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Computer Vision Lab

Nikolay Falaleev

Profile

Nikolay S. Falaleev

London, United Kingdom, nikolasent@gmail.com

Head of AI, Kaggle Master

Visionary AI leader with a decade of experience translating research into commercial success. Expertise in real-time and scalable multi-modal AI solutions for precision-critical applications. Proven ability to lead cross-functional teams and deliver end-to-end project execution.

Skills & Expertise

Leadership:   AI Strategy & Vision, Technical Leadership, Cross-Functional Team Leadership, Project Management, Scaling AI Solutions, Mentoring, Research & Development
Core Expertise:   Deep Learning, Computer Vision, MLOps & AI Infrastructure, Semantic Segmentation, Object Detection & Tracking, Depth Estimation, Video Event Detection, Sensor Fusion, 3D Scene Reconstruction, Localization & Pathplanning
AI/ML Technologies:   PyTorch, TensorRT, TensorFlow, TFLite, ONNX, Scikit-learn, NumPy, MLflow, vLLM, LangChain, vector DBs
Multimedia Frameworks:   OpenCV, FFmpeg, DeepStream, GStreamer
Deployment:   gRPC, FastAPI, Docker, Cloud Infrastructure (Azure, AWS, DigitalOcean), CI/CD Pipelines
Languages:   Python, C/C++, R, SQL
DS & Analytics:   Statistical Modeling, Optimization, Numerical Methods, Data Analysis, Data Visualization
Hardware:   Industrial Cameras, Optics, LiDARs, Depth Cameras, IMUs, DL Accelerators, Sensor Fusion

Experience

10/2020 - present, Head of AI Sportlight Technology

AI-Driven Analytics for Elite Sports (EPL, SPL, NBA, NHL, and others)

  • Defined and implemented the company’s AI strategy, leading cross-functional teams in developing a real-time system that integrates multi-camera video and LIDAR data for insights through multi-modal data fusion (e.g., object detection, tracking, pose estimation, action spotting).
  • Architected and deployed an operational AI system used across all English Premier League (EPL) stadiums, the Saudi Pro League (SPL), and other venues worldwide, achieving industry-leading player tracking accuracy and reducing data QA time and costs by 12x.
  • Led the transition to real-time data processing, enabling scalability for deployments across various sports and geographies, scaling AI operations by 50x.
  • Ensured adherence to best practices in AI experimentation, software development, and data handling to foster technological excellence and support scalable commercial applications.
  • Positioned the company for future growth by introducing innovations that expanded the AI product roadmap to new markets and sports.

08/2017 - 08/2020, Leading Computer Vision R&D Constanta/OSAI

Led the research, development, and deployment of cutting-edge Computer Vision systems for real-time sports analytics, transforming data capture and analysis for tennis, basketball, billiards, and esports. Spearheaded projects that replaced human scouting with automated, highly accurate CV solutions, driving significant cost savings and enhancing data quality for betting, coaching, and broadcast applications.

  • Pioneered a real-time spatio-temporal video processing architecture, published in a CVPR 2020 paper. The system was showcased at high-profile events: Tokyo 2020 Olympics, JOOLA North American Teams Championships and National Table Tennis Championship 2020, and has been deployed in numerous venues worldwide for 24/7 commercial operation. The system is 52% faster than humans, 40% cheaper and has accuracy above 97.5%. SBJ article.
  • Architected and deployed Computer Vision modules for real-time video analysis of 100,000s of eSports tournaments monthly, including FIFA, NHL, and NBA.
  • Led the end-to-end development of a Computer Vision module for pool-9 real-time analysis, overseeing the entire project from data collection to product deployment. This system delivered significant cost savings. Video demo, Blog article [rus].

10/2014 - 07/2017 Junior Researcher Academic Research Institute

Developed and analyzed stable finite-difference schemes for modeling transient heat and mass transfer in three-dimensional geometries using C/C++. Contributed to six peer-reviewed scientific publications and presented findings at three conferences.

Selected Publications

  1. Falaleev N., Chen R. Enhancing Soccer Camera Calibration Through Keypoint Exploitation, // MMSports ‘24: Proceedings of the 7th ACM International Workshop on Multimedia Content Analysis in Sports, 2024, pp. 65-73, DOI: 10.1145/3689061.3689074, GitHub repo.

  2. Cioppa A., Giancola S., …, Falaleev N., et al. SoccerNet 2023 challenges results, // Sports Engineering, V. 27, 2024, DOI: 10.1007/s12283-024-00466-4, ArXiv:2309.06006.

  3. Voeikov R., Falaleev N., Baikulov R. TTNet: Real-time temporal and spatial video analysis of table tennis. // The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 884-885, DOI: 10.1109/CVPRW50498.2020.00450, ArXiv:2004.09927.

Google Scholar profile

ResearchGate profile

Patents

  • RU106765 “Device for producing 3D images by a single camera”. A special method for depth map calculation based on an image, produced by the device, was also developed and the gear is able to work as a range finder.

