Dildar Ali

Dildar Ali (দিলদার আলী)

I am a Postdoctoral Fellow in the Department of Computer Science at National Tsing Hua University (NTHU) Taiwan. I primarily work on Submodular Functions, Combinatorial Optimization, Algorithmic Data Management, and Multi-agent Systems. I completed my Ph.D. in Theoretical Computer Science in the Department of Computer Science and Engineering at Indian Institute of Technology (IIT) Jammu, India, in April 2026 under the supervision of Prof. Suman Banerjee and Prof. Yamuna Prasad. Prior to my doctoral studies, I served as a Project Associate at Indian Institute of Technology (IIT) Bhilai, India under the supervision of Prof. Rajat Moona (Director, IIT Gandhinagar) and Prof. Dhiman Saha (IIT Bhilai) on a MeitY-funded research project in collaboration with C-DAC Noida.

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News

31/03/2026- One paper accepted in The 24^th Symposium on Experimental Algorithms (SEA 2026), Copenhagen, Denmark.

12/03/2026- One paper submitted in The 30^th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2026), Naples, Italy.

06/03/2026- One paper submitted in The 27^th IEEE Conference on Mobile Data Management (MDM 2026), Athens, Greece.

08/02/2026- One paper accepted in The 30^th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2026), Hong Kong, China.

20/12/2025- One paper accepted in The 18^th International Conference on Agents and Artificial Intelligence (ICAART 2026), Marbella, Spain.

25/10/2025- One paper accepted in The 36^th Australasian Database Conference (ADC 2025), Sydney, Australia.

06/10/2025- One paper accepted in The ACM 13^th International Conference on Data Science (ACM IKDD CODS 2025), Pune, India.

16/07/2025- One paper accepted in The 22^th European Conference on Multi-Agent Systems (EUMAS 2025), Bucharest, Romania.

10/06/2025- One paper accepted in The International Journal of Data Science and Engineering.

20/05/2025- Two paper accepted in The 36^th International Conference on Database and Expert Systems Applications (DEXA 2025), Bangkok, Thailand.

09/02/2025- One paper accepted in The 29^th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2025), Sydney, Australia.

17/11/2024- One paper accepted in The 12^th IEEE International Conference on Big Data (BigData 2024), Washington DC, USA.

04/10/2024- One paper submitted in The 25^th SIAM Data Mining Conference (SDM 2025), Virginia, USA.

15/10/2024- One paper accepted in The 35^th Australasian Database Conference (ADC 2024), Tokyo, Japan.

03/09/2024- One paper accepted in The 25^th International Web Information Systems Engineering Conference (WISE 2024), Doha, Qatar.

11/06/2024- One paper accepted in The 28^th European Conference on Advances in Databases and Information Systems (ADBIS 2024), Bayonne, France.

11/05/2024- One paper accepted in The International Journal of Data Science and Analytics.

26/04/2024- One paper submitted in ACM Transactions on Spatial Algorithms and Systems.

27/11/2023 One paper accepted in 39^th ACM/SIGAPP Symposium On Applied Computing (SAC 2024), Avila, Spain.

19/10/2023- One paper submitted to the International Journal of Knowledge and Information Systems (KAIS).

31/07/2023- One paper submitted in IEEE Transactions on Knowledge and Data Engineering.

22/12/2022- Awarded Travel Grant of 1200 USD from Association for the Advancement of Artificial Intelligence, USA, to attend The 37^th AAAI Conference in Artificial Intelligence.

01/11/2022- One paper accepted in 37^th AAAI Conference on Artificial Intelligence (AAAI 2023), Washington DC, USA.

16/08/2022- One paper accepted in 18^th International Conference on Advanced Data Mining and Applications(ADMA 2022), Brisbane, Australia.

Research

I'm interested in Algorithmic Optimization and Computational Social Choice, which studies collective decision-making problems from a computational lens. I enjoy thinking about issues at the interface of economics and computer science. Representative papers are highlighted.

