Amazon Physical Science Fellowship winners announced

Award recognizes three individuals who have shown the skills necessary to bridge the gap between fundamental scientific results in the physical sciences and the development of impactful technologies.

The Amazon Physical Science Fellowship was developed to foster collaboration between Amazon and the physics community for the purpose of accelerating the time from fundamental discovery to real-world application. More than 2,000 physics professors from around the world were invited to identify game-changing discoveries from the past two decades that could lead to products and services that will positively impact future generations.

These three selected fellows demonstrated an ability to identify scientific results from across the physical sciences with the potential to provide broad, positive impacts to society.

The winners are listed below.

Xiwen Gong

Xiwen Gong, PhD, is an assistant professor of chemical engineering at the University of Michigan, where she focuses on developing the next generation of soft electronic materials and devices by utilizing a transdisciplinary approach that unites physics, chemistry, and engineering.

Xiwen Gong
Xiwen Gong

Before joining the University of Michigan, Gong — who is also by courtesy an assistant professor of electrical and computer engineering, materials science and engineering, macromolecular science and engineering, and applied physics — worked as a post-doctoral fellow with Zhenan Bao, the K. K. Lee Professor of Chemical Engineering, at Stanford University’s Department of Chemical Engineering. At Stanford, Gong focused on developing soft and stretchable semiconductors and devices for wearable electronics (inSPIREd Talk). In 2018, Gong earned her PhD in electrical and computer engineering with Edward Sargent, University Professor of electrical and computer engineering, at the University of Toronto. During her PhD studies, Gong focused on the design of novel materials for solar energy harvesting, light emitting, and sensing. Her work has been published in Nature, Nature Materials, Nature Photonics, and other leading science publications. Gong received the Extraordinary Potential Prize and the “Rising Stars in EECS 2017” (Stanford University). In 2018, she was selected as one of the fourteen inaugural Schmidt Science Fellows.

Eric Ma

Eric Y. Ma PhD, is an assistant professor in physics and electrical engineering and computer science and the Georgia Lee Chair in Physics at the University of California, Berkeley. His research focuses on electromagnetic-matter interaction in uncommon regimes.

Eric Ma
Eric Ma

On the one hand, he develops new instruments that use microwave and light to probe the fundamental properties of quantum materials. On the other hand, he creates new devices and structures that use unconventional materials and inverse design to generate, manipulate, and detect electromagnetic fields. His research interests are expansive, though he is particularly excited about beyond-von–Neumann computing and human-computer interface.

Before joining UC Berkeley, Ma earned his PhD in applied physics at Stanford University, where he also conducted postdoc studies in applied physics and electrical engineering. He was also briefly a senior scientist at Apple. Ma is passionate about advancing access to undergraduate research and broadening collaborations between physics and engineering.

Tomas Martin

Tomas Martin, PhD, is senior lecturer in materials physics within the School of Physics at the University of Bristol, and director of the university’s Master of Science in Nuclear Science and Engineering program.

Tomas Martin
Tomas Martin

After earning a PhD at Bristol investigating the electronic properties of diamond surfaces, Martin worked in the renewable energy industry as a bank’s engineer on wind and solar power projects around the world, followed by four years as David Cockayne Junior Research Fellow in Materials at the University of Oxford. Martin is editor-in-chief of the scientific journal Materials Today Communications and is a published science fiction author.'

Martin’s research uses advanced microstructural characterization techniques to understand the structure and chemistry of materials for nuclear power plants, semiconductor devices and aerospace. His work aims to take a holistic approach to materials characterization using a combination of experimental techniques and computer modeling to understand the mechanisms behind materials behavior across the length scales, from individual atomic defects to large-scale stresses and chemistry changes in engineering components.

Martin is part of the core academic team running the University of Bristol’s Interface Analysis Centre microscope facility. His research group uses techniques including atom probe tomography, focused ion beam and electron microscopy, complemented by computational modeling, to understand materials degradation challenges such as corrosion, creep and radiation damage. He works with collaborators in many fields of academic research, as well as with industrial partners including EDF Energy, NNL, Rolls Royce and UKAEA.

Below are the Review Board of the Amazon Physical Science Fellowship, a distinguished group from academia and industry.

Review Board members

Philip Kim.jpg
Philip Kim

Philip Kim - Professor Philip Kim received his B.S in physics at Seoul National University in 1990 and received his Ph.D. in Applied Physics from Harvard University in 1999. He was Miller Postdoctoral Fellow in Physics from University of California, Berkeley during 1999-2001. He then joined the Department of Physics at Columbia University as a faculty member from 2002-2014. In 2014, he moved to Harvard University, where he is Professor of Physics and Professor of Applied Physics.

