SOUTH SAN FRANCISCO, Calif.--(BUSINESS WIRE)--insitro, a machine learning-powered drug discovery and development company, today announced the appointment of Emily Fox, Ph.D., as senior vice president of AI/machine learning. In this role, she will oversee those areas as well as data science and computational biology, inclusive of data modalities that span genetics, omics, imaging, clinical data, and molecular design.

加利福尼亚州南旧金山——（商业新闻短讯）——insitro是一家以机器学习为动力的药物发现和开发公司，今天宣布任命EmilyFox博士为人工智能/机器学习高级副总裁。在这个职位上，她将监督这些领域以及数据科学和计算生物学，包括跨越遗传学，组学，成像，临床数据和分子设计的数据模式。

Dr. Fox, a professor in the Department of Statistics and Department of Computer Science at Stanford University, has made groundbreaking contributions in the application of machine learning in healthcare, with her pioneering work directly translating into patient impact..

福克斯博士是斯坦福大学统计系和计算机科学系的教授，她在机器学习在医疗保健中的应用方面做出了开创性的贡献，她的开创性工作直接转化为对患者的影响。。

'AI leaders of Emily’s caliber who simultaneously have an understanding of biology and health are rare, and we are privileged to have recruited her to lead our AI/ML teams,” said Daphne Koller, Ph.D., co-founder and CEO. “Her groundbreaking work in machine learning, alongside her track record in translating research into impactful applications in healthcare, aligns perfectly with our mission to revolutionize drug discovery through data-driven approaches.

联合创始人兼首席执行官达芙妮·科勒（DaphneKoller）博士说：“像艾米丽这样有能力同时了解生物学和健康的人工智能领导者是罕见的，我们很荣幸能招募她来领导我们的人工智能/人工智能团队。”。“她在机器学习方面的开创性工作，以及她将研究转化为医疗保健领域有影响力的应用的记录，与我们通过数据驱动方法彻底改变药物发现的使命完全一致。

Emily's stellar track record of innovation and leadership, recognized with prestigious awards and accolades, underscores her exceptional contributions to the field. We're thrilled to welcome her wealth of knowledge and visionary insight to our team at insitro.'.

艾米丽在创新和领导力方面取得了卓越的成绩，并获得了久负盛名的奖项和赞誉，突显了她在这一领域的杰出贡献。我们非常高兴地欢迎她丰富的知识和富有远见的见解加入我们insitro的团队。”。

“The transformative power of machine learning in redefining biology is increasingly recognized, and insitro is uniquely positioned to translate this potential to improve human health through the seamless integration of massive biological and clinical datasets together with cutting-edge machine learning methods, providing insights never uncovered before,” said Dr.

“机器学习在重新定义生物学方面的变革能力越来越得到认可，insitro具有独特的地位，可以通过将大量生物学和临床数据集与尖端机器学习方法无缝集成，将这种潜力转化为改善人类健康的潜力，提供前所未有的见解，”Dr。

Fox. “These insights are going to be transformative in accelerating the discovery of novel targets and the development of molecules that interact with them, marking a new era in therapeutic innovation. As someone passionate about machine learning for human health, to be at a company where machine learning is a core part of the mission and is integrated into the entire pipeline from data collection to scientific discovery and drug development is truly inspiring.”.

狐狸。“这些见解将在加速新靶点的发现和与它们相互作用的分子的发展方面产生变革，标志着治疗创新的新时代。作为一个对机器学习促进人类健康充满热情的人，在一家机器学习是使命核心部分的公司，从数据收集到科学发现和药物开发的整个流程中都融入了机器学习，这确实令人鼓舞。”。

Prior to joining Stanford, Dr. Fox established, grew, and led the Health AI team at Apple, where she was a Distinguished Engineer. At Apple, her team collaborated cross-functionally on health and wellness projects leveraging Apple’s ecosystem of devices and software, as well as studies with partners including Aetna, Johnson & Johnson, Eli Lilly, and the Seattle Flu Study.

在加入斯坦福大学之前，福克斯博士在苹果建立、成长并领导健康人工智能团队，她是一名杰出的工程师。在苹果公司，她的团队在利用苹果设备和软件生态系统的健康和健康项目上进行了跨职能合作，并与安泰、强生、礼来和西雅图流感研究等合作伙伴进行了研究。

She also served as the Amazon Professor of Machine Learning in the Paul G. Allen School of Computer Science & Engineering and Department of Statistics at the University of Washington. Her academic research has focused on discovering interpretable latent structure from complex, high-dimensional scientific and clinical datasets, with an emphasis on data arising in genomics and neuroscience, and on machine learning for healthcare applications, including the use of wearable devices and other in-the-wild sensing modalities..

