Welcome to my website!

Educations:

• Ph.D. in. Electrical Engineering at the University of Southern California (current program)

• Master in Computer Science at the University of Southern California

• Master in Electrical Engineering at Sharif University of Technology

• Bachelor in Electrical Engineering at the University of Tehran

Current Research Interest:

• Knowledge-graph Infused LLMs

• Graph Neural Networks (GNN)

• Reinforcement Learning (RL)

• Federated Learning (FL)  

• Large Language Models (LLM)

• Natural Language Processing (NLP)

• Distributed Computing

Previous Research Interest:

• Blockchain

• Optimization for Network Problems

• Covid-19 Risk Estimation through Mathematical and Data-Driven Modeling

• Coded Distributed Computing

Achievements:

• Technical Committee Member of the 2nd Graph Neural Networking (GNNet) Workshop 2023

• Invited Paper at IEEE International Conference on Collaboration and Internet Computing (CIC) 2023

• The 1st Place Winner of the 2nd Student Design Competition on Networked Computing on the Edge - 2022

• 2022 Best Poster - Honorable Mentioned by Ming Hsieh Department of Electrical and Computer Engineering

• Winner-2020 International Covid-19 Computational Challenge-City of Los Angeles & RMDS Lab (licensed by USC Stevens Center for Innovation)

• Nominee Best Paper Award and Best Student Paper Award - ACM Supercomputing Conference (SC’19)

• Best Paper Award - IEEE Globecom Conference, 2017

• Gold Medalist - Iranian nationwide electrical engineering Olympiad

• Ranked 1st among all undergraduate electrical engineering students in the Telecommunication Department, University of Tehran

Selected Papers:

• M. Kiamari and B. Krishnamachari, “Graph-Knowledge-Infused Vision-Language Models,” to be submitted to Journal of Machine Learning Research (JMLR) 2024

• M. Kiamari, Y. Zhao, and B. Krishnamachari, “New low-cost auto-encoder Graph Neural Networks,” to be submitted to Journal of Machine Learning Research (JMLR) 2024

• M. Kiamari, B. Krishnamachari, and S. Yun, “Blizzard: a Distributed Consensus Protocol for Mobile Devices," MDPI Journal on Mathematics, Feb. 2024

• X. Li, M. Karakas, O. Hanna, M. Kiamari, J. Coleman, C. Fragouli, B. Krishnamachari and G. Verma, “Online Allocation of Sensing and Computation in Large Graphs,” invited paper at 9th IEEE International Conference on Collaboration and Internet Computing (CIC), Atlanta, USA, 2023

• M. Kiamari and B. Krishnamachari ,"Gcnscheduler: Scheduling distributed computing applications using graph convolutional networks", Proceedings of the International Graph Neural Networking, December 2022, Rome, Italy 

• M. Kiamari, GS Ramachandran, Q Nguyen, E. Pereira, and J. Holm, B Krishnamachari “COVID-19 Risk Estimation using a Time-varying SIR-model,” Proceedings of the 1st ACM SIGSPATIAL International Workshop on Modeling and Understanding the Spread of COVID-19, Seattle WA USA, November 2020 

• M. Kiamari, B. Krishnamachari, M. Naveed, and S. Yun, “Distributed Consensus for Mobile Devices using Online Brokers,” in Proceedings of IEEE International Conference on Blockchain and Cryptocurrency (ICBC) 2020, Toronto, Canada, May 2020

• K. Narra, Z. Lin, M. Kiamari, S. Avestimehr, M. Annavaram, “Distributed Computing Using Speed Adaptive Coding,” International Conference for High Performance Computing, Networking, Storage, and Analysis 2019 (nominated for both Best Student Paper and Best Paper finalist)

• M. Kiamari, C. Wang, and S. Avestimehr, “On Heterogeneous Coded Distributed Computing,” in Proceedings of Globecom Conference 2017, December 2017 (Received Best Paper Award)

• M. Kiamari, C. Wang, and S. Avestimehr, “SINR-Threshold Scheduling with Binary Power Control for D2D network,” in Proceedings of Globecom Conference 2017, December 2017 

• M. Kiamari, C. Wang, and S. Avestimehr, “Coding for Edge-Facilitated Wireless Distributed Computing with Heterogeneous Users,” Asilomar Conference on Signals, Systems, and Computers, October 2017

Work Experiences:

• Graduate Research Assistant                                       Present

University of Southern California                                       Los Angeles,  CA

• Internship                                                                   May. 2016 – Aug. 2016

Docomo Innovations, Inc.                                                     Palo Alto, CA

Professional Services:

• IEEE Transaction on Information Theory 

• IEEE Transaction on Communications 

• IEEE Transaction on Networking 

• IEEE Globecom Conference 

• IEEE International Symposium on Information Theory 

Teaching Assistant Experiences:

• EE503: Probability for Electrical Engineering, USC

• EE559: Pattern Recognition, USC

• EE660: Machine Learning from Signal, USC

• EE559: Pattern Recognition, USC

• EE512: Stochastic Processes for Financial Engineering, USC

• EE512: Stochastic Processes for Financial Engineering, USC

Selected Graduate Courses:

• Fundamental Concepts of Analysis

• Database Systems

• Introduction to Mathematical Statistics

• Deep Learning and its Applications

• Stochastic Calculus and Mathematical Finance

• Applied Natural Language Processing 

• Large-Scale Optimization for Machine Learning 

• Machine Learning 

• Mathematical Pattern Recognition 

• High-Dimensional Statistics and Big Data Problems 

• Foundation of Artificial Intelligence

• Analysis of the Algorithm

• Information Theory and Compression 

• Random Processes in Engineering

• Probability for Electrical Engineering 

• Numerical Optimization 

• Advanced Communications

Selected Course Projects:

• Citation Prediction for NeurIPS Papers using Natural Language Processing (NLP)

  • Collecting the title, authors & their bios, and abstract of all the published NeurIPS papers

  • Applied NLP algorithms (spaCy for NER, NLTK, TextBlob, RAKE for extracting keywords) to extract authors & their affiliations, keywords of the title and abstract of the papers as features for a classification task

  • Used a variety of models from the Scikit-learn package to build classifiers with over 90% accuracy

• Memory-Efficient Convolutional Neural Network (CNN) Acceleration

  • Implemented ADMM-based optimization methods for both unstructured and structured pruning on convolutional neural networks, such as LeNet-5, AlexNet, and VGG16. Utilized different compressing schemes to encode the pruned model (sparse weight matrices) to reduce the amount of data that must be moved throughout the memory hierarchy

• Deep Reinforcement Learning (Deep RL) with Tensorflow

  • Implemented the REINFORCE algorithm: rollout storage, policy network and training loop

  • Tested RL on various environments with engineering reward function as well as implementing Actor-Critic architecture

• Deep Neural Network (Deep NN) with Tensorflow

  • Implemented the forward and backward passes as well as the NN training procedure for CNNs, RNNs, and GANs

Presentation:

• GCNScheduler (the first GCN-based scheduling scheme), Research Festival at Ming Hsieh Department of Electrical and Computer Engineering - Received 2022 Best Poster - Honorable Mentioned