LIV: Learning, Inference, & Vision Group

Principal Investigator: Erik Sudderth, UC Irvine Computer Science · CV

LIV group members of the HPI Research Center in Machine Learning and Data Science at UC Irvine at a Sept. 2023 retreat in Rheinsberg, Germany.

LIV group and alumni at the December 2017 Conference on Neural Information Processing Systems in Long Beach, CA.

LIV Group Members

• Sakshi Agarwal, PhD student.
• Harry Bendekgey, PhD student.
• Mehrnaz Motamed, PhD student.
• Debora Sujono, PhD student.
• Ali Younis, PhD student.
• Federica Zoe Ricci, PhD student, co-advised by Michele Guindani.

LIV Alumni: PhD Theses

• Soumya Ghosh, IBM Research.
  2015 Brown PhD: Bayesian Nonparametric Discovery of Layers and Parts from Scenes and Objects.
• Gabriel Hope, Visiting Assistant Professor, Harvey Mudd College.
  2023 UC Irvine PhD: Prediction-Constrained Latent Variable Models.
• Michael Hughes, Assistant Prof. of Computer Science at Tufts University.
  2016 Brown PhD: Reliable and Scalable Variational Inference for Nonparametric Mixtures, Topics, and Sequences.
• Geng Ji, Facebook AI.
  2019 UC Irvine PhD: Efficient Variational Inference for Hierarchical Models of Images, Text, and Networks.
• Daeil Kim, founder of AI. Reverie.
  2017 Brown PhD: Scalable Bayesian Nonparametric Models for Networks and Documents.
• Jason Pacheco, Assistant Prof. of Computer Science at Univ. of Arizona.
  2016 Brown PhD: Variational Approximations with Diverse Applications.
• Zhile Ren, applied research scientist at Apple.
  2018 Brown PhD: Semantic Three-Dimensional Understanding of Dynamic Scenes.

LIV Alumni: Masters & Undergraduate Research

• Madina Abdrakhmanova, MS 2019: Prediction Constrained Factor Analysis.
• Samuel Ainsworth, ScB 2016.
• Michael Bryant, ScM 2012: Truly Nonparametric Online Variational Inference for Hierarchical Dirichlet Processes.
• Soravit Changpinyo, ScB 2012 (honors): Learning Image Attributes using the Indian Buffet Process.
• Xiaoyin Chen, BS 2021: Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints.
• Rajkumar Kothapa, ScM 2011: Max-Product Particle Belief Propagation.
• Andrew Miller, ScM 2010: Image and Audio Annotation: Approximate Inference in Dense Conditional Random Fields.
• Daniel Milstein, ScB 2015, ScM 2017: Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces.
• Mengrui Ni, ScM 2015: Variational Inference for Beta-Bernoulli Dirichlet Process Mixture Models.
• Carl Olsson, ScM 2016: Scene Category Context for 3D Object Detection with RGBD Cameras.
• Sonia Phene, ScB 2015 (honors): Parallelization of Variational Inference for Bayesian Nonparametric Topic Models.
• Roshan Rao, ScB 2017 (honors): Protein Structure Prediction from Low-Resolution Electron Density Data using Particle Belief Propagation.
• Jake Soloff, ScB 2016.
• William Stephenson, ScB 2015 (honors): Variational Inference for Hierarchical Dirichlet Process Based Nonparametric Models.
• Donglai Wei, ScB 2011.
• Leah Weiner, ScM 2017.