About

Bosen Jin is an assistant professor in the School of Integrative Plant Science, Soil and Crop Sciences Section. He is an environmental chemist whose research and extension programs apply analytical chemistry, microbiology, soil science, and machine learning to investigate the environmental fate of emerging organic contaminants (EOCs), including per- and polyfluoroalkyl substances (PFAS), pesticides, and pharmaceuticals, in agricultural and soil ecosystems. His overarching goal is to provide mechanistic insights that inform evidence-based remediation and management strategies for contaminated agricultural systems. His ongoing projects include monitoring and identifying EOCs in agricultural systems through developing non-target and suspect screening workflows using high-resolution mass spectrometry, quantifying sorption, leaching, and transformation kinetics in soil–water–plant systems, applying machine learning and modeling approaches to predict contaminant fate and guide mitigation strategies, and assessing the impacts of emerging contaminants on soil microbiomes and biogeochemical processes. Jin's extension program connects fundamental environmental chemistry with practical soil and water management, working with farmers, extension educators, and agencies to develop best practices for managing PFAS and pesticide contamination, assessing their impacts on soil microbial health, and providing tools for integrated soil health evaluation. Through workshops, field collaboration, and applied research, his group aims to translate laboratory findings into actionable, field-scale solutions that support sustainable land use, pollution prevention, and resilient agricultural landscapes.

Research topics

• Computer Science
• Mathematics
• Mathematical optimization
• Algorithm
• Statistics
• Machine Learning
• Combinatorics
• Mathematical economics
• Economics
• Finance
• Discrete mathematics

Selected publications

• Learning-Augmented Online Bipartite Fractional Matching

  ArXiv.org · 2025-05-25

  preprintOpen access

  Online bipartite matching is a fundamental problem in online optimization, extensively studied both in its integral and fractional forms due to its theoretical significance and practical applications, such as online advertising and resource allocation. Motivated by recent progress in learning-augmented algorithms, we study online bipartite fractional matching when the algorithm is given advice in the form of a suggested matching in each iteration. We develop algorithms for both the vertex-weighted and unweighted variants that provably dominate the naive "coin flip" strategy of randomly choosing between the advice-following and advice-free algorithms. Moreover, our algorithm for the vertex-weighted setting extends to the AdWords problem under the small bids assumption, yielding a significant improvement over the seminal work of Mahdian, Nazerzadeh, and Saberi (EC 2007, TALG 2012). Complementing our positive results, we establish a hardness bound on the robustness-consistency tradeoff that is attainable by any algorithm. We empirically validate our algorithms through experiments on synthetic and real-world data.

  PublisherOA PDFDOI

• A $$\frac{4}{3}$$-approximation algorithm for half-integral cycle cut instances of the TSP

  Mathematical Programming · 2025-02-17

  article1st authorCorresponding
  PublisherDOI

• The two-stripe symmetric circulant TSP is in P

  Mathematical Programming · 2025-04-17 · 1 citations

  article
  PublisherDOI

• The Online Submodular Assignment Problem

  2024-10-27

  article

  Online resource allocation is a rich and var-ied field. One of the most well-known problems in this area is online bipartite matching, introduced in 1990 by Karp, Vazirani, and Vazirani. Since then, many variants have been studied, including AdWords, the generalized assignment problem (GAP), and online submodular welfare maximization. In this paper, we introduce a generalization of GAP which we call the submodular assignment problem (SAP). This generalization captures many online assignment problems, including all classical online bipartite matching problems as well as broader online combinatorial optimization problems such as online arboricity, flow scheduling, and laminar restricted allocations. We present a fractional algorithm for online SAP that is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(1-1/e)$</tex>-competitive. Additionally, we study several integral special cases of the problem. In particular, we provide a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(1\ -1/e-\varepsilon){-}$</tex> competitive integral algorithm under a small-bids assumption, and a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(1\ -1/e)$</tex>-competitive integral algorithm for online submodular welfare maximization where the utility functions are given by rank functions of matroids. The key new ingredient for our results is the construction and structural analysis of a “water level” vector for polymatroids, which allows us to generalize the classic water-filling paradigm used in online matching problems. This construction reveals connections to submodular utility allocation markets and principal partition sequences of matroids.