	Group Trip Planning Query Problem with Multimodal Journey Dildar Ali Suman Banerjee, Yamuna Prasad, The 36^th International Conference on Database and Expert Systems Applications (DEXA-2025), Bangkok, Thailand. [Core Rank- B] Paper Link We study the Group Trip Planning (GTP) Query Problem with Multimodal Journey, where a group of users must visit one Point of Interest (PoI) from each required category while minimizing the total travel cost. Unlike existing work, our model jointly selects both the PoIs and transportation modes. We propose an efficient solution approach, analyze its complexity, and evaluate it on real-world datasets. Experimental results show significant reductions in travel time and cost compared to baseline methods.
	Influential slot and tag selection in billboard advertisement Dildar Ali Suman Banerjee, Yamuna Prasad, The 36^th International Conference on Database and Expert Systems Applications (DEXA-2025), Bangkok, Thailand. [Core Rank- B] Paper Link We introduce the Context-Dependent Influential Billboard Slot Selection Problem, where billboard influence varies with context. We prove the problem is NP-hard and show that the influence function is bi-monotone and bi-submodular. To solve it, we propose efficient orthant-wise stochastic, incremental, and lazy greedy algorithms with approximation guarantees. Experiments on real-world datasets demonstrate superior performance over existing methods with reasonable computational cost.
	Fairness Driven Slot Allocation Problem in Billboard Advertisement Dildar Ali Suman Banerjee, Shweta Jain, Yamuna Prasad, The 29^th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2025), Sydney, Australia. [Core Rank- A] Paper Link We introduce the Fair Billboard Slot Allocation Problem, which aims to allocate billboard slots among advertisers fairly while maximizing utility. Using the maximin fair share criterion, we develop an efficient allocation algorithm with theoretical guarantees. Experiments on real-world datasets show that our approach achieves balanced and fair allocations while maintaining high advertiser utility.
	Multi-Slot Tag Assignment Problem in Billboard Advertisement Dildar Ali Suman Banerjee, Yamuna Prasad, The 35^th Australasian Database Conference, Tokyo, Japan, 2024. [Core Rank- B] Paper Link We study the Multi-Slot Tag Assignment Problem in billboard advertising, which aims to assign tags to selected billboard slots to maximize the number of zonal influence demands satisfied. We prove the problem is NP-hard and propose an efficient approximation algorithm with theoretical analysis. Experiments on real-world datasets demonstrate that our approach outperforms baseline methods in satisfying influence demands.
	An Effective Tag Assignment Approach for Billboard Advertisement Dildar Ali, Harishchandra Kumar Suman Banerjee, Yamuna Prasad, The 25^th International Web Information Systems Engineering Conference, Doha, Qatar, 2024. [Core Rank- A] Paper Link We study the Tag Assignment Problem in Billboard Advertisement, which aims to assign product tags to billboard slots to maximize influence. We model the problem as a novel One-to-Many Bipartite Matching (OMBM) framework and propose an efficient iterative solution with theoretical analysis. Experiments on real-world datasets demonstrate the effectiveness and scalability of the proposed approach.
	Influential Billboard Slot Selection under Zonal Influence Constraint Dildar Ali, Suman Banerjee, Yamuna Prasad, The 28^th European Conference on Advances in Databases and Information Systems, Bayonne, France, 2024. [Core Rank- B] Paper Link This paper introduces the Influential Billboard Slot Selection Problem Under Zonal Influence Constraint. We propose a simple, greedy approach to solve this problem. Though this method is easy to understand and simple to implement due to the excessive number of marginal gain computations, this method is not scalable. We design a branch and bound framework with two bound estimation techniques that divide the problem into different zones and integrate the zone-specific solutions to obtain a solution for the whole. We implement both the solution methodologies with real-world billboard and trajectory datasets and several experiments have been reported. We compare the performance of the proposed solution approaches with several baseline methods. The results show that the proposed approaches lead to more effective solutions with reasonable computational overhead than the baseline methods.
