I'm a PhD student at Northeastern University's Khoury College of Computer Science working with Professor Predrag Radivojac. My research interests are in data bias in machine learning; structured prediction and evaluation; geometric deep learning; and representation and generation of structured data. I've worked on applications in computer vision, medical image processing, graphical models for characterizing environmental contamination, and projects on network behavior in computational biology and in economics. Currently, I am actively working on methods for biological sequence representation and detection and quantification of bias in datasets.
April 2023 We are hosting the 5th Critical Assessment of protein Function Annotation algorithms (CAFA 5) with Kaggle for the first time. We're looking forward to the expanded participation by the broader ML community with this effort. Join the challenge and try your hand at protein function prediction: CAFA 5 in collaboration with Kaggle
October 2019 This Fall I will be working as a intern with IBM Research's AI Reasoning team in Yorktown, NY and the MIT-IBM AI Lab in Cambridge, MA on some graph learning problems that I am very excited about.
February 2019 I am joining Nvidia Research for an internship in the AI Algorithms research group led by Anima Anandkumar. I am delighted to join this excting new group!
December 2018 My co-authors and I are heading to NeurIPS 2018 in Montreal to present our paper A Neural Framework for Learning DAG to DAG Translation at the Relational Representation Learning workshop.
May 2018 I'm excited to be joining Microsoft as a Research Intern this summer working on graph-to-graph translation models!
April 2018
I had the great opportunity to attend the CRA-W Grad Cohort in San Francisco along with my fellow CCIS PhD students Lucianna Kiffer and Everlyne Kimani. We were featured in this story in CCIS news:
CCIS students inspired by conference for graduate women in computing, Erica Yee, CCIS News, June 20, 2018.
CAFA-evaluator: A Python Tool for Benchmarking Ontological Classification Methods
Damiano Piovesan, Davide Zago, Parnal Joshi, M. Clara De Paolis Kaluza, Alexander Miguel Monzon, Walter Reade, Iddo Friedberg, Predrag Radivojac, Silvio CE Tosatto.
Bioinformatics Advances 2024.
[paper]
[github]
An approach to identifying and quantifying bias in biomedical data
M. Clara De Paolis Kaluza, Shantanu Jain, Predrag Radivojac.
Pacific Symposium on Biocomputing 2023.
[paper]
[github]
Deep Imitation Learning for Bimanual Robotic Manipulation
Fan Xie, Alex Chowdhury, M. Clara De Paolis Kaluza, Linfeng Zhao, Lawson Wong, Rose Yu.
Advances in Neural Information Processing Systems (NeurIPS), 2020.
[paper]
[github]
Subsurface Source Zone Characterization and Uncertainty Quantification Using Discriminative Random Fields
Masoud Arshadi, M. Clara De Paolis Kaluza, Eric L. Miller, Linda M. Abriola
Water Resources Research 56 (3), 2020
[paper]
A Neural Framework for Learning DAG to DAG Translation
M. Clara De Paolis Kaluza, Saeed Amizadeh, Rose Yu.
Neural Information Processing Systems, relational representation learning workshop, 2018.
[pdf]
[bibtex]
[poster]
Subset Selection for Sequential Data
Ehsan Elhamifar, M. Clara De Paolis Kaluza.
Advances in Neural Information Processing Systems, 2017.
[pdf]
[bibtex]
[poster]
Online Subset Selection via Submodular and Convex Optimization
Ehsan Elhamifar, M. Clara De Paolis Kaluza.
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
[pdf]
[bibtex]
Incorporation of CT-based measurements of trunk anatomy into subject-specific musculoskeletal models of the spine influences vertebral loading predictions
Alexander G Bruno, Hossein Mokhtarzadeh, Brett T Allaire, Kelsey R Velie, M. Clara De Paolis Kaluza, Dennis E Anderson, Mary L Bouxsein.
Journal of Orthopaedic Research, 2017.
Evaluation of a new approach to compute intervertebral disc height measurements from lateral radiographic views of the spine
Brett T Allaire, M. Clara De Paolis Kaluza, Alexander G Bruno, Elizabeth J Samelson, Douglas P Kiel, Dennis E Anderson, Mary L Bouxsein.
European Spine Journal, 2016.
Markov Random Field Models for Quantifying Uncertainty in Subsurface Remediation
M. Clara De Paolis Kaluza, Eric L. Miller, Linda M. Abriola.
In IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015.
Goal-Conditioned Dynamic Graph Model for Task and Motion Planning
M. Clara De Paolis Kaluza, Christopher Paxton, Animesh Garg, Anima Anandkumar, Rose Yu
Nvidia Research internship, Summer 2019
[poster presented at WiML 2019]
Security Scan for OpenStack
M. Clara De Paolis Kaluza, Dilip Makwana, Hao Wu, Chi Zhang. In collaboration with Trilio.
CS 6620 Fundamentals of Cloud Computing, Spring 2018, Northeastern University.
[demo][company write-up]
Subset Selection for Subspace Clustering
M. Clara De Paolis Kaluza.
EECE 7370 Advanced Computer Vision Final Project, Spring 2017, Northeastern University.
[report]
Recommender System for Yelp Dataset Using Matrix Factorization Methods
M. Clara De Paolis Kaluza.
CS 6220 Data Mining Techniques Final Project, Fall 2016, Northeastern University.
[slides]
[report]
[code]
Estimating the Fundamental Matrix of a Random Walk Transition Matrix
M. Clara De Paolis Kaluza.
Math 250 Graph Algorithms Final Project, Spring 2016, Tufts University.
[report]
Content-Aware Video-Frame Resizing Using Seam Carving
M. Clara De Paolis Kaluza, Sutawat Poomcharoenwatana.
ECE 520 Digital Image Processing and Communication Final Project, Fall 2009, Boston University.
[report]