Position: AI/ML engineer, Core ML researcher
Category: Data Science
English level: fluent
Years of experience: 3

Educational Accomplishments:
- Graduated Summa Cum Laude with GPA 4.44/4.5 (rank 1/115 students)
- Class valedictorian
- Best Student award
- President award
- Top Academic Excellence Award
- Global Vision Scholarship (South Korea)
- Master's degree CGPA: 4.5/4.5
- Obtained Ph.D. in computer science and engineering

Professional Accomplishment:
- AI-Connect contest winner
- Winner of ML contest by National IT promotion agency (NIPA)
- ACM-ICPC winner

Work accomplishment:
- reduced CNN computational and memory cost by 21-30% 
- achieved 2.8x CNN training speed-up with 6-10x fewer parameters
- designed a new face liveness detection model to prevent spoof attacks
- optimized ML system pipeline to reduce each user request (1000+ per minute) under 400ms on CPU for iOS, Android, and web services
** AI researcher at DeltaX, Seoul, Korea
  •  Led team of engineers for the X-Cabin project (computer vision-based solution for in-cabin control system)
  •  Developed hand gesture recognition solution for a futuristic hologram-based car infotainment system.
  •  Developed left-item detection, seat-belt detection, driver monitoring system, and occupant monitoring system using in-cabin fisheye camera and deployed the solution to low-power embedded devices.

** Head ML Engineer at UZINFOCOM LLC
  •  Designed and trained DL-based liveness detection models to prevent face spoof attacks using Keras, PyTorch, Tensorflow, OpenCV
 •  Implemented a new face spoof artifact detection model and converted Keras and PyTorch models to ONNX runtime
 •  Designed system’s overall workflow and built a central data processing pipeline: face detection, blurriness detection, head pose estimation, artifact filter, occlusion detection, liveness detection, and face recognition
 •  Optimized vision backend pipeline to reduce each user request (1000+ per minute) under 400ms on CPU.

** Core ML Researcher at Intelligent Mobile Lab
 •  Researched and led various ML projects and provided end-to-end solutions by modeling, experimenting, designing, optimizing, and troubleshooting new and state-of-the-art methods; developed novel learning algorithms for CNN and DBN models
 •  Authored and co-authored prestigious SCI(E) journal publications
 •  Optimized large solutions and pruned large networks to improve inference time and memory costs and deployed them onto embedded devices with limited computational resources, e.g., Jetson Nano, AGX, Raspberry Pi (pruning, ONNX, ROS, TensorTR)

** Head AI Engineer at ITMED LLC - Ministry of Health of the Republic of Uzbekistan
 •  Developed DL models for breast cancer diagnosis and implemented the system for Radiology-Oncology PACS and RIS systems
 •  Built APIs and workflow to get DICOM data from SYNAPSE, then process, inference, and prepare diagnosis results for Radiologists

My technical skills: proficient in Python, Tensorflow, Keras, Pytorch, ONNX, OpenCV, ROS, GitLab, Linux; previously used C++, MATLAB

Soft skills: project management, teamwork, technical writing, task organization, meeting deadlines, problem solving
Friendly environment and a fantastic team