Dr. Supreeth Mysore

Highly skilled and experienced AI-ML EngIneer with a Ph.D. In Computer Science (around 8+ years experience), seeking a position that requires expertise in C++ and Python programming, parallel programming, and strong knowledge of Computer Architecture and Operating Systems.

RESEARCH INTEREST: Hardware modelIng Bayesian statistics. PPA. Data Analysis MachIne LearnIng. Computer architecture. LInear Algebra PROFESSIONAL SKILLS Highly strategic and Inquisitive Senior AI-ML EngIneer and Data Scientist with a Ph.D. In Computer Science from the University of Kentucky, possessing a broad range of experience in data-centered projects across various industries with hardware architecture. Demonstrated ability to leverage advanced statistical techniques to obtain actionable insights from data. Proficient in Python, PySpark, Hive datasets, SQL, NoSQL, and Machine Learning principles, as well as experience in cloud environments such as AWS. Performance tuning and debugging experience and skills. Strong C/C++/Python programming skills Proficient in Python, PySpark, Hive datasets, SQL, NoSQL, and Machine Learning principles, as well as experience in cloud environments such as AWS such as Sagemaker Excellent computer vision or machine learning skills Excellent programming skills with C++ Excellent 2D and 3D computer vision Strong oral/written communication. Detail-oriented team player with strong organizational skills. Ability to handle multiple projects simultaneously with a high degree of accuracy.

• AGNI-Iso latency, in memory DRAM based StoB generator for in-deep learning”
• ISQUED 2023 (Best Papernominated Finalist)
• ATRIA: A Bit-Parallel Stochastic Arithmetic Based Accelerator for In-DRAM CNN Processing”
• ISVLSI 2021(Best Paper nominated Finalist)
• ODIN: A Bit-Parallel Stochastic Arithmetic Based Acceleratorfor In-Situ Neural Network Processing in Phase
• Change RAM”
• ARXIV 2021
• A Scalable Stochastic Number Generator for Phase Change Memory based in-memory stochastic processing”,
• 2019 IEEE, ESWEEK, NY
• Presented Conference paper in Synopsys Conference SNUGS 2017 (Synopsys Users Group) “At speed
• Noise Fixing with Next Generation Tools” dated 12th June 2017
• Presented Conference paper at Ansys conference (AESE 2017) “Faster PDN Convergence Methodology for
• Large Designs” dated on 9th August 2017
• ACADEMIC PHD
• PROJECT
• Stochastic in memory compute architecture for deep learning application Period:- Jan’19 - present
• Low power adiabatic High Speed hardware Cryptographic circuit Period:- Aug18-Jan’19
• ACADEMIC P.G
• PROJECT
• Power-aware Design Methodology Front End Validation Tool Kit Period: - Oct’14-Jun’15
• Sense amplified Gated Differential Adiabatic clock distribution Period: - Jun’14-Oct’14
• Self-Gated Resonant energy recovery flip-flop Period: - Jan’14-May’14
• Low Power Energy Recovery Clock Distribution and Generation Period: - Jul’13-Jan’14
• MINI P.G PROJECT
• TCAD 3D-CMOS simulation and analysis at 45nm node (GF) Period: - Jun’13 – Aug’13
• Tunnel FET Drain Current characterization and simulation Period: - Jun’14- Aug’14
• 16-bit processor CISC architecture creation from RTL to GDSII validation using Cadence CAD tool
• Period: - Jan’14 – Mar’14
• U.G
• PROJECT
• Arm Based mobile as Medical Aid using Android Period: -Aug’11-Apr’12
• MINI U.G
• PROJECT
• C8051 based wireless message via mobile handset with LCD annunciation system
• Period:-Feb’09-Apr’09,
• Python Learning and mathematical modelling (2021)
• Data Analysis (Coursera 2021)
• Udacity
• Deep Learning for AI (nano degree 2019)
• AI algorithm using python (nano degree 2019)
• Courserao Machine Learning (Stanford university)o Deep learning concepts(deep learning.ai)o Bayestain Statistics (University of Santa Cruz)o DataScience (University of Michigan)
• Cleanroom Workshop and Basic concepts in IIT, Chennai, India
• EDX online courseso Nanotechnology (courtesy of edx courses)o Graphene analysis (Courtesy of edx courses)
• Coursera online course on: -o VLSI CAD: Logic to Layout (University of Illinois)o Learning How to Learn: Powerful mental tools to help you master tough subjects
• (University of
• California)o Python Interactive scripting course (University of Michigan)o R language for Data mining (Courtesy of IBM)
• Alison Online Course on: -o Interactive Perl Lectures
• Verification Academy (Courtesy Mentor Graphics): -o Power-Aware verificationo SOC Metricso Basic OVM conceptso System Verilog Assertiono Testbench Accelerator
• Udemy academyo Soc verification using system Verilog assertion
• O Learnt in-depth study using system Verilog assertion and system Verilog coverage
• O Testbench writing for ovm/uvm from Scratcho UPF 2.0 (Courtesy of Acellera.org)