Intro

I’m a Research Scientist with a strong technical background in HW/SW co-design and Edge AI. In summary I’m:

and I have:

4+ years experience as Research Scientist (industrial)
1+ years experience as Data Scientist and Research Assistant (academic & industrial)
2 years experience as FPGA Engineer/RTL Designer (industrial)

Experience

Consult with clients to develop and implement distributed, edge, and federated machine learning solutions
Provide expertise in machine learning model optimization and deployment
Communicate technical concepts to non-technical stakeholders
Collaborate with cross-functional teams to ensure successful implementation of machine learning solutions
Stay up-to-date with industry trends in machine learning and best practices

Research on Federated Learning optimization led to SoTA performance via drift elimination
Research on Transfer Learning led to a filed patent
Research on multiple Meta Learning and Few-shot Learning projects
Research on Multi-hypothesis Transfer Learning and out of distribution generalization
Collaborated with R&D team in designing an AI library for Imagia research
Collaborated with IT in porting Polyaxon on a cluster of NVIDIA DGX systems

Research on predicting human behaviour from fMRI data
Developing a CNN framework for detecting corrosion in aircrafts using D-Sight technology (DAIS)
Optimizing calculation of minimum distance to shore from AIS-GIS streaming data using CUDA and OpenMP
Research on sparsity, activation functions and normalization

Design & Imp. of TDM switches on FPGAs supporting up to 16k x 16k channels (in VHDL)
Multi-channel I2C master controller supporting 16 modules with error checking & correction
SPI & USART Peripheral interfaces
Embedded Processors, RTOS
Focus on speed optimization on Altera Cyclone series

Design & Imp. of lightweight AES modules used in STM4 lines
Multi-channel I2C master controller supporting 16 modules with error checking & correction.
SPI & USART Peripheral interfaces.
Focus on area optimization on Xilinx Virtex 4, 6 series

Co-instructor, ML for Big Data, CSCI-6515, Dalhousie University, Fall 2020
Teacher Assistant, ML for Big Data, CSCI-6515, Dalhousie University, Fall 2018
Teacher Assistant, Digital Circuits, ECED-2200, Dalhousie University, Winter 2016
Teacher Assistant, System Analysis, ECED-3401, Dalhousie University, Fall 2016
Instructor, Computer Architecture, Chehelsotoon Inst. for Higher Edu, Fall 2015
Instructor, System Programming, Chehelsotoon Inst. for Higher Edu, Fall 2015
Teacher Assistant, Java Programming, University of Guilan, Winter 2009
Teacher Assistant, Algorithms, University of Guilan, Winter 2010

Varno, Farshid, Marzie Saghayi, Laya Rafiee, Sharut Gupta, Stan Matwin, and Mohammad Havaei. “Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation.” European Conference on Computer Vision. – ECCV (2022).
Varno, Farshid, Lucas May Petry, Lisa Di Jorio, and Stan Matwin. “Learn Faster and Forget Slower via Fast and Stable Task Adaptation.” arXiv preprint arXiv:2007.01388 (2020).
Varno, Farshid, Behrouz Haji Soleimani, Marzie Saghayi, Lisa Di Jorio, and Stan Matwin. “Efficient neural task adaptation by maximum entropy initialization.” arXiv preprint arXiv:1905.10698 (2019).
Jiang, Xiang, Mohammad Havaei, Farshid Varno, Gabriel Chartrand, Nicolas Chapados, and Stan Matwin. “Learning to learn with conditional class dependencies.” In international conference on learning representations. – ICLR (2018).
Saghayi, Marzie, Jonathan Greenberg, Christopher O’Grady, Farshid Varno, Muhammad Ali Hashmi, Bethany Bracken, Stan Matwin, Sara W. Lazar, and Javeria Ali Hashmi. “Brain network topology predicts participant adherence to mental training programs.” Network Neuroscience 4, no. 3 (2020): 528-555.

Varno, Farsheed, Behrouz Haji Soleimani, Marzie Saghayi, Lisa Di Jorio, and Stan Matwin. Method and system for initializing a neural network. https://patents.google.com/patent/WO2020225772A1. _ EP WO CA CN_ (2020)

Vice-president of Public Relations, Toastmasters International, Dal Toastmasters, 2020.
Experienced leading teams of 2-3 researchers during several projects.
Mentored two masters students, now working as Senior Data Scientists in USA & Brazil.
Reviewer at International Conference on Computer Vision (ICCV 2023, upcoming).
Reviewer at Computer Vision and Pattern Recognition (CVPR 2023, 5 papers).
Reviewer at European Conference on Computer Vision (ECCV 2022, 2 papers).
Reviewer at the 2nd FedVision Workshop (FedVision 2023, 2 papers).
Selected conference program committee member & volunteering experience:
- International Conference on Learning Representations (ICLR, Remote 2020)
- SIGKDD Conference on Knowledge Discovery and Data Mining (KDD, Halifax 2017).
- Confoo (Montreal, 2023).