Possessing an R&D background enables me to keep track of emerging trends that continue to evolve due to algorithmic breakthroughs, hardware advancements, technological commoditization, and the availability of new data. I am always eager to work on advanced technologies that provide key organizational capabilities required to maintain a competitive edge.

My recent focus has largely been on applied machine learning, even though i am been keeping an eye on progress in theoretical realm. Theoretical Machine learning is full of fascinating open problems like generalization, intelligent exploration vs exploitation, counter-factual evaluation, Meta-Learning and Sample Efficient Learning. Progress in solving these problems can have a major impact on application domains like industrial automation, healthcare, and education. The decision-making side of machine learning has been relatively neglected but its equally important. It involves individual high-stake decisions, decisions in the context of multiple decision makers, explanations for decisions and dialog about decisions.

Research Projects 🔭

My Research projects align with my interest in studying decision-making under uncertainty from a computational perspective and my work was published in top-tier conferences. Specifically, I explored complex problems such as utilizing Memory Augmented Neural Networks to generate recommendations that aid businesses in their operational process execution. Additionally, I developed a Clinical Decision Support System for Sepsis Management using Deep Reinforcement Learning. One significant challenge in this field is data privacy, particularly in sensitive domains, which hinders research and limits access to datasets. In one of my papers, I addressed this issue by applying Federated Learning and Differential Privacy techniques to extract process models from geographically distributed process logs.

Process Analytics

AI will play a key role in operational and strategic Business decision-making. I have written a survey paper, providing a broad overview of AI's impact on supporting and improving business processes.

A few of my projects were aimed at building tools that provide decision support at operational and strategic levels during the execution of knowledge-intensive processes.

  • At the operational level, AI can provide recommendations to support business process executions. Deep Learning architectures like LSTMs, MANNs and Transformers provide methods for modeling sequential data problems. In DeepProcess: project, we use Memory Augmented Neural Networks for Predictive Process Monitoring. see [Paper] [Blog_post]
  • At the Strategic level, AI can help firms come up with robust plans. We explore this in the Alpha-GS project: Alpha-GS - Decision making in adversarial settings using Game tree Search Combined With satisfiability(SAT) [Blog Post]
Healthcare and Decision Support Systems:

The digitization of healthcare data, coupled with algorithmic breakthroughs in AI, is poised to significantly impact healthcare delivery in the coming years. I am particularly interested in the application of AI to assist clinicians in patient treatment while preserving privacy of their sensitive data. Although scientific knowledge can guide interventions, there is a crucial need to swiftly navigate the space of decision-making policies to identify effective strategies that support patients throughout the care process.

  • In MIMIC-RL I investigated the problem of developing a Clinical Decision Support System for Sepsis Management using Deep Reinforcement Learning. In our implementation, we sought to answer the following question: Given a patient's specific characteristics and physiological information at each time step as input, can our proposed framework learn an optimal treatment policy that prescribes the appropriate intervention (e.g., use of a ventilator) at each stage of the treatment process, in order to improve the final outcome (e.g., patient mortality)? see [Blog_post] for more details.
  • Federated Learning: Medical data is often geographically dispersed and distributed, and privacy concerns can prevent the construction of a centralized data warehouse. By employing techniques such as federated learning and differential privacy, we can extract process models from distributed healthcare process logs. In this paper, we explore the framework of federated learning in the context of distributed process mining.

Language Understanding and Search:

"80% of all information created today is unstructured (free text with little structural explanation). In 2017 alone the information created was expected to be greater than the previous 5000 years combined. In addition to sheer volume, the rate of information creation is accelerating rapidly, 10x per year by 2025, which is also the year each human is expected to interact with connected devices nearly 5000 times each day."

Recent advances in large language models (LLMs) present an opportunity to reimagine AI systems, using language as a medium for facilitating human-AI interaction. For instance, providing high-quality answers to medical queries necessitates an understanding of the medical context, recall of pertinent medical knowledge, and reasoning with expert information. I have been involved in various academic and industry projects related to information retrieval and knowledge discovery:

Great scientists tolerate ambiguity very well. They believe the theory enough to go ahead; they doubt it enough to notice the errors and faults so they can step forward and create the new replacement theory. If you believe too much you'll never notice the flaws; if you doubt too much you won't get started. It requires a lovely balance. - Richard Hamming