THURSDAY, MAY 7 @ 12:30 PM – 4:30 PM EDT



Speakers Include

Manisha Verma

Manisha Verma

Research Scientist at VerizonMedia

Bethany Doran

Bethany Doran

Founder at Lumas Health, Inc

Amanda Makulec

Amanda Makulec

Senior Data Visualization Lead at Excella

Kiruthika Sankaran

Kiruthika Sankaran

Data Scientist at KeyMe

Chandni Kazi

Chandni Kazi

Data Scientist at Great Place to Work US

Shannon Kalisky

Shannon Kalisky

Product Mgr, Analytics & Data Science at Esri

sample of topics covered:

Machine learning best practices
Data strategy and governance
Data ethics and bias
Recommendation engines
Designing ML pipelines efficiently
Career paths
Hiring and Recruitment
Seeking mentorship with purpose
The importance of female-first spaces
Navigating male-dominated teams
Overcoming imposter syndrome
Data science teams: managing, building, collaboration
Integrating open source tools into your workflows
Data and AI for emerging platforms
Data governance

About DSSe

DSSe is an initiative to elevate and connect the voices of women in data science and encourage companies to set a better habit to generally consider women for data-intense roles.

DSSe is important because the field of Data Science is still young — for it to fulfill its true potential, we need voices that reflect the full experience of the world we live in to contextualize our data-derived insights.

By elevating women and other underrepresented groups, we have the opportunity to build a truly incredible and thriving field that influences decision-making at every level in a meaningful way.


12:30pm - 12:40pm


12:40pm - 1:10pm

Invisible Women in the Workplace

Chandni Kazi – Data Scientist at Great Place to Work US

The world has a major design flaw. It wasn’t built for half its users: women. Even in our minds, we are often conditioned to set men as the default. When environments – physical, political, and social – are not designed with women in mind, they prevent 50% of the population from innovating, leading, and creating at their full potential. When spaces are designed for women, the people in those spaces are more resilient against stereotypes, negative imagery, unconscious biases, and other “bugs” that they experience in their day-to-day lives.

In this talk, attendees will:

  • Gain insights from 4.5 million employees about their workplace experience
  • Learn why and how workplaces can become women-first spaces
  • Walk away with action steps to increase the visibility of the women around you

About the speaker

Chandni Kazi is a lover of ramen. To support this habit, she daylights as a Data Scientist at Great Place to Work, the global authority on workplace culture. She has co-authored white papers such as Women in the Workplace Study, Defining the World’s Best Workplaces, and a five-part series on Innovation. Her research has been featured in Fortune magazine as well as in the Wall Street Journal. Her goal is to use data to help eliminate social injustices that take place within organizations. Additionally, she chairs her company’s philanthropic group and volunteers her expertise to local nonprofits in need of data support.

1:15pm - 1:45pm

Monitoring System Alerts with Machine Learning

Manisha Verma – Research Scientist at VerizonMedia

Given that VerizonMedia hosts services that span thousands of machines, it is important to monitor the machines or individual services (for example databases or web servers) for failures and identify which components might be failing as fast as possible so the end-user is not affected. Our model aims to control the number of alerts that are sent out to software reliability engineers or folks at operation centers.

At the moment, we have a monitoring system, where people configure what they want to monitor (for example CPU memory, disk write/read failures) and alert the product/service owners about any discrepancies in the series listed in these configurations.

Our objective was to train and test several machine learning models to predict which alerts were important and what alerts to escalate to SREs and SEs for resolution. We use techniques from text mining and Hawkes processes to eventually build a classifier to label each alert. I wanted to give the audience an overview of how machine learning could yield improvements in monitoring thousands of machines and reduce engineer workload.

This talk will be discussing TensorFlow, sklearn, python and a library for Hawkes processes.

About the speaker

Manisha Verma is a Research Scientist in New York City. She completed Ph.D. in Computer Science from UCL, London. Her research interests are data mining, information retrieval with an emphasis on applying NLP and IR techniques to large scale alert noise management and time series retrieval for incident resolution across platforms. Some of her work has been published at conferences such as RecSys, CIKM, WSDM, ECIR and SIGIR.

1:50pm - 2:20pm

Should a black box algorithm decide who lives and dies during the Covid-19 outbreak?