Public Talks

11/2024     London PyTorch Meetup
      Optimising Video Pipelines for Neural Network Training with PyTorch. Slides, GitHub repo.
11/2024     Computer Vision Summit London 2024
      Transforming Athlete Performance with LIDAR and Video Data. Keynote presentation, 250+ attendees.
11/2023     Computer Vision Summit London 2023
      Deploying unparalleled accuracy in athletes’ performance analysis with LIDAR and video data processing. Keynote presentation, 250+ attendees.
06/2023     CVPR, 9th International Workshop on Computer Vision in Sports
      Presentation on a DL approach to camera calibration. GitHub repo.
07/2022     GSIG Solutions Showcase: Athlete tracking: AI, computer vision, machine learning
      LIDAR and visual data fusion for athlete tracking.

Prizes and Awards

2023     SoccerNet Camera Calibration Challenge 2023, CVPR
      Top-1. DL approach to camera calibration from football broadcast videos. The challenges was held at CVPR 2023.
2018     Quick, Draw! Doodle Recognition Challenge, Kaggle
      Top-4% (50/1316). Application of deep neural networks to classification of users drawings into 300+ classes with 50M train samples
2018     TGS Salt Identification Challenge, Kaggle
      Top-0.5% (14/3234). The deep-learning based approach helps to segment seismic images which is crucial for oil and gas company drillers.
2018     Lyft Perception Challenge, Udacity
      Top-3% (4/155) and the fastest neural network pipeline. The solution is based on LinkNet34 for real-time multiclass semantic segmentation.
2018     IEEE’s Signal Processing Society - Camera Model Identification, Kaggle
      Top-3% (15/583). The solution is based on convolutional neural networks as classifiers for camera models captured a given image.
2016     Image-Based Localization Challenge, Udacity
      The 3d place. A deep learning approach based on GoogLeNet inception module was applied for “localization as classification” method for localization of an autonomous vehicle.

Education

09/2015 - 07/2017     Master of Science with Honours in Materials Science, Department of Materials Science
      Lomonosov Moscow State University (MSU), Grade: 5.0/5.0
09/2011 - 07/2015     Bachelor of Science in Materials Science, Department of Materials Science
      Lomonosov Moscow State University (MSU)

Continuing education

07/2021 - 08/2021 Oxford Machine Learning Summer School 2021
  Certificate
03/2021 - 08/2021 Intel Edge AI for IoT Developers Nanodegree
  Udacity, Certificate
01/2020 - 06/2020 C++ Nanodegree
  Udacity, Certificate
11/2016 - 10/2017 Self-Driving Car Engineer Nanodegree
  Udacity, Certificate, graduated with the first ever cohort
11/2012 - present >30 MOOCs in Computer and Data Sciences, Robotics, Computer Vision and Machine Learning
  Coursera, Udacity, edX and etc.

Clubs & Societies

Founded a non-commercial organization OniroAI (Github) for independent research and development in Computer Vision and Artificial Intelligence.

Other Publications

Nanotechnology:

  1. Eliseev Andrei A., Kumskov A.S., Falaleev N.S., Zhigalina V.S., Eliseev Artem A., Mitrofanov A.A., Petukhov D.I., Vasiliev A.L., Kisilev N.A. Mass Transport Through Defects in Graphene Layers // Journal of Physical Chemistry C, 2017, DOI: 10.1021/acs.jpcc.7b06100

  2. N. S. Falaleev, A.S. Kumskov, V.G. Zhigalina, I.I. Verbitskiy, A.L. Vasiliev, A. A. Makarova, D. V. Vyalikh, N.A. Kiselev, A.A. Eliseev. Capsulate structure effect on SWNTs doping in RbxAg1−xI@SWNT composites // CrystEngComm, 2017, DOI: 10.1039/c7ce00155j

  3. Andrei A. Eliseev, Nikolay S. Falaleev, Nikolay I. Verbitskiy, Andrei A. Volykhov, Lada V. Yashina, Andrei S. Kumskov, Victoria G. Zhigalina, Alexander L. Vasiliev, Alexey V. Lukashin, Jeremy Sloan, Nikolay A. Kiselev. Size-dependent structure relations between nanotube and encapsulated nanocrystal. // ACS Nano Letters, V. 17 I. 2, 2017, P. 805–810 DOI: 10.1021/acs.nanolett.6b04031

  4. N.A. Kiselev, A.S. Kumskov, V.G. Zhigalina, A.L. Vasiliev, J. Sloan, N.S. Falaleev, N.I. Verbitskiy, A.A. Eliseev. The structure and continuous stoichiometry change of 1DTbBrx@SWCNTs // Journal of Microscopy. V. 262, I. 1, April 2016, P. 92–101 DOI: 10.1111/jmi.12348

  5. Lukashin A.V., Falaleev N.S., Verbitskiy N.I., Volykhov A.A., Verbitskiy I.I., Yashna L.V., Kumskov A.S., Kiselev N.A., Eliseev A.A. Quasi free-standing one-dimensional nanocrystals of PbTe grown in 1.4 nm SWNTs. // Nanosystems: physics, chemistry, mathematics. V. 6, I. 6, 2015, P. 850-856 DOI: 10.17586/2220-8054-2015-6-6-850-856