Ali Salehi

Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.

? Tutorial for this page

Demo Data from VLDB 2026 / reviewers in the wild / expert

dblp:35/95 · DBLP profile ↗

← Back

11ranked-venue papers

4as first author

4since 2021 · last 2025

—ORCID · conflict

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

Databases, data management, data science and information retrieval · 5 · 1 first-authorArtificial intelligence and machine learning · 4 · 2 first-author · 4 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 first-author

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		Machine translation · 77% Information extraction and text analysis · 23%
Computer networks 2 papers		Internet of things and sensor networks · 100%
Databases, data mining, and information retrieval 1 paper		Data stream processing · 77% Database theory · 23%
Computer graphics and multimedia 1 paper		Visualization and visual analytics · 100%
Computer architecture, parallel and distributed computing, and storage systems 1 paper		Distributed systems · 100%

Topics — the 10 heaviest of 11, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Natural language and speech › Machine translation parallel corpora	0.9	1	2025	PARME: Parallel Corpora for Low-Resourced Middle Eastern Languages · ACL (1) 2025
Natural language and speech › Information extraction and text analysis › multilingual NLP multilingual language resources	0.3	1	2025	PARME: Parallel Corpora for Low-Resourced Middle Eastern Languages · ACL (1) 2025
Visualization and visual analytics › information visualization › quantitative data visualization sensor data visualization	0.1	1	2009	Environmental Monitoring 2.0 · ICDE 2009
Internet of things and sensor networks › wireless sensor network environmental monitoring	0.1	1	2009	Environmental Monitoring 2.0 · ICDE 2009
Internet of things and sensor networks sensor data management	0.1	1	2009	Environmental Monitoring 2.0 · ICDE 2009
Distributed systems publish/subscribe systems	0.1	1	2009	Scalable Delivery of Stream Query Results · Proc. VLDB Endow. 2009
Internet of things and sensor networks › wireless sensor network middleware	0.1	1	2006	A Middleware for Fast and Flexible Sensor Network Deployment · VLDB 2006
Internet of things and sensor networks › wireless sensor network sensor deployment	0.1	1	2006	A Middleware for Fast and Flexible Sensor Network Deployment · VLDB 2006
Database theory query containment	0.0	1	2009	Scalable Delivery of Stream Query Results · Proc. VLDB Endow. 2009
Internet of things and sensor networks wireless sensor network	0.0	1	2006	A Middleware for Fast and Flexible Sensor Network Deployment · VLDB 2006