The focus of Prof. Kim’s group research is the mesoscopic investigation of transport phenomena, particularly, electric, thermal and thermoelectrical properties of low dimensional nanoscale materials. These materials include carbon nanotubes, organic and inorganic nanowires, 2-dimensional mesoscopic single crystals, and single organic molecules.

Professor Kim also received numerous honors and award including Tomassoni-Chisesi Prizes (2018); Vannevar Bush Faculty Fellowship (2018); Oliver E. Buckley Prize, American Physical Society (2014); Dresden Barkhausen Award (2012); IBM Faculty Award (2009); and Ho-Am Science Prize (2008). He is Elected member of the American Academy of Arts and Science (2020) and American Physical Society Fellow (2007). He graduated 21 PhD students and trained 32 postdoctoral fellows.

Young-Kee Kim - Young-Kee Kim is the Louis Block Distinguished Service Professor of Physics and Senior Advisor to the Provost for Global Scientific Initiatives at the University of Chicago. She is an experimental particle physicist, and devotes much of her research to understanding the origin of mass for fundamental particles.

Young-Kee Kim copy.jpg
Young-Kee Kim

Between 2004 and 2006, she co-led the CDF experiment at Fermilab and was Deputy Director of Fermilab between 2006 and 2013. She is currently working on the ATLAS particle physics experiment at the Large Hadron Collider at CERN as well as on accelerator physics research. Prior to Chicago, Young-Kee Kim was Professor of Physics at University of California, Berkeley. She was born in South Korea, and earned her BS and MS in Physics from Korea University, in 1984 and 1986, respectively, and her Ph.D. in Physics from the University of Rochester in 1990.

She conducted her postdoctoral research at Lawrence Berkeley National Laboratory. Young-Kee is a Fellow of the National Academy of Sciences, the American Academy of Arts and Sciences, the American Physical Society, the American Association for the Advancement of Science, and the Sloan Foundation. She received the Ho-Am Prize, the Women in Science Leadership Award from the Chicago Council of Science and Technology, the University of Rochester’s Distinguished Scholar Medal, and Korea University’s Alumni Award.

Hideo Mabuchi image.jpg
Hideo Mabuchi

Hideo Mabuchi - Hideo Mabuchi received an AB in Physics from Princeton and a PhD in Physics from Caltech. He served as Chair of the Department of Applied Physics at Stanford from 2010-2016.

His early scientific research was focused on understanding open quantum systems, quantum measurement, and the quantum-to-classical transition. In recent years his research group has turned towards fundamental issues of quantum engineering, such as quantum nonlinear dynamics, quantum feedback control and quantum model reduction. Along the way his group has also worked substantially on single-molecule biophysics, quantum information science, and quantum materials.

Major awards include the inaugural Mohammed Dahleh Distinguished Lectureship (UCSB) and a Fellowship from the John D. and Catherine T. MacArthur Foundation.

Matt McIlwain - Managing Director, Madrona Venture Group - Madrona is a venture capital firm based in Seattle, investing in mainly seed and Series A technology-based companies. For over two decades, the firm has been helping technology entrepreneurs launch and grow world-class companies

Matt McIlwain.jpg
Matt McIlwain

At Madrona, Matt invests in a broad range of software and data driven companies with a focus on cloud computing, dataware, intelligent applications and the intersections of innovation (where life science and data science intersect).

He believes in the Learning Loop for entrepreneurs who journey from curiosity to triangulation and decision making. This leads to positive outcomes and ongoing learnings. Matt has been named several times to the Forbes Midas List and list of Top 100 Venture Capitalists by CB Insights and The New York Times.

He was named Emerging Company Director of the year by the Puget Sound Business Journal. In 2011, he received the Washington Policy Center’s Champion of Freedom Award. Matt is a board member (and previous chair) of Fred Hutchinson Cancer Research Center and a board member of Washington Policy Center.

Matt enjoys going on adventures with his family, discussing public policy issues and trying out new technologies. Matt is a graduate of Dartmouth College and holds an MBA from Harvard Business School and a Master’s in Public Policy from Harvard’s Kennedy School of Government.

José Onuchic - José Onuchic is the Harry C & Olga K Wiess Professor of Physics and Astronomy, Chemistry and Biosciences at Rice University and the co-Director of the NSF-sponsored Center for Theoretical Biological Physics. His research looks at theoretical methods for molecular biophysics and gene networks.