  PublisherDOI

• The Online Submodular Assignment Problem

  arXiv (Cornell University) · 2024-01-13

  preprintOpen access

  Online resource allocation is a rich and varied field. One of the most well-known problems in this area is online bipartite matching, introduced in 1990 by Karp, Vazirani, and Vazirani [KVV90]. Since then, many variants have been studied, including AdWords, the generalized assignment problem (GAP), and online submodular welfare maximization. In this paper, we introduce a generalization of GAP which we call the submodular assignment problem (SAP). This generalization captures many online assignment problems, including all classical online bipartite matching problems as well as broader online combinatorial optimization problems such as online arboricity, flow scheduling, and laminar restricted allocations. We present a fractional algorithm for online SAP that is $(1-\frac{1}{e})$-competitive. Additionally, we study several integral special cases of the problem. In particular, we provide a $(1-\frac{1}{e}-ε)$-competitive integral algorithm under a small-bids assumption, and a $(1-\frac{1}{e})$-competitive integral algorithm for online submodular welfare maximization where the utility functions are given by rank functions of matroids. The key new ingredient for our results is the construction and structural analysis of a "water level" vector for polymatroids, which allows us to generalize the classic water-filling paradigm used in online matching problems. This construction reveals connections to submodular utility allocation markets and principal partition sequences of matroids.

  PublisherOA PDFDOI

• Sample Complexity of Posted Pricing for a Single Item

  2024-01-01 · 1 citations

  article1st authorCorresponding
  PublisherDOI

• High Probability Complexity Bounds for Adaptive Step Search Based on Stochastic Oracles

  SIAM Journal on Optimization · 2024-07-02 · 8 citations

  article1st authorCorresponding
  PublisherDOI

• A Lower Bound for the Max Entropy Algorithm for TSP

  Lecture notes in computer science · 2024-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• The Online Submodular Assignment Problem

  arXiv (Cornell University) · 2024-12-05

  preprintOpen access

  Online resource allocation is a rich and varied field. One of the most well-known problems in this area is online bipartite matching, introduced in 1990 by Karp, Vazirani, and Vazirani [KVV90]. Since then, many variants have been studied, including AdWords, the generalized assignment problem (GAP), and online submodular welfare maximization. In this paper, we introduce a generalization of GAP which we call the submodular assignment problem (SAP). This generalization captures many online assignment problems, including all classical online bipartite matching problems as well as broader online combinatorial optimization problems such as online arboricity, flow scheduling, and laminar restricted allocations. We present a fractional algorithm for online SAP that is (1-1/e)-competitive. Additionally, we study several integral special cases of the problem. In particular, we provide a (1-1/e-epsilon)-competitive integral algorithm under a small-bids assumption, and a (1-1/e)-competitive integral algorithm for online submodular welfare maximization where the utility functions are given by rank functions of matroids. The key new ingredient for our results is the construction and structural analysis of a "water level" vector for polymatroids, which allows us to generalize the classic water-filling paradigm used in online matching problems. This construction reveals connections to submodular utility allocation markets and principal partition sequences of matroids.

  PublisherOA PDFDOI

• Sample complexity analysis for adaptive optimization algorithms with stochastic oracles

  Mathematical Programming · 2024-04-29 · 8 citations

  article1st authorCorresponding
  PublisherDOI

Frequent coauthors

• David P. Williamson

  14 shared

• Miaolan Xie

  Cornell University

  7 shared

• Katya Scheinberg

  Cornell University

  7 shared

• Vasilis Gkatzelis

  5 shared

• Nathan Klein

  Institute for Advanced Study

  5 shared

• Monika Henzinger

  Institute of Science and Technology Austria

  5 shared

• Siddhartha Banerjee

  Cornell University

  5 shared

• Artur Gorokh

  3 shared