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Date: September 27, 2022
Time: 05:00 pm, IST
Venue: CSE Seminar Room-104

Kshitij Gajjar

Dr. Kshitij Gajjar

IIT Jodhpur


Title of the talk: How to store a graph?

Abstract: How does one store a graph in the database of a computer? Typically, the vertices are labelled by the set: {1, 2, 3, ..., n}. The edges can be denoted in several different ways: adjacency matrix, incidence matrix, adjacency list. But, what if the vertices are labelled in a somewhat more creative way, so that the labels of the vertices themselves denote their adjacencies? This entirely eliminates all the need for storing the edges! This topic is part of a heavily researched field called graph labelling, with connections to coding theory and information theory. In this talk we will explore a type of graph labelling known as sum labelling. This is based on joint work with Henning Fernau (University of Trier, Germany). Our paper can be accessed using the link:


Title of the talk: Perspectives on AI Ethics

Abstract: In recent years, we have witnessed widespread adoption of AI tools across various sectors, making them capable of impacting lives. Today AI-powered tools aid critical decision-making in policy, law and order, recruiting, healthcare and education. However, they also bring the massive risk of making wrong decisions, which can turn one’s life upside down, as we have seen with COMPASS in the US judicial system and the A-level grading fiasco in the UK’s education system. Implementation of AI and ML tools without much deliberation on its ethical and social impact has resulted in unfair outcomes, often resulting from algorithmic and data biases. In this talk, I will introduce the concept of AI Ethics, why it is essential and the gaps in the present-day AI Ethics discourse. The talk will also examine the notion of AI Ethics from a social and cultural perspective.


Title of the talk: Compression of Deep Learning Models for NLP

Abstract: RNNs and LSTMs have been used for quite some time for various NLP tasks. But these models are large especially because of the input and output embedding parameters. In the past three-four years, the field of NLP has made significant progress thanks to Transformer based models like BERT, GPT, T5, etc. But these models are humongous in size. Real-world applications however demand a small model size, low response times, and low computational power wattage. In this talk, I will discuss four different types of methods for compression of such models for text, in order to enable their deployment in real industry NLP applications and projects. The four types of methods include pruning, quantization, knowledge distillation, and other Transformer based methods.


Title of the talk: Deep Learning case-studies: Recommender systems for online shopping and Task-oriented Natural Language Generation system

Abstract: In this talk, I present some of my past work that uses deep learning in two completely different contexts: One being recommender systems for online shopping and the other is on task-oriented chat-bots. Deep Learning has seen a wide range of applications of late in both academia and industry. Through these two case studies, we showcase the versatile nature of deep learning in solving complex business problems in two very different domains that are currently trending in the industry - online recommendations and task-oriented chat bots. We also discuss some practical performance metrics and trade-offs that need to be considered for scaling solutions in both the case studies.


Title of the talk: Computational Mechanism Design for Social Decisions

Abstract: Artificial Intelligence deals with machines that take smart decisions. For a large spectrum of decision problems, particularly when the decision involves multiple self-interested agents, such intelligence is beyond the scope of 'learning from data'. In this talk, I am going to address 'strategic multi agent systems' from a game theoretic viewpoint -- a tool used in mathematics and microeconomics to analyze the behavior of rational and intelligent agents -- and show how robust AI systems can be built with such agents. In the process, I will discuss two problems of social importance, namely, social distancing and peer grading, where computational mechanism design approaches are useful. The tools developed based on these ideas will also be briefly presented.


Title of the talk: Parameterized Algorithms for Model Counting

Abstract: Propositional model counting problem (#SAT) is a generalization of SAT, where the aim is to compute the number of satisfying assignments of a formula φ. Model counting is #P-complete even for 2CNF, although checking satisfiability of 2CNF can be done in polynomial time. We consider #SAT parameterized by the treewidth (tw) of the primal graph of the input CNF formula. The best-known algorithm runs in time 2^tw n^O(1), where n is the number of variables. One of the main challenges is whether we can have a faster algorithm, even if we allow a (multiplicative) approximation. In this talk we will see a lower bound on the running time of such algorithms assuming Strong Exponential Time Hypothesis (SETH). For monotone formulas, given a tree decomposition of width w for the primal graph of the input formula, for any \epsilon> 0, we will see a 2^{(1−\epsilon)w} n^O(1)-time 2^{\epsilon n}-approximation algorithm.


