IBM Research Staff members in the Experimental Quantum Computing
Inspired by Women’s History Month and International Women’s Day and Women in Data Science, IBM and its partners are amplifying the work of incredible women trailblazers and contributors to data science and quantum computing. Join us on March 25 in NYC for a day of talks, hands-on workshops, and networking to learn more about IBM’s revolutionary launch into Quantum Computing, the current state of AI and Data Science and meet the women leading the charge. You can join us for the whole day – or for parts of the day.

Date and Location
Monday March 25, 2019 – 10am networking for 11am start ; The formal activities end at 6:00pm.
AT&T Long Lines Building, 33 Thomas Street, New York , NY
An iconic brutalist building with no windows!

PDF for publicizing the event – please share with your colleagues, faculty, students etc:

Outline of Women in Quantum Computing & Data Science Day – Mar 25, 2019

Agenda subject to change

Module 1 – 10:00am start – Quantum Computing

10:00-11:00am – Networking
11:00-12:30pm – Bringing Quantum to Your Enterprise ; Introducing Qiskit, Quantum in Practice
Please bring your laptop if you want to try the hands-on portion and access a quantum computer

  • Hanhee Paik – Research Staff Member, Experimental Quantum Computing, IBM

Module 2 – 12:30pm start – Careers & Mentoring

12:30-1:15pm – Networking
1:15-2:00pm – How women can succeed in a world built without them?

  • Eileen Scully, Founder The Rising Tides – Making the Workplace Better for Women

Module 3 – 2:00PM start – Data Science Showcase

2:00-2:30pm – Networking – Data Science mentors available :
Zairah Mustahsan,
Vaisakhi Mishra,
Aishwarya Srinivasan

2:30-4:40pm – Speakers include:

  • 2:30pm – Zairah Mustahsan, Data Scientist, IBM – Using Watson Studio & WML for Visual Recognition
  • 2:50pm – Vaisakhi Mishra, Data Scientist, IBM – Data Journalism
  • 3:10pm – Aishwarya Srinivasan, Data Scientist, IBM – Connecting AI to Business,
  • 3:30pm – Kaoutar El Maghraoui, Senior Research Scientist, IBM – AI & DevOps
  • 3:50pm – Emily Dodwell, Senior Inventive Scientist, AT&T – From Theory to Practice: A Machine Learning Use Case for Advertising at AT&T
  • 4:10pm – Rachel Bellamy, Principal Researcher & Manager, Human-AI Collaboration, IBM – AI Fairness
  • 4:30-4:40pm – Grand Finale – All speakers Q&A

Module 4 – 4:40PM start – Data Science Experience Center – Sharing Experiences

4:40-5:00pm – Networking

Speaker – 5:00-5:20pm

  • Sabra S. Bhat, Data Strategy Consultant, KPMG – on the intersection of data science and consulting

Panel – 5:20-6:00pm

Agenda subject to change

Candidate questions for panelists from Eileen Scully

Q: I’d like to introduce everyone on today’s panel by sharing in a few sentences, tell us the moment your career took off, and what made that happen for you.
(did you choose your career or did it choose you)

Q: If there was one thing you would advise every other woman that she must do in her career, what would that be?
(prioritization / time management)
(sponsors / personal board of directors)

Q: Tell us about a time you found and used your voice. What can you tell other women about finding and using their voice and their strength?

Q: How do you build credibility when you are the youngest or newest member of your team?

Q: We hear so much about male allies, the role of men in promoting women, and how we need them to sponsor our professional journeys to accelerate our success. What we don’t hear enough about is how we as women can do that for each other. What do you feel is the best way for women to do this for other women, and what examples do you have from your own career about how this has worked for you?

Q: Companies are starting to change their cultures to bring more women to the forefront in positions of power and influence. But that only happens when women are viewed as equally capable of leading successfully. Thinking about women in the workplace, how do you encourage the women at your organization to support and advance each other?

Q: As we look for career mentors, what qualities would you suggest to women are the most critical?
What are the challenges to this relationship?

Bios and Abstracts

Rachel Bellamy

Bio: Rachel Bellamy – IBM Research, USA – Rachel is a Principle Research Scientist and manages the AI Consumability group at IBM T J Watson Research Center, Yorktown Heights, New York. In these roles, she leads an interdisciplinary team of human-computer interaction experts, user experience designers and user experience engineers. Her team is currently working on the user experience for several of IBM Research’s AI projects, including the AI Fairness 360 toolkit ( and rule-based machine-teaching for Watson Assistant. Rachel received her doctorate in cognitive psychology from University of Cambridge, UK in 1991. She received a Bachelor of Science in psychology with mathematics and computer science from University of London in 1986. Before coming to IBM Research, she worked in Apple Computer’s Advanced Technology Group, where she conducted research on collaborative learning and led an inter-disciplinary team that worked with the San Francisco Exploratorium and schools to pioneer the design, implementation and use of media-rich collaborative learning experiences for K-12 students. She holds many patents and has published more than 70 research papers.

