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Our instructors combine technical and business knowledge with years of hands-on teaching experience

Our Story

Cognitir (pronounced: cog-ni-teer) was founded by David Haber and Neal Kumar in August 2015 because both founders are passionate about teaching and find joy in helping others acquire knowledge. The main reason for this is simple: knowledge is a key ingredient in fostering innovation. If individuals can learn skills and problem solving techniques that help them solve the world's toughest problems, what types of innovation will the world experience? The possibilities are endless.

Since founding Cognitir, David and Neal have worked collaboratively to create numerous technical training courses that have empowered thousands of students and professionals around the world.

Our Team

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Rehgan Avon


Rehgan is an Industrial Systems Engineering graduate from The Ohio State University and has held positions as a Systems Engineer and Analyst at Fortune 500 companies. Currently she is an ETL Developer and Data Analyst for Clarivoy, a software start up company based in Columbus, Ohio. Rehgan was also one of the original founding members of the Big Data and Analytics Association at The Ohio State University. During her time at Ohio State she also served as a tutor for Calculus and Computer Science courses.

Rehgan has led Cognitir workshops at Northwestern University, University of Maryland - College Park, University of Michigan - Ann Arbor, University of North Carolina - Chapel Hill among other places.

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Gabrielle Agrocostea


Gaby is a data scientist at GroupM in New York. Previously, she worked in the start up world at Collibra where she focused on sales. She brought visibility and analytics to the sales organization by implementing metrics and models in Python. Gaby also gained experience at Shareablee, where her main focus was around understanding social media engagement and correlations with customer segmentation.

Gaby has an in-depth understanding of data science from her educational background. She graduated from Fordham University with a B.A. in Mathematics. After 7 years of working in the corporate finance field for companies such as Blackrock and Morgan Stanley, she went to Columbia University and was a part of the first class of graduates to earn her M.S in Data Science. It was there that Gaby discovered and developed a passion for data science and machine learning.

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Andrawes Al Bahou


Andrawes is a Machine Learning Engineer at Credit Suisse in Zurich, where he has developed data and analytics products spanning exotic option pricing, alternative data mining and anti money laundering. Prior to his career in finance, he completed research at ETH Zurich in deep learning applied to robotic computer vision and medical imaging.

Andrawes received his MSc degree from ETH Zurich in electronic engineering and high-performance computing. He received his undergrad in Electronic Engineering from UCL in London. Andrawes has been teaching since 2015 when he was founding member of the Applicable Mathematics Society at the London School of Economics.

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Yu Chen


Yu is currently a data scientist at Everyday Labs, an edtech firm, where he focuses on building data-driven products that help public school districts improve attendance and academic outcomes. He has built machine learning attrition models, financial data ETL pipelines, and natural language processing features for technology startups, financial services companies, global entertainment distributors, and large multinationals.

Yu is also an Adjunct Professor of Data Science and Operations at USC, where he teaches graduate courses on natural language processing and business analytics.

Yu received his MBA from UCLA Anderson and his Masters in Computer Science from Syracuse University.

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Philip Couto

Teaching Assistant

Philip holds a bachelor's degree in commerce with a major in finance from Concordia university in Montreal, Canada. His interest in data science was sparked after attending Cognitir's Python for Business Students workshop in 2019. Since then he has passed CFA level 1, completed an internship at PSP Investments, one of Canada's largest pension funds, in the Private Market Risk team as well as two internships with the Canadian Federal Government's Data Analytics team at Transport Canada.

At Transport Canada, he played a pivotal role in the adoption of Microsoft Power BI by creating a comprehensive training course targeted to new users which was delivered to 130 members of the Financial Planning and Analysis community across the country. After briefly tasting finance and analytics, he decided to pursue his interest in data science and its applications to business further by enrolling at Ivey Business School's MSc in Business Analytics expecting to graduate in December 2021.

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David Guo

Teaching Assistant

David is a senior at the University of California, Davis, majoring in Computer Science. He is currently in his third year at UC Davis and is planning to graduate a year early to start applying what he has learnt in university. During his studies, he also served as a tutor in Computer Science courses, such as Operating Systems, Introduction to Programming, and Object-oriented Programming to help fellow students build strong foundations in these topics. Here at Cognitir, David is a teaching assistant for a variety of different courses.

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David Haber

Co-Founder & Advisor

David manages Cognitir's technologies, European business activities and recruiting efforts.

David has experience working with and advising both startups and large corporations. As part of his role as Head of AI at Daedalean, he co-created a framework for the safe use of machine learning technologies in aviation together with the European Union Aviation Safety Agency (EASA). Previously, David was a lead engineer at Soma Analytics (acquired by Prenetics), applying AI in healthcare. He also worked in Deutsche Bank's Asia Pacific Head Office in Singapore where he optimized large-scale financial data transactions.

He holds an MEng (First-Class Honours) in Computer Science from Imperial College London (UK) where he focused on statistical machine learning. He presented his work at international conferences, won several awards for his work and has given 100+ talks and workshops internationally.