Title of the talk: Merely Fun with Algorithms

Abstract: In this talk, we will design and analyze "cool" (beautiful and efficient) algorithms for simple problems that are of recreational nature. Emphasis will be given on fundamental concepts in an interactive manner. Afterall the goal is to have fun!!


Title of the talk: Intelligent Occupant sensing in Car Interiors

Abstract: Traditional occupant sensing methods employ physical sensors and buttons to detect and react to explicit driver/passenger requests in a passenger vehicle. We explore a computer vision based approach to understand implicit requests from the occupants. Our Interior Assist system uses images captured from an in-car camera sensor that are processed by a tiny deep neural network to react to the dynamic scenarios within the passenger vehicle.
In this talk we will specifically focus on how gesture recognition can be used for occupant sensing in car interiors to enhance user experience and comfort, what are the challenges faced and some future research directions.


Title of the talk: Matching under preferences: Stability to popularity

Abstract: Matching under preferences is a research area that finds numerous applications in practice as diverse as assigning students to colleges, workers to firms, kidney donors to recipients, users to servers in a distributed internet service, to just name a few. Indeed this topic is at the heart of the intersection between Computer Science and Economics, and has been recognized as such by a Nobel Memorial prize in Economics. In the first part of the talk, I will discuss canonical problems in this area, namely, stable matching and its generalization, popular matching. In the second part of the talk, I will present a result that resolved the arguably main open problem in the subarea of popular matching: In an arbitrary graph, deciding if a popular matching exists is NP-complete.


Title of the talk: Towards Precision Oncology using Machine Learning on Medical Images

Abstract: Current approach to cancer treatment is moving away from a "carpet bombing" to a "surgical strike." This means that each patient's cancer is profiled for specific molecular or genomic characteristics to prescribe targeted therapies. The cost of these tests remain unaffordable by a vast majority of the population in low-income countries, such as India. On the other hand, general pathology and radiology has become ubiquitous even in tier 3 cities. By using the latest advances in machine learning or artificial intelligence, our research group develops and validates tests that can utilize inexpensive medical imaging modalities to analyze subtle visual patterns of various subtypes of cancer to aid precision medicine. In this talk we will give a few examples of successful results and the process that was required to get there.


Title of the talk: Data-efficient Machine Learning for the Diagnosis of Chest Radiographs

Abstract: Modern deep learning methods are data-hungry. While such methods may require millions of annotated training data, humans can learn new concepts with only a few annotated examples. To mimic this incredible cognitive ability of human beings, different methods for data-efficient machine learning have been proposed in recent years. Few-shot learning and Zero-shot learning are two of the most promising avenues among these methods. In the last few years, several few-shot and zero-shot learning methods have been designed for problems related to natural images. However, such methods are relatively rare in the field of radiology diagnosis. In this webinar, we will present several approaches for few-shot and zero-shot diagnosis of chest radiographs. Our methods show promising results on publicly available chest x-ray datasets.


Title of the talk: Transparent, Trustworthy and Privacy-Preserving Supply Chains

Abstract: Over the years, supply chains have evolved from a few regional traders to globally complex chains of trade. Consequently, supply chain management systems have become heavily dependent on digitisation for the purpose of data storage and traceability of goods. However, current approaches suffer from issues such as scattering of information across multiple silos, susceptibility of erroneous or untrustworthy data, inability to accurately capture physical events associated with the movement of goods and protection of trade secrets. Our work aims to address above mentioned challenges related to traceability, scalability, trustworthiness and privacy. To support traceability and provenance, a consortium blockchain based framework, ProductChain, is proposed which provides an immutable audit trail of product's supply chain events and its origin. The framework also presents a sharded network model to meet the scalability needs of complex supply chains. Next, we address the issue of trust associated with the qualities of the commodities and the entities logging data on the blockchain through an extensible framework, TrustChain. TrustChain tracks interactions among supply chain entities and dynamically assigns trust and reputation scores to commodities and traders using smart contracts. For protecting trade secrets, we propose a privacy-preservation framework PrivChain, which allows traders to keep trade related information private and rather return computations or proofs on data to support provenance and traceability claims. The traders are in turn incentivised for providing such proofs. A different privacy-preservation approach for decoupling the identities of traders is explored in TradeChain by managing two ledgers: one for managing decentralised identities and another for recording supply chain events. The information from two ledgers is then collated using access tokens provided by the data owners, i.e. traders themselves.


