class: research_scientist · conf: 0.93 | dreamer · conf: 0.99

Ashuta Bhattarai

Research Scientist & ML Engineer

Deep learning · Computer vision · Generative modeling · Applied AI systems

Ashuta Bhattarai
[ 01 · Introduction ]

Hi, I'm Ashuta. I'm a research scientist and ML engineer who spends most days deep in computer vision, generative modeling, and applied AI, but I like to think there's more to me than the models I train. I did my PhD at the University of Delaware, and along the way I've worked on everything from medical imaging to video synthesis to red-teaming large language models. Outside of work, I'm usually curled up with a good novel, chasing down a tricky puzzle, or arguing over triple word scores in a game of Scrabble. I love to travel whenever I get the chance, and I have a quieter side that leans toward literature, poetry, and art. I sometimes let that side show on Medium, where I write a bit differently than I do in a research paper.

[ 02 · My work life so far... ]

Research Scientist Consultant

May 2026 – Present
Fusemachines Inc.
  • Leading research direction for a real time interactive video avatar system designed for AI driven recruitment, extending an existing voice based interview agent to a fully expressive, lip synced visual experience.
  • Investigating state of the art generative modeling approaches including NeRF based and diffusion based architectures to achieve photorealistic, real time avatar rendering and facial animation.
  • Collaborating with a cross functional research team to define technical feasibility, model selection criteria, and evaluation benchmarks for production ready deployment.

Machine Learning Engineer Consultant

Dec 2025 – May 2026
Quasar Intelligence
  • Architected the end to end Bone Health Index prediction pipeline spanning phantomless HU calibration, 3D preprocessing, multi domain segmentation, and risk score generation from volumetric CT scans across 4 major scanner vendors, with sub 60 second inference.
  • Built a scalable, reproducible ML workflow using PyTorch and MONAI, processing hundreds of CT scans with standardized cross vendor compatibility, enabling downstream clinical validation and research deployment.
  • Designed an LLM powered clinical reporting pipeline that ingests model generated measurements (bone density, muscle mass, visceral fat) and produces structured, per patient BHI narrative reports via prompt engineering.

AI Red Teaming Consultant

Aug 2025 – Apr 2026
Handshake AI
  • Conducted adversarial red teaming of frontier multimodal LLMs by engineering highly technical prompts designed to expose model failure modes, boundary violations, and reasoning breakdowns across computer science domains.
  • Performed systematic failure analysis to document why and how models failed, producing structured evaluations that informed model improvement and alignment efforts.
  • Served as a senior peer reviewer, evaluating and quality controlling red teaming contributions from other fellows to ensure technical rigor and consistency of findings.

Graduate Research Assistant

Feb 2020 – May 2025
Video/Image Modeling and Synthesis (VIMS) Lab · University of Delaware, Newark, DE
  • Developed a self supervised pretraining algorithm for video understanding by reconstructing temporally shuffled frames, matching VideoMAE performance on action recognition benchmarks without requiring intact temporal sequences.
  • Built a deep learning pipeline for Sickle Cell Retinopathy detection from OCT scans with Nemours Children's Hospital, achieving mAP 0.86 on 500+ clinical volumes, published at MICCAI 2024 in the top 11% of 2,869 submissions.
  • Mentored 3 junior researchers on deep learning methodology, experimental design, and PyTorch workflows, enabling independent experimentation and contributing to 2 additional conference submissions.

Machine Learning Engineer

Jan 2018 – Jan 2020
Ekbana Solutions · Lalitpur, Nepal
  • Developed instance segmentation and object detection pipelines for satellite imagery using Mask RCNN and RetinaNet, building end to end workflows from data preparation to model inference for ship detection in large scale aerial scenes.
  • Built time series forecasting models for retail sales using PyTorch and Scikit learn, predicting demand patterns that guided store inventory and stocking decisions, improving supply chain efficiency.
[ 03 · Education ]

PhD, Computer Science (M.S. en route)

University of Delaware · Newark, DE
Feb 2020 – May 2025

B.E., Computer Engineering

Tribhuwan University · Lalitpur, Nepal
Nov 2013 – Sep 2017
[ 04 · Publications ]
  1. [1] Bhattarai A, Jin J, and Kambhamettu C. Analyzing Adjacent B-Scans to Localize Sickle Cell Retinopathy in OCTs. Medical Image Computing and Computer Assisted Intervention (MICCAI), 2024.
  2. [2] Bhattarai A, Kambhamettu C, and Jin J. CUNet: Towards continuous multi-class contour detection for retinal layer segmentation in OCT images. 29th IEEE International Conference on Image Processing (ICIP), 2022.
  3. [3] Bhattarai A. Towards Multi-Scale Inter-Frame Attention to Improve Deep Learning Tasks. Ph.D. dissertation, Dept. of Computer and Information Sciences, University of Delaware, Newark, DE, USA, 2025.
[ 05 · Technical Skills ]
Languages
PythonMATLABC++SQL
Machine Learning
Deep LearningComputer VisionMedical Image AnalysisVideo UnderstandingObject DetectionInstance SegmentationSelf-Supervised LearningLLM Red-TeamingPrompt EngineeringTime Series ForecastingDiffusion Models
Frameworks & Libraries
PyTorchTensorFlowMONAIHuggingFace TransformersNumPyPandasOpenCVScikit-learn
MLOps & Tools
Weights & BiasesTensorBoardGitLinuxAWS SageMaker