Jose Onuchic.jpg
José Onuchic

He introduced the concept of protein folding funnels. Energy landscape theory and the funnel concept provide the framework needed to pose and to address the questions of protein folding and function mechanisms. He developed the tunneling pathways concept for electron transfer in proteins. He is also interested in stochastic effects in genetic networks with applications to bacteria decision-making and cancer. Further expanding his ideas coming from energy landscapes for protein folding, his group is now exploring chromatin folding and function and therefore modeling the 3D structure of the genome. He has received much recognition for his achievements. He was elected to the National Academy of Sciences in 2006.

He received the ICTP Prize in honor of Heisenberg in Trieste, Italy (1989) and the Beckman Young Investigator Award (1992). He is a fellow of the American Physical Society (1995), the American Academy of Arts and Sciences (2009), the Brazilian Academy of Sciences (2009), the Biophysical Society (2012) and the American Association for the Advancement of Science (2017). He received the Einstein Professorship by the Chinese Academy of Sciences (2011).

In 2014 he received the Diaspora Prize from the Ministry of Foreign Affairs and the Ministry of Industrial Development and Foreign Trade from Brazil. In 2015 he received The International Union of Biochemistry and Molecular Biology Medal. In 2018 he received National Order of Scientific Merit by the Brazilian National Council in Science and Technology. He received the 2019 American Physical Society’s Max Delbruck Prize in Biological Physics and was elected to Pontifical Academy of Sciences in 2020.

Babak Parviz - Vice President | Amazon — Babak is a Vice President at Amazon, and has led the launch of products/services such as Amazon Care, Amazon Comprehend Medical, Echo Frames, and Amazon Explore.

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Babak Parviz

Prior to joining Amazon in 2014, Babak was with Google as a Distinguished Engineer and Director at Google [x] where he built Google Glass and founded the robotic surgery and the active contact lens programs.

Babak received his BA in Literature (University of Washington), BS in Electronics (Sharif University of Technology), MS in Physics and MS and PhD in Electrical Engineering (University of Michigan), and completed his postdoctoral fellowship in Chemistry and Chemical Biology at Harvard University. He has received numerous recognitions including NSF Career Award, MIT Technology Review 35, University of Michigan Bicentennial Alumni Award, Time magazine’s best invention of the year, and IEEE CAS Industrial Pioneer Award.

Simone Severini - Simone Severini is a Professor of Physics of Information at University College London and is the Director of Quantum Computing at AWS.

Simone Severini.jpg
Simone Severini

As Director, Simone contributed to grow the initiatives of AWS in Quantum Technologies, including the Amazon Braket service and the AWS Center for Quantum Computing in partnership with Caltech.

During his academic career, Simone served as a grant reviewer for EPSRC (UK), NSF (US), NSFC (China), European Commission, Research Council of Norway, National Science Center (Poland), Dutch Research Council, Israel Science Foundation, MITACS (Canada), NSERC (Computer Science Evaluation Group), The Royal Society (International Exchanges Committee).

Our inspiration

Building on the formulation of Maxwell’s equations in 1865, Heinrich Hertz demonstrated in 1888 that radio waves can be generated, transmitted, and detected in a laboratory setting. Though Hertz doubted this discovery would lead to any practical application, it provided the game-changing experimental results that inspired Guglielmo Marconi to develop a viable radio system, transmitting the first signals across the Atlantic Ocean in 1902.

It took fourteen years from an important experimental observation by Hertz until the radio became widespread. Now, we are interested in identifying key scientific findings since the year 2000 and working with big thinkers who can help foster their development into engineered products and services at a much faster pace. We aim to identify the modern-day equivalents of the Hertz experiment.

