pixie.jpg
Location: Princeton, NJ, USA
Faculty advisor: Sanjeev Arora

Pixie

We're an eclectic team of research-oriented undergraduate and graduate students in Princeton's CS and math departments.

Individually, our specialties span a wide gamut, from machine learning theory to computer vision to distributed systems. We're united by a passion for the multifaceted field of artificial intelligence, and a vision of bringing change and surprise to the world through our research. Using a combination of tried-and- true techniques in natural language processing and freshly minted methods in deep learning, we hope to bring to you a socialbot that will understand and react to the social context, providing endless interesting and empathetic conversation.

Niranjani P. - Team leader

I'm a second year PhD student in Computer Science, advised by Professor Barbara Engelhardt. In 2013, I graduated from the University of Cambridge in Information and Computer Engineering (BA, MEng). Following that, I was with a start-up for two years, working on the research and development of speech recognition software. My current research interests are primarily in machine learning methods motivated by clinical medicine, spanning reinforcement learning, time series modelling, natural language processing and knowledge representation.

Alex B.

I'm a second year CS PhD student advised by Han Liu working on statistical learning and deep learning. At Princeton, I've worked on robustness of machine learners to attack (paper accepted at NIPS) and online hyperparameter optimization for deep networks. I also did a research internship at Google working on transfer learning for speech recognition with deep recurrent networks. Before Princeton I worked at Wynyard on stochastic process models of crime and distributed network security software for Apache Spark. My undergraduate research was on signal processing algorithms for ventilator management in the intensive care unit.

Ari S.

I'm a second year Computer Science PhD student working with Han Liu. I am interested in both general machine learning methodologies and applications in computer vision, robotics, and natural language processing. I am supported by an NDSEG Fellowship. Before Princeton I completed a research fellowship at the National Institutes of Health, focusing on computer-aided diagnostics. I developed software for automated detection of pathologies (e.g., enlarged lymph nodes, tumors) on CT and MRI images. Prior to NIH, I studied mathematics as an undergraduate at the University of Florida.

Cyril Z.

I'm a PhD student in Computer Science, studying algorithms and machine learning theory. I received my B.S. in Computer Science from Yale University, where I worked on fast Laplacian solvers, exoplanet physics, and various artsy things. I dream of uniting the beauty and rigor of theoretical computer science with the humanism and pragmatism of its applications.

Daniel S.

Daniel is a second-year graduate student working at the intersection of artificial intelligence and distributed systems. After receiving his bachelor's in Computer Science from Harvard, he spent five years in industry working on three-dimensional computer vision, constructing laser scanners with high dynamic range, and cluster computing on three-dimensional data. In the last year, he has built a robot that autonomously scans large indoor spaces in real time powered with a distributed computing back end. He was also on the MIT-Princeton team that took 3rd place at the 2016 Amazon Picking Challenge (top non-industry entrant). He currently works on deadline computing.

Davit B.

I graduated UCL majoring in Computer Science supervised by Prof. Lourdes Agapito. I developed Cyclop War during New Year's night. Launched multi-platform casual game Froo Zoo played by 100K users at age 17. At 18 I was featured by TechCrunch and started Newsly. At 19 I founded Cyclop. I am inspired by Elon Musk, Steve Jobs, DeepMind and the possible applications of Recurrent Neural Networks in vision. I am also co-founder Castly.tv, which is a video on demand platform that lets users sync-watch movies with friends and family. Started my PhD at 20.

Holden L.

I am a third-year PhD student advised by Sanjeev Arora. My research is on provable algorithms for machine learning, including areas such as neural networks, natural language processing, and reinforcement learning. I graduated with at B.Sc. in Mathematics from MIT in 2013 and M.A.St. in Mathematics from the University of Cambridge in 2014. My other interests include creative writing, teaching, science fiction, and rationality.

Jason G.

I majored in applied math and computer science in USTC between 2010 and 2014 and joined the Statistical Machine Learning (SMiLe) lab at Princeton in Sept. 2014 for graduate study under the supervision of Prof. Han Liu. I worked on CUDA programming for real time rendering algorithm in USTC. In the summer of 2013, I developed a set of computer vision toolkits for microscopy video archive processing while working as a research intern at the Oxford Center for Applied Math. My recent research focuses on automatic feature engineering and variable selection in the presence of heavy noise and multicolinearity.

Karan S.

