Faculty
Brief Bio
Deepak Nadig is currently an Assistant Professor in the Department of Computer and Information Technology (CIT) at Purdue University. He is also the Director of the Cloud-native, Cyberinfrastructure and Networks (CYAN) Lab at Purdue CIT. He received his Ph.D. in Computer Engineering from the University of Nebraska-Lincoln (UNL) in 2021. Deepak was the Director of Technology and Research at SOLUTT Corporation, India, from 2009 to 2015. At SOLUTT, he was instrumental in leading the networking and wireless operations and responsible for the design, development, consulting and training services in 4G/LTE and multi-gigabit wireless technologies. He was an IEEE-certified Wireless Communications Professional (IEEE WCP®) from 2014 to 2019.
He has served on the technical program committees of several conferences and as a reviewer for leading journals such as IEEE/ACM Transactions on Networking, IEEE Journal on Selected Areas in Communications (IEEE JSAC), Computer Networks, IEEE INFOCOM, IEEE Globecom, IEEE/ACM International Symposium on Quality of Service (IWQoS), IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN) and IEEE Sarnoff Symposium. He has published in several leading IEEE/ACM conferences and journals. He is a member of the IEEE, IEEE Communication Society and ACM.
Prof. Nadig was the recipient of the 2017 IEEE ANTS best paper award, the 2019 Milton E. Mohr Fellowship, and a fellow of the Preparing Future Faculty (PFF) program at UNL. His research interests are computer networks, cloud-native infrastructure, software- defined and programmable networks, network virtualization, AI/ML applications for networking and network security. Purdue University has supported his research.
Graduate
Appal Badi
Appal is a Doctor of Technology (D.Tech) student at Purdue University. He is the Head of Data Products at Silicon Valley Bank, CA. He has an MBA from the University of Illinois at Urbana-Champaign, a graduate certificate in cloud computing from Austin University and a Bachelor’s in computer science from India. He has over 20 years of experience in various roles, including leadership positions in Information Technology and Data Science in different companies across the globe. He has extensive experience building and managing complex data warehouses, data governance and cloud platforms to support internal/external reporting and analytics needs for organizations. He also worked on process optimization for organizations using various analytical tools. His research interests are in data and cloud observability.
Ayush Shridhar
Ayush is a Master’s student in Computer and Information Technology at Purdue University. His research interests are in Cloud-native MLOps and Cloud Autoscaling.
Publications
1. | Shridhar, Ayush; Nadig, Deepak: Heuristic-based Resource Allocation for Cloud-native Machine Learning Workloads. In: 2022 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), pp. 415-418, 2022, ISSN: 2153-1684. (Type: Proceedings Article | Abstract | Links | BibTeX)@inproceedings{10227727, As machine learning workloads become computationally demanding, there is an increased focus on distributed machine learning to train and deploy models across multiple machines in a cloud-native cluster. However, optimizing a machine learning model’s lifecycle to facilitate efficient resource utilization is still an active area of research. The approach typically involves a manual effort to partition the models into distinct layers and decide how to store these distinct layers on a distributed computing framework. However, distributing distinct layers across nodes can induce a network latency bottleneck in the machine learning pipeline. Further, the above process becomes more inefficient as models become increasingly complex. In this paper, we present a heuristic-based approach to distributed model training. Further, we analyze the resource utilization metrics from a sample machine learning pipeline deployed on a KubeFlow MLOps framework testbed. |
Isham Jitender Mahajan
Isham Mahajan is a Master’s student in Computer and Information Technology at Purdue University. He has a Bachelor’s degree in Information Technology from the Manipal Institute of Technology, India. He was a Graduate Research Assistant at Purdue’s Rosen Center for Advanced Computing (RCAC), where he automated and optimized JupyterHub deployments on Kubernetes. Isham’s research interests lie in the economic aspect of cloud technology, specifically in reducing deployment costs by fine-tuning container resources within Kubernetes. He is also passionate about creating efficient, cost-effective solutions for cloud application developers.
Karumuri Meher Hasanth
Hasanth is a Master’s student in Computers and Information Technology at Purdue University. His research interests are cloud-native network service mesh (NSM) and NSM performance optimization. Hasanth was an intern at Samsung, where he worked on DevOps pipelines. Before joining Purdue, he worked as a Site Reliability Engineer at ADP, acquiring skills in system reliability, availability, and performance enhancement. His current expertise is DevOps, Kubernetes, cloud technologies (AWS), and Python automation. He has a Bachelor’s degree in Electronics and Communication Engineering from Vellore Institute of Technology, India.
Shoaib Basu
Shoaib is a Master’s student in Computer and Information Technology at Purdue University. His research interests are in Cloud Accelerators and network offloading.