Hi there! I’m a PhD student in the Theory of Computation Group at Columbia University, where I am extremely fortunate to be advised by Daniel Hsu and Jeannette Wing. My broad research areas are: algorithmic statistics, machine learning theory, and online learning.
A bit more specifically, my research focuses on statistical learning in settings where the training and test distributions differ: this might arise, for instance, from distribution shift or demand for a more fine-grained notion of accuracy on subgroups. I also like thinking about online learning, sequential decision-making, and all the cool theory that comes out of it.
I typically go by Sam, and my pronouns are he/him/his.
Learning Tensor Representations for Meta-Learning.
Samuel Deng, Yilin Guo, Daniel Hsu, Debmalya Mandal.
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.
A Separation Result Between Data-oblivious and Data-aware Poisoning Attacks.
Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Abhradeep Thakurta.
Advances in Neural Information Processing Systems (NeurIPS), 2021.
An Attack on InstaHide: Is Private Learning Possible with Instance Encoding?
Nicholas Carlini, Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Shuang Song, Abhradeep Thakurta, Florian Tramèr.
IEEE Symposium on Security and Privacy (Oakland), 2021.
Ensuring Fairness Beyond the Training Data.
Debmalya Mandal, Samuel Deng, Suman Jana, Jeannette Wing, Daniel Hsu.
Advances in Neural Information Processing Systems (NeurIPS), 2020.
Biased Programmers? Or Biased Data? A Field Experiment on Operationalizing AI Ethics.
Bo Cowgill, Fabrizio Dell’Acqua, Samuel Deng, Daniel Hsu, Nakul Verma, Augustin Chaintreau.
21st ACM Conference on Economics and Computation, 2020.
Methodological Blind Spots in Machine Learning Fairness: Lessons from the Philosophy of Science and Computer Science
Samuel Deng, Achille Varzi.
NeurIPS Workshop on Human-Centric Machine Learning, 2019.
Undergraduate Senior Thesis, 2019. full pdf
I love teaching, and I am passionate about developing my own pedagogical practices. In particular, it’s important to me to think about ways in which pedagogy in computer science and math education could be improved to be more effective, empathetic, and equitable to diverse student needs.
On the teaching front, I’ve had the pleasure of:
I’m currently developing a syllabus and material for Mathematics for Machine Learning, a bridge course for Columbia CS students to strengthen mathematical foundations for studying machine learning.
Alongside Hadleigh Schwartz, I am currently Ph.D coordinator for Columbia’s Emerging Scholars Program (ESP), a peer-taught, discussion-based seminar course for first and second-year CS students focused on group problem-solving, collaboration, and introducing beginning computer scientists to the breadth of the subject.