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

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US, MA, N.reading
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US, CA, San Francisco
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US, WA, Seattle
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US, MA, North Reading
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US, WA, Seattle
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US, CA, San Francisco
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IN, KA, Bengaluru
RBS (Retail Business Services) Tech team works towards enhancing the customer experience (CX) and their trust in product data by providing technologies to find and fix Amazon CX defects at scale. Our platforms help in improving the CX in all phases of customer journey, including selection, discoverability & fulfilment, buying experience and post-buying experience (product quality and customer returns). The team also develops GenAI platforms for automation of Amazon Stores Operations. As a Sciences team in RBS Tech, we focus on foundational ML research and develop scalable state-of-the-art ML solutions to solve the problems covering customer experience (CX) and Selling partner experience (SPX). We work to solve problems related to multi-modal understanding (text and images), task automation through multi-modal LLM Agents, supervised and unsupervised techniques, multi-task learning, multi-label classification, aspect and topic extraction for Customer Anecdote Mining, image and text similarity and retrieval using NLP and Computer Vision for product groupings and identifying duplicate listings in product search results. Key job responsibilities As a Data Scientist, you will be responsible to design and deploy scalable GenAI, NLP and Computer Vision solutions that will impact the content visible to millions of customer and solve key customer experience issues. You will develop novel LLM, deep learning and statistical techniques for task automation, text processing, image processing, pattern recognition, and anomaly detection problems. You will define the research and experiments strategy with an iterative execution approach to develop AI/ML models and progressively improve the results over time. You will partner with business and engineering teams to identify and solve large and significantly complex problems that require scientific innovation. You will help the team leverage your expertise, by coaching and mentoring. You will contribute to the professional development of colleagues, improving their technical knowledge and the engineering practices. You will independently as well as guide team to file for patents and/or publish research work where opportunities arise. The RBS org deals with problems that are directly related to the selling partners and end customers and the ML team drives resolution to organization level problems. Therefore, the Data Scientist role will impact the large product strategy, identifies new business opportunities and provides strategic direction which is very exciting.
IN, KA, Bengaluru
We are looking for a Senior Applied Scientist to help establish and lead the technical direction of our newly formed team in Bangalore. In this role, you will drive the research and development of next-generation machine learning models spanning computer vision, audio processing, and multimodal semantic understanding. You will help define the science roadmap, tackle high-ambiguity problems across modalities, and deliver solutions that operate at scale. This is a rare opportunity to shape the technical vision, culture, and long-term research agenda of a greenfield site. Key job responsibilities Model Development & Technical Leadership: Architect and drive development of advanced deep learning models for CV, audio understanding, and multimodal semantic fusion — setting the technical bar and defining best practices for the team. End-to-End Ownership: Own complex ML programs end-to-end — from identifying high-impact problems, designing data strategies and evaluation frameworks, through experimentation, optimization, and deployment at production scale. Research & Innovation: Define the science roadmap for your area; drive novel research directions in multimodal learning and deliver results that advance both the product and the broader field. Publications & Thought Leadership: Maintain an active publication record at top-tier venues (e.g. CVPR, NeurIPS, ICASSP, ICCV, ACL) and represent the team externally in the research community. Mentorship & Culture Building: Mentor scientists and engineers, raise the technical bar through hiring, and play a foundational role in establishing the Bangalore site's culture, processes, and scientific identity. A day in the life An Applied Scientist with the Alexa Edge AI team will lead science solution design, run experiments, research new algorithms, and find new ways of optimizing the customer experience; while setting examples for the team on good science practice and standards. Besides theoretical analysis and innovation, a Sr. Applied Scientist will also drive cross functional collaboration with talented engineers and scientists to put algorithms and models into production. About the team The Alexa Edge AI team has a mission to deliver best in class, resource efficient multimodal AI models in support of various perception (vision, audio and speech) and semantic understanding based applications for devices like Echo Show series within Amazon.
IN, KA, Bengaluru
The Alexa Edge AI team is seeking a talented and motivated Applied Scientist to join our newly established team in Bangalore. In this role, you will design, develop, and deploy state-of-the-art machine learning models spanning computer vision (CV), audio (including speech) processing, and multimodal semantic understanding for both edge and cloud deployment. You will work at the intersection of multiple modalities to build systems that can perceive, interpret, and reason about the world — pushing the boundaries of what's possible in unified multimodal intelligence. This is a unique opportunity to be a founding member of a brand-new site, shaping the team culture, technical direction, and research agenda from the ground up. Key job responsibilities Model Development: Design and build deep learning models for computer vision, audio understanding, and multimodal semantic fusion — including architectures that enable joint reasoning across visual, auditory, and textual modalities. End-to-End Ownership: Own the full ML lifecycle — from problem formulation, data strategy, and annotation design through experimentation, evaluation frameworks, model optimization, and deployment at scale. Research & Innovation: Stay at the frontier of CV, audio ML, and multimodal learning; identify and apply cutting-edge techniques and contribute to the scientific community through papers at top-tier venues (CVPR, NeurIPS, ICASSP, ICCV, ACL). Mentorship & Culture Building: As a founding member of the Bangalore site, help hire, onboard, and establish the technical practices that define the team's culture. A day in the life An Applied Scientist with the Alexa Edge AI team will support science solution design, run experiments, research new algorithms, and find new ways of optimizing the customer experience; while setting examples for the team on good science practice and standards. Besides theoretical analysis and innovation, an Applied Scientist will also work closely with talented engineers and scientists to put algorithms and models into production. About the team The Alexa Edge AI team has a mission to deliver best in class, resource efficient multimodal AI models in support of various perception (vision, audio and speech) and semantic understanding based applications for devices like Echo Show series within Amazon.
IN, KA, Bengaluru
The Alexa Edge AI team is seeking a talented and motivated Applied Scientist to join our newly established team in Bangalore. In this role, you will design, develop, and deploy state-of-the-art machine learning models spanning computer vision (CV), audio (including speech) processing, and multimodal semantic understanding for both edge and cloud deployment. You will work at the intersection of multiple modalities to build systems that can perceive, interpret, and reason about the world — pushing the boundaries of what's possible in unified multimodal intelligence. This is a unique opportunity to be a founding member of a brand-new site, shaping the team culture, technical direction, and research agenda from the ground up. Key job responsibilities Model Development: Design and build deep learning models for computer vision, audio understanding, and multimodal semantic fusion — including architectures that enable joint reasoning across visual, auditory, and textual modalities. End-to-End Ownership: Own the full ML lifecycle — from problem formulation, data strategy, and annotation design through experimentation, evaluation frameworks, model optimization, and deployment at scale. Research & Innovation: Stay at the frontier of CV, audio ML, and multimodal learning; identify and apply SOTA techniques and contribute to the scientific community through papers at top-tier venues (CVPR, NeurIPS, ICASSP, ICCV, ACL). Mentorship & Culture Building: As a founding member of the Bangalore site, help hire, onboard, and establish the technical practices that define the team's culture. A day in the life An Applied Scientist with the Alexa Edge AI team will support science solution design, run experiments, research new algorithms, and find new ways of optimizing the customer experience; while setting examples for the team on good science practice and standards. Besides theoretical analysis and innovation, an Applied Scientist will also work closely with talented engineers and scientists to put algorithms and models into production. About the team The Alexa Edge AI team has a mission to deliver best in class, resource efficient multimodal AI models in support of various perception (vision, audio and speech) and semantic understanding based applications for devices like Echo Show series within Amazon.