Women in STEM

SPY Benchmark Trader

Women have every opportunity to excel in STEM (Science, Technology, Engineering, and Math) career fields. The CloudQuant team has worked with several talented women in our careers and hope to encourage other women to join us in the world of Financial Technology and Algorithmic Trading.

We encourage women to:

1. Become crowd researchers using STEM and Data Science skills to develop trading algorithms.

2. Review our career page and to apply for jobs. Internships, both full time and part time, are available for most semesters, summer, and winter breaks.


Sonal Gupta, Female Data Science Leader

What is it like to be a female in Data Science?

Sonal Gupta is an MBA graduate student at Case Western Reserve University in Ohio. She has five years experience in leading software development teams, product development, and consulting engagements.  She has the ability to analyze large volumes of data and generating actionable insights.  We asked her what her experience has been like as a female in data science and invite you to read her response below.
  Did I always want to be a part of the Science Technology Engineering and Math (STEM) culture?  This new culture is vastly expanding across my home country of India and is a large part of my past already.  Computer classes were always a part of my schooling but I related to them a lot more when I began to learn coding languages such as BASIC, C, and C++.  Learning a computer language was not as tedious a task, as let’s say cooking is for me, but laying the foundation was not easy.   I was told by my teachers, parents, and mentors that “failures are your best teacher.” I trusted this saying and never negotiated with it. With time I failed more and in doing so learned more.  Through my studies, I realized that my imagination enjoyed creating something virtually. Learning any language, apart from computer language, stipulated much more effort from me, so I stuck to the latter. An uncontrollable desire to push forward and seek new horizons made me set new goals.   My friends and I challenged each other every day to create a new chunk of code, and whatever we built was our own creation and we loved it. Our first collaboration was a computer game of chess, followed by many more projects. Whether it was intra-school or interschool competitions, our team of friends won medals for our work. Computers became my second love because my mom is my first love. She is the one who has seen me thrive, who has seen me at times challenged and who knows whatever may come I will survive the tides.   No guess that my love for coding would land me a job as a developer, after my engineering studies. At India’s Tech Mahindra, an information technology services and solutions company, I pursued my passion for coding and got into the JAVA development team. I have so much respect for this language because it is ever evolving. The culture in the organization resonated with my work style. Researching and coding was always on my agenda but I also started developing business acumen. I met the right kind of people who motivated me and kept my thirst for knowledge kindled. I worked on several projects but one of them helped me make a mark.   While working on the project I came up with the idea of merging the request calls to the database into a single unit so that the time consumed in the process of data creation be reduced by 80%. I conceptualized and initiated the development of the UI application and had to collaborate with several teams to bring in the specifics of the project. I asked other members to volunteer and the team effort brought in accolades after seven months. The client was really happy and I was promoted on his recommendation.   In my life, I have always found a way through any hard work to leverage my skills and create something new. I believe men and women are born with equal rights to live on this earth and to pursue their dreams. I chose a path that interested me and will pave it further based on my decisions. It is foolish to live someone else’s life. If this is what interests you, I say go for it. Courage is the most important factor in leading yourself to new heights.  
  To learn more about Ms. Sonal Gupta please check out her LinkedIn page.
stock charts

AI & Machine Learning News. 16, April 2018

How more women in AI could change the world

“Alexa” or “Alexander”, the female AI voice plays into the familiar gender stereotypes of women as helpful, subservient, and non-threatening. “By codifying AI and intelligent machines as women, we’re reinforcing our own sexism and misogyny — including toward real, human women.” The gender gap in AI is striking, but unsurprising given the dearth of women in computer science and STEM fields. According to some estimates, as few as 13.5 percent of the machine learning field is female, while 18 percent of software developers and 21 percent of computer programmers identify as women. Targeted ads are already feeding bias, with certain algorithms perpetuating the pay gap by targeting listings for higher-paying jobs toward men. It doesn’t take much imagination to envision how much worse this could get. 2018-04-15 00:00:00 https://venturebeat.com/2018/04/15/how-more-women-in-ai-could-change-the-world/ CloudQuant Thoughts: AI can greatly benefit from the female point of view, action needs to be taken to balance the genders.  

Can Humans Understand How Robots Invest?

For a guy who built a robot he hopes will banish human emotion from the investing process, Chida Khatua spends a lot of time trying to figure out how it thinks. The fund EquBot’s model makes recommendations for, the AI Powered Equity ETF, launched in October and has quickly amassed $136 million in assets, making it one of the most successful ETF debuts of 2017. Any AI system is likely to make investment decisions that look puzzling, says Zachary Lipton, an assistant professor in the machine learning department at Carnegie Mellon University. In the strictest sense, a model “is not operating according to logical rules. The model is just spewing out statistical correlations,” he says. “It’s not giving you logic—there isn’t actually a chain of coherent logical reasoning that tells you how to invest in the stock market. If there was one, you wouldn’t need the model in the first place.” 2018-04-10 00:00:00 https://www.bloomberg.com/news/articles/2018-04-10/can-humans-understand-how-robots-invest CloudQuant Thoughts: And yet a misstep will have the SEC asking “why it traded in this fashion”…  

AVBytes: AI & ML Developments this week – Stanford’s NLP Course Projects, R Package for Anomaly Detection, Create Deep Learning Dataset, etc.