I'm a second year Ph.D., advised by Prof. Elad Hazan. My research is focused on the design of interactive learning algorithms involving feedback-driven data collection. My recent work deals with complex, structured decision-making systems, involving partial feedback, ubiquitous in online advertising, clinical decision making. I graduated from the Indian Institute of Technology, Kanpur in 2015 with the distinction of being awarded the President's Gold Medal for the best academic performance. In 2014, as a research intern at Microsoft Research, Redmond, I worked on Programming-by-Natural-Language techniques to translate natural language prompts into structured queries over knowledge bases.

Mikhail K.

I am an MSE student in the Department of Computer Science interested in developing algorithms and models for computational problems. My research has focused on machine learning, natural language processing, mathematical optimization, scientific computing, and partial differential equations. I received an A.B. in Mathematics with Honors from Princeton University in 2016. My thesis was supervised by Professor Sanjeev Arora.

Nikunj S.

I am a first year Masters student in the Computer Science department. I am interested in Machine Learning, deep learning and NLP.

Oluwatosin A.

I am currently a First-year Master's CS student. My undergraduate degree was in Electrical Engineering (summa cum laude) at The George Washington University. So the world of CS (especially AI) is relatively new to me. I find it interesting to learn about topics in different subject areas, and I am hoping to learn with and contribute to the Princeton team with my skills and persistence.

Sanjeev Arora - Faculty advisor

Professor of Computer Science, Princeton University. Interests include Theory, Algorithms, Machine Learning and NLP.

