Katie Collins

Hi, I’m Katie! I’m a Machine Learning PhD candidate in the Computational and Biological Learning (CBL) Lab at the University of Cambridge, where I am supervised by Adrian Weller MBE and advised by Richard Turner, and am a part-time Student Researcher at Google Brain with Krishnamurthy (Dj) Dvijotham and Miltos Allamanis. I am also a Student Fellow at the Leverhulme Centre for the Future of Intelligence (CFI) and volunteer with the Human-Oriented Automated Theorem Proving, led by Sir Tim Gowers. I am grateful to the Marshall Scholarship, King’s College, and the Cambridge Trust for additional funding support for my studies.

I’m particularly excited about scalable trustworthy machine learning, probabilistic modeling, and data-efficient human-machine teaming. I maintain a keen interest in computational cognitive science, stemming from my undergraduate research with Josh Tenenbaum (with whom I happily enjoy continuing to collaborate!).

I recently completed an MPhil in Machine Learning and Machine Intelligence from the University of Cambridge and obtained a Bachelors of Science from MIT in 2021 in Brain and Cognitive Sciences, with minors in Computer Science and Biomedical Engineering. During my undergrad, I founded the MITxHarvard Women in AI Group in an effort to bringing more diverse voices to the table in AI!

Outside of research, I love to run (!) and used to run competitively for MIT.

Papers

You can find the most up-to-date listing on Google Scholar profile.

Human Uncertainty in Concept-Based AI Systems
Katherine M. Collins, Matthew Barker^, Mateo Espinosa Zarlenga^, Naveen Raman^, Umang Bhatt, Mateja Jamnik, Ilia Sucholutsky, Adrian Weller, and Krishnamurthy (Dj) Dvijotham.
Under submission (2023).
Code and Data (forthcoming)

Human-in-the-Loop Mixup
Katherine M. Collins, Umang Bhatt, Weiyang Liu, Vihari Piratla, Ilia Sucholutsky, Bradley Love, and Adrian Weller.
Under submission (2023). Earlier version at AAAI Workshop R^2HCAI and Best Demo/Poster at HCOMP Demo Track (2022).
Code and Data (forthcoming) Video

Harms from Increasingly Agentic Systems
Alan Chan, Rebecca Salganik, Alva Markelius, Chris Pang, Nitarshan Rajkumar, Dmitrii Krasheninnikov, Lauro Langosco, Zhonghao He, Yawen Duan, Micah Carroll, Michelle Lin, Alex Mayhew, Katherine M. Collins, Maryam Molamohammadi, John Burden, Wanru Zhao, Shalaleh Rismani, Konstantinos Voudouris, Umang Bhatt, Adrian Weller, David Krueger, Tegan Maharaj.
Under submission (2023).

Eliciting and learning with soft labels from every annotator
Katherine M Collins^, Umang Bhatt^, Adrian Weller.
AAAI HCOMP (2022).
Code and Data Project Page

Structured, flexible, and robust: benchmarking and improving large language models towards more human-like behavior in out-of-distribution reasoning tasks
Katherine M. Collins^, Lionel Wong^, Jiahai Feng, Megan Wei, and Joshua B. Tenenbaum.
CogSci (2022). Invited Talk. Awarded Travel Grant for Paper.
Code and Data Project Page Turing Institute Talk

Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface
Tuan Anh Le, Katherine M Collins, Luke Hewitt, Kevin Ellis, Samuel J Gershman, Joshua B Tenenbaum.
ICLR (2022).
Code

Learning signal-agnostic manifolds of neural fields
Yilun Du, Katherine M. Collins, Joshua B. Tenenbaum, Vincent Sitzmann.
NeurIPS (2020).
Code Project Page

Deep representation learning improves prediction of LacI-mediated transcriptional repression
Alexander S Garruss, Katherine M Collins, George M Church.
PNAS (2020).

Sequence-to-function deep learning frameworks for engineered riboregulators
Jacqueline A Valeri^, Katherine M Collins^, Pradeep Ramesh^, Miguel A Alcantar, Bianca A Lepe, Timothy K Lu, Diogo M Camacho.
Nature Communications (2020).
Code

Next-generation machine learning for biological networks
Diogo M Camacho, Katherine M Collins, Rani K Powers, James C Costello, James J Collins.
Cell (2018).

^Contributed equally