Ayaan Haque

Hi! I'm Ayaan Haque, a 20 y/o research scientist at Luma AI. I'm also a student at UC Berkeley studying EECS, where I'm advised by Angjoo Kanazawa. I'm currently interested in training generative models. Recently, I worked on Genie, Luma's text to 3D foundation model which can generate high-fidelty 3D objects in seconds.

In a past life, I was a builder and hacker (I'm a MLH Top-50 Hacker!), and now I'm exploring deep-tech startups. Other than that, I enjoy writing, watching/playing sports, eating out with friends, and just having a good time. My ongoing goal and dream:

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• [Jan 2024] Joining Luma (now a Series B company) full-time!
• [October 2023] Gave oral talk on Instruct-NeRF2NeRF at ICCV in Paris!
• [July 2023] Instruct-NeRF2NeRF accepted to ICCV 2023 (Oral)!
• [May 2023] Joining Luma AI, a Series A startup building the future of 3D!
• [Mar 2023] Released new pre-print Instruct-NeRF2NeRF!
• [August 2022] Starting my undergrad at UC Berkeley to study EECS!

Since I've worked on a wide variety of topics (and am still exploring new topics), I've split my publications into currently relevant works and previous works. Only relevant works are listed below. For a full list (and chronological list) of my papers, visit my Google Scholar.

We propose a method for editing NeRF scenes with text-instructions. Given a NeRF of a scene and the collection of images used to reconstruct it, our method uses an image-conditioned diffusion model (InstructPix2Pix) to iteratively edit the input images while optimizing the underlying scene, resulting in an optimized 3D scene that respects the edit instruction.

We introduce self-supervised MeshCNN, or SSL-MeshCNN, a novel mesh-specialized contrastive learning method to perform downstream segmentation with limited-labeled data. We create an augmentation policy tailored for meshes, enabling the network to learn efficient visual representations through contrastive pre-training.

We propose SDCNL to address the unexplored problem of classifying between depression and more severe suicidal tendencies using web-scraped data. Our method introduces a novel label correction method to remove inherent noise in web-scraped data using unsupervised learning combined with a deep-learning classifier based on pre-trained transformers.

We introduce MultiMix, a joint semi-supervised classification and segmentation model employing a confidence-based augmentation strategy for semi-supervised classification along with a novel saliency bridge module that guides segmentation and provides explainability for the joint tasks.

We propose EC-GAN, which combines a Generative Adversarial Network with a classifier to leverage artifical GAN generations to increase the size of restricted, fully-supervised datasets using semi-supervised algorithms. Mentored by Microsoft Postdoc and Princeton University PhD Jordan T. Ash.

We propose Noise2Quality (N2Q), a novel, self-supervised IQA model which predicts SSIM Image Quality maps from low-dose CT. We propose a self-supervised regularization task of dose-level estimation creating a multi-tasking framework to improve performance.

We introduce SSWL-IDN, a novel self-supervised CT denoising window-level prediction surrogate task. Our method is task-relevant and related to the downstream task, yielding improved performance over recent methods.

We expand upon MultiMix (in ISBI 2021). Our extended manuscript contains a detailed explanation of the methods, saliency map visualizations from multiple datasets, and quantitative (performance metrics tables) and qualitative (mask predictions, Bland Altman plots, ROC curves, consistency plots).

• Generative AI startup building the future of visual AI, raised $43 million Series B
• Released Genie, a text to 3D foundation model which can generate high-fidelty 3D objects in seconds

• Working on NeRFs and diffusion models

• Proposed "SSL-MeshCNN", a novel self-supervised algorithm for segmenting non-uniform, irregular 3D meshes
• Introduced new SimCLR-inspired stochastic augmentation policy for mesh-specialized contrastive learning
• Matched accuracy of fully-supervised training (90.50%) with just 67% of labels on benchmark datasets
• Wrote paper accepted to AAAI 2023, available on ArXiv

• Worked on learning from limited labeled data for clinical imaging tasks using unsupervised, self-supervised, and semi-supervised techniques
• Developed research skills by running hundreds of experiments, writing papers, preparing supplementals, and writing rebuttals

• Accelerator providing early-stage startups with software and human capital instead of direct funding (closed as of late 2021)
• Developing a Waitlist API which is to be sold to porfolio companies in the program, where companies can establish waitlists for their products to build a market
• Using React.js, MongoDB, Flask and other web dev/backend tech, integrating Stripe payment features and referral features, writing documentation

Outside of research, I enjoy building practical applications in both competitive and casual formats.

