Kilian Weinberger

About

Kilian Weinberger is a professor in the Department of Computer Science at Cornell University and a field member in statistics. He received his Ph.D. from the University of Pennsylvania in machine learning under the supervision of Lawrence Saul, and his undergraduate degree in mathematics and computing from the University of Oxford. His research focuses on machine learning and its applications, including learning under resource constraints, metric learning, AI in science, computer vision, autonomous vehicles, Gaussian processes, and deep learning. Weinberger has worked as an associate professor at Washington University in St. Louis and as a research scientist at Yahoo! Research in Santa Clara. He has received numerous awards, including the Outstanding AAAI Senior Program Chair Award in 2011, an NSF CAREER award in 2012, the Daniel M. Lazar '29 Excellence in Teaching Award in 2016, and the Ann S. Bowers Teaching and Advising Excellence Award in 2024. He is an ACM and AAAI fellow, a 2021 Blavatnik National Awards Finalist, and a member of the Sloan Research Fellowships Selection Committee since 2024.

Research topics

• Artificial Intelligence
• Computer Science
• Machine Learning
• Natural Language Processing
• Mathematics
• Geography
• Remote sensing
• Biology
• Computational biology
• Computer vision

Selected publications

• BERTScore: Evaluating Text Generation with BERT

  arXiv (Cornell University) · 2020 · 605 citations

  We propose BERTScore, an automatic evaluation metric for text generation. Analogously to common metrics, BERTScore computes a similarity score for each token in the candidate sentence with each token in the reference sentence. However, instead of exact matches, we compute token similarity using contextual embeddings. We evaluate using the outputs of 363 machine translation and image captioning systems. BERTScore correlates better with human judgments and provides stronger model selection perform…

• End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection

  2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 2020 · 202 citations

  Reliable and accurate 3D object detection is a necessity for safe autonomous driving. Although LiDAR sensors can provide accurate 3D point cloud estimates of the environment, they are also prohibitively expensive for many settings. Recently, the introduction of pseudo-LiDAR (PL) has led to a drastic reduction in the accuracy gap between methods based on LiDAR sensors and those based on cheap stereo cameras. PL combines state-of-the-art deep neural networks for 3D depth estimation with those for…

  DOI

• Denoising Vision Transformers

  Lecture notes in computer science · 2024-11-26 · 19 citations

  book-chapter
  PublisherDOI

• Determining subpopulation methylation profiles from bisulfite sequencing data of heterogeneous samples using DXM

  Nucleic Acids Research · 2021 · 15 citations

  Epigenetic changes, such as aberrant DNA methylation, contribute to cancer clonal expansion and disease progression. However, identifying subpopulation-level changes in a heterogeneous sample remains challenging. Thus, we have developed a computational approach, DXM, to deconvolve the methylation profiles of major allelic subpopulations from the bisulfite sequencing data of a heterogeneous sample. DXM does not require prior knowledge of the number of subpopulations or types of cells to expect. W…

  DOI

• Towards high-accuracy bacterial taxonomy identification using phenotypic single-cell Raman spectroscopy data

  ISME Communications · 2025-01-01 · 6 citations

  articleOpen access

  Abstract Single-cell Raman Spectroscopy (SCRS) emerges as a promising tool for single-cell phenotyping in environmental ecological studies, offering non-intrusive, high-resolution, and high-throughput capabilities. In this study, we obtained a large and the first comprehensive SCRS dataset that captured phenotypic variations with cell growth status for 36 microbial strains, and we compared and optimized analysis techniques and classifiers for SCRS-based taxonomy identification. First, we benchma…

  PublisherOA PDFDOI

Recent grants

• RI: AF: Small: Collaborative Research: Differentially Private Learning: From Theory to Applications

  NSF · $250k · 2016–2021

• III: Small: Collaborative Research: Towards Interpretable Machine Learning

  NSF · $250k · 2015–2021

• CAREER: New Directions for Metric Learning

  NSF · $478k · 2012–2015

Frequent coauthors

• Bharath Hariharan

  37 shared

• Mark Campbell

  36 shared

• Jacob R. Gardner

  33 shared

• Wei‐Lun Chao

  33 shared

• Geoff Pleiss

  32 shared

• Yurong You

  31 shared

• Gao Huang

  26 shared

• Felix Wu

  Columbia University

  24 shared

Labs

• Kilian Weinberger LabPI

Education

• Ph.D., machine learning

  University of Pennsylvania

• B.A., mathematics and computing

  University of Oxford

Awards & honors

• Outstanding AAAI Senior Program Chair Award (2011)
• NSF CAREER award (2012)
• Daniel M. Lazar '29 Excellence in Teaching Award (2016)
• Ann S. Bowers Teaching and Advising Excellence Award (2024)
• ACM Fellow

Similar researchers at Cornell University

• Jon Kleinbergh-122
• Deborah Estrinh-120
• Wen Sunh-102