I’m an experienced Manager of Software and Data Science (Lead 15+ Developers Simultaneously)

Management and Leadership:

☑ I have a B.A. Degree in Business Managment. Also I have a LL.B Degree in Law. I’m an Authorized Israeli Lawyer but I’m not practicing.

☑ I’m an Autodidact. I can take a project from inception to delivery. I can Analyze. Identify. Provide Structured and automated algorithmic solutions for business startegy needs to enhance company growth

☑ I consider myself a strong communicator and influencer and cross department collaborator

☑ I have set successful Protocols. Workflows. Rules. Standards for efficient company process-oriented work  

☑  I am a Strategic thinker. Recognizing opportunities. Defining solutions. Executing strategies that drive business growth 

 ☑ I Build high-performing teams. Provide guidance. Mentorship. Recognize and Reward achievements

 

 

 

I’m proficient in the following sciences: Mathematics,  Physics,  Computer Science, Statistics

Mathematics:

Final Israel Open University Grades: Statistics: 96 ,Discrete Math: 96  ,Differential And Integrable Math: 82 , Linear Algebra: 91 

  • Proof by induction, Proof by contradiction.

  • Linear Algebra – Vector-Matrix Linear Transformation,Pre-image of sets,subspaces,subsets, ranges under transformations, Image of Transformation,Rotations,Scaling, R2,R3,Rn, Unit Vectors, Projections, Distributive Properties, Associativity Properties ,Composition Properties, Inverse Determinant Properties,Transpose, Null space, Column space, Rank,Reduced Row-Echelon Form ,Linear Independence,Matrix Powers via Diagonlization.

  • Concept of Set, Injection, Surjection and Bijection, Concept of groups, Analysis, , Differential equations, Sequences, Series, Convergence, Limits, Calculus , Continuity, Ordinary differential equations (ODE), partial differential equations (PDE) and stochastic differential equations (SDE).

  • Orthogonal Complements, Projection Matrix,Least squares approximation,basis change,Orthonormal Bases using the Gram-Schmidt Process, EigenDecomposition (Eigenvalues and Eigenvectors),SVD, Covariance Matrices and PCA Reduction, Vector/Matrix dot and cross products.

  • Euclidean Geometry ,Non-Euclidean Geometry, Proofs,  Projective Geometry, Sphere Geometry, Hyperbolic Geometry,Trigonometry,Fourier Analysis, Signals Analysis, Time Series Analysis
 

Physics:

  • Displacement ,Acceleration, Velocity ,Kinematic Formulas, Projectile Motion
  • Centripetal Force, Centripetal Acceleration, Gravity,Mass,Weight
  • Work, Potential Energy, Kinetic Energy, Thermal Energy, Power, Springs, Hooke’s Law, Conservation of Energy, Mechanical Advantages.
  • Angular Velocity and Speed,Rotational Kinematic Formulas,Moments,Torque Moment of Inertia,Rotational Inertia,Rotational Kinetic Energy,Angular Momentum,Cross Product and Torque
  • Normal Force, Contact Force,Balanced and Unbalanced Forces,Force of Friction ,Static and Kinetic Friction, Force of Tension
 

Computer Science:

  • Operating Systems, Data Structures ,Set theory,Combinatorics,Algorithms,Computer Graphics,Logic,Image Processing and Analysis,cyber security, Signal, Image And Data processing,Computer Architecture,Random graphs,Complexity Theory, Storage Systems, Internet of Things (IOT),Internet Networking,Computability, Software Verification, Distributed Systems,Compilation,Database Management Systems,Reverse Engineering,Artificial Intelligence,Cryptology,Robotics,Concurrent Programming,Computer Vision ,NLP, Reinforcement Learning,Computational Geometry,Data Analysis, File Compression
 

Statistics:

  • Statistical Methods,Measures of location,Central Tendency,Dispersion,Probability Theory,Random Variables
    Expectation of Random Variable,Standard and Continuous  probability distributions,
    Bivariate and multivariate Distributions,Bivariate Transformations.
  • Correlation and Regression,Limit Laws,Order Statistics, Sampling Distribution,Tests of Significance
    Index Numbers,Time Series.

