Allen Liu
Assistant Professor of Computer Science
Studies machine learning theory and algorithms, including statistical learning, language model foundations and quantum learning.
Allen Liu's lab at NYU works on learning theory & optimization and large language models. 16 papers since 2023, 13 at top venues, mostly FOCS and STOC. Applications close .
At a glance
- papers, 2023–2026
- 4/ year
- at T1 venues
- 81%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 1 papers2026 · T1 1 · collecting
- 2 papers2025 · T1 1
- 4 papers2024 · T1 2
- 9 papers2023 · T1 9
T1 venuesOtherVenues: FOCS, STOC, COLT, ICLR, ICML
Research focuspaper titles that use each topic's words
- Learning theory & optimization6 papers
- Large language models2 papers
- language model1 paper
- quantum learning0 papers
- statistical learning0 papers
Paper list
From the lab homepage.
20261 paper
20252 papers
- QIP Short Plenary, STOCLearning the closest product state (opens in a new tab)
- STOC (T1)Model Stealing for Any Low-Rank Language Model (opens in a new tab)
20244 papers
- STOC (T1)An Optimal Tradeoff between Entanglement and Copy Complexity for Quantum State Tomography (opens in a new tab)
- QIP Invited Plenary, FOCS , QuantaHigh-Temperature Gibbs States are Unentangled and Efficiently Preparable (opens in a new tab)
- QIP Invited Plenary, Best Student Paper, STOC , QuantaLearning Quantum Hamiltonians at any Temperature in Polynomial Time (opens in a new tab)
- FOCS (T1)Structure learning of Hamiltonians from real-time evolution (opens in a new tab)
20239 papers
- STOC (T1)A New Approach to Learning Linear Dynamical Systems (opens in a new tab)
- NeurIPS (T1)Constant Approximation for Individual Preference Stable Clustering (opens in a new tab)
- FOCS (T1)Matrix Completion in Almost-Verification Time (opens in a new tab)
- SODA (T1)Robust Voting Rules from Algorithmic Robust Statistics (opens in a new tab)
- COLT (T1)Semi-Random Sparse Recovery in Nearly-Linear Time (opens in a new tab)
- STOC (T1)Settling the Robust Learnability of Mixtures of Gaussians (opens in a new tab)
- ICML (T1)Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems (opens in a new tab)
- FOCS (T1)The Full Landscape of Robust Mean Testing: Sharp Separations between Oblivious and Adaptive Contamination (opens in a new tab)
- FOCS (T1)When Does Adaptivity Help for Quantum State Learning? (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to NYUFall 2027
Computer Science PhD (Courant / Tandon)
- Round
- Single round (December)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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Your notes
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Data
Some numbers could not be verified:
- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Not collected
- Alumni
- Not collected
Tiers are venue classifications, not lab rankings.
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