• [May-Aug '24]: I will spend the summer at the Machine Intelligence, Neural Design (MIND) group at Apple (Seattle) as a Machine Learning Research Intern.
• [Dec, '23]: Our research focused on enhancing the efficiency of Tensor Program Generation is currently under review in the journal Applied Sciences.
• [May-Aug, '23]: I will be joining the Neural Engine team in the Video Engineering BU as an AI Compiler intern for the summer at Apple!
• [Summer '23]: Serving in Artifact Evaluation Committee at MICRO ’23!
• [Feb, '23]: Paper on "Transfer Learning Across Heterogeneous Features For Efficient Tensor Program Generation" has been accepted for publication at the International Workshop on Extreme Heterogeneity Solutions at PPoPP 2023.
• [Jan 13, '23]: Successfully completed my Research Proficiency ExamRPE Report!

I have had the pleasure to intern at great places with exceptional researchers:

• [Summer '23]: I interned at Apple Inc., USA and contributed to the development of hardware-aware Deep Learning training and inference solutions.
• [Summer '22]: I interned at Cadence Design Systems, USA (remote) under the supervision of Eric Stotzer on the correcness of tensor compilers using fuzzing and metamorphic mutation-based techniques.
• [Summer '21]: I interned at Argonne Leadership Computing Facility, ANL, Illinois, under the supervision of Murali Emani on HPC-FAIR - a framework to manage data and AI models for analyzing and optimizing scientific applications.
• [Summer '20]: I interned at Lawrence Livermore National Laboratory, California, under the supervision of Chunhua Liao on DataRaceBench (DRB) Suite for evaluating OpenMP correctness tools aimed to detect data races.
• [Summer '14]: I interned at Indian Statistical Institute, Kolkata, India as a JNCASR Fellow under the supervision of Prof Subhamoy Maitra on optimizing Stream Ciphers and its variants.

Before pursuing higher studies, I have spent three quality years working in various capabilities at Fidelity India.

• [July '16 - July '19]: Worked with big data technologies (Spark, Hadoop, IBM Netezza DB, Apache NiFi) and Amazon's cloud services (S3, EMR, Lambda, and Cloudwatch) to streamline the data flow pipelines.

• President: IACS Student Association, Stony Brook University, May, 2021 - Aug, 2022.
• Artifact Evaluation Committee: ASPLOS, 2022; Supercomputing, 2022
• Presenter: Supercomputing, 2020; IPDPS, 2021; Supercomputing, 2021; Computing Frontiers, 2022
• Secretary: IACS Student Association, Stony Brook University, Aug, 2020 - May, 2021.
• Organizer (Technical Support): Languages and Compilers for Parallel Computing (LCPC) Workshop, 2020
• AWS Educate Student Ambassador: May, 2020 - July, 2021.

CSE 331: Computer Security Fundamentals

Disclaimer: The following papers may have copyright restrictions. Downloads will have to adhere to these restrictions. They may not be reposted without explicit permission from the copyright holder. Any opinions, findings, and conclusions or recommendations expressed in these materials are those of the author(s) and do not necessarily reflect the views of the sponsors listed in the publications.

• Verma, Gaurav and Raskar, Siddhisanket and Xie, Zhen and Malik, Abid M and Emani, Murali and Chapman, Barbara. (2023, Feb). Transfer Learning Across Heterogeneous Features For Efficient Tensor Program Generation. In Proceedings of the 2nd International Workshop on Extreme Heterogeneity Solution.
  PDF
• Verma, G., Finviya, S., Malik, A. M., Emani, M., & Chapman, B. (2022, May). Towards neural architecture-aware exploration of compiler optimizations in a deep learning {graph} compiler. In Proceedings of the 19th ACM International Conference on Computing Frontiers [Workshop] (pp. 244-250).
  PDF
• Verma, G., Emani, M., Liao, C., Lin, P. H., Vanderbruggen, T., Shen, X., & Chapman, B. (2021, November). HPCFAIR: Enabling FAIR AI for HPC Applications. In 2021 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC) (pp. 58-68). IEEE.
  PDF
• Liao, C., Lin, P. H., Verma, G., Vanderbruggen, T., Emani, M., Nan, Z., & Shen, X. (2021, November). HPC Ontology: Towards a Unified Ontology for Managing Training Datasets and AI Models for High-Performance Computing. In 2021 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC) (pp. 69-80). IEEE.
  PDF
• Verma, G., Gupta, Y., Malik, A. M., & Chapman, B. (2021, June). Performance evaluation of deep learning compilers for edge inference. In 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) (pp. 858-865). IEEE.
  PDF
• Verma, G., Shi, Y., Liao, C., Chapman, B., & Yan, Y. (2020, November). Enhancing dataracebench for evaluating data race detection tools. In 2020 IEEE/ACM 4th International Workshop on Software Correctness for HPC Applications (Correctness) (pp. 20-30). IEEE.
  PDF
• Verma, G., Gawande, S. M., Bhura, M., & Koolagudi, S. (2016). Polygonal Meshes Predicated Watermarking Algorithm to Avert Misinterpretation of ATM Cards. Procedia Computer Science, 89, 587-596.
  PDF
• Verma, G., Nandewal, A., & Chandrasekaran, K. (2015, April). Cluster Based Routing in NDN. In 2015 12th International Conference on Information Technology-New Generations (pp. 296-301). IEEE.
  PDF
• Ketankumar, D. C., Verma, G., & Chandrasekaran, K. (2015). A green mechanism design approach to automate resource procurement in cloud. Procedia Computer Science, 54, 108-117.
  PDF