Amazon and UCLA announce fellowship recipients

The Amazon Fellows fulfill the Science Hub for Humanity and Artificial Intelligence's mission of researching the societal impact of artificial intelligence

The Science Hub for Humanity and Artificial Intelligence, launched in October 2021 to facilitate collaboration between academic researchers and Amazon scientists, today announced the second cohort of Amazon Fellows. The fellowships are aimed at graduate students pursuing research into artificial intelligence and its impact on society.

Related content
The UCLA Science Hub seeks to address challenges to humanity through research using artificial intelligence, bringing together academic and industry scientists.

The fellowships provide PhD students at UCLA Samueli School of Engineering with up to two quarters of funding during the academic year to pursue independent research projects. The Amazon Fellows study within the departments of computer science, electrical and computer engineering, bioengineering, and mechanical and aerospace engineering. In addition to project funding, they will be invited to apply to intern at Amazon.

Top row, left to right, Sanae Amani Geshnigani, Kewei Cheng, Zi-Yi Dou, Kai Fukami, and Luzhe Huang; second row, left to right, Alexander Johnson, Tung Nguyen, Alexander Schperberg, and Zhouxing Shi; and bottom row, left to right, Zhaoqiang Wang, Yu Yang, Da Yin, and Zhe Zeng. The UCLA logo is on the bottom right.
The Science Hub for Humanity and Artificial Intelligence's second cohort of Amazon Fellows are: top row, left to right, Sanae Amani Geshnigani, Kewei Cheng, Zi-Yi Dou, Kai Fukami, and Luzhe Huang; second row, left to right, Alexander Johnson, Tung Nguyen, Alexander Schperberg, and Zhouxing Shi; and bottom row, left to right, Zhaoqiang Wang, Yu Yang, Da Yin, and Zhe Zeng.

What follows is the list of fellows, their areas of research, and their UCLA faculty advisors:

Sanae Amani Geshnigani is pursuing a PhD in electrical and computer engineering; her advisor is Lin Yang, assistant professor of electrical and computer engineering.

“My research goal is to expand the applicability of bandit and reinforcement learning algorithms to new application domains: specifically, safety-critical and distributed physical systems, such as robotics, wireless networks, the power grid and medical trials.”

Kewei Cheng, is pursuing a PhD in computer science; her advisor is Yizhou Sun, professor of computer science.

“My research interests mainly focus on knowledge graph reasoning with a specific concentration on neural-symbolic reasoning, and more generally in machine learning and network science.”

Zi-Yi Dou is pursuing a PhD in computer science; his advisor is Nanyun Peng, assistant professor of computer science.

“My research has been centered around advancing the field of artificial intelligence with an aim of helping people around the globe by allowing computers to interact with them through natural language and help them accomplish tasks. State-of-the-art models still struggle with gathering information from diverse modalities and languages, and generalizing well to novel scenarios. To overcome these limitations, my current research goal is to build robust multimodal and multilingual AI models and comprehensively evaluate them along multiple dimensions and domains.”

Related content
Models that map spoken language to objects in an image would make it easier for customers to communicate with multimodal devices.

Kai Fukami is pursuing a PhD in mechanical and aerospace engineering; his advisor is Kunihiko Taira, professor, computer science.

“My academic interest belongs to fluid dynamics which is a discipline to study flows around us such as air and water. In particular, I am working on the design of artificial-intelligent techniques and machine-learning methods to understand and control turbulent flows from limited sensor measurements.”

Luzhe Huang is pursuing a PhD in electrical and computer engineering; his advisor is Aydogan Ozcan, Chancellor's Professor and the Volgenau Chair for Engineering Innovation.

“In the past decade, AI has revolutionized many fields, including robotics, computer vision, and natural language processing, and greatly improved our daily life. When it comes to microscopy imaging, despite some researches exploring the integration of AI and microscopy imaging, critical challenges remain for real-world applications and prevent the advance of AI to benefit a broad group of users in biology, pathology and medical science. I am fortunate to be studying on this frontier of human’s knowledge and develop technologies to conquer these challenges using my interdisciplinary knowledge in both AI and optics.”

Alexander Johnson, is pursuing a PhD in electrical and computer engineering; his advisor is Abeer Alwan, professor of electrical and computer engineering.