她还曾担任华盛顿大学保罗·艾伦计算机科学与工程学院和统计系的亚马逊机器学习教授。她的学术研究集中在从复杂的高维科学和临床数据集中发现可解释的潜在结构，重点是基因组学和神经科学中产生的数据，以及用于医疗保健应用的机器学习，包括使用可穿戴设备和其他野生传感方式。。

Her work has been recognized with her selection as a CZ Biohub - San Francisco Investigator (2022-2027) and serving as the NeurIPS Program co-chair in 2019. She has also been awarded a Presidential Early Career Award for Scientists and Engineers (PECASE), Sloan Research Fellowship, ONR Young Investigator award, and NSF CAREER award.

她的工作因被选为CZ Biohub旧金山调查员（2022-2027年）并于2019年担任NeurIPS计划联合主席而获得认可。她还获得了总统科学家和工程师早期职业奖（PECASE）、斯隆研究奖学金、ONR青年研究员奖和NSF职业奖。

Her Ph.D. thesis was recognized with the Leonard J. Savage Thesis Award in Applied Methodology and MIT EECS Jin-Au Kong Outstanding Doctoral Thesis Prize..

她的博士论文获得了Leonard J.Savage应用方法论论文奖和麻省理工学院EECS Jin Au Kong杰出博士论文奖。。

Fei-Fei Li, an AI pioneer, Sequoia Professor of Computer Science and founding Co-Director of Stanford’s Human-Centered AI Institute, Stanford University, said, “Having had the privilege of knowing and working with both Emily Fox and Daphne Koller for many years, I’ve witnessed firsthand their definitive contributions to machine learning, particularly in healthcare.

人工智能先驱、红杉（Sequoia）计算机科学教授、斯坦福大学（Stanford University）以人为中心的人工智能研究所（Stanford's Human-Centered AI Institute）创始联合主任李飞飞（Fei Fei Li）表示：“多年来，我有幸了解艾米丽·福克斯（EmilyFox）和达芙妮·科勒（DaphneKoller），并与他们一起工作，我亲眼目睹了他们对机器学习的决定性贡献，尤其是在。

With Emily joining insitro, their combined efforts in leveraging AI for biology discovery and drug development are set to revolutionize the field. It’s collaborations like these that highlight the power of diverse minds in AI, driving not just technological progress, but societal betterment as well.”.

随着Emily加入insitro，他们在利用人工智能进行生物学发现和药物开发方面的共同努力将彻底改变该领域。正是这样的合作突出了人工智能中多样化思维的力量，不仅推动了技术进步，而且推动了社会的改善。”。

About insitro

关于insitro

insitro is a drug discovery and development company applying machine learning (ML) and generative AI to data at scale to decode biology for transformative medicines. At the core of insitro’s approach is the convergence of in-house generated multi-modal cellular data and high-content phenotypic human cohort data.

insitro是一家药物发现和开发公司，将机器学习（ML）和生成人工智能应用于大规模数据，以解码转化药物的生物学。insitro方法的核心是内部生成的多模式细胞数据和高内容表型人类队列数据的融合。

These data are used to develop ML models that expand insitro’s data tensor via imputation, uncover underlying biologic state, and elucidate high-impact genetic modulators of disease. These powerful models rely on extensive biological and computational infrastructure and allow insitro to advance novel targets and patient biomarkers, design therapeutics, and inform clinical strategy.

这些数据用于开发ML模型，通过插补扩展insitro的数据张量，揭示潜在的生物学状态，并阐明疾病的高影响力遗传调节剂。这些强大的模型依赖于广泛的生物学和计算基础设施，并允许insitro推进新的靶标和患者生物标志物，设计治疗方法并为临床策略提供信息。

insitro is advancing a wholly owned and partnered pipeline of insights and therapeutics in neuroscience, oncology, and metabolism. Since launching in 2018, insitro has raised over $700 million from top tech, biotech, and crossover investors, and from collaborations with pharmaceutical partners. For more information on insitro, please visit www.insitro.com..

insitro正在推进神经科学，肿瘤学和新陈代谢领域的全资和合作的见解和治疗渠道。自2018年推出以来，insitro已从顶尖技术、生物技术和交叉投资者以及与制药合作伙伴的合作中筹集了7亿多美元。有关insitro的更多信息，请访问www.insitro.com。。