pixel-wise NeRF, enabling improved local spatial context modeling for underwater image restoration.
- Achieved state-of-the-art results on the newly curated Underwater View Synthesis (UVS) benchmark across 12 calibrated scenes, with +11% perceptual similarity and +4% restoration quality over prior methods.
- Developed as part of Master’s thesis at IIT Madras and later published at ICLR 2024 (Tiny Papers Track).
Machine Learning Intern, Preimage
Sep 2022 – Dec 2022 (Bangalore, India)
- Adapted a transformer-based multi-view stereo (MVS) pipeline for dense 3D geometry reconstruction from aerial (drone) imagery, optimizing performance for sparse-view and large-scale outdoor scenes.
- Improved feature representation using an adaptive feature pyramid network (FPN) that leverages sinusoidal embeddings conditioned on scene-specific depth bounds, increasing reconstruction accuracy by ~10% in challenging outdoor scenarios.
- Deployed large-scale reconstruction experiments on Azure VMs, leveraging AWS S3 for dataset management and PyTorch Lightning with CUDA for efficient distributed training.
Machine Learning Intern, Asilla Japan
May 2022 – Jul 2022 (Remote)
- Enhanced an abnormal activity detection pipeline for surveillance systems by fine-tuning the quantization mechanism used to learn a latent “normal action” dictionary and flag deviations as anomalous behavior.
- Reduced runtime latency by ~15%, enabling deployment on 20+ real-time CCTV feeds at Hankyu Nishinomiya Gardens Mall, Japan.
Image Processing Intern, GalaxEye Space
Dec 2021 – Jan 2022 (Chennai, India)
- Built a super-resolution neural network to upsample low-quality remote-sensing data in the form of SAR images, along with a generative model to predict RGB optical images from the super-resolved SAR output.
- Conducted experiments on cross-public datasets, leading to increased super-resolution quality even at scales up to 16x.
Education
New York University (NYU), Tandon School of Engineering
Sep 2023 – May 2025 (New York, NY)
- M.S. in Computer Engineering
- Cumulative GPA: 3.93/4.0
Indian Institute of Technology Madras (IITM)
Aug 2018 – Jul 2023 (Chennai, India)
- B.Tech in Mechanical Engineering & M.Tech in Robotics (Dual Degree)
- Cumulative GPA: 3.51/4.0
- Minors in Computing, Artificial Intelligence & Machine Learning
Featured Projects
Here are some of the projects I'm proud of. Feel free to check them out.
U2NeRF (ICLR '24)
Self-supervised transformer-based framework achieving joint underwater image restoration and 3D reconstruction, embedding physics-informed light modeling for realistic view synthesis.
3DGS Enhancer for Sparse Views
Diffusion-guided framework integrating 2D video priors with 3D Gaussian Splatting to improve sparse-view reconstruction, enhancing 3D view consistency through pose-conditioned feature alignment.
Counterfactual Image Generation Using Text Guidance
Text-guided counterfactual generation framework combining CLIP and Stable Diffusion to inpaint high-confidence regions, achieving fine-grained attribute manipulation while preserving over 90% of original content.
Satellite Image Super-Resolution
GAN-based system that fuses SAR and optical images to turn low-resolution satellite data into sharper, more detailed outputs using cross-domain feature alignment.
Implicit 3D Surface Mesh Reconstruction
Implicit ML-framework that reconstructs 3D geometry from point clouds, learning a continuous function (occupancy or SDF) over space.
Steel Surface Defect Detection
Deep-learning model that detects and segments defects on steel surfaces from images, generating pixel-wise masks for precise localization and handling various defect types.
Honors & Recognition
Key accomplishments and recognitions received throughout my career.
Intel Impact Award
Intel Corporation
Awarded the "Impact Award" during my Graduate Engineering Internship for delivering high-quality work ahead of schedule and demonstrating exceptional productivity.
Published Author - ICLR '24
ICLR Conference, Vienna
Research paper titled "U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields" accepted and published at the prestigious International Conference on Learning Representations (ICLR), Tiny Papers track.
Bronze Medal – Inter-IIT Tech Meet 2023
IIT Kanpur, India
Awarded Bronze (3rd place) in the Chandrayaan Moon Mapping Challenge organized by ISRO, representing the IIT Madras contingent at the 2023 Inter-IIT Tech Meet.
All India Rank 1467 - JEE Advanced
IIT Kanpur, India
Secured an All India Rank of 1467 in the highly competitive JEE Advanced, placing among the top 0.1% engineering aspirants in India.
National Talent Search Exam (NTSE) Scholarship
NCERT, India
Awarded the prestigious NTSE Scholarship, granted to Grade 10 students ranking in the top 0.4% nationwide.
Get In Touch
I'm currently open to new opportunities and collaborations.. If you have a project in mind or just want to say hi, feel free to reach out!
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