Abstract: Putting Fair AI into Practice – Fairness is an increasingly important concern as machine learning models are used to support decision making in high-stakes applications such as mortgage lending, hiring, and prison sentencing. This talk will introduce an open source Python toolkit for algorithmic fairness, AI Fairness 360 (AIF360). This extensible open source toolkit can help you examine, report, and mitigate discrimination in machine learning models. It is designed to translate algorithmic research from the lab into the actual practice of domains as wide-ranging as finance, human capital management, healthcare, and education.

Sabra S. Bhat

Bio: Sabra S. Bhat – Data Strategy Consultant, KPMG – Sabra is a data strategy consultant with KPMG’s Digital Enablement practice. She is specifically aligned to the Healthcare and Life Sciences Sector with clients interested in leverage data integration tools, machine learning, predictive modelling among other advanced analytic tools to drive better insights and stay on top of the disruptive wave within healthcare. Prior to consulting, Sabra worked at a digital health innovation lab, the NYC Department of Health, UNFPA, and Mercy Corps – highlighting a diverse and somewhat non-traditional career trajectory. All experiences shaped her passion for the intersection of healthcare, policy, and tech. She received her BA in Anthropology at Bryn Mawr College and an MPA from Columbia University.

Noemi Derzsy

Bio: Noemi Derzsy – is a Senior Inventive Scientist at AT&T Labs within the Data Science and AI Research organization, doing lots of science with lots of data. Holding a PhD in Physics and research background in Network Science and Computer Science, her interests revolve around the study of complex systems and complex networks through real-world data. Previously, she was a Data Science Fellow at Insight Data Science NYC and a postdoctoral research associate at Social Cognitive Networks Academic Research Center at Rensselaer Polytechnic Institute.

Emily Dodwell

Bio: Emily Dodwell – is a Senior Inventive Scientist in the Data Science and AI Research organization at AT&T Labs, where she currently focuses on predictive modeling for advertising applications, the creation of interactive tools for data analysis and visualization, and research concerning ethics and fairness in machine learning. She is a member of R Forwards, the R Foundation task force on women and other underrepresented groups. Prior to joining AT&T Labs in 2015, Emily taught high school math for three years at Choate Rosemary Hall. She received her M.A. in statistics from Yale University and B.A. in mathematics from Smith College.

Kaoutar El Maghraoui

Bio: Kaoutar El Maghraoui – is a senior research scientist at IBM Research AI organization where she is focusing on innovation at the intersection of systems and artificial intelligence. Kaoutar has extensive and deep expertise in HPC, systems software, cloud computing, machine learning, and AI. Kaoutar holds a PhD. degree from Rensselaer Polytechnic Institute, USA. She received several awards including the Robert McNaughton Award for best thesis in computer science, IBM’s Eminence and Excellence award for leadership in increasing Women’s presence in science and technology, IBM’s award for contributions to the foundational POWER software technologies and promoting these systems in Africa, and 2 IBM’s outstanding accomplishment awards for contributions to building cognitive virtual technical agents. She is a senior member of ACM, IEEE Computer Society, and the Society of Women Engineers. Kaoutar is the chair of the Arab Women in Computing organization and avid supporter and active member of several women in science and technology initiatives. Dr. Kaoutar is a frequent speakers at various technical conferences.

Abstract: Operationalizing & Scaling AI Operationalizing AI has become a major endeavor in both research and industry. Automated, operationalized pipelines that manage the AI application lifecycle – from data preparation, to model training, to model turning, to runtime performance monitoring, to automated retraining – will form a significant part of tomorrow’s infrastructure workloads. Operationalizing AI is still a big hurdle for data scientists. It is the last mile that is crucial for data science to meet production IT and for business value to be created. This talk will introduce ModelOps, a framework to operationalize AI for business through an integrated lifecycle management approach. ModelOps defines abstractions for the domain of AI operations, provides a set of reusable and composable pipeline templates, and contains plugins to execute the pipelines on various target platforms (e.g., local developer machine, public/private clouds, edge devices).

Vaisakhi Mishra

Bio: Vaisakhi Mishra – Data Science Elite USA – is a Data Scientist in IBM Data and AI group. She received her Master’s in Information Management from University of Washington, Seattle, specializing in Business Intelligence and Data Science. During her capstone project she worked extensively with UNICEF to discover and bridge immunization gaps in SAARC and Central African Countries. Prior to joining IBM’S Data Science group, she worked with IBM supply chain engineering, developing a cognitive tool to match resources to projects and shadow project opportunities. Currently as part of the Data Science Elite Team, she is an advocate for adopting data science techniques for solving niche business problems in Oil and Gas and Healthcare industry.