Title of the talk: Powering the Next Era of Analytics and AI with GPU

Abstract: Artificial Intelligence has played a key role from predicting, minimizing and stalling Pandemic outbreak such as Coronavirus to making autonomous vehicles a reality. The world of computing is going through an incredible change. With deep learning and AI, computers are learning to write their own software. Learn how Deep learning relies on GPU eco system helping the research and data science community to solve use cases in domains like Healthcare, Autonomous Driving, Financial and many more.


Title of the talk: What Is Software Engineering Anyway? Reflections on 50 Years of Software Engineering and the Road Ahead!

Abstract: This talk discusses the nature of software engineering from multiple perspectives, its historical contributions, and presents a future perspective based on current research trends. What can Software Engineering do for the design of AI/ML systems? Are they energy efficient?


Title of the talk: Explainable AI (XAI) using Nonlinear Decision Trees

Abstract: For many years, practitioners have been interested in solving optimization and AI related problems to find a single acceptable solution. With the advent of efficient methodologies and human quest for knowledge, optimization and AI systems are now embedded with existing knowledge or modified to extract essential knowledge with which they solve problems. Efforts to explain AI systems with interpretable rules are also getting attention. In this talk, we present knowledge-driven optimization and explainable AI applications using a novel nonlinear decision tree approach in solving a variety of practical problems.


Title of the talk: Decision-making in the face of uncertainty

Abstract: Future is about a large complex system of systems that need to operate in an increasingly dynamic environment where the changes cannot be deduced a-priori. Typically, a complex system of systems is understood in terms of its various parts and interactions between them. Moreover, this understanding is typically partial and uncertain from which the overall system behavior emerges over time. With the overall system behavior hard to know a-priori and conventional techniques for system-wide analysis either lacking in rigor or defeated by the scale of the problem, the current practice often exclusively relies on human expertise for analysis and synthesis leading to decision-making. This is a time-, effort-, cost- and intellect-intensive endeavor. The talk will present an approach aimed at overcoming these limitations and also illustrate its efficacy on a few representative real-world problems.


Title of the talk: Brain Variable Reward Structure for Cooperative Machine Learning in IoT Network

Abstract: Recent advances in machine learning research have resulted in state-of-the-art techniques where the Reinforcement Learning (RL) agents are focused on either using value-based methods or policy-based methods with the goal of reducing variance in the reward signal, thereby trying to reach an optimal state in the shortest period. Metrics such as the number of iterations taken to reach optimal reward structure or the number of interactions needed with the environment to achieve this are generally used as key performance indicators. There is a large body of research work that shows how the agents can achieve this using either large amounts of training data or using complex algorithms that require power and resource-intensive computational elements. But such a strategy may not be applicable for resource and power-sensitive network of IoT devices and more importantly differs fundamentally from how humans learn. To overcome this challenge, we have been looking at the field of neuroscience to derive inspiration from how the human brain works, specifically towards the release of dopamine in response to variable reward structure. Typical RL systems focus on receiving observations from the environment, calculating reward, and then deciding on the next set of actions at fixed intervals or based on fixed responses. However, scientific research on human brain activity has shown higher activity in dopamine release in response to rewards received at variable times. In this presentation, findings on two particularly interesting areas in neuroscience and psychology are presented, one is related todopamine-based reward-stimulated learning which supports the concept of cooperative learning. It has been shown that the active dopamine release activity will be available to increase the processing of new information. Second, is related to the study that has found that cooperative groups generate more participation and stimulate multiple brain regions. In such an environment, the efficiency of the network increases dramatically. We will discuss how RL techniques can learn from this behavior, especially in an IoT system that may contain several nodes. Rather than expecting agents running on all the nodes at fixed time intervals, our research investigates the efficiency gain by invoking agents at different time instances, thereby providing them with an opportunity to receive reward signals. Just like variable reward structure results in increased dopamine activity in human brains, such an approach can help achieve higher efficiency in IoT systems.


