VLDB 2026 Research / reviewers in the wild / expert
Chris Alvin
dblp:149/1259
· DBLP profile ↗
7ranked-venue papers
4as first author
3since 2021 · last 2025
0000-0001-6044-2159ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 4 · 2 first-author · 2 since 2021Artificial intelligence and machine learning · 2 · 1 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 1 · 1 first-author
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Benchmarking Artificial Intelligence Models for Daily Coastal Hypoxia Forecasting
Magesh Rajasekaran, Md Saiful Islam Sajol, Chris Alvin, Supratik Mukhopadhyay, Yanda Ou, Z. George Xue |
IEEE Big Data | 3 |
| 2023 | Program analysis using empirical abstraction
Vivian M. Ho, Chris Alvin, Jimmie D. Lawson, Supratik Mukhopadhyay, Brian Peterson |
Int. J. Softw. Tools Technol. Transf. | 2 |
| 2021 | Static generation of UML sequence diagramsabstractAbstract UML sequence diagrams are visual representations of object interactions in a system and can provide valuable information for program comprehension, debugging, maintenance, and software archeology. Sequence diagrams generated from legacy code are independent of existing documentation that may have eroded. We present a framework for static generation of UML sequence diagrams from object-oriented source code. The framework provides a query refinement system to guide the user to interesting interactions in the source code. Our technique involves constructing a hypergraph representation of the source code, traversing the hypergraph with respect to a user-defined query, and generating the corresponding set of sequence diagrams. We implemented our framework as a tool, StaticGen (supporting software: StaticGen ), analyzing a corpus of 30 Android applications. We provide experimental results demonstrating the efficacy of our technique (originally appeared in the Proceedings of Fundamental Approaches to Software Engineering—20th International Conference, FASE 2017, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2017, Uppsala, Sweden, April 22–29, 2017). Chris Alvin, Brian Peterson, Supratik Mukhopadhyay |
Int. J. Softw. Tools Technol. Transf. | 1 |
| 2020 | Empirical Abstraction
Vivian M. Ho, Chris Alvin, Supratik Mukhopadhyay, Brian Peterson, Jimmie D. Lawson |
RV | 2 |
| 2017 | Synthesis of Problems for Shaded Area Geometry Reasoning
Chris Alvin, Sumit Gulwani, Rupak Majumdar, Supratik Mukhopadhyay |
AIED | 1 |
| 2017 | StaticGen: Static Generation of UML Sequence Diagrams
Chris Alvin, Brian Peterson, Supratik Mukhopadhyay |
FASE | 1 |
| 2014 | Synthesis of Geometry Proof ProblemsabstractThis paper presents a semi-automated methodology for generating geometric proof problems of the kind found in a high-school curriculum. We formalize the notion of a geometry proof problem and describe an algorithm for generating such problems over a user-provided figure. Our experimental results indicate that our problem generation algorithm can effectively generate proof problems in elementary geometry. On a corpus of 110 figures taken from popular geometry textbooks, our system generated an average of about 443 problems per figure in an average time of 4.7 seconds per figure. Chris Alvin, Sumit Gulwani, Rupak Majumdar, Supratik Mukhopadhyay |
AAAI | 1 |