 

 

I have proven work experience as a R&D Engineer in: Data Scientist, Computer Vision, Autonomous, Algorithm, Python

Data Scientist:

  • Deep Machine Learning
    • Designer/Trainer/Researcher.
    • Fully Connected Networks , Convolutional Networks
    • Train/Validation/Inference Increasing accuracy:
      •  Over-fitting: Regularization(L1 sum of Absolute Coefficients for sparse, L2 sum of squared for dense,lambda for taming), Dropout, Batch Normalization,Early-stop.
      • General Hyper Parameter Tuning: Learning rates, Optimizers, Grid-Search.
      • Under-fitting: Unbalanced Data, Data Augmentation, Bias/Variance Trade-off Tuning.
      • Data Preparation -Cleansing,Imputing vs Generalization Tradeoff, Data Preprocessing -Dropping/Dummy Vars/Categorical-Discrete Continuous Encoding, Tensoring.
      • False Positive(T1)/False Negative(T2) Errors(Wrongs), Accuracy (TP+TN/Total),Recall (TP/TP + FN   –   T2 penalized), Precision(TP/ TP+FP –   T1 penalized)
      • Harmonic Mean(F1) – 2⋅Precision∗Recall​/Precision+Recall, F-beta (1 + \beta^2)∗Precision∗Recall​/beta^2∗Precision+Recall, Roc Area(TP/TotalT,FP/TotalN)
      • Mean Absolute,Mean Squared(Differential),R2 (MSE for predicted model/MSE for average line)
    • Supervised:Classification, Linear& Polynomial&Logistic Regression, SVM(kernel trick, c-value(lower margin better classification),RBF Kernel-mountain gammas), K-Dimensional Tree,Decision tree(information gain/entropy states reduction by approximation on sample batches for fast convergence/(Shannon entropy  by sum of probability logs of difference of parent and average of it’s children instead of tiny probability products)).
    • Unsupervised Clustering:  KNN, K-means(scree plot elbow) . Gaussian Mixture Models. Cluster Validation,anomalities,Hierarchical,  Density
    • Unsupervised Dimentionality Reduction: Random Projection , Independent Component Analysis ,PCA
    • RNN, Attention (multiplicative and additive), LSTM, GRU.
    • Transfer Learning.
    • Up-Transpose (DeConvolutions)/Aliasing.
    • Perception and Detection based deep learning, semantic segmentation(YOLO(Iou/Nms), RCNN, instance segmentation).
    • VggNet, ResNet, GoogLeNet/Inception ,MobileNet, HF-Net.
    • NLP – Captioning NN’s, Feature Extraction,TF-IDF,Topic modeling,seq2seq,ASR,CFG
    • Behavioral Cloning.
    • Adverserial Training(GAN).
    • Deep Reinforcement Learning(Deep Q-network,Actor-Critic).
 

Computer Vision:

  • Detection
    • Feature Extraction – SIFT(scale robust), SURF and HOG(noise robust), Hough Transforms(polar spaces parameterization as lines xcoos-ycos = d),Generalized Hough Transforms(displacement vectors r,  gradients theta, peaks ), Color Spaces(e.g. keep yellow by converting in HSV instead of loosing it in grayscale) , Spatial Information, Color Histograms.
    • Laplacian (second derivative filter), Difference of Gaussians , Canny Edge Detection (high threshold joined by lower threshold)
    • Sliding windows/patches with overlap, changing small scales for horizons and large for closer images.
    • Ray Tracing with floats, with integers(bresemham).
    • Template matching with threshold.
    • False-Posivite reduction with next frame threshold,
    • Duplicate reduction using Iou Centers and Nms.
    • Tracking(moving predictions with detection as a closed cycle loop)
    • Line detection applying polynomial calculations of curves and estimation of curvature through radius by applying 2nd derivatives.
  • 3D Perception
      • 3D point clouds.
      • “Voxels” using PCL.
      • Bundle Adjustments.
      • Triangulation.
      • Eigen Vector Decomposition for high dimensional domain computations in different basis spaces.
  • General
      • Recognition and Classification by Prior Generatives and by Posterior Discriminatives in Detection/Verification/Identification Instances and Categorization/Scene/Context Generics.
      • Computing the radial/tangential distortion rectifying transformation matrix.
      • Perspective transforms -fitting polynomials, masking , thresholding,  parallel line convergence fitting ,smaller to bigger enhancement, map(top view/bird view).
      •  Thresholding angles (such as gradients) using trigonometric functions such as Arctan2.
      • Camera matrix transformation from 3d to 2d.
      • Interpolation using linspace.
 