“My research focuses on improving speech technology performance for children’s speech and African American English (AAE) speech in order to provide more equitable outcomes in early education. Speech technologies perform well for certain demographics (ie. able-bodied, adult, first-language speakers of mainstream dialects). However, they perform much worse for underrepresented groups (eg. young children, speakers of non-mainstream dialects, people with speech-related disabilities, etc.). Child speakers of AAE often show poorer reading and oral language performance than their white counterparts as a result of the orthographic mismatch between their spoken dialect and mainstream American English (MAE) taught in their classrooms. ASR systems trained to recognize AAE could give these students additional teaching support and help bridge this performance gap. However, this is a difficult low-resource problem given the small number of publicly available, labeled datasets for AAE speech in comparison to those for MAE speech. Thus, novel methods for low-resource dialects are needed in order to bring ASR systems for AAE-speaking children to the level of current data-driven ASR approaches for MAE.”

Tung Nguyen is pursuing a PhD in computer science; his advisor is Aditya Grover, assistant professor of computer science.

“Deep learning has grown rapidly in both scale and generalizability over the past decade. However, the majority of the real-world advances are made in the field of vision or language, while sequential decision-making paradigms such as reinforcement learning (RL) have lagged behind and only showed limited successes for controlled domains such as games. Sequential decision making in the real world is more challenging, because 1) the inputs are high-dimensional with long-range spatiotemporal dependencies; 2) agents need to quantify uncertainty to balance exploration and exploitation; and 3) active online interactions with the environment can be very expensive or even infeasible in high-stakes applications. My research goal is to address these challenges, and thereby enable robust sequential decision making for real-world applications. I outline my past research and future plans below.”

Alexander Schperberg is pursuing a PhD in mechanical and aerospace engineering; his advisor is Dennis Hong, professor of mechanical and aerospace engineering.

Related content
Teaching robots to stow items presents a challenge so large it was previously considered impossible — until now.

“My goal is to facilitate the dream of one day seeing diverse sets of wheeled, aerial, legged, and underwater robots being used ubiquitously towards reducing the burdens of society. Robotics and AI technology have the enormous potential to support humanity by performing tasks too dangerous for human workers, or through human-robot interactions. Unfortunately, while the potential use of robotics is an exciting prospect, they are still not commonly used due to a justified concern for both their safety and cost. For example, to make robots safer typically demands high-fidelity sensor and computer components. Thus, these robots are very expensive and are still seen as a luxury item rather than a product for everyday use. More troubling is that those from economically challenged and/or underprivileged groups may not have access and potentially cannot reap the benefit from this technology. Ideally, creating new robots using off-the-shelve or inexpensive components would greatly expand the robotic field and rapidly benefit society for all.”

Zhouxing Shi is pursuing a PhD in computer science; his advisor is Cho-Jui Hsieh, associate professor of computer science.

“My research interest is trustworthy machine learning and responsible AI, and I am currently working on the formally verifiable robustness of machine learning models especially neural networks.”

Zhaoqiang Wang is pursuing a PhD in bioengineering; his advisor is Liang Gao, assistant professor of bioengineering.

“Cardiovascular diseases (CVDs) are the leading cause of death globally, taking an estimated 17.9 million lives each year. In the United States, it is reported that approximately 82.6 million people currently live with at least one type of CVD, which contributes to a significant healthcare burden. To elucidate the underlying mechanism, researchers replicate the cardiac disease model in well-established genetic systems such as mouse and zebrafish. These model animals possess the essential common physiology as humans, but intelligent microscopy is critically necessary to reveal their heart morphology and dynamics.”

Yu Yang is pursuing a PhD in computer science; her advisor is Baharan Mirzasoleiman, assistant professor of computer science.

“My research contributes to the foundations of large-scale machine learning. Learning from massive datasets is financially and environmentally expensive. Moreover, large real-world data are usually biased toward large sub-populations, and often contain noisy or malicious examples that harm the generalization performance of the trained models. To address these problems, my research primarily focuses on understanding and improving the training data or learning objectives for resource-efficient and accountable learning.”

Da Yin is pursuing a PhD in computer science; his advisor is Kai-Wei Chang, associate professor computer science.

“I propose to utilize external knowledge to promote the effectiveness and inclusivity of neural models. Specifically, the framework of building models enhanced with external knowledge is usually separated into three important stages: 1) understanding what knowledge is not well learned by neural models; 2) acquiring knowledge necessary for specified domains; and 3) injecting knowledge to strengthen model’s capability.”

Zhe Zeng is pursuing a PhD in computer science; her advisor is Guy Van den Broeck, associate professor of computer science.

