The MIT Startup Exchange Lightning Talks and Startup Exhibit listed below are part of the full agenda for 2022 MIT Efficient AI and Computing Technologies Conference, running from 9:00 AM - 6:00 PM, EST.
Computing – from hardware and software to algorithms and AI – is the intellectual backbone supporting advancement across science and technology fields, molding society in profound ways. With skyrocketing demand for computer science education, as well as the pressing need for concrete, sustainable, and responsible solutions across technological domains, the MIT Schwarzman College of Computing invests in new artificial intelligence, data science, and computer science research. The 2022 MIT Efficient AI and Computing Technologies Conference will showcase the most recent developments and tangible impacts of computing technologies in AI hardware and software for improved efficiency and explainability, as well as their applications in diverse technological areas. Presented by the MIT Schwarzman College of Computing and the MIT Industrial Liaison Program, you will hear insights from MIT faculty, MIT Startup Exchange entrepreneurs, and industry executives from presentations, lightning talks, exhibitions, and a panel discussion.
Ariadna joined MIT Startup Exchange in a new role as Events Leader in September 2019. She has responsibility for the development and execution of events featuring startups, and for helping to promote collaboration and partnerships between MIT-connected startups and industry. She works closely with the Industrial Liaison Program (ILP), also within Corporate Relations, and with other areas around the MIT innovation ecosystem and beyond. Prior to this, Ariadna worked for over a decade at Credit Suisse Group in New York City and London in a few different roles in event management and later became a Director for client strategy. She has combined her experience in the private sector with work in non-profits as a Consultant and Development Director at the New York Immigration Coalition, Immigrant Defense Project and Americas Society/Council of the Americas. Ariadna also served on the Board of the Riverside Clay Tennis Association in NY for several years. She earned her B.A. in Political Science and Communications from New York University (NYU), also doing coursework at the Instituto Tecnológico y de Estudios Superiores de Monterrey (ITESM) in Mexico City, and her M.A. in Sociology from the City University of New York (CUNY).
Jeff received his PhD his EECS from UC Berkeley as an NDSEQ fellow, was a Batelle Post-Doctoral Scholar at MIT, and is an Entrepreneurial Research Fellow at Active. Prior to Sync, he was a technical staff member at MIT Lincoln Laboratory.
Dr. Michael Fleder’s MIT research forms the basis for Covariance.ai - a machine-learning startup that enables companies to track competitors and markets with previously-impossible clarity and precision. Michael’s work has featured in MIT News (2021, 2019) and leading modeling conferences. Michael has extensive background in robotics (MIT, NASA/JPL), quantitative trading, and technology advising for C-Suite at retail banks. Michael earned his bachelor’s, master’s and doctorate degrees from MIT.
Henry Valk graduated from Wake Forest University in 2017 with a degree in Cognitive Psychology. Upon graduating, he worked in Dr. David Poeppel's cognitive neuroscience lab at NYU where he conducted research on neural synchrony in collaborative learning environments. Since that time, he has worked as a Data Scientist at Pison Technology.
Daisy Zhuo, PhD, is a Co-Founding Partner at Interpretable AI. During her PhD in Operations at MIT, she has developed a range of cutting-edge machine learning techniques such as Optimal Imputation and Robust Classifications, with publications in top machine learning and operations research journals. These algorithms have since become the core software modules of Interpretable AI. With expertise in mixed integer optimization and machine learning, she continues to research and develop new machine learning algorithms at Interpretable AI as well as applying them to solve real world industry problems. She has led the development of successful solutions in a wide range of industries including health care, insurance, finance, real estate, and manufacturing.
Elaheh Ahmadi is a co-founder and CEO of Themis AI. She received her BSc and MEng in Electrical Engineering and Computer Science from MIT. Ahmadi and her peers at MIT CSAIL spun-off Themis AI with the vision to bring fair AI into the industry. Themis AIis a leader in providing high-performance and risk-robust AI solutions — identifying and tackling bias, uncertainty, and other real-world generalization challenges.
Sebastian is the CEO of Ubicept which provides new imaging solutions in challenging environments such as seeing motion in the dark at unprecedented quality. He has spent his career exploring new kinds of imaging with a focus on signal processing and information extraction. He holds BS/MS/PhD degrees in Electrical Engineering from the Karlsruhe Institute of Technology (KIT), Germany.
Dr. Andy Wang is the founder and CEO of Prescient, a Boston-based technology startup that provides data automation and data management software for the edge. Prior to Prescient, he was the founder and CTO of GTI IoT Technology, which developed low-power wireless sensing solutions. Dr. Wang received his Ph.D. degree in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology.
Julie Choi is VP and Chief Growth Officer at MosaicML responsible for marketing, community, and customer relationships. Prior to MosaicML, she was VP / GM of AI Products and Research Marketing at Intel Corporation, responsible for Intel's product marketing, ecosystem partnerships, and sales enablement at global scale. During Julie's tenure, Intel reported over $4.5B revenue for AI technologies and became a credible provider of compute for AI workloads. Prior to Intel, Julie led product marketing at Hewlett Packard Enterprise, Mozilla and Yahoo, focused on developer and enterprise audiences. Julie holds a bachelor’s degree from MIT and a master’s degree from Stanford, both in Management Science.
Di Wu, PhD, is the co-founder and CEO of OmniML, focusing on enabling Edge AI for all ML tasks and all hardware. OmniML is an enterprise artificial intelligence (AI) company that aims to effortlessly empower edge AI everywhere. The company enables and amplifies powerful machine learning capabilities to edge devices by bringing greater speed, accuracy, and efficiency in AI through deep learning models that bridge the gap between those devices and AI applications.
Previously, Di was a software engineer in PyTorch at Scale team in AI Infra at Facebook, focusing on large-scale AI systems using hardware acceleration.
Before Facebook (Meta), Di worked on hardware acceleration for computational genomics at a high-tech startup company that was later acquired by Xilinx. He received his PhD in CS from UCLA focusing on system design for domain-specific hardware acceleration, including deep learning, medical imaging, cognitive computing, etc.
Mark Tibbetts is Product and Data Science Lead based out of Arundo’s Houston office. He gained his PhD in High Energy Physics at Imperial College in 2010 before joining Berkeley National Laboratory as a postdoctoral researcher working on the Large Hadron Collider at CERN, Switzerland. In 2016 he joined Arundo’s Oslo office as a Data Scientist and then moved to Houston in 2019 to take on the role of Lead Data Scientist. He is currently the Product Manager for Arundo’s Marathon application which delivers actionable machine learning insights to heavy asset operations and maintenance teams.
Sync Computing | Covariance | Interpretable AI | Themis AI | UbiCept
Prescient | MosaicML | OmniML | Arundo | Pison
Startups at Lunch Exhibit Only
• Leela AI: Resilient AI Solving Real-World Problems
• ServiceMob: Ontology Based Analytics Cross Industry for Service/Support Centers
• Modzy: ModelOps Platform for AI at Scale
• Pathr: You Can Learn a Lot from a Dot
Disclaimer: MIT Startup Exchange can make introductions that ideally provide open ended discussions in order to share mutual interests and potentially create common ground that incite the parties to collaborate. MIT Startup Exchange introductions may eventually lead to mutual partnerships, but that is not in any way guaranteed by MIT, MIT Corporate Relations, MIT Industrial Liaison Program (ILP) or MIT Startup Exchange, which takes no responsibility for these outcomes and no formal part in such discussions following our introduction. MIT Startup Exchange and its activities and events are not for purposes of soliciting investment or offering securities.