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

query merging · 0.2query grouping · 0.2middleware · 0.2contour plotting · 0.2

Year	Publication	Venue	Position
2025	PARME: Parallel Corpora for Low-Resourced Middle Eastern Languages abstract Sina Ahmadi, Rico Sennrich, Erfan Karami, Ako Marani, Parviz Fekrazad, Gholamreza Akbarzadeh Baghban, Hanah Hadi, Semko Heidari, Mahîr Dogan, Pedram Asadi, Dashne Bashir, Mohammad Amin Ghodrati, Kourosh Amini, Zeynab Ashourinezhad, Mana Baladi, Farshid Ezzati, Alireza Ghasemifar, Daryoush Hosseinpour, Behrooz Abbaszadeh, Amin Hassanpour, Bahaddin Jalal Hamaamin, Saya Kamal Hama, Ardeshir Mousavi, Sarko Nazir Hussein, Isar Nejadgholi, Mehmet Ölmez, Horam Osmanpour, Rashid Roshan Ramezani, Aryan Sediq Aziz, Ali Salehi, Mohammadreza Yadegari, Kewyar Yadegari, Sedighe Zamani Roodsari. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025. Sina Ahmadi, Rico Sennrich, Erfan Karami, Ako Marani, Parviz Fekrazad, Gholamreza Akbarzadeh Baghban, Hanah Hadi, Semko Heidari, Mahîr Dogan, Pedram Asadi, Dashne Bashir, Mohammad Amin Ghodrati, Kourosh Amini, Zeynab Ashourinezhad, Mana Baladi, Farshid Ezzati, Alireza Ghasemifar, Daryoush Hosseinpour, Behrooz Abbaszadeh, Amin Hassanpour, Bahaddin Jalal Hamaamin, Saya Kamal Hama, Ardeshir Mousavi, Sarko Nazir Hussein, Isar Nejadgholi, Mehmet Ölmez, Horam Osmanpour, Rashid Roshan Ramezani, Aryan Sediq Aziz, Ali Salehi, Mohammadreza Yadegari, Kewyar Yadegari, Sedighe Zamani Roodsari	ACL (1)	30
2023	DDCNet: Deep dilated convolutional neural network for dense prediction Ali Salehi, Madhusudhanan Balasubramanian	Neurocomputing	1
2023	DDCNet-Multires: Effective Receptive Field Guided Multiresolution CNN for Dense Prediction Ali Salehi, Madhusudhanan Balasubramanian	Neural Process. Lett.	1
2022	UniMorph 4.0: Universal Morphology abstract The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation, and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements on several fronts that were made in the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 66 new languages, including 24 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g., missing gender and macrons information. We have amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet. Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa, Nizar Habash, Witold Kieras, Gábor Bella, Brian Leonard, Garrett Nicolai, Kyle Gorman, Yustinus Ghanggo Ate, Maria Ryskina, Sabrina J. Mielke, Elena Budianskaya, Charbel El-Khaissi, Tiago Pimentel, Michael Gasser, William Lane 0002, Mohit Raj, Matt Coler, Jaime Rafael Montoya Samame, Delio Siticonatzi Camaiteri, Esaú Zumaeta Rojas, Didier López Francis, Arturo Oncevay, Juan López Bautista, Gema Celeste Silva Villegas, Lucas Torroba Hennigen, Adam Ek, David Guriel, Peter Dirix, Jean-Philippe Bernardy, Andrey Scherbakov, Aziyana Bayyr-ool, Antonios Anastasopoulos, Roberto Zariquiey, Karina Sheifer, Sofya Ganieva, Hilaria Cruz, Ritván Karahóga, Stella Markantonatou, George Pavlidis, Matvey Plugaryov, Elena Klyachko, Ali Salehi, Candy Angulo, Jatayu Baxi, Andrew Krizhanovsky, Natalia Krizhanovskaya, Elizabeth Salesky, Clara Vania, Sardana Ivanova, Jennifer C. White, Rowan Hall Maudslay, Josef Valvoda, Ran Zmigrod, Paula Czarnowska, Irene Nikkarinen, Aelita Salchak, Brijesh Bhatt, Christopher Straughn, Zoey Liu, Jonathan Washington, Yuval Pinter, Duygu Ataman, Marcin Wolinski, Totok Suhardijanto, Anna Yablonskaya, Niklas Stoehr, Hossep Dolatian, Zahroh Nuriah, Shyam Ratan, Francis M. Tyers, Edoardo Maria Ponti, Grant Aiton, Aryaman Arora, Richard J. Hatcher, Ritesh Kumar 0002, Jeremiah Young, Daria Rodionova, Anastasia Yemelina, Taras Andrushko, Igor Marchenko, Polina Mashkovtseva, Alexandra Serova, Emily Tucker Prud'hommeaux, Maria Nepomniashchaya, Fausto Giunchiglia, Eleanor Chodroff, Mans Hulden, Miikka Silfverberg, Arya McCarthy, David Yarowsky, Ryan Cotterell, Reut Tsarfaty, Ekaterina Vylomova	LREC	44
2012	Capturing sensor data from mobile phones using Global Sensor Network middleware abstract Mobile phones play increasingly bigger role in our everyday lives. Today, most smart phones comprise a wide variety of sensors which can sense the physical environment. The Internet of Things vision encompasses participatory sensing which is enabled using mobile phones based sensing and reasoning. In this research, we propose and demonstrate our DAM4GSN architecture to capture sensor data using sensors built into the mobile phones. Specifically, we combine an open source sensor data stream processing engine called ‘Global Sensor Network (GSN)’ with the Android platform to capture sensor data. To achieve this goal, we proposed and developed a prototype application that can be installed on Android devices as well as a AndroidWrapper as a GSN middleware component. The process and the difficulty of manually connecting sensor devices to sensor data processing middleware systems are examined. We evaluated the performance of the system based on power consumption of the mobile client. Charith Perera, Arkady B. Zaslavsky, Peter Christen, Ali Salehi, Dimitrios Georgakopoulos 0001	PIMRC	4
2010	Collaborative information analysis for sensor-enabled scientific applications abstract Data collected from sensor networks are often analysed by cross-domain scientists who produce results that are requested by a variety of clients. In such a collaborative environment, scientific experiments include data collection form sensors, and data analysis performed by scientists. To meet the c Ali Salehi, Mukkaddim Pathan, Dimitrios Georgakopoulos 0001, David Deery	CollaborateCom	1