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Zeke Hochberg


Zeke is an experienced educator turned data scientist currently working at Nielsen Marketing Cloud. His team uses machine learning to improve digital advertising audience targeting. Zeke was a high school teacher for five years and is a former Teach For America Corps Member. He first taught in Dallas, Texas, before moving to Brooklyn to be a founding teacher at a new charter high school. Zeke chose to move into data science to combine his interests in math and linguistics, and hopes to do more work with natural language processing and deep learning moving forward.

Zeke has a BA in cognitive science from The University of Virginia, and an MA in Teaching from Relay Graduate School of Education.

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Seth Johnston


Seth is currently a data scientist/product manager at Verizon Connect where he builds data-driven features for connected vehicle products. He uses his expertise in machine learning and neural networks to build algorithms for predictive vehicle maintenance. He also leads data visualization for customer feedback based on his natural language processing work. He has several patents pending for Verizon related to vehicle maintenance and safety.

Seth received his undergraduate in Statistics from Brigham Young University and his Masters in Data Analytics from the Georgia Institute of Technology, both with an emphasis in Business.

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Jon Karp


Jon is a former science teacher and current business analyst at Teach For America, leading projects focused on data and foundational technologies. Since 2016, Jon has spearheaded efforts to revamp reporting, analytics, and data integration systems at TFA, including significant work with SQL, Power BI, and data modeling.

Prior to his work in IT, Jon taught for four years with Teach For America in St. Louis, MO, also serving as science department chair during his final year. He received a Bachelor of Science in Earth and Ocean Sciences from Duke University, where he cut his teeth on climate and chemical oceanography datasets.

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Jit Kumar


Jit leads operations and FP&A for Cognitir. He also plays a pivotal role in recruiting talent and product management. He previously worked for Broadcom Corporation where he was a Sr. Manager and led teams that designed Baseband Processors for 3G cell phones, and Read Channel Devices for Hard Disk Drives. Prior to joining Broadcom, he spent 26 years as a Technical Manager at Bell Labs, Lucent Technologies, and Agere Systems where he led large teams that did pioneering work in the development of VLSI chips for Data Networking and Speech Recognition/Synthesis.

Jit received his B.Tech from Indian Institute of Technology, Kanpur (India) and an MSEE from University of Notre Dame (USA). He previously served on the Conference Board and Technical Committee on VLSI of Signal Processing Society (IEEE) for 10 years. He has published extensively in leading IEEE Conferences and technical journals and is author/coauthor of 19 papers. He also has two patents to his credit.

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Jack Khayoyan


Jack received his undergraduate degree in Economics, as well as his Masters in Applied Economics, from UCLA. In addition, he is a CFA Charterholder. He has an extensive background in business and financial matters, including business valuation, accounting, finance, etc. He is also an experienced programmer with expertise in big data and data science. Jack has a unique understanding of business, having founded multiple successful startup companies. He is a member of CFAI and CFALA.

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Neal Kumar


At Cognitir, Neal advises on the company's strategy and growth efforts.

Previously, Neal led training seminars for a top financial training provider and consistently received top reviews from attendees and created two training courses that were used in seminars worldwide. He also founded a consulting practice to help executives with strategic initiatives ranging from product management to M&A. Before his consulting and training careers, Neal was a Teach For America Corps Member and taught high school mathematics in Saint Louis Public Schools (USA). In addition to his education and training careers, Neal was an investment banker at Lazard, JPMorgan, and Houlihan Lokey.

Neal received his MBA from London Business School (UK) and BBA in Finance from the University of Notre Dame (USA). He is also a CFA Charterholder and a Member of the CFA Institute Education Advisory Committee (EAC) Working Body where he helps shape CFA Program Content.

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Timothy Marble


Timothy Marble is the founder of Eucalyptus Global Advisors LLC, a systematic quantitative investment firm. Previously he led the Data Science effort at, where he used machine learning to develop an alternative credit scoring model for underbanked customers in Kenya.

Prior to this Tim founded of Eucalyptus Partners LLC, a proprietary trading firm and SEC Registered Broker/Dealer. At Eucalyptus Tim was responsible for all aspects of development and management of a portfolio of systematic and algorithmic trading strategies, and was a member of NYSE ARCA, CBOT, NYMEX and COMEX. Prior to forming Eucalyptus, he held research roles with variety of hedge fund and proprietary trading firms.

Tim holds an MSc in Software Engineering from Oxford and an MBA with concentration in Statistics and Econometrics from the University of Chicago – Booth.

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Kevin McPherson

Teaching Assistant

Kevin is a Venture for America (VFA) fellow in Kansas City, Missouri. VFA provides experience to young professionals in high growth startups in emerging U.S. cities to prepare fellows to become future founders and successful entrepreneurs. As part of VFA, Kevin is a data scientist and machine learning engineer at Bellwethr, a company democratizing machine learning via their B2B platform around customer retention and automation related to the customer lifetime.

Kevin holds a bachelors degree in biology from Emory University. In addition to his coursework, Kevin has conducted genetics, biophysics, and cell physiology research at National Institutes of Health and Stanford University. Kevin has many interests, and hopes to combine his love of the physical and life sciences with machine learning through various projects. Kevin believes in the power of mentorship and teaching as a way to unlock interesting interdisciplinary insights.