Autonomous:

  • Estimation
    • Sensors – Lidars(Infra red (velodyne) rain or fog – cycles pulses of point clouds), Radars(radio wave doppler, adaptive cruise control,velocity measurement rain/fog/underground fov),Accelerometers, Gyros.
    • Extended Kalman Filters(unimodal), Unscented Kalman Filters, Particle Filters(multimodal),Monte-Carlo(multimodal).
    • Multi Sensor Fusion – DeMorgans  law for independent measurements followed by max
    • RANSAC, PCA/T-SNE.
    • Gaussian Naive Bayes Probabilities (estimating measurements and noise), Maximum Likelihood, Continuous Probabilities.
    • Pedestrian location heading and speed.
  • Localization
    • rotations/quaternions(avoiding gymbel lock)/Euler (visualization).
    • Image-based.
    • Sensor Fusion Based.
    • Simultaneous Localization and Mapping (SLAM).
    • IMU for localization.
    • GNSS+RTK (real-time kinematic) – High accuracy of GPS.
  • Mapping
      • Configuration space.
      • Dense mapping vs. Sparse mapping(landmarks).
      • Structure from Motion(SFM).
  • Motion Path Planning(Map,Goal Value,Cost Value)
    • Trajectory over a fixed receding time horizon.
    • A* search and navigation algorithm around obstacles,Dynamic A*.
    • Partially observable Markov decision processes (POMDPs).
    • Behavior tree/Finite state machine motion.
    • Detour,Lane shift Policies.
    • Optimal min neighbor policy.
    • Probabilistic roadmap -Dijkstra’s shortest path , Voronoi graph and medial axis graphs.
    • Two-and-a-half-dimensional , Random Sampling,
    • Rapidly-exploring Random Tree (RRT).
    • Dynamic programming – for each point there is a path(not just the start).
  • Control
    • PID Control – cross track error. (proportional , integral, differential )
    • Model Predictive Control (MPC)- optimize control actuations over a given time horizon.
    • Bicycle model(steering angle, brake, heading)
    • Ackerman steering model.
    • Differential drive model for robots.
    • Slip angles and slip ratios.
    • Linear-quadratic regulator (LQR).
    • Twiddling techniques.
    • Statics(equilibrium),Kinematics(forces),Kineto-statics(1 time frame),Dynamics(all time frame) models.
 

Algorithm:

    • Sorting and searching
    • Graph algorithms:
      • Graph traversal (DFS, BFS) and applications
      • Minimum spanning tree
      • Shortest path
      • Matchings
      • Network flow
      • Traveling salesman problem
      • Hamilton cycle
    • Algorithm design:
      • Divide-and-conquer
      • Graph traversal
      • Greedy
      • Dynamic Programming(store the results of subproblems)
      • Reductions
      • Use of advanced data structures
    • Algorithm correctness:
      • Proofs and proof techniques (assumptions, basic logic inference and induction)
    • Algorithm analysis:
      • Time and space complexity
      • Asymptotic analysis Big O(Theta,Omega,O)
      • Worst/Best/Average case analysis
 

Python:

  • NN Frameworks – Pytorch,Tensorflow,Keras,Lua,KerasRL, Tensorforce
  • DataScience -(Python, Pandas, NumPy, SK-Learn, Shapely)
  • Autonomous – Networkx, PyMC
  • Computer Vision – PIL, OpenCV, SK-Image,PCL
  • Visualization – Matplotlib,Plotly
  • NLP -Nltk, Textblob, Spacy

 

● Further Programming Languages I have work experience with(to state only a very few):

    • c++ – eigen, boost 
    • C# – WPF,WCF,Web Api  
    • Ecma javascript 2019 ,React, Angular 5,TS  
    • Matlab 

● Professional .NET core developer: Web/ Desktop Apps; Multi-Thread Async; P/Invoke; Design Patterns; Architecting/Framework designer;

● Experienced system analyst: Business needs delivery focused. Proficient in identifying and setting priorities.

● Fast Learner and Problem solver: Highly motivated, can contribute to any project. Productive in both team-based and self-managed projects, including system design and technical documentation, peer training and review. Excellent English; written and verbal

 

Yair Shinar