2009	Environmental Monitoring 2.0 abstract A sensor network data gathering and visualization infrastructure is demonstrated, comprising of global sensor networks (GSN) middleware and Microsoft SensorMap. Users are invited to actively participate in the process of monitoring real-world deployments and can inspect measured data in the form of contour plots overlayed onto a high resolution map and a digital topographic model. Users can go back in time virtually to search for interesting events or simply to visualize the temporal dependencies of the data. The system presented is not only interesting and visually enticing for non-expert users but brings substantial benefits to environmental scientists. The easily installed data acquisition component as well as the powerful data sharing and visualization platform opens up new ground in collaborative data gathering and interpretation in the spirit of Web 2.0 applications. Sebastian Michel 0001, Ali Salehi, Liqian Luo, Nicholas Dawes, Karl Aberer, Guillermo Barrenetxea, Mathias Bavay, Aman Kansal, K. Ashwin Kumar, Suman Nath, Marc Parlange, Stewart Tansley, Catharine van Ingen, Feng Zhao 0001, Yongluan Zhou	ICDE	2
2009	Knowing When to Slide - Efficient Scheduling for Sliding Window Processing abstract We consider sliding window query execution scheduling in stream processing engines. Sliding windows are an essential building block to limit the query focus at a particular part of the stream, based either on value count or time ranges. These so called sliding window predicates specify the execution condition for the query. Due to the often massive amount of registered queries, efficient algorithms to check these predicates are essential. While there exists a comprehensive set of works on the stream processing techniques, the actual algorithms to intelligently decide on the sliding behaviors is not extensively addressed in the existing works. In this paper we propose a set of algorithms for managing and sharing sliding decisions. This work introduces the concept of the batch sliding and sliding graphs to improve the sliding decision of the stream processing engines. Our algorithms can be efficiently used in large-scale stream processing systems where data arrives at high rates and a large number of user queries are registered to these data streams. Our evaluation results show the suitability of this approach in the real world applications. Ali Salehi, Mehdi Riahi, Sebastian Michel 0001, Karl Aberer	Mobile Data Management	1
2009	Scalable Delivery of Stream Query Results abstract Continuous queries over data streams typically produce large volumes of result streams. To scale up the system, one should carefully study the problem of delivering the result streams to the end users, which, unfortunately, is often overlooked in existing systems. In this paper, we leverage Distributed Publish/Subscribe System (DPSS), a scalable data dissemination infrastructure, for efficient stream query result delivery. To take advantage of DPSS's multicast-like data dissemination architecture, one has to exploit the common contents among different result streams and maximize the sharing of their delivery. Hence, we propose to merge the user queries into a few representative queries whose results subsume those of the original ones, and disseminate the result streams of these representative queries through the DPSS. To realize this approach, we study the stream query containment theories and propose efficient query grouping and merging algorithms. The proposed approach is non-intrusive and hence can be easily implemented as a middleware to be incorporated into existing stream processing systems. A prototype is developed on top of an open-source stream processing system and results of an extensive performance study on real datasets verify the efficacy of the proposed techniques. Yongluan Zhou, Ali Salehi, Karl Aberer	Proc. VLDB Endow.	2
2007	Infrastructure for Data Processing in Large-Scale Interconnected Sensor Networks abstract With the price of wireless sensor technologies diminishing rapidly we can expect large numbers of autonomous sensor networks being deployed in the near future. These sensor networks will typically not remain isolated but the need of interconnecting them on the network level to enable integrated data processing will arise, thus realizing the vision of a global "sensor Internet." This requires a flexible middleware layer which abstracts from the underlying, heterogeneous sensor network technologies and supports fast and simple deployment and addition of new platforms, facilitates efficient distributed query processing and combination of sensor data, provides support for sensor mobility, and enables the dynamic adaption of the system configuration during runtime with minimal (zero-programming) effort. This paper describes the global sensor networks (GSN) middleware which addresses these goals. We present GSN's conceptual model, abstractions, and architecture, and demonstrate the efficiency of the implementation through experiments with typical high-load application profiles. The GSN implementation is available from http://gsn.sourceforge.net/. Karl Aberer, Manfred Hauswirth, Ali Salehi	MDM	3
2006	A Middleware for Fast and Flexible Sensor Network Deployment Karl Aberer, Manfred Hauswirth, Ali Salehi	VLDB	3