	Towards Regret Free Slot Allocation in Billboard Advertisement Dildar Ali, Suman Banerjee, Yamuna Prasad, International Journal of Data Science and Analytics, 2024 [Rank- Q1]. Paper Link In this paper, we solve the Regret Minimization problem in the context of billboard advertisement and pose it as a discrete optimization problem. We propose four efficient solution approaches for this problem and analyze them to understand their time and space complexity. We implement all the solution methodologies with real-life datasets and compare the obtained results with the existing solution approaches from the literature. We observe that the proposed solutions lead to less regret while taking less computational time.
	Minimizing Regret in Billboard Advertisement under Zonal Influence Constraint Dildar Ali, Suman Banerjee, Yamuna Prasad, International Journal of Knowledge and Information Systems, 2025 [Rank- Q1]. Paper Link In this paper, we study this problem as a discrete optimization problem and propose four solution approaches. The first one selects the billboard slots from the available ones in an incremental greedy manner, and we call this method the Budget Effective Greedy approach. In the second one, we introduce randomness with the first one, where we perform the marginal gain computation for a sample of randomly chosen billboard slots. The remaining two approaches are further improvements over the second one. We analyze all the algorithms to understand their time and space complexity.
	Regret Minimization in Billboard Advertisement under Zonal Influence Constraint Dildar Ali, Suman Banerjee, Yamuna Prasad, The 39^th ACM/SIGAPP Symposium On Applied Computing, Avila, Spain, 2024. [Core Rank- B] Paper Link In a typical billboard advertisement technique, a number of digital billboards are owned by an influence provider, and several commercial houses approach the influence provider for a specific number of views of their advertisement content on a payment basis. If the influence provider provides the demanded or more influence, then he will receive the full payment or else a partial payment. In the context of an influence provider, if he provides more or less than an advertiser’s demanded influence, it is a loss for him. This is formalized as ‘Regret’, and naturally, in the context of the influence provider, the goal will be to allocate the billboard slots among the advertisers such that the total regret is minimized. In this paper, we study this problem as a discrete optimization problem and propose two solution approaches.
	Influential Billboard Slot Selection using Spatial Clustering and Pruned Submodularity Graph Dildar Ali, Suman Banerjee, Yamuna Prasad, International Journal of Data Science and Engineering, 2025 [Rank- Q1]. Paper Link In this paper, we formulate the Influential Billboard Slot Selection Problem as a discrete optimization problem and show that this problem is NP-Hard and hard to approximate within a constant factor. We propose a Pruned Submodularity Graph-based solution approach to solve this problem with its detailed analysis and illustration with a problem instance.
	Efficient Algorithms for Regret Minimization in Billboard Advertisement Dildar Ali, Ankit Kumar Bhagat, Suman Banerjee, Yamuna Prasad, The 37^th AAAI Conference on Artificial Intelligence, Washington DC, USA, 2023. [Core Rank- A^*] Paper Link Nowadays, billboard advertising has emerged as an effective outdoor advertisement technique. In this case, a commercial house approaches an influence provider for a specific number of views of their advertisement content on a payment basis. If the influence provider can satisfy this, then they will receive the full payment else a partial payment. If the influence provider provides more or less than the demand, this is certainly a loss to them. This is formalized as ‘Regret’ and the goal of the influence provider will be to minimize the ‘Regret’. This paper proposes simple and efficient solution methodologies to solve this problem. Efficiency and effectiveness have been demonstrated by experimentation.
	Influential Billboard Slot Selection using Pruned Submodularity Graph Dildar Ali, Suman Banerjee, Yamuna Prasad, 18^th International Conference on Advanced Data Mining and Applications(ADMA), Brisbane, Australia, 2022. [Core Rank- B] Paper Link In this paper, we formulate the Influential Billboard Slot Selection Problem as a discrete optimization problem and show that this problem is NP-Hard and hard to approximate within a constant factor. We propose a Pruned Submodularity Graph-based solution approach to solve this problem with its detailed analysis and illustration with a problem instance.