Marcin von Grotthuss, PhD

Marcin von Grotthuss is a Senior Computational Scientists at the Broad Institute of MIT and Harvard. He earned the Ph.D. in bioinformatics at Radboud University Nijmegen, Netherlands and gained his professional experiences at Sanford-Burnham Institute (La Jolla), University of Washington (Seattle), Harvard University, University of Cambridge, University of California Irvine, and the Brigham and Women’s Hospital / Harvard Medical School. In his work, initially, he applied machine-learning technics to estimate the biomedical properties of small molecules. Next, he worked on distant sequence-structure-function relationships in proteins. After that, he was focused on the genomics of model organisms and human genetics (1000 Genomes Project). Currently, he develops knowledge portals for complex traits like type 2 diabetes, stroke, and cardiovascular diseases. And he is one of the instrumental scientists in the consortium that builds a Biomedical Data Translator.

Strategies for Building AI-ready Data Sources and (Semi)autonomous Reasoning Agents Operating on Top of Them(Business Talk)

Dr. Biplav Srivastava

Biplav Srivastava is a Professor of Computer Science at the AI Institute at the University of South Carolina. Previously, he was at IBM for nearly two decades in the roles of a Research Scientist, Distinguished Data Scientist and Master Inventor. Biplav is an ACM Distinguished Scientist, AAAI Senior Member, IEEE Senior Member and AAAS Leshner Fellow for Public Engagement on AI (2020-2021). His focus is on promoting goal-oriented, ethical, human-machine collaboration via natural interfaces using domain and user models, learning and planning. He applies these techniques in areas of social as well as commercial relevance with particular attention to issues of developing countries (e.g., transportation, water, health and governance). Biplav’s work has lead to many science firsts and high-impact commercial innovations ($B+), 150+ papers and 50+ US patents issued, and awards for papers, demos and hacks. He has interacted with commercial customers, universities and governments, been on multilateral bodies, and assisted business leaders on technical issues.

A Human-Machine Collaboration Built on Trust and Accountability(Business Talk)

Jekaterina Novikova, PhD

Jekaterina Novikova is a Director of Machine Learning at Winterlight Labs, Toronto/Canada, where she leads a team of research scientists and engineers. She focuses on strategic leadership in machine learning, helping teams to incorporate state-of-the-art research into business priorities, prioritize opportunities, and develop roadmaps. Jekaterina holds a PhD from the University of Bath/UK and did her PostDoc at the Heriot-Watt University in Edinburgh/UK.

Development and evaluation of natural language interfaces is a key area of Jekaterina’s research, with applications ranging from human-robot spoken dialogue systems to machine learning-based diagnostic platforms that detect cognitive and mental diseases from human speech. Jekaterina has authored over 40 peer-reviewed papers in this area, that were published and presented at top-tier conferences, such as EMNLP and ACL.

In recent years, Jekaterina was invited to be the keynote speaker at various conferences and workshops across the globe, such as CogX 2017 in London/UK, Re-Work AI Summit 2018 in Toronto/Canada, MLconf 2019 in San Francisco/US, ODSC East 2020 Virtual. Jekaterina’s work and outreach activities have been recognized with a nomination of “30 Influential Women Advancing AI in Canada”, as well as best research paper nominations at the conferences HAI and SigDIAL.

Solving Real-life Challenges in Detecting Cognitive Diseases from Speech using ML(Business Talk)

Rich Dutton

Rich Dutton is the Head of Machine Learning for Corporate Engineering at Google, where he leads a team of 15 engineers and data scientists across NYC and Austin. Prior to this role, Rich was a tech lead in Bigtable at Google following a 15 year career working in data and analytics across both tech and finance in the US (New York and Seattle), Europe and Asia.

How Google Uses AI and Machine Learning in the Enterprise(Business Talk)

Javed Ahmed, PhD

Javed is an economist and data scientist with experience in banking, finance, forecasting, risk management, consulting, policy, and behavioral economics. He has led development of analytic applications for large organizations including Amazon and the Federal Reserve Board of Governors, and served as a researcher with the Office of Financial Research (U.S. Treasury). He holds a PhD in financial economics and MA in statistics from U.C. Berkeley, as well as undergraduate degrees in operations management and systems engineering from the University of Pennsylvania. Currently, Javed is a Senior Data Scientist on the Corporate Training team at Metis, where he works with companies to upskill their staff in data science and analytics.

Tackling Ethical Risk and Bias in Machine Learning Applications(Business Talk)

Susan Gregurick, PhD

Susan K. Gregurick, Ph.D., was appointed Associate Director for Data Science and Director of the Office of Data Science Strategy (ODSS) at the National Institutes of Health (NIH) on September 16, 2019. Under Dr. Gregurick’s leadership, the ODSS leads the implementation of the NIH Strategic Plan for Data Science through scientific, technical, and operational collaboration with the institutes, centers, and offices that comprise Dr. Gregurick was previously the Division Director for Biophysics, Biomedical Technology, and Computational Biosciences at the National Institute of General Medical Sciences (NIGMS). In this role Dr. Gregurick led the institutes effort to reimagine the NIGMS technology programs including early stage, concept development, focused technology programs, development and dissemination centers, through National and Regional Resources to support state-of-the-art facilities, equipment, technologies, research tools, software, and service. Prior to joining the NIH, Susan was a program manager for the Department of Energy where she oversaw the development and implementation of the DOE Systems Biology Knowledgebase, which is a framework to integrate data, models, and simulations together for a better understanding of energy and environmental processes. During Susan’s academic career she was a Professor of Computational Biology at the University of Maryland, Baltimore County and her research interests include dynamics of large biological macromolecules. Susan holds a Ph.D. in Computational Chemistry and her areas of expertise are computational biology, high-performance computing, neutron scattering and bioinformatics.

Dave Stuart

Dave Stuart is a senior technical executive within the US Department of Defense, where he’s leading a large-scale effort to transform the workflows of thousands of enterprise business analysts through Jupyter and Python adoption, making tradecraft more efficient, sharable, and repeatable. Previously, Dave led multiple grassroots technology adoption efforts, developing innovative training methods that tangibly increased the technical proficiency of a large noncoding enterprise workforce.

Jupyter as an Enterprise “Do It Yourself” (DIY) Analytic Platform(Business Talk)

Mark Weber

Mark Weber is an applied researcher and Strategy & Operations Lead at the MIT-IBM Watson AI Lab, a $250 million partnership funding over 200 scientists making fundamental breakthroughs in AI. Through the lab’s corporate membership program, which he runs, Mark works closely with global leaders across multiple sectors on the creative challenge of bridging fundamental science to real-world impact. Mark’s current applied research includes neuro-symbolic generative modeling for construction monitoring, graph deep learning for anti-money laundering, and supply chain demand forecasting. Mark also oversees strategic engagements with IDEO, the International Monetary Fund, and the Internal Revenue Service. Prior to IBM Research, Mark was a graduate researcher at the MIT Media Lab and a fellow at the MIT Legatum Center for Development & Entrepreneurship while he earned his M.B.A in finance from MIT Sloan. There he led the development of an open-source protocol called b_verify for verifiable records in supply chain finance. Before his foray into technology, Mark spent the first chapters of his career focused on political economy and development. He produced three documentary films on these subjects, most notably the critically acclaimed film Poverty, Inc