Titash Mandal is a graduate student pursuing a Master's degree in Computer Science from New York University with extensive experience in programming, technical skills, and academic projects. He has worked as a teaching assistant and lead instructor for K-12 STEM programs where he designed curriculum and taught topics in computer science and cybersecurity. His research experience includes developing algorithms for image analysis and retinal disease classification.

1. TITASH MANDAL
“A recognized student leader, mentor & innovator with extensive programming, technical and collaborative skills.”
Email: tm2761@nyu.edu | Phone:(716)-275-5897 | Address: 309E, 49th Street, Apartment 10-F, New York, NY – 10017
Website: titashmandal.me | LinkedIn: https://www.linkedin.com/in/titash-mandal-9a6899117 | GitHub: https://github.com/Titash21
EDUCATION
New York University, Tandon School of Engineering, Master of Science in Computer Science, Brooklyn, NY GPA: 3.9/4.00 May 2018
KIIT University, Bachelor of Technology in Computer Science & Engineering, Bhubaneswar, India CGPA: 9.36/10 April 2016
PROGRAMMING/TECHNICAL SKILLS
• Programming Languages: Core Java, Python, C, C++, HTML, CSS, Java Script
• Operating Systems: Windows OS, Linux (Ubuntu), Mac OS
• Other Skills: Wireshark, IntelliJ, MATLAB, Python IDLE, Atom, MS Word, PowerPoint, Excel, SQL, Android Studio, Eclipse
• Data Skills: Tensorflow, Hadoop, PySpark, Matplotlib, MLLib, Python’s Scikit-learn Pandas, Numpy , Coursera Certification in Neural Networks
WORK EXPERIENCE
Teaching Assistant, Computer Science, Brooklyn, NY September 2017 - Present
 Instructed two weekly Python programming labs to undergraduate students, provided weekly programming assignments and mentoring sessions to clarify doubts.
 Debugged programs, created coding assignment challenges, clarified several programming concepts and assisted in grading homework’s and examinations.
Lead Instructor, K-12 STEM Program - Computer Science for Cybersecurity, Brooklyn, NY June 2017 - Present 2017
 Mentored a successful pilot program, featured in the Wall St. Journal for introducing Cybersecurity among high school kids.
 Designed and taught Computer Science and Cybersecurity concepts like- Mono-alphabetic and Polyalphabetic ciphers, Networking Protocols, Cryptography,
Stenography, Python, Databases, Website Development and Social Engineering topics like - Phone Snooping, security breaches in social networking websites.
 Programmed the cyber security challenges and programming activities using tools like Python, Flask, Stepic, Stegsolve, Autopsy, PHPMyAdmin, SQL.
Graduate Research Assistant, New York University, Brooklyn, NY Feb 2017 - June 2017
 Developed novel algorithms for color and image analysis, segmentation, feature quantification and statistical analysis related to retinal diseases.
 Performed Retinal Image grading and implemented Image Segmentation algorithms for identifying the drusen deposits in the eye of patients.
Research Assistant, IIT Kharagpur, India May 2014 - July 2014
 Formulated a C program to test power dissipated in the Memory Built in Self-Test architecture.
 Proposed an algorithm for grouping the memory cores based on distance and timing constraint enabling faster calculation.
 Researched the reuse of the available memory core network to act as a Test Access Mechanism bringing down the area overhead as well as reduce testing power.
ACADEMIC PROJECTS
Implemented NVidia’s Research Paper on Progressive Growing of Generative Adversarial Networks. (Tensorflow, Python) April 2018 – May 2017
 Built and trained generator and discriminator networks to generate synthetic images from latent noise using Pixel normalization, WGAN Loss, Sliced Wasserstein
distance, slow progressive growing from lower resolution to higher resolution on CELEBA and LSUN datasets.
Implemented a Deep Neural Network and Convolutional Neural Network on cifar10. (Tensorflow, Python, Pandas, Numpy) Feb 2018 – March 2017
 Built and trained a deep L-layer Neural Network and analyzed matrix and vector dimensions to check neural network implementations.
 Illustrated hyper parameter tuning to improve the performance of forward propagation and backward propagation.
 Implemented convolution and pooling operation with padding, strides and proper filter for image multi-class classification.
Analyzing levels of air pollution, explaining changes in global land temperatures and relating pollution to deaths and diseases using Big Data Technology.
(Spark, PySpark, SparkSQL, MLLib, Matplotlib, R) October 2017 - October 2017
 Performed data analysis to determine the most polluted cities in US based on AQI levels of gases using PySpark and SparkSQL.
 Correlated multiple datasets of Pollution and Global Land Temperature to determine how pollution levels effect yearly average temperatures in different cities.
 Joined three datasets namely: Pollution Dataset, Land Temperature Dataset and Global Death dataset to analyze changes in yearly death rates due to pollution.
 Performed an exploratory analysis on daily tweets to see current awareness using Machine Learning Algorithms identify positive and negative sentiments.
Applying Data Science and Data Mining tools to analyze the factors causing Doctors to prescribe branded vs generic drugs. October 2017 - October 2017
 Performed feature engineering to identify the most import attributes using entropy and mutual information, converted the categorical attributes to numerical
attributes, removed selection bias in the dataset, identified and mitigated concept drift and chose accuracy as the performance measure for the problem.
 Implemented feature reduction using Principle Component Analysis and Tree Based Pruning to avoid memory leakage.
 Applied various ML Algorithms like random forest, decision tree, ada-boost to identify the best regression algorithm for the problem set and generate conclusions.
Analyzing primary factors for customer churn in an organization using Predictive Modeling (Python) October 2017 - October 2017
 Implemented a predictive model to identify factors causing customer churn using Decision Trees and identified correlation between the driving factors of churn.
Motion detection to navigate a game in Optical Flow (Python, Opencv2) May 2017 - May 2017
 Developed a program which performs real time analysis of a video captured by the webcam to detect a specific colored object in the video frame using opencv.
 Applied the Lucas Kanade algorithm to the individual video frames to determine the flow and velocity vectors of the object.
 Implemented an algorithm to link the displacement vectors calculated during the object’s motion to navigate a game in real-time using motion detection.
LEADERSHIP AWARDS & APPOINTMENTS
 Graduate Orientation Caption, NYU, Brooklyn, NY Jan 2018 – Jan 2018
 Executive Board Member, Web Master, SHPE (Society of Hispanic Professionals), Brooklyn, NY December 2017- Present
 Student Leadership Award Recipent, NYU, Brooklyn, NY March 2017-March 2017
 Participant and Mentee – Womentorship Program, NYU, Brooklyn, NY Jan 2017- December 2017
 Lead Dancer, Team Leader, Member, UG, KIIT University, Bhubaneswar, India April 2013 - April 2015