Dima Drusvyatskiy
Professor
Designs and analyzes algorithms for large-scale nonsmooth and stochastic optimization motivated by data science.
Dima Drusvyatskiy's lab at UCSD works on learning theory & optimization and data management & big data. 14 papers since 2023, 6 at top venues, mostly JMLR and COLT. Applications close .
At a glance
- papers, 2023–2026
- 4/ year
- at T1 venues
- 43%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 5 papers2025 · T1 2
- 4 papers2024 · T1 1
- 5 papers2023 · T1 3
T1 venuesOtherVenues: JMLR, COLT, NeurIPS, PNAS
Research focuspaper titles that use each topic's words
- stochastic6 papers
- Learning theory & optimization3 papers
- stochastic optimization1 paper
- Data management & big data0 papers
- data science0 papers
Paper list
From the lab homepage.
20255 papers
- Found. Comput. Math.Active manifolds, stratifications, and convergence to local minima in nonsmooth optimization (opens in a new tab)
- Math. Program.Gradient descent with adaptive stepsize converges (nearly) linearly under fourth-order growth (opens in a new tab)
- PNAS (T1)Linear Recursive Feature Machines provably recover low-rank matrices (opens in a new tab)
- COLT (T1)Online covariance estimation in nonsmooth stochastic approximation (opens in a new tab)
- Found. Comput. Math.The radius of statistical efficiency (opens in a new tab)
20244 papers
- Ann. StatAsymptotic normality and optimality in nonsmooth stochastic approximation (opens in a new tab)
- IMA Inf. InferenceFlat minima generalize for low-rank matrix recovery (opens in a new tab)
- JMLR (T1)Stochastic approximation with decision-dependent distributions: asymptotic normality and optimality (opens in a new tab)
- SIAM J. Optim.Stochastic Optimization over Proximally Smooth Sets (opens in a new tab)
20235 papers
- NeurIPS (T1)Aiming towards the minimizers: fast convergence of SGD for overparametrized problems (opens in a new tab)
- JMLR (T1)Multiplayer performative prediction: learning in decision dependent games (opens in a new tab)
- Math. Program.Stochastic algorithms with geometric step decay converge linearly on sharp functions (opens in a new tab)
- JMLR (T1)Stochastic approximation under distributional drift (opens in a new tab)
- Proc. Amer. Math. Soc., 151(11), 4751-4756The slope robustly determines convex functions (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to UCSDFall 2027
UCSD CSE Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- See program page
- English
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- Fee
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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
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Tiers are venue classifications, not lab rankings.
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