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

☑ 𝗜 𝗮𝗺 𝗮𝗻 𝗮𝘂𝘁𝗼𝗱𝗶𝗱𝗮𝗰𝘁, 𝘁𝗵𝗮𝘁 𝗰𝗮𝗻 𝘁𝗮𝗸𝗲 𝗮 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗳𝗿𝗼𝗺 𝗶𝗻𝗰𝗲𝗽𝘁𝗶𝗼𝗻 𝘁𝗼 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘆, 𝗮𝗻𝗮𝗹𝘆𝘇𝗶𝗻𝗴, 𝗶𝗱𝗲𝗻𝘁𝗶𝗳𝘆𝗶𝗻𝗴 𝗮𝗻𝗱 𝗽𝗿𝗼𝘃𝗶𝗱𝗶𝗻𝗴 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗮𝗻𝗱 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘀𝘁𝗮𝗿𝘁𝗲𝗴𝘆 𝗻𝗲𝗲𝗱𝘀 𝘁𝗼 𝗲𝗻𝗵𝗮𝗻𝗰𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗴𝗿𝗼𝘄𝘁𝗵

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

☑ 𝗜 𝗮𝗺 𝗮𝗻 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝗱 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿, 𝗜 𝗵𝗮𝘃𝗲 𝘀𝗲𝘁 𝘀𝘂𝗰𝗰𝗲𝘀𝘀𝗳𝘂𝗹 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀 𝗮𝗻𝗱 𝗿𝘂𝗹𝗲𝘀 𝗳𝗼𝗿 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝘄𝗼𝗿𝗸

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

Mathematics:

Final Israel Open University Grades:

Statistics: 96 out of 100

Discrete Math: 96 out of 100

Differential And Integrable Math: 82 out of 100

Linear Algebra: 91 out of 100

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:

Algorithms

- 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 (Basic)
- c# – WPF,WCF,Web Api (Expert)
- Ecma javascript 2019 ,React, Angular 5,TS (Moderate)
- Matlab(Basic)

● Professional .NET 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