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The OTS ANCHOR (Analytics, Insights, and Centralized Hub for OTS Reporting) team is part of the WW IT Operations & RME organization, responsible for delivering data solutions that drive operational excellence across Amazon's global IT infrastructure. We build data pipelines, predictive models, develop analytical intelligence frameworks, and create AI-powered solutions that transform how Amazon manages, triages, and resolves IT operational incidents at scale pointing towards the Letting Data Tell the Story north start vision. We are seeking a Data Scientist to help own the end-to-end data science charter for critical operational workstreams — from forecasting IT service demand and capacity planning to designing intelligent severity triaging models and building the analytical backbone for agentic AI solutions. You will work backwards from complex operational problems to create models and solutions that directly impact the efficiency and reliability of Amazon's IT operations worldwide. Key job responsibilities - Design, develop, and deploy machine learning models for IT operations forecasting, including incident volume prediction, capacity planning, and workforce demand modeling - Build and maintain severity classification and intelligent triaging models that process thousands of daily IT service tickets across global regions - Develop anomaly detection systems to identify emerging patterns in operational data, supporting proactive incident prevention - Apply econometric and statistical methods to measure the impact of operational interventions and inform resource allocation decisions - Partner with engineering teams to productionize ML models via automated pipelines using AWS services (SageMaker, Step Functions, Lambda, Redshift) - Design and conduct experiments to empirically validate model performance and operational impact - Contribute to data quality monitoring frameworks and help maintain standards for operational data integrity - Collaborate with cross-functional teams (ServiceNow platform, Decision Intelligence, Field Operations) to translate analytical insights into actionable recommendations - Communicate findings, model performance, and recommendations to stakeholders and senior leaders through written documents and presentations - Contribute to the team's science roadmap, identifying opportunities to apply ML/AI (including Generative AI) to operational challenges A day in the life Amazon Benefits: Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan
CN, 31, Shanghai
As WWGS is committed to building an AI-native, agentic organization, the heart of SSG's evolution is to reinvent how we engage sellers with an AI-first mindset. Delivering that ambition at scale demands breakthrough innovation in our foundational capabilities, at ever-faster speed. To meet these priorities with the pace and focus they require, we are standing up a dedicated team, organized as a POD model - a lean, cross-functional team led by a single owner who holds end-to-end decisions and the business outcome, so we can move at the speed the business demands. Our priority is to build the capability to reach the right sellers at scale, anchored on two pillars: Seller Tagging and Good Contact. Through Seller Tagging, we will turn today's fragmented, siloed seller signals into a unified seller intelligence capability that deepens and broadens how well we truly understand every seller. Through Good Contact, we will resolve the seller identity gaps across our channels and systems, so that every engagement and every system that depends on it, can consistently reach the right seller contact. The Data Scientist will be part of the POD team and support the team to establish structured data governance and model training framework, and trail predict models with real business use cases. Key job responsibilities Design and deliver seller segmentation and propensity models at scale — incorporating GMS, category, growth trajectory, engagement signals, and lifecycle stage. Build contact quality scoring and lifecycle management systems (coverage optimization, dormancy detection, reactivation modeling). Define success metrics, experimentation frameworks (A/B, causal inference), and measurement methodology for seller engagement interventions. Productionize ML models and data products — partner with engineering to deploy seller scores, contact quality indices, and recommendation signals. Explore LLM/GenAI applications: automated insight generation from seller data, contact intent classification, and intelligent report synthesis. Serve as the science representative in bi-weekly NBS theme reviews; present findings and proposals to theme Bar Raisers and leadership. Collaborate with BIE team members to democratize analytical outputs via dashboards and self-serve tools. Contribute to cross-marketplace seller behavior analysis supporting Global Expansion strategy (IN, KR, VN, LATAM). Evaluate, integrate, and iterate on AI systems — assess new AI/ML tools, frameworks, and third-party models for applicability to seller intelligence use cases.
US, MA, North Reading
Robotics Applied Scientist II Intern/Co-op (2027) Are you passionate about robotics and research? Do you want to solve real customer problems through innovative technology? Do you enjoy working on scalable research and projects in a collaborative team environment? Do you want to see your science solutions directly impact millions of customers worldwide? We’re looking for current PhD students with a passion for robotic research and applications to join us in 2027 as Robotics Applied Scientist II Interns/Co-ops to shape the future of robotics and transform automation at Amazon’s scale. By applying to this role, you will be considered for Robotics Applied Scientist II Intern/Co-op (2027) opportunities across various Robotics teams at Amazon with different robotics research focus. Internship positions are available for multiple locations, durations (3 to 6+ months), and year-round start dates (winter, spring, summer, fall). Join us at the forefront of Applied Science, where your contributions will shape the future of Robotics! Key job responsibilities As a Robotics Applied Scientist II Intern/Co-op, you will work alongside global experts to advance the state of the art in