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Ishan Mehta


Ishan is a graduate of the California Institute of Technology holding degrees in economics and chemistry. While at Caltech, Ishan developed an econometric model for various currency pairs and used it to successfully develop an algorithmic trading platform. Additionally, Ishan has collaborated with the City of Pasadena to drive citywide economic growth through development and implementation of various traffic/parking allocation strategies. Furthermore, Ishan served as a consultant at Mars & Co where he worked closely with C-level executives to find synergies across its two primary business arms.

Ishan has led Cognitir workshops at Claremont McKenna College and Ohio State University among other places.

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John Min


John is based out of Brooklyn, New York, working at SITO as a data scientist focused on big data engineering and geospatial analytics. Currently, he works primarily with Spark using Scala for data engineering and model building tasks and then, switches over to Python for geospatial visualizations.

After graduating from Duke University with a B.S. in Economics, he continued his studies in the M.A in Quantitative Methods in the Social Sciences and M.S. Operations Research programs at Columbia University, where he fostered his passion for statistics, data science, and machine learning. During graduate school, he worked as a research assistant for the Center for Computational Learning Systems @ Columbia University, a machine learning lab where he worked on a smart energy project for commercial real estate buildings. Subsequently, he has worked as a data scientist at COTA, an oncology data startup, and as a developer for Democracy Works, a political non-profit.

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Deep Pulusani


Deep works as a senior software engineer in the ever-evolving startup world in Los Angeles. He studied finance and psychology at the University of Texas at Austin, and the intersection of the two disciplines led to his interest in poker and game theory. Using principles from probability and statistics, Deep beat a field of 1000 players to win a World Series of Poker bracelet in 2013, and has won in tournaments and cash games around the world. Deep has also acted as a coach, mentor, and backer to other high-level poker professionals.

As a software engineer, Deep spends most of his time working with emerging technologies. His open source work with WebAssembly appeared on the main stage of the Google I/O developer conference in 2017. He is currently working with a FinTech startup to apply machine learning techniques to consumer banking information.

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Derek Sasthav


Derek has held various positions in business intelligence, data analytics, and management consulting roles where he has delivered innovative solutions to executive-level clients. He currently works at AMEND Consulting, a Cincinnati-based management consulting firm, where he is focused on building analytics capabilities for clients in the middle market. At AMEND, he has worked on impactful data science projects including price volume mix analysis, production scheduling optimization, and operational KPI reporting. Previously, he worked at the IBM North American Analytics Center working on predictive modeling for crime rates in urban areas. Derek studied Industrial Engineering at Ohio State University, where he was president of the Big Data and Analytics Association, a student group focused on teaching data science to students. During this time, he has become proficient in many analytics tools including Python, R, SQL, Microsoft PowerBI, and more.

Derek has led Cognitir workshops at Chicago Booth, Columbia University, London Business School, and UCLA among other places.

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Will Suess


Will is a senior quantitative analyst at a leading independent investment research firm where he focuses on delivering differentiated and actional quantitative analysis for a global subscriber base. He leads the quantitative group where they leverage Python and R and a few cloud service providers to generate investment rankings for 27,000 securities globally. Previously, Will has held positions at buy side investment firms where he has developed a variety of investment models often utilizing custom back testing, portfolio optimization and attribution. It was during his time on the buyside where he took his love of statistics and data science and discovered the usefulness of incorporating machine learning into quantitative investment processes.

Will is a CFA Charterholder and holds a B.B.A. in both Finance and Economics from the University of Iowa.

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Kristy Wedel


Kristy is a Sr. Manager, Data Scientist at Abbott in Columbus, OH. With over ten years of experience in the analytics and visualization space, she has developed solutions for marketing, finance, and various other business units. At Abbott, she has hosted lunch and learns, office hours, and user groups in R, Power BI, and SQL. She is passionate about data literacy and making it easier to identify actionable insights from data.

Kristy holds a B.S. in Mathematics from The Ohio State University and various certifications in SQL Server, Database Management, R, and Python. Kristy has previously tutored students in mathematics at Columbus State Community College. She is also a contributing author to the book Analytics Interpreted: A Compilation of Perspectives (2021).

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Ryan West


Ryan is a Venture for America fellow, a program for young professionals that provides the resources and ongoing support to prepare fellows to become successful entrepreneurs. As part of the fellowship, he has worked as a Machine Learning Engineer at Nexosis, Inc., implementing traditional data science workflows into Nexosis' automated machine learning platform and generalizing time series models to forecast on datasets across multiple industries. For MLconf's 2017 Atlanta conference, he was selected to present his research about automating time series model selection.

Ryan holds a degree in Systems Science & Engineering from Washington University in St. Louis and is also a physics graduate from Knox College. Outside of his coursework, he worked as a research assistant using Mössbauer spectroscopy to analyze the electronic structure of Iron Dibromide compounds. As a teaching assistant and tutor for physics and engineering courses, Ryan discovered his love of teaching and passion for breaking complex theoretical concepts into approachable practical material.

Want to join our team? Explore our open positions here.