Kushal Mukherjee
Papers
2
Total Citations
58
H-Index
2
About
Kushal Mukherjee is a researcher whose work bridges pattern recognition and intelligent automation, with a particular focus on advancing IT operations. His early foundational research introduced a novel approach to pattern classification by combining wavelet-based feature extraction with probabilistic finite state automata—a method that has garnered 55 citations and remains influential in machine learning and signal processing communities. This work demonstrated how complex, high-dimensional data could be effectively reduced and modeled for accurate classification tasks. More recently, Mukherjee has turned his attention to the evolving landscape of process automation, specifically addressing the challenges of knowledge-intensive workflows in IT operations. His 2023 paper on hybrid automation proposes a pragmatic framework where conversational interfaces enable seamless collaboration between human operators and automated bots. This work tackles the critical problem of bootstrapping automation in environments where full end-to-end automation is impractical, offering a scalable pathway for enterprises to integrate AI into complex operational tasks. By focusing on the intersection of human expertise and machine efficiency, Mukherjee’s research provides actionable insights for designing resilient, adaptive automation systems—a contribution that is increasingly vital as organizations seek to balance productivity gains with the nuanced demands of real-world IT environments.
Research Focus
Key Achievements
Top Papers
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