Timezone: »
Anne Carpenter, Hui Ting Grace Yeo, Jian Zhou, Maria Chikina, Alexander Tong, Benjamin Lengerich, Aly O. Abdelkareem, Gokcen Eraslan, Andrew Blumberg, Stephen Ra, Daniel Burkhardt, Emanuel Flores Bautista, Frederick Matsen, Alan Moses, Zhenghao Chen, Marzieh Haghighi, Alex Lu, Geoffrey Schau, Jeff Nivala, Luke O'Connor, Miriam Shiffman, Hannes Harbrecht and Shimbi Masengo Wa Umba Papa Levi present in a lightning round.
Author Information
Anne Carpenter (Broad Institute of Harvard and MIT)
Dr. Carpenter is an Institute Scientist and Merkin Fellow at the Broad Institute of Harvard and MIT. Her research group develops algorithms and strategies for large-scale experiments involving images. The team’s open-source CellProfiler software is used by thousands of biologists worldwide (www.cellprofiler.org). Carpenter is a pioneer in image-based profiling, the extraction of rich, unbiased information from images for a number of important applications in drug discovery and functional genomics. Carpenter has been named an NSF CAREER awardee, an NIH MIRA awardee, a Massachusetts Academy of Sciences fellow (its youngest at the time), a Genome Technology “Rising Young Investigator”, and is listed in Deep Knowledge Analytics’ top-100 AI Leaders in Drug Discovery and Advanced Healthcare.
Jian Zhou (UTSW)
Assistant Professor, Lyda Hill Department of Bioinformatics, UT Southwestern CPRIT Scholar in Cancer Research, Cancer Prevention and Research Institute of Texas
Maria Chikina (University of Pittsburgh)
Alexander Tong (Yale University)
Ben Lengerich (Carnegie Mellon University)
Aly Abdelkareem (University of Calgary)
Gokcen Eraslan (Broad Institute of MIT and Harvard)
Stephen Ra (Pfizer R&D)
Daniel Burkhardt (Yale University)
Frederick A Matsen IV (Fred Hutchinson Cancer Research Center)
Alan Moses (University of Toronto)
Zhenghao Chen (Calico Lifesciences)
Marzieh Haghighi (Broad Institute of MIT and Harvard)
Alex Lu (University of Toronto)
I'm a PhD student at the University of Toronto. I'm part of the Computer Science Department, and I research computational biology under Alan Moses. I focus on unsupervised machine learning techniques for the analysis of microscopy images. I believe that microscopy images contain rich information about biology, but they're underused because analysis of these images has traditionally been subjective and time-consuming, requiring biologists to look at each image manually. This approach is incompatible with current technologies, where robots can take tens of thousands of images in a single experiment. I develop ways for computers to "look" at these images, automatically discovering interesting biology for us. In some cases, the computer can identify patterns that are too complex for us to identify by human eye, or organize its findings systematically to make novel biological insights. This allows us to discover new biology from microscopy images, in an objective and systematic way.
Geoffrey Schau (Oregon Health & Science University)
Jeff Nivala (University of Washington)
Miriam Shiffman (MIT)
Hannes Harbrecht (University of Cambridge)
Levi Masengo Wa Umba (University of Pretoria)
Joshua Weinstein (University of Chicago)
More from the Same Authors
-
2021 Spotlight: Neural Additive Models: Interpretable Machine Learning with Neural Nets »
Rishabh Agarwal · Levi Melnick · Nicholas Frosst · Xuezhou Zhang · Ben Lengerich · Rich Caruana · Geoffrey Hinton -
2021 : A sandbox for prediction and integration of DNA, RNA, and proteins in single cells »
Malte Luecken · Daniel Burkhardt · Robrecht Cannoodt · Christopher Lance · Aditi Agrawal · Hananeh Aliee · Ann Chen · Louise Deconinck · Angela Detweiler · Alejandro Granados · Shelly Huynh · Laura Isacco · Yang Kim · Dominik Klein · BONY DE KUMAR · Sunil Kuppasani · Heiko Lickert · Aaron McGeever · Honey Mekonen · Joaquin Melgarejo · Maurizio Morri · Michaela Müller · Norma Neff · Sheryl Paul · Bastian Rieck · Kaylie Schneider · Scott Steelman · Michael Sterr · Daniel Treacy · Alexander Tong · Alexandra-Chloe Villani · Guilin Wang · Jia Yan · Ce Zhang · Angela Pisco · Smita Krishnaswamy · Fabian Theis · Jonathan M Bloom -
2021 : Interpretable Transfer Learning for Pulmonary Disease Detection on Chest X- Rays »
Levi Masengo Wa Umba -
2022 : Learning representations of cell populations for image-based profiling using contrastive learning »
Robert van Dijk · John Arevalo · Shantanu Singh · Anne Carpenter -
2021 : Multimodal Single-Cell Data Integration + Q&A »
Daniel Burkhardt · Smita Krishnaswamy · Malte Luecken · Debora Marks · Angela Pisco · Bastian Rieck · Jian Tang · Alexander Tong · Fabian Theis · Guy Wolf -
2021 Poster: Neural Additive Models: Interpretable Machine Learning with Neural Nets »
Rishabh Agarwal · Levi Melnick · Nicholas Frosst · Xuezhou Zhang · Ben Lengerich · Rich Caruana · Geoffrey Hinton -
2019 : Phenotype »
Nir HaCohen · David Reshef · Matthew Johnson · Sam Morris · Aurel Nagy · Gokcen Eraslan · Meromit Singer · Eliezer Van Allen · Smita Krishnaswamy · Casey Greene · Scott Linderman · Alexander Wiltschko · Dylan Kotliar · James Zou · Brendan Bulik-Sullivan -
2019 Poster: The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers »
Alex Lu · Amy Lu · Wiebke Schormann · Marzyeh Ghassemi · David Andrews · Alan Moses -
2019 Poster: Learning Sample-Specific Models with Low-Rank Personalized Regression »
Ben Lengerich · Bryon Aragam · Eric Xing -
2018 Poster: Generalizing Tree Probability Estimation via Bayesian Networks »
Cheng Zhang · Frederick A Matsen IV -
2018 Spotlight: Generalizing Tree Probability Estimation via Bayesian Networks »
Cheng Zhang · Frederick A Matsen IV -
2013 Workshop: Deep Learning »
Yoshua Bengio · Hugo Larochelle · Russ Salakhutdinov · Tomas Mikolov · Matthew D Zeiler · David Mcallester · Nando de Freitas · Josh Tenenbaum · Jian Zhou · Volodymyr Mnih