Abstract: Data Journalism – Data Science is not just data modelling or machine learning. It is about uncovering what story data holds! A Data Scientist should see the data beyond what it shows at the first glance, explore its use, model it but also communicate effectively. I will deep dive into data science as data journalism, to explore and solve business problems effectively.
Data Journalism: Data Science is not just data modelling or machine learning. It is about uncovering what story data holds! A Data Scientist should see the data beyond what it shows at the first glance, explore its use, model it but also communicate effectively. I will deep dive into data science as data journalism, to explore and solve business problems effectively.

Zaira Mustahsan

Bio: Zairah Mustahsan – is a Data Scientist in IBM Data and AI group. Zairah is a Data Scientist in Watson Discovery, a service embedded with NLP capabilities, making it easy to build AI solutions that find relevant answers in complex, disparate data with speed and accuracy. She is also involved with a corporate-wide education program on AI Skills, where she serves as a subject matter expert and course content reviewer. She served as an instructor on two, week-long residency programs held in NY in 2019, mentoring teams in the areas of Sentiment Analysis, Time Series Data Analysis and Visual Recognition. Prior to joining IBM, Zairah received her M.S. in Computer Science from the University of Pennsylvania.

Abstract : Using Watson Studio & WML for Visual Recognition – Zairah will talk about how to solve a data science problem, by going over an easy to understand visual recognition problem. The talk will cover some tips and tricks on working across a data science pipeline, and give a gentle introduction to IBM’s tools and services like Watson Studio and Watson Machine Learning (WML). The talk will conclude with a high-level overview of transfer learning and explainability of AI solutions.

Hanhee Paik

Bio: Hanhee Paik -is a research staff member at IBM Q and has been studying superconducting qubit systems since the beginning of the field. Through her research career, she has been focusing on understanding and improving coherence mechanisms of superconducting qubits, and developing novel superconducting multi-qubit architectures. She received a PhD at Joint Quantum Institute, University of Maryland, and during her postdoc at Yale University pioneered a new design of superconducting qubits with breakthrough coherence time. She was integral in the development of the 16-qubit IBM Q Experience cloud device, which is available for free to the public.

Abstract: Bringing Quantum to You; Introducing Qiskit – On Mar 14, 2019 a joint study between IBM & MIT published in Nature magazine, showed that theory may become reality: Quantum computers can fortify certain kinds of machine learning.
On Mar 25, 2019 at the Women in Data Science and Quantum Computing in NYC, Hanhee Paik, research staff member at IBM Q, will first go though the basic concepts of quantum computing and its possible applications for science and industry. Then she will introduce IBM Q Experience and Qiskit, an open-source quantum computing software designed for today’s quantum processors. During the session, she will demonstrate a few examples together with you! Bring your laptops (optional).

Eileen Scully

Bio: Eileen Scully – is the Founder and CEO of The Rising Tides, which works to make the workplace better for women through consulting and advising corporations. She is an international speaker, and author of “In the Company of Men: How Women can Succeed in a World Built Without Them”, set for publication in the spring of 2019. She is a SheSource Expert with the Women’s Media Center, and has been interviewed by Forbes, the Boston Globe, Standard and Poor’s Global Market Intelligence, Thrive Global, Psychology Today, and Inc.. In June of 2016, she was invited by the Obama White House to participate in the United State of Women, one of five thousand global advocates for women and girls.

Aishwarya Srinivasan

Aishwarya Srinivasan – is a Data Scientist in the IBM Data Science & AI Elite Team. It is an international team of machine learning and AI experts who work with clients to solve complex business use-cases. Aishwarya is a post-graduate in Data Science from Columbia University; she strives for innovation. She is an energetic and enterprising researcher in machine learning and reinforcement learning. Aishwarya is an extrovert by nature and looks out for any learning opportunity. She utilizes her entrepreneurial skill to engage with clients. She is an advocate for Women in Data Science and actively participates in events and conferences to inspire budding data scientists. She is focused on expanding her horizons in the machine learning research community including her recent patent award won in 2018 for developing a Reinforcement Learning Model for Machine Trading. “Belief in your dreams, conviction to make them a reality, and pliability towards any obstacle – will act like wind under your wings.”

Directions to 33 Thomas Street from Grand Central Terminal

Please bring your photo-id. Note that the entrance door rotates at 33 Thomas Street when you enter the doorway.
Bring your laptops if you want to try the hands-on for Quantum Computing via QisKit.

Directions Link :

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