“How can we build artificial intelligence systems that are able to make efficient and re-liable inference under complex, noisy and highly structured real-world scenarios? One primary challenge to tackle this question is that probabilistic inference in such systems is, in general, computationally intractable. While current machine learning techniques heavily emphasize on scaling up probabilistic inference, they are at the cost of harming inference reliability. One promising direction is to combine probabilistic machine learning techniques and the formal verification techniques. My research interests primarily lie in bridging between AI and formal methods for such purposes.”

Related content

US, WA, Seattle
The retail pricing science and research group is a team of scientists and economists who design and implement the analytics powering pricing for Amazon’s on-line retail business. The team uses world-class analytics to make sure that the prices for all of Amazon’s goods and services are aligned with Amazon’s corporate goals. We are seeking an experienced high-energy Economist to help envision, design and build the next generation of retail pricing capabilities. You will work at the intersection of economic theory, statistical inference, and machine learning to design new methods and pricing strategies to deliver game changing value to our customers. Roughly 85% of previous intern cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. Key job responsibilities Amazon’s Pricing Science and Research team is seeking an Economist to help envision, design and build the next generation of pricing capabilities behind Amazon’s on-line retail business. As an economist on our team, you will work at the intersection of economic theory, statistical inference, and machine learning to design new methods and pricing strategies with the potential to deliver game changing value to our customers. This is an opportunity for a high-energy individual to work with our unprecedented retail data to bring cutting edge research into real world applications, and communicate the insights we produce to our leadership. This position is perfect for someone who has a deep and broad analytic background and is passionate about using mathematical modeling and statistical analysis to make a real difference. You should be familiar with modern tools for data science and business analysis. We are particularly interested in candidates with research background in applied microeconomics, econometrics, statistical inference and/or finance. A day in the life Discussions with business partners, as well as product managers and tech leaders to understand the business problem. Brainstorming with other scientists and economists to design the right model for the problem in hand. Present the results and new ideas for existing or forward looking problems to leadership. Deep dive into the data. Modeling and creating working prototypes. Analyze the results and review with partners. Partnering with other scientists for research problems. About the team The retail pricing science and research group is a team of scientists and economists who design and implement the analytics powering pricing for Amazon’s on-line retail business. The team uses world-class analytics to make sure that the prices for all of Amazon’s goods and services are aligned with Amazon’s corporate goals.
US, CA, San Francisco
The retail pricing science and research group is a team of scientists and economists who design and implement the analytics powering pricing for Amazon's on-line retail business. The team uses world-class analytics to make sure that the prices for all of Amazon's goods and services are aligned with Amazon's corporate goals. We are seeking an experienced high-energy Economist to help envision, design and build the next generation of retail pricing capabilities. You will work at the intersection of statistical inference, experimentation design, economic theory and machine learning to design new methods and pricing strategies for assessing pricing innovations. Roughly 85% of previous intern cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. Key job responsibilities Amazon's Pricing Science and Research team is seeking an Economist to help envision, design and build the next generation of pricing capabilities behind Amazon's on-line retail business. As an economist on our team, you will will have the opportunity to work with our unprecedented retail data to bring cutting edge research into real world applications, and communicate the insights we produce to our leadership. This position is perfect for someone who has a deep and broad analytic background and is passionate about using mathematical modeling and statistical analysis to make a real difference. You should be familiar with modern tools for data science and business analysis. We are particularly interested in candidates with research background in experimentation design, applied microeconomics, econometrics, statistical inference and/or finance. A day in the life Discussions with business partners, as well as product managers and tech leaders to understand the business problem. Brainstorming with other scientists and economists to design the right model for the problem in hand. Present the results and new ideas for existing or forward looking problems to leadership. Deep dive into the data. Modeling and creating working prototypes. Analyze the results and review with partners. Partnering with other scientists for research problems. About the team The retail pricing science and research group is a team of scientists and economists who design and implement the analytics powering pricing for Amazon's on-line retail business. The team uses world-class analytics to make sure that the prices for all of Amazon's goods and services are aligned with Amazon's corporate goals.
US, WA, Bellevue
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Python is necessary, and experience with SQL and UNIX would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of interns from previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
US