• Team Captain of 5 total members (shoutout Viraaj, Adithya, Ishaan, and Sajiv)
• Created numerous projects (listed in Projects section)
• 🏆 33x Award Winner, 9x First Place, 22x Top 3, $10,000+ in earnings
• Chosen for MLH Top 50 Hackers Class of 2021, one of five high schoolers

• FRC Robotics Software Team, ML-Specialist (FTC Captain 9th Grade)
• Worked with AI/ML for in-game object detection and using predictive models for shot selection, work on shooter trajectory modeling, write documentation
• 🏆 2021 Skills Competition Finalist in Carbon Group 🏆 2021 Engineering Excellence Award 🏆 CalGames 2019 Finalist 🏆 ChezyChamps 2019 Semi-Finalist

I've just listed a few of my favorite projects, and the remaining are available on my Github. On Github, I have 500+ commits and ~300 stars across all my repositories. Check out this cool commit graph, and check this out for my Github stats.

Contribute (in small parts) to large-scale open-source project. Helped implement research methods (Instruct-NeRF2NeRF, CLIP-based NeRF) into main repository.

Stack: Python, PyTorch

SuiSense is a progressive web application that uses Artificial Intelligence (AI) and Natural Language Processing (NLP) to distinguish between depressive and suicidal phrases and help concerned friends and family determine whether their struggling loved one is on the path to suicide. SuiSense uses an implementation of SDCNL.

🏆 4th Place Congressional App Challenge 2020 🏆 2nd Place @ GeomHacks 2020 🏆 HM @ MLH Summer League SHDH 2020

Stack: Python, HTML, CSS, JavaScript, Tensorflow, PyTorch, BERT, Flask, PythonAnywhere, Pandas, Sci-Kit Learn

This mobile, on-the-go system is designed for clinics in Bangladesh to screen patients for Diabetic Retinopathy (DR) using a smartphone camera with a retinal attachment. The purpose of this rig is to allow precise positioning of the smartphone to any patient's left and right eye such that the images can be efficiently fed into Drishti's AI algorithms for DR diagnosis. The system is completely adjustable for all head sizes. It is made of readily available components that can be purchased at many local hardware stores, and is designed for low-cost fabrication. All assembly tools are common household tools or easily purchasable/rentable from a local hardware store. The 3D-printed components can be printed on low-end machines and with cheap PLA filament. We designed this system to be completely collapsable, such that it can fit into a standard size backpack.

Stack: SolidWorks, Hardware Materials

Tickbird is an advanced Swift mobile app based on the TesseractOCR neural network framework allowing visually impaired patients to aurally understand their prescriptions or the labels on their pill bottles in order to gain independence and avoid the prospect of lethal miscommunication regarding necessary medicines from their doctors. Moreover, the app's smart profiling feature not only finds the nearest pharmacy containing the user's prescription, but it also uses AI/ML algorithms to detect and set notifications for the times the user has to take or refill their medicine.

🏆 2x Award Winner @ OmniHacks 2019 🏆 App Store April 2020, 1000+ Impresions

Stack: Swift, Xcode, IOS, Firebase, TesseractOCR, Ruby

I write on Medium (semi-regularly) to share my thoughts with the world. Here are a few of my favorite medium articles that I have written.

A response to an article discussing our purpose in this world combined with a discussion of my own purpose

This article is a reponse to my friend's article, where he discusses critiques of our Saratoga society. In my article, I respond to his ideas and then share my own story of finding my purpose in life.

Coding, winning prizes, and proving ourselves

Authored by Ayaan Haque, Adithya Peruvemba, Viraaj Reddi, Sajiv Shah, and Ishaan Bhandari

This article travels through the journey of my team, Haleakala Hacksquad, and how we became great hackers.

Website template from Jon Barron