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CM Murad advises graduates to lead with empathy, integrity, social responsibility
No full textSindh Chief Minister Syed Murad Ali Shah, Patron of IBA Karachi, presided over the convocation for the Class of 2024, celebrating the graduates\u27 achievements and resilience. He highlighted IBA\u27s upcoming 70th anniversary, marking seven decades of excellence in education and leadership. The CM emphasized the institute\u27s academic rigor, with 67% of its faculty holding PhDs, and its focus on preparing graduates for advancements in AI, data science, and digital transformation.
Shah recognized IBA\u27s alumni network of over 18,000 as role models for graduates and urged them to lead with empathy, integrity, and social responsibility. He called on graduates to use their knowledge to create positive change and contribute to Pakistan\u27s development. The event also set the stage for IBA’s 70th-anniversary celebrations, reflecting on its lasting impact on education and leadership
Resilient Reinforcement Learning with Reward Shaping
No full textThis research enhances the robustness of reward-shaping-based reinforcement learning agents against adversarial attacks by investigating a critical vulnerability in the process of reward function generation and deploying a targeted defense mechanism to mitigate this weakness. Reinforcement learning agents are increasingly deployed in critical real-world scenarios where data integrity cannot be guaranteed, making their robustness against adversarial attacks essential for reliable performance. In reward shaping, a common and efficient approach is to learn a reward function from user feedback on sample data. However, this process is vulnerable to adversarial attacks, as ensuring the integrity of the feedback is challenging. Malicious actors can intentionally provide incorrect feedback to corrupt the learned policy. Existing research lacks a comprehensive understanding of the impact of such attacks, and the current methods for reward function design are not robust against data poisoning attacks.
In this work, we first explore how an effective attack mechanism can be designed by injecting noisy data into user feedback provided to the reinforcement learning agent. Secondly, we develop a defense mechanism based on the K-Nearest Neighbors (KNN) algorithm, which protects the reward function learning process from noisy data. Our experiments involved generating an Oracle agent that always provides correct feedback, simulating a perfect user. Subsequently, we systematically corrupted the feedback from the oracle to simulate an attack. The experiments covered scenarios both with and without the reward function and included varying levels of noise in the training data. The Mountain Car domain was used as a testbed.
The results demonstrated that the learned reward function significantly improved the agent\u27s performance. However, as noise levels in the training data increased, the agent\u27s performance degraded, highlighting the impact of data quality in efficient policy learning. Furthermore, the KNN-based defense mechanism detected noisy data with high accuracy across different noise levels, as indicated by a consistently low number of noisy data points predicted as clean. Our findings underscore the importance of analyzing potential vulnerabilities in the reinforcement learning process. Moreover, a straightforward
technique like KNN can effectively detect and mitigate noisy data, further improving the system\u27s robustness
Deep Reinforcement Learning Applications in Stock Trading
No full textThe equity market is characterized by its inherent volatility and unpredictability, yet it is governed by underlying patterns and structures. The most successful traders are ones who have managed to accumulate small wins over a period, rather than investing in a single stock that rose tremendously. However, in today’s fast-moving economy, it is more difficult for us to place our bets on a 20+ year investment horizon, and it would be more beneficial to the investors to have multiple wins during that period.
Through this study our aim is to leverage algorithms that use various methods of Deep Reinforcement Learning to identify the dynamics of stock movement and attempt to optimize them by altering different parameters with the hope of improving their performance and understanding the sensitivity of each model’s performance to those parameters. We aim to train our model in a manner wherein it learns a medium-term strategy rather than keeping an investment horizon of multiple decades.
To achieve this, we used the stock data from the Dow Jones 30 Index to train our model, using each stock’s closing price and a handful of technical indicators. The data was then used to train our Agent through different models, such as the Advantage Actor-Critic (A2C), the Proximal Policy Optimization (PPO), the Deep Deterministic Policy Gradient (DDPG), the Soft Actor-Critic (SAC), and the Twin Delay Deep Deterministic Policy Gradient (TD3) models, whose training was curtailed to a finite period for each episode. Our objective is to identify the optimal episodic length to achieve the best results.
Through our experiments, we managed to chart the performance of various trained models against the different parameters used to train them. We noticed that clipping the investment horizon to a shorter period resulted in more waning results, whereas longer training episodes led to far more stable and positive results
Maximizing Profitability through Assortment Analysis & Search Algorithm Enhancement
No full textThe project was conducted to provide the data science team with a data set that has an SKU level expected profit margin so that it can be incorporated in their search & recommendations algorithm to show people all those items that are more profitable for Daraz and thus can help the Daraz in achieving profitability. Daraz is currently on the course of achieving positive unit economics and has been working on multiple projects that can help the company to make this a reality. The company already has access to world-class tools and resources which include its search and algorithm team which has developed algorithms to fuel its research and they keep on making it better with the power of data. We undertook this project keeping in mind the focus of the company this year, we identified that besides the changes that Daraz is making business-wise, there should be something that the Data Science team can do from its point of view to help the organization achieve this feat and hence the idea for this project came into existence
HR SUITE - AI Powered Job Application and Automation Systems
No full textThe recruitment process is an integral and resource-intensive aspect of every organization, but with advancements in technology, it has become possible to automate many of the manual tasks involved. This report presents a comprehensive solution for automating job application processing by utilizing Artificial Intelligence (AI) and Optical Character Recognition (OCR) technologies. The proposed solution analyzes and processes resumes, assesses candidate qualifications based on job descriptions, and provides recruiters with a suitability rating to streamline decision-making.
