Graduate

Although treatment algorithms vary, surgery is the primary treatment modality for most solid cancers. In oncologic tumor resection, the preferred outcome is complete cancer removal as residual tumor left behind is considered treatment failure. However complete tumor removal needs to be balanced with functional preservation and minimizing patient morbidity including prevention of inadvertent nerve injury. The inability of surgeons to visually distinguish between tumor and normal tissue including nerves leads to residual cancer cells being left behind at the edges of resection, i.e. positive surgical margins (PSM). PSM can be as high as 20-40% in breast cancer lumpectomy, 21% for radical prostatectomy, and 13% for HNSCC. Similarly, using white light reflectance alone which is the current standard of care in operating rooms, nerve dysfunction following surgery has been reported to be as high as ~2-40% ranging from immediate post op to long-term dysfunction.

Molecular imaging with fluorescence provides enhanced visual definition between diseased and normal tissue and have been shown to decrease PSM in both animal models and patients. Molecular imaging with fluorescence can also provide enhanced visualization of important structures such as nerves to improve preservation and minimize inadvertent injury. Our laboratory has extensive experience in development of both nerve and tumor injectable markers for surgical visualization. In presentation we will discuss the development of nerve and tumor markers combinations to improve intraoperative visualization – aka Precision Surgery TM.

The SCIEN Colloquia are now offered via Zoom - Please register by going to https://stanford.zoom.us/meeting/register/tJctd-utrT4rHtT5OO34glASg1vol-PCGuXR

Lidar systems are used in diverse applications, including autonomous driving and industrial automation. Despite the challenging requirements for these systems, most lidars in the market utilize legacy technologies such as scanning mechanisms, long-coherence-length or edge-emitting lasers, and avalanche photodiodes, and consequently offer limited performance and robustness at a relatively high price point. Other systems propose to utilize esoteric technologies which face an uphill struggle towards manufacturability. This talk will describe two complementary technologies, leveraging on years of progress in adjacent industries. When combined with novel signal processing algorithms, these deliver a high-resolution, long-range and low-cost solid-state flash lidar system, breaking the performance envelope and resulting in a camera-like system. We will discuss design trade-offs, performance roadmap into the future and remaining challenges.

Please register by going to https://stanford.zoom.us/meeting/register/tJAtcO2gqz0iGN0p94z4JU_I9-EQuQ3lBi7N

The talks are also videotaped and posted the following week on talks.stanford.edu for Stanford students, staff, faculty and SCIEN Affiliate Member companies.

If you wish to receive announcements about future talks, please add yourself to the SCIEN distribution list by going here.

Group testing involves testing individual items together as a combined group, rather than separately, to better understand each item. This process is used in numerous applications, including the screening of blood donations, the detection of sexually transmitted diseases, the discovery of chemical compounds for new pharmaceuticals, and the estimation of virus transmission rates from insects to plants. Applied in appropriate settings, group testing can greatly reduce associated testing costs and increase testing efficiency. This is why group testing has played an important role with increasing testing capacity during the COVID-19 pandemic. My presentation examines the statistical and non-statistical aspects of group testing for SARS-CoV-2, the virus that causes COVID-19. I will explain how group testing is being used now and how it could be implemented better to maximize its impact.

We begin with FDA's Guidance for Industry on (a) Master Protocols for Efficient Clinical Trial Designs to Expedite Development of Oncology Drugs and Biologics in 2018; (b) Enrichment Strategies for Clinical Trials to Support Determination of Effectiveness of Human Drugs and Biological Products in March 2019; and (c) Adaptive Designs for Clinical Trials of Drugs and Biologics in November 2019. We then describe their biostatistical and biopharmaceutical underpinnings, focusing on recent advancements in adaptive group sequential trial designs and statistical methods for their analysis, and conclude with challenges and opportunities for statistical science in precision medicine and regulatory submission.

This is joint work with Tze Lai and Nikolas Weissmueller.

Dear EE Community,

Based on the feedback from our last EE Town Hall (6/11/20), we would like to continue the discussion regarding equity and inclusion. At this next town hall, a few of our campus partners will join the conversation and we will have a Q&A session moderated by the Graduate Life Office.

No RSVP is needed to attend. If you would like to provide feedback to the EE Department's Culture, Equity, and Inclusion Committee, you may submit your feedback here: https://stanforduniversity.qualtrics.com/jfe/form/SV_ctC1kwfhPRiCt3D.

The panelists include:

• EE Department Chair: Prof. Stephen Boyd
• EE Culture, Equity, and Inclusion Committee: Prof. Eric Pop (committee chair and host) & other committee members
• Associate Dean for Graduate and Career Education and Diversity, School of Medicine: Ayodele Thomas
• Director of Equity and Inclusion, School of Engineering: Lourdes Andrade
• CAPS Psychologist: Helen Hsu
• Graduate Life Office: Ken Hsu and Chidel Onuegbu (moderators of Q&A session)

We hope you can make it. Please stay safe, take care, and check-in with one another.

Sincerely,
Electrical Engineering Department
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Zoom Info available to EE community mailing list

Charlie and Prof. Levis invite you to attend weekly meetups for everyone contributing to FLIGHT this summer. 

Tuesdays at 8PM PDT.

Here's the URL: https://stanford.zoom.us/j/91397287160

password: join ee125-project for meeting ID and password

EE New Student Orientation

The orientation will be virtual this year. We will host breakout sessions and meetings via Zoom.

More details to follow within a few weeks!

Please let Tiffany** know if you have any questions. 

**Graduate Admissions Specialist & Student Life Coordinator, P: 650-725-9925, E: tiffany@ee.stanford.edu

Despite their many applications over the last decades, entangled and single-photon sources from probabilistic sources such as spontaneous parametric downconversion have limited performance due to the presence of unwanted multi-pair events. For example, it is not possible to produce a heralded single photon with a probability exceeding 25%. However, by incorporating low-loss multiplexing (in our case, time-bin multiplexing), one can greatly enhance device performance. We will review our recent work in this area, and speculate about the future.

Joint analyses of genomic datasets obtained in multiple different conditions are essential for understanding the biological mechanism that drives tissue-specificity and cell differentiation. But it still remains computationally challenging even when the number of conditions is moderate.

I will present CLIMB (Composite LIkelihood eMpirical Bayes), a statistical methodology which learns patterns of condition specificity present in genomic data by leveraging pairwise information. CLIMB provides a generic framework facilitating a host of downstream analyses, such as clustering genomic features sharing similar conditional-specific patterns and identifying which of these features are involved in cell fate commitment. It improves upon existing methods by boosting statistical power to identify biologically meaningful signals while retaining interpretability and computational tractability. We illustrate CLIMB's value on a CTCF ChIP-seq dataset measured in 17 different cell populations and an RNA-seq dataset measured in three committed hematopoietic lineages. These analyses demonstrate that CLIMB captures biologically relevant clusters in the data and improves upon commonly-used pairwise comparisons and unsupervised clusterings typical of genomic analyses.

These informal & informational lunches consist of several students and faculty. There is no set agenda during the discussion and it is completely informal. Students are encouraged to share their experience within the department, research, their plans, and feedback.

Registration is required.
Sign up as soon as you can because the roundtable discussion session is very popular and fills up quickly! Space is limited; you will receive a confirmation email if you are confirmed to attend. Please contact Tiffany, Student Life Coordinator if you have any questions, tdtran@stanford.edu. Thank you!
The student/faculty roundtable discussions are organized by the Student Life Committee.

Professor and Chair, Stephen Boyd
EE Faculty: Srabanti Chowdhury, Jelena Vuckovic, Ada Poon, and Dan Congreve