Dildar Ali (দিলদার আলী)

I am a Ph.D. Research Scholar being advised by Dr. Suman Banerjee and Dr. Yamuna Prasad in the CSE Department at Indian Institute of Technology Jammu, India since January, 2022. I primarily work in Submodular Functions, Algorithms and Economics, Data Management, and Game Theory.

I completed my Master of Technology in Computer Science and Engineering from Aliah University (A State University), Kolkata, India, in 2020. I have secured 1st rank in graduation and 2nd rank in master in the university final examination. I have also worked as a Project Associate at Indian Institute of Technology Bhilai, India under the supervision of Prof. Rajat Moona (Director, IIT Gandhinagar) and Dr. Dhiman Saha (Assistant Professor, IIT Bhilai) on a MeitY-funded research project in collaboration with C-DAC Noida.

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News

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

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

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

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

11/06/2024- One paper accepted in The 28th 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 39th 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 37th AAAI Conference in Artificial Intelligence.

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

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

Fairness Driven Slot Allocation Problem in Billboard Advertisement
Dildar Ali Suman Banerjee, Shweta Jain, Yamuna Prasad, The 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2025), Sydney, Australia. [Core Rank- A]
Paper Link

In billboard advertisement, a number of digital billboards are owned by an influence provider, and several commercial houses (which we call advertisers) approach the influence provider for a specific number of views of their advertisement content on a payment basis. Though the billboard slot allocation problem has been studied in the literature, this problem still needs to be addressed from a fairness point of view. In this paper, we introduce the Fair Billboard Slot Allocation Problem, where the objective is to allocate a given set of billboard slots among a group of advertisers based on their demands fairly and efficiently. As fairness criteria, we consider the maximin fair share, which ensures that each advertiser will receive a subset of slots that maximizes the minimum share for all the advertisers. We have proposed a solution approach that generates an allocation and provides an approximate maximum fair share. The proposed methodology has been analyzed to understand its time and space requirements and a performance guarantee. It has been implemented with real-world trajectory and billboard datasets, and the results have been reported. The results show that the proposed approach leads to a balanced allocation by satisfying the maximin fairness criteria. At the same time, it maximizes the utility of advertisers.

Multi-Slot Tag Assignment Problem in Billboard Advertisement
Dildar Ali Suman Banerjee, Yamuna Prasad, The 35th Australasian Database Conference, Tokyo, Japan, 2024. [Core Rank- B]
Paper Link

Nowadays, billboard advertising has emerged as an effective advertising technique due to higher returns on investment. Given a set of selected slots and tags, how to effectively assign the tags to the slots remains an important question. In this paper, we study the problem of assigning tags to the slots such that the number of tags for which influence demand of each zone is satisfied gets maximized. Formally, we call this problem the Multi-Slot Tag Assignment Problem. The input to the problem is a geographical region partitioned into several zones, a set of selected tags and slots, a trajectory, a billboard database, and the influence demand for every tag for each zone. The task here is to find out the assignment of tags to the slots, such the number of tags for which the zonal influence demand is satisfied is maximized. We show that the problem is NP-hard, and we propose an efficient approximation algorithm to solve this problem. A time and space complexity analysis of the proposed methodology has been done. The proposed methodology has been implemented with real-life datasets, and a number of experiments have been carried out to show the effectiveness and efficiency of the proposed approach. The obtained results have been compared with the baseline methods, and we observe that the proposed approach leads to a number of tags whose zonal influence demand is satisfied.

An Effective Tag Assignment Approach for Billboard Advertisement
Dildar Ali, Harishchandra Kumar Suman Banerjee, Yamuna Prasad, The 25th International Web Information Systems Engineering Conference, Doha, Qatar, 2024. [Core Rank- A]
Paper Link

Billboard Advertisement has gained popularity due to its significant outrage in return on investment. To make this advertisement approach more effective, the relevant information about the product needs to be reached to the relevant set of people. This can be achieved if the relevant set of tags can be mapped to the correct slots. Formally, we call this problem the Tag Assignment Problem in Billboard Advertisement. Given trajectory, billboard database, and a set of selected billboard slots and tags, this problem asks to output a mapping of selected tags to the selected slots so that the influence is maximized. We model this as a variant of traditional bipartite matching called One-To-Many Bipartite Matching (OMBM). Unlike traditional bipartite matching, a tag can be assigned to only one slot; in the OMBM, a tag can be assigned to multiple slots while the vice versa can not happen. We propose an iterative solution approach that incrementally allocates the tags to the slots. The proposed methodology has been explained with an illustrated example. A complexity analysis of the proposed solution approach has also been conducted. The experimental results on real-world trajectory and billboard datasets prove our claim on the effectiveness and efficiency of the proposed solution.

Influential Billboard Slot Selection under Zonal Influence Constraint
Dildar Ali, Suman Banerjee, Yamuna Prasad, The 28th 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, "Towards Regret Free Slot Allocation in Billboard Advertisement", International Journal of Data Science and Analytics, 2024 [Impact Factor- 3.4].
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 (KAIS), 2023 (Submitted).
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 39th 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, ACM Transactions on Spatial Algorithms and Systems (Submitted).
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 37th 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, 18th 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.

Teaching Assistantship

1. Machine Learning (CSL774), Instructor: Dr. Shaifu Gupta

2. Database Management System (CSL362), Instructor: Dr. Suman Banerjee

3. Design and Analysis of Algorithms (CSC006P1M), Instructor: Dr. Harkeerat Kaur

4. Theory of Computation (CSL019U3M), Instructor: Dr. Suman Banerjee

5. Discrete Mathematical Structures (CS-203), Instructor: Dr. Sumit Kumar Pandey

Student Guided

1. Atharva Sanjay Tekawade, Under-Graduate Research Program, IIT Jammu (Pursuing MS at Georgia Tech, Georgia, USA)

2. Ankit Kumar Bhagat, Rise-Up Intern, University of Delhi

3. Tejash Gupta, Rise-Up Intern, NIT Hamirpur

4. Harishchandra Kumar, Rise-Up Intern, NIT Raipur

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