Ali Malik

Ali R. Malik

AI Research Scientist

I spend my time building systems to help people learn. I also love teaching and trying to make sense of complicated topics in maths, cs, and philosophy.

PhD in Computer Science from Stanford.
Co-creator of [Code in Place].

Research interests: probabilistic reasoning, human-centered learning at scale, generative models, learning theory.

Projects and Publications

From Tarzan to Tolkien: Controlling the Proficiency Level of LLMs for Content Generation
From Tarzan to Tolkien: Controlling the Proficiency Level of LLMs for Content Generation
[PDF] [Code]
A. Malik, S. Mayhew, C. Piech, K. Bicknell
ACL Findings (Bangkok, Thailand) // 2024
NLP AI
TeachNow: Enabling Spontaneous 1:1 Help in Massive Online Courses
TeachNow: Enabling Spontaneous 1:1 Help in Massive Online Courses
[PDF]
A. Malik*, J. Woodrow*, C. Piech
ITiCSE (Milan, Italy) // 2024
AI HCI Education
Learners Teaching Novices: An Uplifting Alternative Assessment
Learners Teaching Novices: An Uplifting Alternative Assessment
[PDF]
A. Malik, J. Woodrow, C. Piech
SIGCSE (Portland, USA) // 2024
HCI Education
Lifting uniform learners via distributional decomposition
Lifting uniform learners via distributional decomposition
[PDF]
G. Blanc*, J. Lange*, A. Malik*, L. Tan*
STOC (Orlando, USA) // 2023
Learning Theory Theory
Popular decision tree algorithms are provably noise tolerant
Popular decision tree algorithms are provably noise tolerant
[PDF]
G. Blanc*, J. Lange*, A. Malik*, L. Tan*
ICML (Baltimore, USA) // 2022
AI Learning Theory
On the power of adaptivity in statistical adversaries
On the power of adaptivity in statistical adversaries
[PDF]
G. Blanc*, J. Lange*, A. Malik*, L. Tan*
COLT (London, UK) // 2022
Learning Theory Theory
Generative Grading: Near human-level accuracy for automated feedback
Generative Grading: Near human-level accuracy for automated feedback
[PDF] [Code]
A. Malik*, M. Wu*, V. Vasavada, J. Song, M. Coots, J. Mitchell, N. Goodman, C. Piech
EDM (Paris, France) // 2021
AI Education
Code in Place: Online section leading for scalable human-centered learning
Code in Place: Online section leading for scalable human-centered learning
[PDF]
C. Piech, A. Malik, K. Jue, M. Sahami
SIGCSE (Virtual) // 2021
Learning at Scale Education
The Stanford Acuity Test: A probabilistic approach for precise visual acuity testing
The Stanford Acuity Test: A probabilistic approach for precise visual acuity testing
[PDF]
C. Piech*, A. Malik*, L. Scott, R. Chang, C. Lin
AAAI (New York, USA) // 2020
AI Health
Calibrated model-based deep reinforcement learning
Calibrated model-based deep reinforcement learning
[PDF] [Code]
A. Malik*, V. Kuleshov*, J. Song, D. Nemer, H. Seymour, S. Ermon
ICML (Long Beach, USA) // 2019
AI RL
Using latent variable models to observe academic pathways
Using latent variable models to observe academic pathways
[PDF]
N. Gruver, A. Malik, B. Capoor, C. Piech, M. Stevens, A. Paepcke
EDM (Montréal, Canada) // 2019
AI Education
BlueBook: Secure, electronic Computer Science exams
BlueBook: Secure, electronic Computer Science exams
[PDF]
A. Malik, B. Capoor, C. Piech
Desktop Application // 2018
Software Education
DeepGIFs
DeepGIFs
[PDF]
A. Malik, M. Troute, B. Capoor
Project // 2018
Deep Learning Vision
WikiRacer
WikiRacer
[PDF]
A. Malik
SIGCSE (Nifty Assignment) // 2018
Algorithms Education