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.

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Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine innovative AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. We leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of robotics foundation models that: - Enable unprecedented generalization across diverse tasks - Integrate multi-modal learning capabilities (visual, tactile, linguistic) - Accelerate skill acquisition through demonstration learning - Enhance robotic perception and environmental understanding - Streamline development processes through reusable capabilities The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. As a Senior Applied Scientist, you will develop and improve machine learning systems that help robots perceive, reason, and act in real-world environments. You will leverage state-of-the-art models (open source and internal research), evaluate them on representative tasks, and adapt/optimize them to meet robustness, safety, and performance needs. You will invent new algorithms where gaps exist. You’ll collaborate closely with research, controls, hardware, and product-facing teams, and your outputs will be used by downstream teams to further customize and deploy on specific robot embodiments. Key job responsibilities As a Senior Applied Scientist in the Foundations Model team, you will: - Leverage state-of-the-art models for targeted tasks, environments, and robot embodiments through fine-tuning and optimization. - Execute rapid, rigorous experimentation with reproducible results and solid engineering practices, closing the gap between sim and real environments. - Build and run capability evaluations/benchmarks to clearly profile performance, generalization, and failure modes. - Contribute to the data and training workflow: collection/curation, dataset quality/provenance, and repeatable training recipes. - Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs - Stay current with latest developments in foundation models and robotics, assist in literature reviews and research documentation, prepare technical reports and presentations, and contribute to research discussions and brainstorming sessions. - Work closely with senior scientists, engineers, and leaders across multiple teams, participate in knowledge sharing, support integration efforts with robotics hardware teams, and help document best practices and methodologies.
US, CA, Sunnyvale
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CN, 31, Shanghai
As a Sr. 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