The past week saw some intriguing developments in machine learning and deep learning. Stanford released a list of all its NLP course projects for 2018 (it’s a goldmine of knowledge), the Google Research team unveiled its deep neural network to extract audio by looking at a person’s face, a R package was released to deal with anomalies in time series, and many other developments happened this week which we have covered under the AVBytes umbrella. 2018-04-15 19:04:53+05:30 https://www.analyticsvidhya.com/blog/2018/04/avbytes-ai-ml-developments-this-week-160418/ CloudQuant Thoughts: These are all great links and worthy of your time, especially the first three.  

AI-triggered unemployment may lead to rise of communism, says Head of BofE

Mark Carney, the governor of Bank of England, has warned that the impact of artificial intelligence could result in mass job loss and wage stagnation. According to Carney, unemployment triggered by artificial intelligence will result in vast inequalities between workers who benefit from the technology and those whose careers are destroyed by it. “If you substitute platforms for textile mills, machine learning for steam engines, Twitter for the telegraph, you have exactly the same dynamics as existed 150 years ago when Karl Marx was scribbling The Communist Manifesto,” said Carney. 2018-04-16 15:12:34+08:00 http://www.ibtimes.sg/ai-triggered-unemployment-may-lead-rise-communism-says-expert-25960 CloudQuant Thoughts: The U.S. Bureau of Census tagged the horse to car transition as “one of the main contributing factors” to the Great Depression.  

UK lawmakers warn of data ‘monopolization’ for AI by big US tech firms

Regulators should review the “potential monopolization of data” by U.S. technology giants in the U.K. that could hamper home-grown development of artificial intelligence (AI), an influential body has recommended. A committee made up of lawmakers from the House of Lords, the upper house of Britain’s parliament, released a report Monday on the need for the ethical development of AI. Professor Richard Susskind, spoke about the “unprecedented concentration of wealth and power in a small number of corporations” such as Alibaba, Alphabet, Amazon, Apple, Facebook, Microsoft and Tencent. The lawmakers said in their report that this was a “view widely held” among a number of witnesses. They took written evidence from 223 witnesses and interviewed 57 people during their investigation. 2018-04-16 00:00:00 https://www.cnbc.com/2018/04/16/uk-lawmakers-warn-of-data-monopolization-for-ai-by-big-us-tech-firms.html CloudQuant Thoughts: The three largest AI firms are all Chinese, Tencent, Ali-Baba and Baidu. Ali-Baba recently upped their investment in SenseTime, taking the AI firm’s valuation to about $3 billion and making it one of the world’s most valuable AI startups.  

Google Semantris Blocks Google has launched a pair of AI word-association games called Semantris

Semantris Arcade presents you with a list of words and an empty text box. One of the words in the list is highlighted, and it’s your job to help the AI identify it by providing clues. Semantris Blocks is a word-association take on Tetris For example, the list might contain the words football, lunch, face, plant, frozen and beer. If the word ‘face’ was highlighted, you might type ‘nose’. This word is more closely related to ‘face’ than any other word in the list, so the AI will be able to guess the right answer. It won’t always be so easy though, and the system might make some unexpected connections – especially when new words start dropping into the top of the list. Semantris Blocks is the same concept, but based around Tetris. You’re given a wall of blocks, some of which are labelled with words. Give a clue related to a word, and (provided the AI agrees) that block will be destroyed. Those of a more cynical nature may suspect the games are in part designed to help ‘train’ Google’s AI, although if that is the case Google isn’t saying so. 2018-04-10 00:00:00 https://research.google.com/semantris/ CloudQuant Thoughts: These are great AI experiments in understanding language and they are fun.

And on the more serious side…


Visualizing Artificial Neural Networks (ANNs) with just One Line of Code

ANN Visualizer is a python library that enables us to visualize an Artificial Neural Network using just a single line of code. It is used to work with Keras and makes use of python’s graphviz library to create a neat and presentable graph of the neural network you’re building.

With advanced in deep learning, you can now visualise the entire deep learning process or just the Convolutional Neural Network you’ve built.

