Jingjing (Jing) Huang

About

Dr. Jingjing (Jing) Huang joined the Pamplin College of Business at Virginia Tech in the Fall of 2014. She holds an Accounting Ph.D. from the University of Oregon, a Master’s Degree in Accounting from Iowa State University, and a Bachelor’s Degree in Business Administration from Shanghai University of Electric Power. She is licensed as a CPA in the state of Iowa. Her research interests include tax, financial accounting, corporate finance, and R&D innovation. Her dissertation examines the role of taxes in foreign earnings management for multinational companies, and her working paper investigates how companies trade off tax incentives against nontax costs in R&D investment decisions. Dr. Huang has previously taught at the University of Oregon and has worked as a federal tax associate at KPMG in Des Moines, Iowa, as well as an accounting intern at Deloitte and Touche in London and at HNI Corporation in Iowa and Hong Kong.

Research topics

• Artificial Intelligence
• Computer Science
• Data Mining
• Computer vision
• Mathematics
• Computer graphics (images)

Selected publications

• DRIT++: Diverse Image-to-Image Translation via Disentangled Representations

  International Journal of Computer Vision · 2020 · 404 citations

  DOI

• Consistent video depth estimation

  ACM Transactions on Graphics · 2020 · 307 citations

  We present an algorithm for reconstructing dense, geometrically consistent depth for all pixels in a monocular video. We leverage a conventional structure-from-motion reconstruction to establish geometric constraints on pixels in the video. Unlike the ad-hoc priors in classical reconstruction, we use a learning-based prior, i.e., a convolutional neural network trained for single-image depth estimation. At test time, we fine-tune this network to satisfy the geometric constraints of a particular i…

  DOI

• Author response for "Network Traffic Prediction Model Based on Convolutional Neural Networks‐Long Short‐Term Memory and <scp>iTransformer</scp>"

  2025-01-16

  peer-review
  PublisherDOI

• MaDCoW: Marginal Distortion Correction for Wide-Angle Photography with Arbitrary Objects

  2025-06-10

  article

  We introduce MaDCoW, a method for correcting marginal distortion of arbitrary objects in wide-angle photography. People often use wide-angle photography—it is the default in smartphone cameras—but very-wide-fields-of-view produce distorted object appearance in image margins. In our system, a user annotates straight lines and regions of interest. MaDCoW solves for a separate linear perspective projection for each region and then jointly solves for a distortion-minimizing projection for the whole…

  PublisherDOI

• PAD3R: Pose-Aware Dynamic 3D Reconstruction from Casual Videos

  ArXiv.org · 2025-09-29

  preprintOpen accessSenior author

  We present PAD3R, a method for reconstructing deformable 3D objects from casually captured, unposed monocular videos. Unlike existing approaches, PAD3R handles long video sequences featuring substantial object deformation, large-scale camera movement, and limited view coverage that typically challenge conventional systems. At its core, our approach trains a personalized, object-centric pose estimator, supervised by a pre-trained image-to-3D model. This guides the optimization of deformable 3D Ga…

  PublisherOA PDFDOI

Recent grants

• CRII: RI: Representation Learning and Adaptation using Unlabeled Videos

  NSF · $173k · 2018–2021

Frequent coauthors

• Ming–Hsuan Yang

  66 shared

• Johannes Kopf

  Alpha Omega Alpha Medical Honor Society

  45 shared

• Chang-Il Kim

  27 shared

• Changhee Jung

  Purdue University System

  24 shared

• Ryan K. Williams

  24 shared

• Ashrarul H. Sifat

  24 shared

• Haibo Zeng

  Virginia Tech

  23 shared

• Xuanliang Deng

  22 shared

Labs

• Accounting and Information SystemsPI

Similar researchers at Virginia Tech

• Robert M. Brownh-65
• Ryan Hamiltonh-52
• Jessica Fergusonh-51