The system integrates OCR for parsing PDF resumes, AI for evaluating qualifications, and role-based access to ensure the recruitment process remains efficient, secure, and data-driven. The solution automates key tasks such as resume parsing, qualification analysis, and suitability evaluation, which minimizes the workload of HR teams and enhances the overall candidate experience.
By providing an automated and scalable approach to recruitment, this project aims to assist organizations in improving hiring efficiency, reducing manual errors, and enhancing data-driven decision-making. The report outlines the architecture of the system, describes its core component
The Jamatkhana and its role in Social Cohesion: A case study of the Ismaili Community in Karachi
No full textIn Pakistan, the field of development studies has slowly begun to recognize the importance of initiating holistic developmental interventions. The significance of social and cultural development is beginning to attract the attention of local and international development agencies in the country. In this context, building social capital is significant for society’s mental and physical well-being, increased standards of living, political goodwill as well as reduced inclination towards violence, crime, and drug use.
This thesis utilises prevalent literature on social cohesion to primarily inquire: How do Jamatkhanas Contribute to Social Cohesion in the Ismaili Community in Karachi? It establishes how this cohesion contributes towards social connections, the formation of a social identity, the support garnered from the community during crisis and, the resilience built against social challenges. This paper fills a gap in social cohesion literature in Pakistan by providing a novel understanding of the experiences of the Ismaili youth in Karachi, one that is pre-disposed to isolation and alienation in a metropolitan city and yet seems to possess characteristics, on account of their shared community identity, which reduce the effect of said isolation.
As hypothesized, the thesis shows a generally positive relationship between social cohesion and reduced feelings of mental distress, isolation, and reduced inclination towards social evils. Respondents in the study also report greater levels of companionship, deeper social connections, and indicate well-built social work and solidarity networks
Alumni Excellence Award Ceremony at IBA University
No full textThe Institute of Business Administration (IBA), Karachi, hosted the IBA Alumni Excellence Awards to honour nine members of its alumni community for their professional achievements and contributions to the community
Enhancing Economic Competitiveness
No full textPakistan will celebrate its centenary in 2047, just twenty-five years from today. To achieve its aspiration of becoming an upper-middle-income country by then, the nation must accelerate and sustain growth at a rate of 6-8 percent annually. While Pakistan’s actual performance was at 6 percent in the first forty years (1950–1990), it has since slipped to 4 percent in the last thirty years. Therefore, to resume the trajectory of higher growth, Pakistan must enhance productivity in both industry and agriculture, adopt an outward-looking strategy, actively participate in international trade, and attract foreign direct investment. History shows that no country has achieved prosperity by relying solely on its domestic markets. The spectacular success of China, despite having a large market of 1.4 billion people, is primarily attributed to its integration into the world economy. Within a mere three decades, China has risen from almost zero to become the world’s top exporting nation. To increase its share in the global market, Pakistan has to become competitive, surpassing other countries in pricing, quality, reliability, and timely delivery of goods and services in demand. This chapter, therefore, focuses on the essential elements that would enhance Pakistan’s competitiveness in the global market, allowing it to recapture lost market share, accelerate economic growth, provide jobs for its youth, and reduce its excessive dependence on external borrowing. This approach aims to break Pakistan’s repeated cycle of entering external financial crises and subsequently resorting to the IMF and friendly countries for bailouts
Green Initiatives in Pakistani Universities: Nexus among Human Resource Practices, Employee Environment Behavior, and Innovative Work with Mediating Role of Work Engagement in Pakistani Universities
No full textThis study aims to develop a model based on social exchange theory (SET) to elucidate the influence of green human resource management (GHRM) on employee on-the-job, off-the-job, and green innovative work behaviors (GIWB). Drawing from the job demand resource model and SET, the study proposes that green work engagement (GWE) mediates the relationship between GHRM work behaviors. A self-administered survey was conducted among 168 employees within higher education institutions in Pakistan to gather data. The primary statistical analysis employed partial least squares structural equation modeling to test the research hypotheses. The results indicate that GHRM significantly predicts employees\u27 green behaviors both at work and away from work, as well as their GIWB. Furthermore, the study demonstrates that GWE plays a significant mediating role in explaining the relationship between GHRM and the aforementioned work behaviors. The findings offer valuable insights for policymakers in higher education institutions on the potential of GHRM to enhance employees\u27 environmental performance. This study contributes to the understanding of GHRM by expanding its scope and addressing knowledge gaps, particularly in higher education settings. Moreover, it highlights the role of GWE as a mediator in improving GIWB through GHRM initiatives
Mock Interviews
No full texthttps://ir.iba.edu.pk/career-development-center-gallery/1033/thumbnail.jp