Bethany Doran – Founder at Lumas Health, Inc

There are many advances in medicine, health, and technology that may occur from the introduction of telemedicine, use of crowd-sourcing and citizen science to track the viruses’ spread, rapid testing, and use of 3-D printing to suppress and treat the deadly disease. However, there is also the potential for harm if there is unmitigated and unrestricted use of tracking without consent of citizens, non-interpretable methods of resource allocation using algorithms (that may influence who gets a ventilator and lives and dies), and limitations on freedom of speech by providers and other citizens alarmed at the current state of practice. In this talk, we will discuss the potential and the perils of new technological advances, as well as provide insight into the long term ethical challenges faced by society long after the virus has passed.

About the speaker

Bethany Doran is a Duke and Columbia trained practicing cardiologist and bioinformaticist. She has a degree in public health with focus on policy. She first learned the power of data while in training, when she taught herself to code to be able to understand how to predict outcomes among patients in large datasets. Her early work formed the basis of national and international guidelines for lipids, and her current research focuses on methods to improve risk prediction using explainable machine learning and statistical modeling in healthcare settings. Her company, Lumas Inc., focuses on data privacy for consumers and makes it easy for companies to comply with CCPA and GDPR regulations and honor consumer data values and preferences. She has been a participant of the 2018 Singularity Incubator, the 2018 Exponential Medicine, and YC120.

2:35pm - 3:05pm

First, Do No Harm: Making Sense of Data in a Pandemic

Amanda Makulec – Senior Data Visualization Lead at Excella

As a community of data scientists, analysts, visualizers, and enthusiasts, the COVID-19 pandemic offered a perfect storm of opportunity to contribute our skills: a ready-to-use data set updated daily by Johns Hopkins University, a sense of urgency as case counts increased, and a wide audience clamoring for information.

But making sense of that information is trickier than creating line charts or simple models. Accurate representation and analysis of data from an emerging pandemic requires expertise in epidemiology, public health, virology, and related disciplines. Plus, the stakes are high: charts and graphs being shared in the public domain informed major policy decisions, and the individual actions that collectively could help to slow the spread of the virus (or not).

During this talk, we’ll unpack the ethical considerations of creating visualizations and models in the middle of a public health crisis, the responsibilities we have as data people, and why we should be led by the first principle from medicine: first, do no harm.

About the speaker

Amanda Makulec is the Senior Data Visualization Lead at Excella and holds a Masters of Public Health from the Boston University School of Public Health. She worked with data in global health programs for eight years before joining Excella, where she leads teams and develops user-centered data visualization products for federal, non-profit, and private sector clients. Amanda volunteers as the Operations Director for the Data Visualization Society and is a co-organizer for Data Visualization DC. Find her on Twitter at @abmakulec

3:10pm - 3:40pm

Different recommendation engine algorithms and their uses

Kiruthika Sankaran – Data Scientist at KeyMe

The talk outlines what is a recommender system and where it is being used. What are the different algorithms that can be used to build better recommender engines? Using python to code and build the recommender engine and compare the performance between different algorithms. Choose the best algorithm based on the dataset and the algorithm’s performance on the dataset.

About the speaker

Kiruthika Sankaran is a data scientist and machine learning expert. She has experience in building recommendation engines and creating insights using predictive models. She is currently working as a Data Scientist in New York, creating analytical solutions for KeyMe, leveraging a wealth of data to make actionable business recommendations.

3:45pm - 4:15pm

Analytics and DataViz with Spatial Data Science

Shannon Kalisky – Product Manager, Analytics & Data Science at Esri

Using maps to frame a question, convey analytical results, and tell a story are some of the aspects that differentiate spatial data science from other fields in data science. Today, more than ever, using the “where” in your data is just as important as the “when.” We’ll talk about what makes an effective spatial visualization, what methods exist to factor in spatial aspects of your data, and how you can clean and contextualize your data with location-based methods and attributes.

About the speaker

Shannon Kalisky is the lead product manager for Analytics and Data Science at Esri, where she works with development and engineering teams to bring spatial data science mainstream. She started her career in GIS where she worked for a variety of organizations ranging from government to Fortune 500 companies, leveraging spatial data to uncover patterns and build predictive models with a combination of GIS and Python. Her undergraduate studies were in geography and her graduate education was in community and regional planning. She is currently pursuing her MBA in global business. When she’s not working behind a computer, you’re most likely to find Shannon with her hands dirty in a garden or at the local hardware store gathering supplies for her next project.

4:15pm - 4:30pm

Wrap up

virtual happy hour @ 7pm

Stay connected with other women in the data science community. Relax, unwind, and raise a glass (of whatever) with us.

Coffee chat @ 12pm the next day

Join us for a cup of coffee or tea and a chat the morning after the DSS Elevate Virtual Conference.


For any queries regarding Registration or Tickets, please contact us via info@formulated.by 

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