November 1st - 3rd, 2022
Responsible AI
Applying AI to help solve social, climate, and humanitarian challenges
Responsible AI Track
As data proliferates and becomes more freely available, the power of driving impact in social sector increases.
See the many ways organizations are applying their data science infrastructure in the name of making the world a better place.
Learn through stories of success and failures, and core practices that are implemented by change makers in the social sector that can differ from industry and academia.
Get exposed to data science & Machine learning workflows and models being utilized steered towards causes like climate change, agriculture, socio-economic impacts, disaster management etc.
Featured Speakers

Animesh Singh
Animesh is primarily responsible for –
• Driving IBM AI and ML Strategy and execution, both externally in open source and internally with Watson, with focus on creating an AI platform for IBM, delivering and growing successful AI projects, and driving adoption of these externally with community partners and internally with AI product teams.
• Provide technical leadership that enables offerings from Watson like Watson Studio, which can run, and scale on IBM Cloud and on prem offerings like ICP for Data. I lead a team, which defines the integration points for our next generation AI platform at Local, Dedicated and Public Cloud layers.
• Building consensus within IBM and the industry around the IBM approach of bringing AI and Cloud together. Leading voice in driving the next generation of the products and setting the direction for widespread adoption.
• Leading multiple initiatives around IBM Watson and Cloud Platform a multi-billion dollar investment from IBM around AI technologies like TensorFlow, Caffe2 etc., built on Cloud.
Animesh is Global Team Leader –
• Leading and collaborating with teams spread across US, China, France, Germany, India, Italy and Japan. • An excellent team builder, motivator, execution lead and implementer. Have demonstrated leadership driving business-enhancing change initiatives with AI and Cloud Computing Solutions
He is a Strategist and Speaker –
• Global speaker, invited to speak at conferences worldwide on IBM strategy and technology. Have spoken in conferences in U.S.A, Canada, France, Japan, Germany, Spain etc.
• Talks at the conferences have garnered more than 105K+ Views on Slideshare (http://www.slideshare.net/AnimeshSingh)
60K+ views on YouTube.
INVENTOR
• 15 filed Patents
• 10 granted
Session on Trusted AI Coming Soon!

Kerry Weinberg
Kerry Weinberg leads Data at League, North America’s leading Health OS. Before joining League, Kerry led Data Science & Engineering for Amgen’s Digital Health & Innovation where her team applied machine learning to better understand human disease, improve Amgen’s ability to reach patients, and improve patient outcomes. Before joining Amgen, Kerry received her MBA and M.S. Biological Engineering from MIT as part of the Leaders for Global Operations Program. She previously led systems engineering efforts for high-speed cell sorters at Beckman Coulter. Kerry holds a B.S. Biological Engineering also from MIT.
A Day in the Life: Data in Digital Health(Business Talk)

Lisa Amini, PhD
Dr. Lisa Amini is the Director of IBM Research Cambridge, which is also home to the MIT-IBM Watson AI Lab, and of IBM’s AI Horizons Network. Lisa was previously Director of Knowledge & Reasoning Research in the Cognitive Computing group at IBM’s TJ Watson Research Center in New York, and she is also an IBM Distinguished Engineer. Lisa was the founding Director of IBM Research Ireland, and the first woman Lab Director for an IBM Research Global (i.e., non-US) Lab (2010-2013). In this role she developed the strategy and led researchers in advancing science and technology for intelligent urban and environmental systems (Smarter Cities), with a focus on creating analytics, optimizations, and systems for sustainable energy, constrained resources (e.g., urban water management), transportation, and the linked open data systems that assimilate and share data and models for these domains. She earned her PhD degree in Computer Science from Columbia University.

Neil Sahota
Neil Sahota is an IBM Master Inventor, United Nations (UN) AI Advisor, author of the book Own the A.I. Revolution., and Chief Innovation Officer at UC Irvine. He is a business solution advisor to several large companies and sought-after keynote speaker. Over his 20+ year career, Neil has worked with enterprises on the business strategy to create next generation products/solutions powered by emerging technology as well as helping organizations create the culture, community, and ecosystem needed to achieve success such as the U.N.’s AI for Good initiative. Neil also actively pursues social good and volunteers with nonprofits. He is currently helping the Zero Abuse Project prevent child sexual abuse as well as Planet Home to engage youth culture in sustainability initiatives.

Dave Thau, PhD
Dave Thau is WWF’s Data and Technology Global Lead Scientist with him over 30 years of software development and conservation experience. He is also a member of the IPBES Knowledge and Data taskforce. Prior to WWF, Dave worked at the California Academy of Sciences, the Kansas University Museum of Natural History, and Google where he helped launch Google Earth Engine. Dave’s work focuses on the fields of data management, sustainability, artificial intelligence, and remote sensing. He holds degrees from the University of California, Los Angeles, the University of Michigan, Ann Arbor, and a doctorate in computer science from the University of California, Davis. He also has an ant named in his honor – the charming Plectroctena thaui.
Artificial Intelligence for Conservation and Sustainability: From the Local to the Global(Talk)

Tempest Van Schaik, PhD
Tempest is passionate about improving lives using sensors, data, and AI. Some of the ways she’s driven impact have been through her startup, SoilCards, which aims to make mobile soil testing accessible to the world’s poorest farmers in order to improve their livelihood and protect the environment. She has also developed novel ways to measure cognitive function and mood in people with depression using wearables. She has used data science to improve physiotherapy for children with cystic fibrosis, and has put principles of responsible AI into practice to build predictive ICU models which treat different patient groups fairly. She is currently a Senior Machine Learning Engineer in Microsoft’s Commercial Software Engineering (CSE) team, where she is an ML Lead for collaborations with some of Microsoft’s biggest healthcare customers. She is a member of CSE’s Responsible AI board and a CSE ambassador for Diversity & Inclusion, because she believes in promoting positive change as a leader in the industry. She has a PhD in Bioengineering from Imperial College London, with an internship at MIT, and an Imperial College Rector’s Award. She is a Technical Advisory Board member of Ultromics Ltd as well as a TEDx and SXSW speaker. Her research has received awards from Innovate UK and the US National Academies of Science Engineering and Medic.