The Amazon Supply Chain Optimization Technology (SCOT) organization is looking for an Intern in Economics to work on exciting and challenging problems related to Amazon's worldwide inventory planning. SCOT provides unique opportunities to both create and see the direct impact of your work on billions of dollars’ worth of inventory, in one of the world’s most advanced supply chains, and at massive scale. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. We are looking for a PhD candidate with exposure to Program Evaluation/Causal Inference. Knowledge of econometrics and Stata/R/or Python is necessary, and experience with SQL, Hadoop, and Spark would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
US, WA, Seattle
The Selling Partner Fees team owns the end-to-end fees experience for two million active third party sellers. We own the fee strategy, fee seller experience, fee accuracy and integrity, fee science and analytics, and we provide scalable technology to monetize all services available to third-party sellers. We are looking for an Intern Economist with excellent coding skills to design and develop rigorous models to assess the causal impact of fees on third party sellers’ behavior and business performance. As a Science Intern, you will have access to large datasets with billions of transactions and will translate ambiguous fee related business problems into rigorous scientific models. You will work on real world problems which will help to inform strategic direction and have the opportunity to make an impact for both Amazon and our Selling Partners.
US, WA, Bellevue
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. We are looking for a PhD candidate with exposure to Program Evaluation/Causal Inference. Some knowledge of econometrics, as well as basic familiarity with Stata or R is necessary, and experience with SQL, Hadoop, Spark and Python would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
US, MA, Westborough
Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun. Amazon Robotics is seeking interns and co-ops with a passion for robotic research to work on cutting edge algorithms for robotics. Our team works on challenging and high-impact projects, including allocating resources to complete a million orders a day, coordinating the motion of thousands of robots, autonomous navigation in warehouses, identifying objects and damage, and learning how to grasp all the products Amazon sells. We are seeking internship candidates with backgrounds in computer vision, machine learning, resource allocation, discrete optimization, search, and planning/scheduling. You will be challenged intellectually and have a good time while you are at it! Please note that by applying to this role you would be considered for Applied Scientist summer intern, spring co-op, and fall co-op roles on various Amazon Robotics teams. These teams work on robotics research within areas such as computer vision, machine learning, robotic manipulation, navigation, path planning, perception, artificial intelligence, human-robot interaction, optimization and more.
US, MA, Westborough
Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun. Amazon Robotics is seeking interns and co-ops with a passion for robotic research to work on cutting edge algorithms for robotics. Our team works on challenging and high-impact projects, including allocating resources to complete a million orders a day, coordinating the motion of thousands of robots, autonomous navigation in warehouses, identifying objects and damage, and learning how to grasp all the products Amazon sells. We are seeking internship candidates with backgrounds in computer vision, machine learning, resource allocation, discrete optimization, search, and planning/scheduling. You will be challenged intellectually and have a good time while you are at it! Please note that by applying to this role you would be considered for Applied Scientist summer intern, spring co-op, and fall co-op roles on various Amazon Robotics teams. These teams work on robotics research within areas such as computer vision, machine learning, robotic manipulation, navigation, path planning, perception, artificial intelligence, human-robot interaction, optimization and more.
US, WA, Bellevue
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Python is necessary, and experience with SQL and UNIX would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
US, MA, Boston
Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun. Amazon Robotics, a wholly owned subsidiary of Amazon.com, empowers a smarter, faster, more consistent customer experience through automation. Amazon Robotics automates fulfillment center operations using various methods of robotic technology including autonomous mobile robots, sophisticated control software, language perception, power management, computer vision, depth sensing, machine learning, object recognition, and semantic understanding of commands. Amazon Robotics has a dedicated focus on research and development to continuously explore new opportunities to extend its product lines into new areas. AR is seeking uniquely talented and motivated data scientists to join our Global Services and Support (GSS) Tools Team. GSS Tools focuses on improving the supportability of the Amazon Robotics solutions through automation, with the explicit goal of simplifying issue resolution for our global network of Fulfillment Centers. The candidate will work closely with software engineers, Fulfillment Center operation teams, system engineers, and product managers in the development, qualification, documentation, and deployment of new - as well as enhancements to existing - operational models, metrics, and data driven dashboards. As such, this individual must possess the technical aptitude to pick-up new BI tools and programming languages to interface with different data access layers for metric computation, data mining, and data modeling. This role is a 6 month co-op to join AR full time (40 hours/week) from July – December 2023. The Co-op will be responsible for: Diving deep into operational data and metrics to identify and communicate trends used to drive development of new tools for supportability Translating operational metrics into functional requirements for BI-tools, models, and reporting Collaborating with cross functional teams to automate AR problem detection and diagnostics