Title of the talk: From Smart-Sensing to Smart Living

Abstract: We live in an era in which our physical and personal environments are becoming increasingly intertwined and smarter due to the advent of pervasive sensing, wireless communications, computing, control and actuation technologies. Indeed, our daily lives in smart cities and connected communities depend on a wide variety of cyber-physical infrastructures, such as smart city, smart energy, smart transportation, smart healthcare, smart manufacturing, etc. Alongside, the availability of wireless sensors, Internet of Things (IoT) and rich mobile devices are empowering humans with fine-grained information and opinion collection through crowdsensing about events of interest, resulting in actionable inferences and decisions. This synergy has led to cyber-physical-social (CPS) convergence with human in the loop that exhibits complex interactions, inter-dependence and adaptations between the engineered/natural systems and human users with a goal to improve human quality of life and experience in smart living environments. However, huge challenges are posed by the scale, heterogeneity, big data, social dynamics, and resource limitations in sensors, IoT and CPS networks. This talk will highlight unique research challenges in smart living , followed by novel frameworks and models for efficient mobility management, data gathering and fusion, security and trustworthiness, and trade-off between energy and information quality in multi-modal context recognition. Case studies and experimental results from smart energy and smart healthcare applications will be presented. The talk will be concluded with directions of future research.


Title of the talk: What would make an intelligent system generally intelligent?

Abstract: Intelligence may be understood as the ability of a system to construct models, usually in the service of solving problems or controlling behavior. Some problems are general enough to require a unified model of the environment of the system, including the intelligent system itself, and the nature and results of its interactions. While many complex organisms implement such models, comparatively little AI research has been dedicated to them. What types of models and algorithms can support such a degree of generality? Can we identify design principles of biological and social systems that we can transfer into AI systems?


Title of the talk: Participatory Budgeting - Making Budgeting Great Again

Abstract: Participatory Budgeting is a grassroots, direct-democracy approach to deciding upon the usage of public funds, most usually in the context of the yearly budget of a municipality. I will discuss this concept, provide some appropriate mathematical models for it, and will concentrate on related algorithmic considerations, most notably novel aggregation methods and generalizations such as incorporations of liquid democracy, project interactions, and the possibility of deciding on hierarchical budgets.


Title of the talk: Trustworthy AI Systems.

Abstract: We are experiencing unprecedented growth in AI in recent years. Coupled with it, we have also seen simple adversarial attacks and bias in AI systems. In this talk, we will look at how to build trustworthy AI systems to prevent and thwart attacks on AI systems and protect AI models. Specifically, we will discuss threat models extended to AI systems for adversarial attacks and their mitigation, bias compensation, and taking advantage of advances in blockchain technology and fully homomorphic encryption (FHE) in building AI systems to make them trustworthy.


Title of the talk: Multi-view invariance and grouping for self-supervised learning

Abstract: In this talk I will present our recent efforts in learning representation learning that can benefit semantic downstream tasks. Our methods build on two simple yet powerful insights - 1) The representation must be stable under different data augmentations or "views" of the data; 2) The representation must group together instances that co-occur in different views or modalities. I will show that these two insights can be applied to weakly supervised and self-supervised learning, to image, video, and audio data to learn highly performant representations. For example, these representations outperform weakly supervised representations trained on billions of images or millions of videos; can outperform ImageNet supervised pretraining on a variety of downstream tasks; and have led to state-of-the-art results on multiple benchmarks. These methods build upon prior work in clustering and contrastive methods for representation learning. I will conclude the talk by presenting shortcomings of our work and some preliminary thoughts on how they may be addressed.


Title of the talk: Picking Random Vertices

Abstract: We survey some recent graph algorithms that are based on picking a vertex at random and declaring it to be a part of the solution. This simple idea has been deployed to obtain state-of-the-art parameterized, exact exponential time, and approximation algorithms for a number of problems, such as Feedback Vertex Set and 3-Hitting Set. We will also discuss a recent 2-approximation algorithm for Feedback Vertex Set in Tournaments that is based on picking a vertex at random and declaring it to /not/ be part of the solution.


Title of the talk: “Why do we need to Optimize Deep Learning Models?”

Abstract: Designing deep learning-based solutions is becoming a race for training deeper models with a greater number of layers. While a large-size deeper model could provide competitive accuracy, it creates a lot of logistical challenges and unreasonable resource requirements during development and deployment. This has been one of the key reasons for deep learning models not being excessively used in various production environments, especially in edge devices. There is an immediate requirement for optimizing and compressing these deep learning models, to enable on-device intelligence. In this talk, I will talk about the different deep learning model optimization techniques and the challenges involved in production ready model optimization.