Distributed Algorithm for High-Utility Subgraph Pattern Mining Over Big Data Platforms. - Alind Khare; Vikram Goyal; Srikanth Baride; Sushil K. Prasad; Michael McDermott; Dhara Shah (HiPC, 2017)

HCudaBLAST: an implementation of BLAST on Hadoop and Cuda. - Nilay Khare; Alind Khare; Farhan Khan (J. Big Data, 2017)

A Simple Dynamic Learning Rate Tuning Algorithm For Automated Training of DNNs. - Koyel Mukherjee; Alind Khare; Ashish Verma 001 (CoRR, 2019)

HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units. - Shenda Hong; Yanbo Xu; Alind Khare; Satria Priambada; Kevin O. Maher; Alaa Aljiffry; Jimeng Sun 001; Alexey Tumanov (KDD, 2020)

CompOFA - Compound Once-For-All Networks for Faster Multi-Platform Deployment. - Manas Sahni; Shreya Varshini; Alind Khare; Alexey Tumanov (ICLR, 2021)

UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification. - Yanbo Xu; Alind Khare; Glenn Matlin; Monish Ramadoss; Rishikesan Kamaleswaran; Chao Zhang; Alexey Tumanov (NeurIPS, 2022)

Subgraph Stationary Hardware-Software Inference Co-Design. - Payman Behnam; Alexey Tumanov; Tushar Krishna; Pranav Gadikar; Yangyu Chen; Jianming Tong; Yue Pan; Abhimanyu Rajeshkumar Bambhaniya; Alind Khare (MLSys, 2023)

SuperFed: Weight Shared Federated Learning. - Alind Khare; Animesh Agrawal; Myungjin Lee; Alexey Tumanov (CoRR, 2023)

ABKD: Graph Neural Network Compression with Attention-Based Knowledge Distillation. - Anshul Ahluwalia; Rohit Das; Payman Behnam; Alind Khare; Pan Li 005; Alexey Tumanov (CoRR, 2023)

Hardware-Software Co-Design for Real-Time Latency-Accuracy Navigation in Tiny Machine Learning Applications. - Payman Behnam; Jianming Tong; Alind Khare; Yangyu Chen; Yue Pan; Pranav Gadikar; Abhimanyu Bambhaniya; Tushar Krishna; Alexey Tumanov (IEEE Micro, 2023)

DεpS: Delayed ε-Shrinking for Faster Once-for-All Training. - Aditya Annavajjala; Alind Khare; Animesh Agrawal; Igor Fedorov; Hugo Latapie; Myungjin Lee; Alexey Tumanov (ECCV (89), 2024)

SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-device Inference. - Alind Khare; Animesh Agrawal; Aditya Annavajjala; Payman Behnam; Myungjin Lee; Hugo Latapie; Alexey Tumanov (ECCV (79), 2024)

DεS: Delayed ε-Shrinking for Faster Once-For-All Training. - Aditya Annavajjala; Alind Khare; Animesh Agrawal; Igor Fedorov; Hugo Latapie; Myungjin Lee; Alexey Tumanov (CoRR, 2024)

SuperServe: Fine-Grained Inference Serving for Unpredictable Workloads. - Alind Khare; Dhruv Garg; Sukrit Kalra; Snigdha Grandhi; Ion Stoica; Alexey Tumanov (NSDI, 2025)

∇QDARTS: Quantization as an Elastic Dimension to Differentiable NAS. - Payman Behnam; Uday Kamal; Sanjana Vijay Ganesh; Zhaoyi Li; Michael Jurado; Alind Khare; Igor Fedorov; Gaowen Liu; Alexey Tumanov (Trans. Mach. Learn. Res., 2025)

Efficient AI Stack: Deployment-Aware Neural Architecture Search and Serving of Deep Neural Networks. - Alind Khare (2025)

Towards Efficient Large Multimodal Model Serving. - Haoran Qiu; Anish Biswas; Zihan Zhao; Jayashree Mohan; Alind Khare; Esha Choukse; Íñigo Goiri; Zeyu Zhang 005; Haiying Shen; Chetan Bansal; Ramachandran Ramjee; Rodrigo Fonseca (CoRR, 2025)

Toward Weight Sharing Paradigm for Efficient AI: Training and Inference Serving. - Payman Behnam; Alind Khare; Dhruv Garg; Alexey Tumanov (ACM SIGOPS Oper. Syst. Rev., 2025)