robotics and artificial intelligence, tackling innovative problems that bridge the gap between research and real-world robotics implementation to innovate and build solutions on behalf of our customers. We are particularly interested in students with specialization and expertise in one or more of the following research areas: - Robotics - Artificial Intelligence - Robotics Manipulation (e.g., robot arm, grasping, dexterous manipulation, end of arm tools/end effector) - Autonomous Mobile Robots - Mobile Manipulation - Multi-Robot Coordination - Autonomous Navigation - Locomotion - Motion/Path Planning - Controls - Perception - Sensing - Robot Learning - Machine Learning - Computer Vision - Large Language Models (LLM), Vision-Language-Action Models (VLA), Vision-Language-Models (VLM) - World Models - Human-Robot Interaction - Robotics Simulation - Optimization - And related! Key responsibilities may include: - Develop novel, scalable algorithms and/or modeling techniques that advance the state-of-the-art in science areas such as robotics, artificial intelligence, or related - Tackle challenging, innovative research problems within robotics, artificial intelligence, or related that bridge the gap between theoretical advancement and practical robotics implementation - Collaborate with cross-functional teams to solve complex problems, and to communicate research findings clearly, leveraging your expertise in robotics, artificial intelligence, or related - Demonstrate the ability to work independently, thrive in a fast-paced, ever-changing environment - Leverage AI-powered tools where applicable to accelerate research, experimentation, and prototyping. - Critically review and validate outputs from AI tools and automated systems. About the team Explore Robotics Teams at Amazon At Amazon, robotics is where innovative research meets real-world deployment. There are many different specialized robotics teams, including the few highlighted below. Learn more about Robotics at Amazon here: https://www.amazon.science/research-areas/robotics Fulfillment Technologies and Robotics We build dynamic partnerships between people and intelligent machines. This intricate collaboration helps Amazon fulfill orders with unmatched accuracy. Working in symphony with our robotic technology, you’ll have the opportunity to extend your technical capabilities by working alongside some of the industry’s most advanced technologies. This includes our fleet of autonomous mobile robots, sophisticated control software, and technologies like language perception, machine learning, object recognition, and semantic understanding of commands. These technologies help us deliver an ever-improving customer and employee experience, as well as improve the safety of our facilities. Amazon Robotics Amazon Robotics is at the epicenter of robotics innovation. We design, test, and build mobile robots and mechatronic systems. Our offices, labs, test floors, and factories are close together, which means we can design the hardware, write the code, test, build, and watch it all come to life. Our areas of focus include advancements in autonomous mobility, AI, robotic management software, and much more. Fauna Fauna is focused on building capable, safe, and genuinely delightful robots for everyday life. Fauna’s goal is simple: make robots people actually want to live and interact with everyday. Fauna develops tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Fauna’s work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but overall experience. Amazon Fulfillment Technologies Amazon Fulfillment Technologies develops tech solutions that create network optimizations for all Amazon sites worldwide. We own network infrastructure and devices in all buildings and labor planning and scheduling technology for fulfillment centers in North America and Europe.
US, NY, New York
The FAIM (Full Funnel Agentic Intelligence and Models) builds the agentic AI systems behind next-generation advertising. The team develops multimodal foundation models and LLM-driven agents that power shopper understanding, audience-aware creative generation, and agentic advertising workflows. As a Principal Applied Scientist for the FAIM team, you will invent the models that help advertisers achieve desired outcomes. Today's state of the art models can setup ad campaigns to a specification, but do not understand advertising strategy well enough to help with abstract goals that real advertisers ask about like "how can I make more money." This is a rare charter to build foundational optimization science where little exists today, spanning campaign recommendation, cross-product budget allocation, incrementality measurement, and long-term-sales modeling. You will set technical direction for a growing team, partner with engineering and product to take models from research to production at Amazon scale, and directly move advertiser ROAS and new-to-brand growth. In this role you will - Own the science vision for developing evaluations of the Ads Agent - Partner with scientists from across Ads to develop best practices for evaluating agent performance both online and offline - Raise the science bar for all agent evaluations developed in Ads. - Own establishing a data driven foundation for making decisions about when new features are ready to launch in Ads Agent. Key job responsibilities - Define the long-term scientific vision for agent evaluations in Ads - Translate ambiguous advertiser needs and competing objectives into a concrete science roadmap. - Invent, prototype, and productionize machine learning and optimization solutions for joint budget allocation across sponsored ad products, spanning the shopper journey from awareness to purchase. - Develop rigorous approaches to incrementality measurement and long-term-sales modeling that ground optimization in true advertiser value. - Design and lead large-scale experiments and analyses to validate hypotheses and guide product direction. - Partner closely with engineering and product to define technical contracts, data schemas, and serving systems that carry models into production. - Raise the technical bar across science and engineering through mentorship, design reviews, and hands-on collaboration. - Grow scientific talent and publish impactful research internally and at top-tier venues.