2018-04-14 16:53:00.006000+00:00 https://towardsdatascience.com/visualizing-artificial-neural-networks-anns-with-just-one-line-of-code-b4233607209e?source=collection_home—4——2—————-  

12 Useful Things to Know About Machine Learning

Machine learning algorithms can figure out how to perform important tasks by generalizing from examples. This is often feasible and cost-effective where manual programming is not.  As more data becomes available, more ambitious problems can be tackled. As a result, machine learning is widely used in computer sincere and other fields. However, developing successful machine learning applications requires a substantial amount of “black art” that is hard to find in textbooks. I recently read an amazing technical paper by Professor Pedro Domingos of the University of Washington titled “A Few Useful Things to Know about Machine Learning.”… 2018-04-12 00:00:00 https://www.kdnuggets.com/2018/04/12-useful-things-know-about-machine-learning.html  

Ten Machine Learning Algorithms You Should Know to Become a Data Scientist

It’s important for data scientists to have a broad range of knowledge, keeping themselves updated with the latest trends. With that being said, we take a look at the top 10 machine learning algorithms every data scientist should know. Machine Learning Practitioners have different personalities. While some of them are “I am an expert in X and X can train on any type of data”, where X = some algorithm, some others are “Right tool for the right job people”. A lot of them also subscribe to “Jack of all trades. Master of one” strategy, where they have one area of deep expertise and know slightly about different fields of Machine Learning. That said, no one can deny the fact that as practicing Data Scientists, we will have to know basics of some common machine learning algorithms, which would help us engage with a new-domain problem we come across. This is a whirlwind tour of common machine learning algorithms and quick resources about them which can help you get started on them… 2018-04-10 00:00:00 https://www.kdnuggets.com/2018/04/10-machine-learning-algorithms-data-scientist.html  

Deep Autoencoders For Collaborative Filtering – Towards Data Science

Predicting the Rating a User would give a Movie — A practical Tutorial. Collaborative Filtering is a method used by recommender systems to make predictions about an interest of a specific user by collecting taste or preferences information from many other users. The technique of Collaborative Filtering has the underlying assumption that if a user A has the same taste or opinion on an issue as the person B, A is more likely to have B’s opinion on a different issue. In this article you will learn how to predict the ratings a user would give a movie based on this user’s taste and the taste of other users who watched and rated the same and other movies. 2018-04-15 11:05:05.455000+00:00 https://towardsdatascience.com/deep-autoencoders-for-collaborative-filtering-6cf8d25bbf1d?source=collection_home—4——1—————-  

A Must-Read Introduction to Sequence Modelling (with use cases)

Artificial Neural Networks (ANN) were supposed to replicate the architecture of the human brain, yet till about a decade ago, the only common feature between ANN and our brain was the nomenclature of their entities (for instance – neuron). These neural networks were almost useless as they had very low predictive power and less number of practical applications. But thanks to the rapid advancement in technology in the last decade, we have seen the gap being bridged to the extent that these ANN architectures have become extremely useful across industries. 2018-04-15 19:13:29+05:30 https://www.analyticsvidhya.com/blog/2018/04/sequence-modelling-an-introduction-with-practical-use-cases/  

Introduction to Bayesian Linear Regression – Towards Data Science

An explanation of the Bayesian approach to linear modeling. The Bayesian vs Frequentist debate is one of those academic arguments that I find more interesting to watch than engage in. Rather than enthusiastically jump in on one side, I think it’s more productive to learn both methods of statistical inference and apply them where appropriate. In that line of thinking, recently, I have been working to learn and apply Bayesian inference methods to supplement the frequentist statistics covered in my grad classes. 2018-04-14 01:37:55.231000+00:00 https://towardsdatascience.com/introduction-to-bayesian-linear-regression-e66e60791ea7?source=collection_home—4——5—————-
Sarah Leonard, MScA, University of Chicago

Interview with Sarah Leonard, STEM Woman and Data Scientist

“It’s exciting to see the growing number of women in Science, Technology, Engineering and Math (STEM); my advice is to not be afraid to jump in headfirst,” said Sarah Leonard, graduate student at the University of Chicago. “It is a difficult field but also lucrative and rapidly growing.” Leonard sat down with CloudQuant to talk about her experiences in data science, her insight as a female in a male dominated world, and the intensive process it took to find her dream job.
Young women getting started in technology

Young Women: Programming Helps!

The first book I got was “Hello World”. The book intimidated me, because I saw programming as a superpower, something only my dad was capable enough to do. I tried to learn it, but I just got bored very quickly. My dad, still persistent, decided it was time to try programming something we loved.
Jessica Titlebaum Darmoni, Founder of The Title Connection

The Future of FinTech is Female

FinTech Women (FTW) had their launch event at Morningstar on Monday, December 11 in an effort to bring together experienced women in the financial technology space and highlight their achievements.
Daily ROIC Prior to improvements

Improving A Trading Strategy

TD Sequential is a technical indicator for stock trading developed by Thomas R. DeMark in the 1990s. It uses bar plot of stocks to generate trading signals. … Several elements could be modified in this strategy. Whether to include the countdown stage, the choice of the number of bars in the setup stage and countdown stage, the parameters that help to decide when to exit and the size of the trade will affect strategy performance. In addition, we could use information other than price to decide whether the signal should be traded.