Latest news

The latest updates, stories, and more about Alexa Prize.
CN, 31, Shanghai
As an Applied Scientist, you will be responsible for bringing new product designs through to manufacturing. You will work closely with multi-disciplinary groups including Product Design, Industrial Design, Hardware Engineering, and Operations, to drive key aspects of engineering of consumer electronics products. In this role, you will use expertise in physical sciences, theoretical, numerical or empirical techniques to create scalable models representing response of physical systems or devices, including: * Applying domain scientific expertise towards developing innovative analysis and tests to study viability of new materials, designs or processes * Working closely with engineering teams to drive validation, optimization and implementation of hardware design or software algorithmic solutions to improve product and customer risks * Establishing scalable, efficient, automated processes to handle large scale design and data analysis * Conducting research into use conditions, materials and analysis techniques * Tracking general business activity including device health in field and providing clear, compelling reports to management on a regular basis * Developing, implementing guidelines to continually optimize design processes * Using simulation tools like LS-DYNA, and Abaqus for analysis and optimization of product design * Using of programming languages like Python and Matlab for analytical/statistical analyses and automation * Demonstrating strong understanding across multiple physical science domains, e.g. structural, thermal, fluid dynamics, and materials * Developing, analyzing and testing structural solutions from concept design, feature development, product architecture, through system validation * Supporting product development and optimization through application of analysis and testing of complex electronic assemblies using advanced simulation and experimentation tools and techniques
IL, Haifa
We are seeking an Applied Scientist to help build Amazon’s next-generation customer memory and personalization systems. Are you interested in building systems that move beyond reacting to customer behavior, to actually understanding and remembering it over time? Our team is building Amazon’s customer memory layer – a system that extracts, curates, and reasons over customer knowledge to power next-generation personalization. This includes transforming noisy, unstructured signals into durable, high-quality representations of customer preferences, intents, and life events, and using them in real time to improve customer experiences. We are part of Amazon’s Personalization organization, a high-performing group that leverages large-scale machine learning, generative AI, and distributed systems to deliver highly relevant customer experiences. We tackle challenging problems at the intersection of information extraction, knowledge representation, LLM reasoning, and recommendation systems. Our systems operate under real-world constraints of scale, latency, and quality, requiring careful tradeoffs between precision, recall, and responsiveness. This team plays a central role in defining how Amazon understands its customers, and how that understanding is applied across the shopping experience. As an Applied Scientist, you will design and build ML and LLM-powered solutions for Amazon's customer memory and personalization systems. You will work on how customer knowledge is extracted, validated, and applied in production systems. You will own the end-to-end delivery of ML solutions, from problem formulation and modeling to offline and online experimentation, and production deployment at scale. You will deliver high-quality, scalable systems that power customer-facing experiences. You will drive work across areas such as fact extraction, memory quality and lifecycle, temporal reasoning, and grounded personalization, while navigating tradeoffs between quality, latency, and coverage. You will collaborate closely with engineering and product teams to translate research into measurable customer impact. Please visit https://www.amazon.science for more information.
US, WA, Seattle
Are you passionate about solving big problems from ground-up? Do you enjoy building new state-of-the-art products at internet scale? Come lead the innovation in this startup team, vertical ad products. This is a green field problem without a known answer or a pattern to follow. We have ambitious vision to simplify full funnel advertising solutions, at scale, with specialized agentic AI-powered models and diversify the demand to strategic verticals including finserv, autos, locals.. etc. We are seeking an experienced Sr Data Scientist to drive innovation in our Ads Foundational Model. In this individual contributor role, you will apply advanced machine learning techniques to improve advertiser performance and customer experience. Key job responsibilities As a Data Scientist on this team, you will: 1. Develop and drive the science strategy for Ads Foundational Model (Ads-FM), aligning it with the program's objectives and overall business goals. 2. Identify high-impact opportunities within Ads-FM program and lead the ideation, planning, and execution of science initiatives to address them. 3. Build and deploy machine learning models using computer vision, natural language processing, and deep learning to evaluate and enhance ad effectiveness. 4. Develop algorithms that extract meaningful signals from image, video, and audio content to predict and improve customer engagement 5. Leverage Amazon's extensive data repository to create predictive models that generate actionable recommendations for more compelling ad creative 6. Collaborate with business leaders and cross-functional teams to implement ML-powered solutions 7. Contribute to the ML roadmap for the Ads-FM program through innovation and research.
US, WA, Seattle
You will build and lead the economics research agenda for measurement, experimentation, and value attribution for Amazon's Devices & Services organization. Your team is the "truth layer" of the Intelligence Core — the shared economics and causal inference capability that serves all Devices product lines, marketing pods, and Finance leadership with causal evidence of what Devices are worth and whether our investments are working. This is not a traditional analytics or measurement role. You will own an active research program in experimentation design — identifying and executing the causal studies that produce the causal inputs for pricing decisions, marketing optimization, and portfolio strategy. Your outputs provide the causal evidence base that L8 peers and senior leadership consume to make billions of dollars in investment decisions across the D&S portfolio. You will also own the economic models that validate and drive execution across the full surface area of marketing spend for devices and services. Key job responsibilities Economic Value: • Downstream value attribution for all Devices product lines — Impact on Prime, subscription lift, consumer spending, advertising value • Alexa+ value isolation and cross-PL attribution • Causal frameworks connecting device sales to Prime acquisition, subscription retention, and ecosystem engagement Marketing Science & Measurement: • Build the marketing science function from scratch • Incrementality measurement for marketing spend across all channels • Attribution methodology, measurement standards, and cross-pod governance • Marketing ROI frameworks for use by category marketers • CCM certification methodology and scenario planning models for optimal investment allocation Experimentation: • Owning the estimation methodology, identification strategies, data inputs/outputs, and refresh cadence • You will build this team's analytics function with AI at its core from day one • Experimentation governance — managing interference across teams, setting standards for causal validity • Evaluation framework for AI agents and autonomous optimization systems
