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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About Sponsored Products and Brands: The Sponsored Products and Brands (SPB) organization at Amazon Ads is re-imagining the advertising landscape through industry leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. About Our Team: The Brand Beacon team is responsible for inventing impressions offerings for brands to increase share of voice via premium experiences, helping brands get discovered, acquire new customers and sustainably grow customer lifetime value. We build end-to-end solutions that enable brands to drive discovery, visibility and share of voice. This includes building advertiser controls, shopper experiences, monetization strategies and optimization features. We succeed when (1) shoppers discover, engage and build affinity with brands and (2) brands can grow their business at scale with our advertising products. About This Role: As a Senior Scientist for the team, you will have the opportunity to apply your deep subject matter expertise in the area of ML, LLM and GenAI models. You will invent new product experiences that enable novel advertiser and shopper experiences. This role will liaise with internal Amazon partners and work on bringing state-of-the-art GenAI models to production, and stay abreast of the latest developments in the space of GenAI and identify opportunities to improve the efficiency and productivity of the team. Additionally, you will define a long-term science vision for our advertising business, driven by our customer’s needs, and translate it into actionable plans for our team of applied scientists and engineers. This role will play a critical role in elevating the team’s scientific and technical rigor, identifying and implementing best-in-class algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling. You will communicate learnings to leadership and mentor and grow Applied AI talent across org. * Develop AI solutions for advertiser and shopper experiences. Build monetization and optimization systems that leverage generative models to value and improve campaign performance. * Define a long-term science vision and roadmap for our advertising business, driven from our customers' needs, translating that direction into specific plans for applied scientists and engineering teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. * Design and conduct A/B experiments to evaluate proposed solutions based on in-depth data analyses. * Effectively communicate technical and non-technical ideas with teammates and stakeholders. * Stay up-to-date with advancements and the latest modeling techniques in the field. * Think big about the arc of development of Gen AI over a multi-year horizon and identify new opportunities to apply these technologies to solve real-world problems. #GenAI
US, WA, Seattle
Amazon Industrial Robotics is seeking exceptional applied science talent to develop AI and machine learning systems that will enable the next generation of advanced manufacturing capabilities at unprecedented scale. We're building revolutionary software infrastructure that combines cutting-edge AI, large-scale optimization, and advanced manufacturing processes to create adaptive production control systems. As a Senior Applied Scientist, you will develop and improve machine learning systems that enable real-time manufacturing flow decisions. You will leverage state-of-the-art optimization and ML techniques, evaluate them against representative manufacturing scenarios, and adapt them to meet the robustness, reliability, and performance needs of production environments. You will invent new algorithms where gaps exist. You'll collaborate closely with software engineering, manufacturing engineering, robotics simulation, and operations teams, and your outputs will directly power the systems that determine what to build next, where to allocate resources, and how to maximize throughput. The ideal candidate brings deep expertise in optimization and machine learning, with a proven track record of delivering scientifically complex solutions into production. You are hands-on, writing significant portions of critical-path scientific code while driving your team's scientific agenda. If you're passionate about inventing the intelligent manufacturing systems of tomorrow rather than optimizing those of today, this role offers the chance to make a lasting impact on the future of automation. Key job responsibilities - Identify and devise new scientific approaches for constraint identification, dispatch optimization, WIP release control, and predictive flow intelligence when the problem is ill-defined and new methodologies need to be invented - Lead the design, implementation, and successful delivery of scientifically complex solutions for real-time manufacturing flow optimization in production - Design and build ML models and optimization algorithms including constraint prediction, starvation risk forecasting, and dispatch optimization - Write a significant portion of critical-path scientific code with solutions that are inventive, maintainable, scalable, and extensible - Execute rapid, rigorous experimentation with reproducible results, closing the gap between simulation and real manufacturing environments - Build evaluation benchmarks that measure model performance against manufacturing outcomes including constraint utilization and throughput rather than traditional ML metrics alone - Influence your team's science and business strategy through insightful contributions to roadmaps, goals, and priorities - Partner with manufacturing engineering, robotics simulation, and applied intelligence teams to ensure scientific approaches are grounded in operational reality - Drive your team's scientific agenda and role model publishing of research results at peer-reviewed venues when appropriate and not precluded by business considerations - Actively participate in hiring and mentor other scientists, improving their skills and ability to deliver - Write clear narratives and documentation describing scientific solutions and design choices