Demo Data from SIGIR 2026 / reviewers in the wild / expert
Ali Taleghani
dblp:99/5934
· DBLP profile ↗
4ranked-venue papers
3as first author
0since 2021 · last 2010
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 3 · 3 first-authorArtificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1
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.
| Theoretical computer science
2 papers |
Automated reasoning and model checking · 100% | |
| Artificial intelligence
1 paper |
Video understanding and tracking · 50% Probabilistic and Bayesian machine learning · 50% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Automated reasoning and model checking › model checking
software model checking |
0.2 | 2 | 2010 | Search-carrying code · ASE 2010 State-Space Coverage Estimation · ASE 2009 |
Automated reasoning and model checking › model checking
explicit-state model checking |
0.1 | 1 | 2009 | State-Space Coverage Estimation · ASE 2009 |
Computer vision › Video understanding and tracking
multi-object tracking |
0.0 | 1 | 2004 | A Boosted Particle Filter: Multitarget Detection and Tracking · ECCV (1) 2004 |
Machine learning › Probabilistic and Bayesian machine learning › monte carlo methods › sequential monte carlo
particle filtering |
0.0 | 1 | 2004 | A Boosted Particle Filter: Multitarget Detection and Tracking · ECCV (1) 2004 |
Methods — techniques the papers use, named apart from their topics
model checking · 0.1monte carlo sampling · 0.1boosting · 0.0boosted particle filter · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | Search-carrying codeabstractIn this paper, we introduce a model-checking-based certification technique called search-carrying code (SCC). SCC is an adaptation of the principles of proof-carrying code, in which program certification is reduced to checking a provided safety proof. In SCC, program certification is an efficient re-examination of a program's state space. A code producer, who offers a program for use, provides a search script that encodes a search of the program's state space. A code consumer, who wants to certify that the program fits her needs, uses the search script to direct how a model checker searches the program's state space. Ali Taleghani, Joanne M. Atlee |
ASE | 1 |
| 2009 | State-Space Coverage EstimationabstractSoftware model checking is the process of systematically exploring a program's state space to find hard-to-discover errors. Because of the exponential size of the state space, an exhaustive search of the state space is often impossible given the memory resources. In such cases, an estimate of how much of the state space is covered can help the verifier to decide whether to employ additional computational resources or to use more aggressive abstraction techniques. Our work focuses on coverage estimation for explicit-state model checking of software programs. In this paper, we present an estimation algorithm that is based on Monte Carlo techniques that sample the unexplored portion of the reachability graph. We implemented our algorithm in Java Pathfinder and evaluated our approach on a suite of Java programs, simulating out-of-memory errors after a known percentage of a program's state space had been searched. Our empirical studies show that, on average, our algorithm's coverage estimates differ from the actual coverage by less than 10 percentage points, with a standard deviation of about 5 percentage points - regardless of whether the actual state-space coverage is low (3%) or high (95%). Ali Taleghani, Joanne M. Atlee |
ASE | 1 |
| 2006 | Semantic Variations Among UML StateMachines
Ali Taleghani, Joanne M. Atlee |
MoDELS | 1 |
| 2004 | A Boosted Particle Filter: Multitarget Detection and Tracking
Kenji Okuma, Ali Taleghani, Nando de Freitas, James J. Little, David G. Lowe |
ECCV (1) | 2 |