US, WA, Seattle
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best.
IN, KA, Bengaluru
Have you ever wondered how that Amazon box with the smile arrives so quickly, where it came from, and how much it cost Amazon to deliver? The WW Amazon Logistics, Business Analytics team manages the delivery of tens of millions of products every week to Amazon's customers, achieving on-time delivery in a cost-effective manner. We are seeking an enthusiastic, customer-obsessed Manager Research Science with strong analytical skills to join our team. This role is crucial in optimizing Amazon's vast delivery network and will have significant impact on the customer experience, particularly in the final phase of delivery. As a Manager Research Science, you will: 1. Address business challenges through building compelling cases and using data to influence change across the organization 2. Develop input and assumptions based on preexisting models to estimate costs and savings opportunities associated with varying levels of network growth and operations 3. Create metrics to measure business performance, identify root causes and trends, and prescribe action plans 4. Manage multiple high-impact projects simultaneously 5. Work with technology teams and product managers to develop new tools and systems supporting business growth 6. Communicate with and support various internal stakeholders and external audiences 7. Implement scheduling solutions, improve metrics, and develop scalable processes and tools The ideal candidate will have: - Extensive experience in operations research and data-driven decision making - Strong analytical and problem-solving skills - Robust program management and research science skills - Ability to work with a team and make independent decisions in ambiguous environments - Customer-obsessed mindset with a focus on improving the Amazon delivery experience This role offers the autonomy to think strategically and make data-driven decisions from day one. Join us in shaping the future of e-commerce delivery and addressing the core challenges in our world-class operations space! Key job responsibilities 1. Advanced Modeling and Algorithm Development: - Design and implement sophisticated machine learning models for logistics optimization - Develop complex time series forecasting algorithms for demand prediction and resource allocation 2. AI and Machine Learning Integration: - Architect and deploy AI-powered systems to enhance decision-making in logistics operations - Implement deep learning techniques for image recognition in package sorting and handling - Develop reinforcement learning algorithms for adaptive scheduling and resource management 3. Big Data Analytics and Processing: - Design and implement distributed computing solutions for processing massive logistics datasets - Utilize cloud computing platforms (e.g., AWS) for scalable data processing and analysis 4. AI-Driven Workflow Optimization: - Design and implement AI agents for autonomous decision-making in logistics processes - Create machine learning models for customer behavior analysis and personalized delivery options 5. Software Development and System Architecture: - Write efficient, scalable code in languages such as Python, Java, or C++ - Develop and maintain complex software systems for logistics optimization - Stay at the forefront of AI and ML research - Publish research findings in top-tier conferences and journals About the team We are Amazon's Last Mile Science and Analytics team, dedicated to improving e-commerce delivery. We work to optimize our vast network, forecast demand using machine learning, and enhance route efficiency. Our efforts focus on developing innovative delivery methods, applying AI to solve complex problems, and conducting geospatial analysis. We create simulations to refine processes and plan capacity effectively. Operating globally, we strive to develop adaptable solutions for diverse markets. We aim to advance logistics science, continually improving speed, efficiency, and customer satisfaction, in support of Amazon's mission to be Earth's most customer-centric company.
US, WA, Seattle
Ever wish you could use your quantitative and critical thinking skills to influence business decisions? Economists at Amazon partner closely with senior management, business stakeholders, scientist and engineers, and economist leadership to solve key business problems. As part of the Content Discovery and Experimentation Science team within Prime Video, you will leverage your expertise in causal inference and experimental design to make Prime Video the best-in-class digital video experience. Key job responsibilities - Build causal models and metrics that capture trade-off decisions when business and customer outcomes do not align - Partner with data scientists and product managers to integrate these metrics into Prime Video's experimentation tooling - Work with finance partners to ensure that the team's product metrics contribute to Prime Video's strategic business and financial objectives - Contribute to technical and business documents to communicate ideas and proposals to various audiences - Educate and advocate for best practices in experimentation and how to use it for decision-making
DE, BE, Berlin
As an Applied Scientist II in the Alexa Conversational Modelling Intelligence team within Alexa AI, you will drive model post-training for Large Language Models that power Alexa+. You'll adopt and adapt state-of-the-art techniques — including supervised fine-tuning, RLHF, and preference optimization — running rigorous experiments and translating findings into production-ready solutions that directly improve the customer experience for millions of users worldwide. You will own the full model development cycle from data curation through training, evaluation, and deployment. Your day-to-day will involve developing evaluation methods and metrics, diagnosing model defects, and iterating on recipes to move concrete quality and efficiency benchmarks. You'll write clean, reproducible code, contribute to shared tooling, and collaborate closely with scientists and engineers to bring models from experimentation to scale. You are technically curious, experiment-driven, and motivated by real customer impact. You will also advance the state of the art by publishing at top-tier NLP/ML conferences (ACL, EMNLP, NeurIPS, ICML, ICLR) — contributing to the broader research community while grounding your work in measurable outcomes. Key job responsibilities As an Applied Scientist II in the Alexa Conversational Modelling Intelligence team, you will own the end-to-end model development lifecycle for LLMs that power Alexa+. You'll design and