The Journey of Shivan Mukherjee: A Rising Star in AI and Machine Learning from Columbia University
Shivan Mukherjee, a bright and innovative computer engineering student at Columbia University, is already making significant strides in the fields of artificial intelligence (AI) and machine learning (ML). With hands-on experience at some of the most renowned companies in the world, including Apple, IBM, and Alix, Mukherjee is shaping up to be a key player in advancing AI technologies. In this article, we will delve into his educational journey, internships, and achievements, including his recent work on high-performance machine learning models and software engineering.
Early Life and Education
Shivan Mukherjee’s academic journey began at Columbia University, where he is currently pursuing a Bachelor’s degree in Computer Engineering, with a minor in Operations Research. His dedication to academics is reflected in his place on the Dean’s List for two consecutive years (2021, 2022). Along with his studies, Mukherjee actively participates in extracurricular activities, including his involvement in the Columbia Organization for Rising Entrepreneurs and the IEEE. His technical coursework spans a range of subjects including Discrete Math, Data Structures in Java, and Probability for Engineers.
Research and Academic Projects
Shivan’s academic pursuits go beyond coursework. He worked as a research assistant in the Columbia Wireless and Mobile Networking Lab, where he collected and analysed 5G mmWave data in a city-scale testbed in West Harlem. This research aimed at exploring indoor-to-outdoor wireless measurements to improve network performance. His experience with technical writing, software-defined radio, and wireless networking has equipped him with the skills needed to tackle complex technical challenges.
Internships: Gaining Real-World Experience
Shivan Mukherjee has honed his skills and knowledge through various internships that have allowed him to gain hands-on experience in software and hardware development, AI engineering, and machine learning research.
Apple – Software Engineering Intern
In the summer of 2024, Mukherjee interned at Apple in San Diego, California, where he was involved in backend system development for Connectivity Engineering. This experience exposed him to cutting-edge technology and provided him with an opportunity to contribute to projects that enhance Apple’s connectivity solutions.
ANAFLASH Inc – Machine Learning Research Intern
Mukherjee also interned as a Machine Learning Research Intern at ANAFLASH Inc., where he worked on neural network model compression and optimisation for edge devices. This role required him to optimise models so that they could run efficiently on devices with limited resources, which is a crucial aspect of deploying AI in real-world applications.
Alix – AI Engineering
Currently, Mukherjee is working as an AI Engineering Intern at Alix, where he is continuing his exploration of AI and machine learning. His responsibilities include developing and optimising algorithms, working on innovative projects that push the boundaries of AI technology. His work at Alix allows him to contribute to the AI landscape while further enhancing his expertise in this rapidly evolving field.
IBM – Machine Learning Researcher
In addition to his internship at Alix, Mukherjee is working part-time at IBM as a Machine Learning Researcher. His focus is on optimising transformer model architectures for long context lengths, an area of particular importance in NLP and other machine learning tasks that require processing long sequences of data. This work contributes to IBM’s efforts in advancing AI models to handle more complex data.
Notable Achievements and Recognition
Shivan Mukherjee’s achievements are not just limited to his internships. He has received multiple awards throughout his academic career, underscoring his talent and commitment to excellence. Some of his key awards include:
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Altice Excellence in Technology Innovator Award (2021): A recognition of his technical innovation and contributions to the field of technology.
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FIRST Global Innovation Challenge Finalist (2021): Shivan was selected as one of 20 teams globally out of 883 teams, showcasing his ability to think creatively and solve complex problems.
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National Merit Finalist (2020): Recognised as one of the top 0.5% of PSAT scorers per state, marking his academic excellence.
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Presidential Volunteer Service Award – Gold (2020): Recognising his contributions to community service.
His experience and accolades demonstrate his strong work ethic, exceptional intellect, and dedication to the field of computer engineering.
Shivan Mukherjee and High-Performance Machine Learning at Columbia
One of the areas where Shivan has made significant contributions is in the realm of high-performance machine learning (HPML). As a student at Columbia University, he is actively engaged in learning and applying advanced concepts of HPML, a field that deals with optimising machine learning algorithms for large-scale, high-speed applications.
What is High-Performance Machine Learning?
HPML refers to the use of high-performance computing (HPC) resources to speed up the training and inference of machine learning models. This involves techniques such as model compression, pruning, quantisation, and distributed training, which help scale models and improve their efficiency.
Mukherjee’s Work on Transformer Models
Shivan’s work as a machine learning researcher at IBM and his focus on optimising transformer model architectures for long context lengths are directly related to HPML. Transformer models, widely used in natural language processing (NLP), require efficient training techniques to handle long sequences of data without compromising performance. Mukherjee’s contributions to this area have the potential to make these models more efficient and scalable, allowing them to process vast amounts of data in real-time.
Future Outlook and Career Goals
With his solid foundation in AI and machine learning, Shivan Mukherjee is on track to become a leading figure in the tech industry. His interest in high-performance machine learning, combined with his internship experience at top tech companies, positions him well to contribute to the development of innovative AI solutions. Moving forward, Shivan plans to continue expanding his knowledge in AI engineering and machine learning research, with the goal of driving advancements in both academia and industry.
Conclusion
Shivan Mukherjee’s journey is a testament to his passion for technology, innovation, and problem-solving. From his academic achievements at Columbia University to his impactful internships at Apple, IBM, and Alix, he is shaping up to be an influential force in the fields of AI and machine learning. His work in high-performance machine learning, particularly with transformer models, demonstrates his ability to apply cutting-edge technologies to solve real-world problems. As he continues to learn and grow, Shivan’s future in AI is undoubtedly bright.
In conclusion, Shivan Mukherjee’s career path illustrates the power of combining academic excellence with hands-on experience. His contributions to the field of AI are making a real difference, and his work on high-performance machine learning is helping to push the boundaries of what is possible in AI. With his dedication, talent, and drive, Shivan is set to make significant strides in the technology world for years to come.
