Deep data from wearable sensors for healthcare
We welcome you to join us in-person and on Zoom for our August eWEAR Seminar.
Date: Tuesday, August 8th from 3:30 pm to 4:30 pm PDT
Salt & Straw ice cream will be provided at 3:15 pm for in-person attendees & a chance to talk with the speakers after the seminar.
Registration: Please click here to register
Amir Bahmani, Ph.D.
3:30 pm to 4:00 pm
“Deep data in precision medicine”
Bjoern Eskofier, Ph.D.
4:00 pm to 4:30 pm
Amir Bahmani, Ph.D.
Lecturer and Director of Deep Data Research Center, Genetics, Stanford University
Amir Bahmani is a Lecturer and Director of Stanford’s Deep Data Research Center (https://deepdata.stanford.edu). He has been working on distributed and parallel computing applications since 2008. Currently, Amir is an active researcher in the NIH Bridge to Artificial Intelligence (Bridge2AI), VA Million Veteran Program (MVP), NIH Human Tumor Atlas Network (HTAN), NIH Human BioMolecular Atlas Program (HuBMAP), Stanford Metabolic Health Center (MHC) and Integrated Personal Omics Profiling (iPOP). Amir successfully created and launched Stanford’s first Cloud Computing course for Biology and Healthcare, offered to students in Biology, Computer Science, Genetics, and Biomedical Data Science departments. In addition to his teaching activities, Amir leads a team of computer scientists bioinformaticians and his team has designed and developed several notable cloud-scale frameworks such as Personal Health Dashboard (PHD) and cloud-based cost saving platforms like Hummingbird and Swarm that are now being used at Stanford and is rapidly spreading to other labs (in several universities (e.g., UPenn, UCLA, Nagoya) to accelerate otherwise-infeasible research studies. The MyPHD platform now has over 10k participants and hosts over 10 studies. His team also created Stanford Data Ocean (SDO), another innovative platform for educating engineers and biologists. SDO is the first serverless multi-omics and wearables data platform to be used for education and training.
We are currently at the beginning stages of a generation-defining revolution in biology. For the past two decades, breakthroughs in our understanding of genetics and genomics, coupled with those in AI and machine learning, have presented us with opportunities to radically improve healthcare around the world. Data is now a digital specimen, but as more and more data is collected, often in different formats and on disparate platforms, new solutions are needed to successfully integrate, store, compute, and secure data. This talk provides a short set of examples for how to handle large-scale medical studies in a secure and scalable fashion. It assesses contemporary realities, identifies potentially promising research directions, and investigates potential impact on the field of bioinformatics from a Computer Science perspective.
Bjoern Eskofier, Ph.D.
Professor and Head of the Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg
Bjoern M. Eskofier (SM, IEEE) heads the Machine Learning and Data Analytics (MaD) Lab at the Friedrich-Alexander-University Erlangen-Nuernberg (FAU). He is also the current speaker of FAU’s Department Artificial Intelligence in Biomedical Engineering (AIBE), of the German Ministry of Economic Affairs and Climate Action’s GAIA-X usecase project “TEAM-X”, and the co-speaker of the German Research Foundation’s collaborative research center “EmpkinS” (www.empkins.de).
Dr. Eskofier studied electrical engineering at the FAU and graduated in 2006. He then did his PhD in biomechanics under the supervision of Prof. Dr. Benno Nigg at the University of Calgary (Canada). He has authored more than 400 peer-reviewed articles, holds five patents, started three spinoff startup companies, and is in a supporting role for further startups. He won several medical-technical research awards, including the “Curious Minds” award in 2021 in “Life Sciences” from Manager Magazin and Merck. In 2016, he was a visiting professor in Dr. Paolo Bonato’s Motion Analysis Lab at Harvard Medical School (February–March), and in 2018, he was a visiting professor in Dr. Alex “Sandy” Pentland’s Human Dynamics group at MIT Media Lab (March–August). He serves as Area Editor for the “IEEE Open Access Journal of Engineering in Medicine and Biology” and Associate Editor for the “IEEE Journal of Biomedical and Health Informatics”. He is also active in the organization of several IEEE and ACM meetings (e.g., BSN, BHI, EMBC, IJCAI, ISWC, and UbiComp), most recently as General Chair of BHI 2023.
Bjoern Eskofier has defined his research and entrepreneurial agenda to revolve around contributions to a “Digital Health Ecosystem”, where patients are connected to other stakeholders within the healthcare system using digital support tools. His digital health research philosophy is that only multidisciplinary teams of engineers, medical experts, industry representatives, and entrepreneurs will have the tools to actually implement changes in healthcare.
The talk will give an overview of the collaborative research center “empatho-kinaesthetic sensory science” (www.empkins.de), which is newly funded by the German Research Foundation (DFG; 12M€). In EmpkinS, we combine research in traditional wearable sensing with novel (radar-based) sensing concept. I will highlight the interdisciplinary research of engineering, ethics, medical, and psychological experts within in EmpkinS.
The systems that are currently being investigated in EmpkinS in laboratory environments will have everyday applicability in the future. This will open up new possibilities in healthcare, which will hopefully contribute to delivering more objective, precise, and personalized medical diagnosis and care decisions in a future AI-supported healthcare system.
Seminar Location: Y2E2 Building, Room 299 (473 Via Ortega, Stanford, CA 94305, Y2E2 Building)
Rates (click here for more):
Per hour = $4.46
Day pass = $35.68
The following three options are available to pay for parking:
- Download the app and set up a Park Mobile account. It is recommended to do this before coming to campus.
- Use ParkMobile’s Zone parking option: no app download or account needed. You can check out as a guest without setting up an account.
- Pay-By-Phone if you don’t have a smartphone or prefer an automated voice system, call ParkMobile at 877.727.5718 to start your parking session.
Safety Protocol: Stanford University Covid-19 Policies. Stanford strongly recommends masking indoors and in crowded outdoor settings.