execute training recipes — including supervised fine-tuning, reinforcement learning from human feedback, and preference optimization — iterating rapidly on data, hyperparameters, and architectures to move quality and efficiency metrics. Your work will directly shape how millions of customers interact with Alexa daily. You will build robust evaluation frameworks to measure model performance, diagnose failure modes, and quantify improvements. This includes developing benchmarks, implementing LLM-as-a-judge pipelines, and conducting rigorous defect analysis to identify where models fall short and why. You'll translate these insights into targeted improvements that close gaps in conversational quality, safety, and fluency. You will collaborate closely with research scientists and engineers to bring models from experimentation to production at scale. You'll contribute to shared tooling and infrastructure, write clean and reproducible code, and document your methods so the team can build on your work. You are also expected to advance the state of the art by publishing findings at top-tier NLP/ML venues (ACL, EMNLP, NeurIPS, ICML, ICLR), ensuring your research drives both customer impact and scientific contribution. A day in the life As an Applied Scientist II, your day will involve launching and monitoring training runs, analyzing experiment results, and iterating on model recipes based on evaluation data. You'll participate in science reviews with fellow researchers, sync with engineering partners on deployment readiness, and deep-dive into model outputs to understand behavioral patterns. You'll balance hands-on experimentation with collaborative problem-solving — working across the Alexa AI organization to align model improvements with customer-facing goals and product priorities. About the team The Alexa Conversational Modelling Intelligence team builds industry-leading LLM-based conversational technologies that customers love. Our mission is to push the envelope in LLMs for Alexa to deliver the best-possible customer experience. As an Applied Scientist, you'll contribute directly to that mission through model development and experimentation.
US, CA, Sunnyvale
MULTIPLE POSITIONS AVAILABLE Employer: AMAZON.COM SERVICES LLC Offered Position: Manager III, Economist Job Location: Sunnyvale, California Job Number: AMZ9803624 Position Responsibilities: Independently manage a team of economists and/or scientists in developing strategic economic analyses and demand estimation models. Translate business questions into econometric methodologies and causal inference analyses. Communicate economic insights to non-technical audiences to guide strategic-level, high-impact business decisions. Scale economic models through cross-functional collaboration with engineering teams. Establish scientific quality standards and research priorities. Drive operational efficiency and research excellence across the team. 40 hours / week, 8:00am-5:00pm, Salary Range: $201,300/year to $272,400/year. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, visit: https://www.aboutamazon.com/workplace/employee-benefits. Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.#0000
US, TX, Austin
What happens when you combine startup speed with Amazon-scale impact? You get this team. Amazon Enterprise Security Products is a newly launched group building intelligent, cloud-agnostic security tools using AI-first development practices. Here, you build AI and you build with AI at the same time. This role is a chance to define and lead the science strategy for the future of security tooling with a small, fast team that ships like a startup but deploys at Amazon scale. We're looking for a Senior Data Scientist who operates at the intersection of applied ML, agentic AI, and security; and who can set technical direction across ambiguous, undefined problem spaces. You won't just build models; you'll decide which problems are worth solving, architect the scientific approach for an entire product area, and raise the bar for how the team applies science. You'll partner with senior and principal engineers, applied scientists, security researchers, and PMs, and your judgment will shape roadmaps, not just deliverables. This is a role for someone who thrives in ambiguity, influences without authority, and turns "too ambitious" into shipped reality. Key job responsibilities - Set the science direction for a product area: Define the modeling strategy, scientific approach, and success metrics for entire categories of AI-first security capabilities, agentic systems, anomaly detection, threat classification, and automated response across multi-cloud environments. Decide where science can move the needle and where it can't. - Own the hardest, most ambiguous problems: Take on undefined, open-ended challenges where the path isn't clear, the data is messy or scarce, and the stakes are high. Frame the problem, choose the approach, and bring others along. - Build with AI to build AI and define how the team does it: Drive adoption of agentic coding tools, LLM-powered workflows, and experimental AI tooling across the science org. Establish the practices that multiply velocity for every scientist, not just yourself. - Architect agentic intelligence: Lead the design of models, embeddings, RAG pipelines, evaluation frameworks, and feedback loops that make multi-agent security systems smart, safe, and customer-ready at scale. Own the science architecture decisions others build on. - Drive technical strategy across teams: Influence roadmaps, dive deep with senior and principal scientists and engineers, and align cross-functional partners around a shared scientific vision. Your recommendations shape what the team invests in next. - Prototype, validate, and scale: Turn ambiguous hypotheses into prototypes in days, validate with real customer signal, and chart the path from prototype to production system that runs reliably at Amazon scale. - Communicate to influence at the executive level: Translate complex modeling results and scientific trade-offs into clear recommendations for engineers, product leaders, and senior executives. Drive organizational decisions with data and earn trust across the company. - Raise the bar and grow others: Mentor data scientists and applied scientists, lead technical and science reviews, and champion AI-first development practices. Shape the science culture and hiring bar of a fast-growing team from the ground floor. A day in the life No two days look the same on this fast-growing, AI-first team. You might start your morning setting direction in a roadmap review; making the call on which science investments will have the biggest customer impact and then dive into architecting an evaluation framework that the whole team will build on. Before lunch, you're pair-prompting with an agentic coding assistant to validate a new approach, then unblocking a teammate stuck on a thorny modeling problem. In the afternoon, you lead a design session with senior and principal scientists and engineers, then distill it into a crisp recommendation for senior leadership. You own ambiguous problems end to end, define how the team works, and see your decisions ripple across the product. This is where builders who want to lead with science come to do their best work. About the team Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices.