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Samuel Tian

dblp:334/0907 · DBLP profile ↗
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3ranked-venue papers
0as first author
3since 2021 · last 2025
0009-0004-1906-5995ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Software engineering, systems software and programming languages · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Image recognition and object detection · 77% Generative modeling · 23%
Software engineering, system software, and programming languages
1 paper
Compilers and program optimization · 77% Program verification · 23%
Network and information security
1 paper
Cryptographic primitives and cryptanalysis · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Image recognition and object detection › image classification
fine-grained image classification
0.812024
On Learning Discriminative Features from Synthesized Data for Self-supervised Fine-Grained Visual Recognition · ECCV (89) 2024
Cryptographic primitives and cryptanalysis
finite field arithmetic
0.712023
CryptOpt: Verified Compilation with Randomized Program Search for Cryptographic Primitives · Proc. ACM Program. Lang. 2023
Compilers and program optimization
verified compilation
0.712023
CryptOpt: Verified Compilation with Randomized Program Search for Cryptographic Primitives · Proc. ACM Program. Lang. 2023
Machine learning › Generative modeling
synthetic data generation
0.212024
On Learning Discriminative Features from Synthesized Data for Self-supervised Fine-Grained Visual Recognition · ECCV (89) 2024
Program verification › formal proof
mechanized proof
0.212023
CryptOpt: Verified Compilation with Randomized Program Search for Cryptographic Primitives · Proc. ACM Program. Lang. 2023

Methods — techniques the papers use, named apart from their topics

symbolic execution · 1.3randomized program search · 1.3SMT solving · 1.3synthesized data · 0.8discriminative feature learning · 0.8
YearPublicationVenuePosition
2025 Uncovering allosteric communication in cancer-related histone mutations
abstract
Abstract Background Histone mutations have been implicated in various cancers, but their mechanistic effects on chromatin dynamics remain largely unexplored. Aim To investigate how allosteric communication influences the functional outcomes of cancer-linked histone mutations. Methods We analyzed forty cancer-associated histone missense mutations using integrated computational modeling and experimental validation to evaluate their long-range structural and functional consequences. Results The allosteric effects of histone mutations were highly position-dependent. Mutations near the N-terminal tails of histones H3 and H4 caused the most pronounced conformational perturbations. Seven histone mutations were predicted to have strong allosteric signatures potentially influencing nucleosome interactions. Experimental assays confirmed that H2BS64Y and H2BS64F substitutions impaired histone function, disrupted H2BK120 ubiquitination, and decreased genome stability. Conclusions These findings indicate that certain histone mutations may exert oncogenic potential through long-range allosteric mechanisms, underscoring the importance of allostery as a key regulatory principle in cancer epigenetics.
Shuxiang Li, Jie Shu, James C. Rober, Austin Macklem, Daniel Espiritu, Tanay Debnath, Samuel Tian, Daniel Tian, Maria J. Aristizabal, Anna R. Panchenko
Briefings Bioinform.7
2024 On Learning Discriminative Features from Synthesized Data for Self-supervised Fine-Grained Visual Recognition
Zihu Wang, Lingqiao Liu, Scott Ricardo Figueroa Weston, Samuel Tian, Peng Li 0001
ECCV (89)4
2023 CryptOpt: Verified Compilation with Randomized Program Search for Cryptographic Primitives
abstract
Most software domains rely on compilers to translate high-level code to multiple different machine languages, with performance not too much worse than what developers would have the patience to write directly in assembly language. However, cryptography has been an exception, where many performance-critical routines have been written directly in assembly (sometimes through metaprogramming layers). Some past work has shown how to do formal verification of that assembly, and other work has shown how to generate C code automatically along with formal proof, but with consequent performance penalties vs. the best- known assembly. We present CryptOpt, the first compilation pipeline that specializes high-level cryptographic functional programs into assembly code significantly faster than what GCC or Clang produce, with mechanized proof (in Coq) whose final theorem statement mentions little beyond the input functional program and the operational semantics of x86-64 assembly. On the optimization side, we apply randomized search through the space of assembly programs, with repeated automatic benchmarking on target CPUs. On the formal-verification side, we connect to the Fiat Cryptography framework (which translates functional programs into C-like IR code) and extend it with a new formally verified program-equivalence checker, incorporating a modest subset of known features of SMT solvers and symbolic-execution engines. The overall prototype is quite practical, e.g. producing new fastest-known implementations of finite-field arithmetic for both Curve25519 (part of the TLS standard) and the Bitcoin elliptic curve secp256k1 for the Intel 12𝑡ℎ and 13𝑡ℎ generations.
Joel Kuepper, Andres Erbsen, Jason Gross, Owen Conoly, Chuyue Sun, Samuel Tian, Adam Chlipala, Chitchanok Chuengsatiansup, Daniel Genkin, Markus Wagner 0007, Yuval Yarom
Proc. ACM Program. Lang.6