Ashutosh Garg, PhD
Bio Coming Soon!
Using AI to Overcome Bias & Make Hiring More Equitable(Talk)

Mikhail Yurochkin, PhD
Mikhail is a Research Staff Member at IBM Research and MIT-IBM Watson AI Lab in Cambridge, Massachusetts. His research interests are Model fusion and federated learning; Algorithmic fairness; Applications of optimal transport in machine learning; Bayesian (nonparametric) modeling and inference. Before joining IBM, he completed Ph.D. in Statistics at the University of Michigan, where he worked with Long Nguyen. He received his bachelor’s degree in applied mathematics and physics from the Moscow Institute of Physics and Technology.

Olaf de Leeuw
As a graduated Mathematician I’m particularly interested in the techniques and math behind algorithms. How do they search for the optimal solution and why is one algorithm faster than the other? In my work as a Data Scientist I develop algorithms or adapt existing solutions to customer needs and put them into production such they can get the most value out of it. In my own time I love to read popular scientific articles or books about mathematics, physics or astrophysics. Besides this I love traveling and cycling.
Towards More Energy-Efficient Neural Networks? Use Your Brain!(Talk)

Denise Anderson
Denise Anderson, MBA, is President and CEO of the Health Information Sharing and Analysis Center (H-ISAC), a non-profit organization dedicated to protecting the global health sector from physical and cyber attacks and incidents through dissemination of trusted and timely information. Denise currently serves as Chair of the National Council of ISACs, sits on the Board of Directors for the Global Resilience Federation (GRF) and the Executive Committee of the Cyber Working Group for the Health and Public Health Sector Coordinating Council. In addition, she participates in numerous industry advisory groups and initiatives and has spoken at events all over the globe. Denise was certified as an EMT (B), and Firefighter I/II and Instructor I/II in the state of Virginia for twenty years and was an Adjunct Instructor at the Fire and Rescue Academy in Fairfax County, Virginia for ten years. She is a graduate of the Executive Leaders Program at the Naval Postgraduate School Center for Homeland Defense and Security.

Dr. Mike Flaxman
Dr. Michael Flaxman is OmniSci’s Product Lead. In addition to leading product strategy at OmniSci, Dr. Flaxman focuses on the combination of geographic analysis with machine learning, or “geoML.” He has served on the faculties of MIT, Harvard and the University of Oregon. Dr. Flaxman has participated in GIS projects in 17 countries. He has been a Fulbright fellow, and served as an advisor to the Interamerican Development Bank, the World Bank and the National Science Foundation. Dr. Flaxman previously served as industry manager for Architecture, Engineering and Construction at ESRI, the world’s largest developer of GIS technology. Dr. Flaxman received his doctorate in design from Harvard University in 2001 and holds a master’s in Community and Regional Planning from the University of Oregon and a bachelor’s in biology from Reed College.
What You'll Learn
Talks + Workshops + Special Events on these topics:
Responsible AI: From Principles to Practice
Using AI to Overcome Bias & Make Hiring More Equitable
Artificial Intelligence for Conservation and Sustainability: From the Local to the Global
Machine Learning and Robotics in Healthcare Devices and Rehabilitation
Advances and Frontiers in Auto AI and Machine Learning
Federated Learning for User Privacy
Explainable AI: human in the loop
Reproducability
AI Risk to Companies
Incident Response in a World of Evolving Threats
and more…
Why Attend?
Accelerate and broaden your knowledge of key areas in Responsible AI
With numerous introductory level workshops, get hands-on experience to quickly build up your skills
Post-conference, get access to recorded talks online and learn from over 100+ high-quality recording sessions that let you review content at your own pace
Take time out of your busy schedule to accelerate your knowledge of the latest advances in data science
Learn directly from world-class instructors who are the authors and contributors to many of the tools and languages used in data science today
Meet hiring companies, ranging from hot startups to Fortune 500, looking to hire professionals with data science skills at all levels
Get speaker insights and training in AI frameworks such as TensorFlow, MXNet, PyTorch, Spark, Storm, Drill, Keras, and other AI platforms
Connect with peers and top industry professionals at our many networking events to discover your next job, service, product, or startup.
Who should attend
The AI for Social Good Track is where industry’s top creative minds gather to discuss and shape the most challenging social problems. Whether you are an expert, or just starting your journey, this is the conference for you.
Data scientists looking to build an understanding of ethical intelligent machines
Data scientists seeking to investigate and define potential adverse biases and effects, mitigation strategies, fairness objectives and validation of fairness
Anyone interested in understanding areas such as fairness, safety, privacy and transparency in artificial intelligence and data
Business professionals and industry experts looking to understand data science ethics in practice
Software engineers and technologists who need to develop algorithms to solve fundamental algorithmic fairness problems
CTO, CDS, and other managerial roles that require a bigger picture view of data science
Technologists in the field of AI Fairness and others looking to learn mitigation strategies, algorithmic advances, fairness objectives, and validation of fairness
Students and academics looking for more practical applied training in data science tools and techniques
ODSC WEST 2022 - November 1st-3rd
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