Our Research Team

Meet the brilliant minds behind AgentForge's groundbreaking research in AI agents and machine learning.

Dr. Sarah Chen

Principal Researcher & Co-Founder

Multi-Modal AI & Computer Vision

Biography

Dr. Chen leads our multi-modal AI research with over 12 years of experience in computer vision and machine learning. She holds a PhD from Stanford University and has published over 40 papers in top-tier conferences.

Education

  • PhD Computer Science, Stanford University (2011)
  • MS Computer Science, MIT (2007)
  • BS Mathematics and Computer Science, Caltech (2005)

Experience

  • Research Scientist, Google DeepMind (2011-2018)
  • Visiting Researcher, Microsoft Research (2010)
  • Research Intern, NVIDIA Research (2009)

Research Interests

Vision-language modelsCross-modal representation learningNeural renderingEmbodied AI

Notable Publications

  • ModalFusion: A Unified Architecture for Multi-Modal Understanding (NeurIPS 2022)
  • CrossVision: Bridging Visual and Linguistic Representations (CVPR 2021)
  • Neural Scene Representation and Rendering (Science 2020)

Awards & Recognition

  • ACM SIGGRAPH Significant New Researcher Award (2023)
  • IEEE Computer Society Technical Achievement Award (2021)
  • MIT Technology Review 35 Under 35 (2018)

Prof. Michael Rodriguez

Research Director

Reinforcement Learning & Decision Systems

Biography

Prof. Rodriguez brings extensive expertise in reinforcement learning and autonomous systems. He previously served as a professor at MIT and has been instrumental in developing several breakthrough RL algorithms that have been deployed in real-world applications.

Education

  • PhD Electrical Engineering, MIT (2008)
  • MS Computer Science, University of California, Berkeley (2004)
  • BS Computer Engineering, University of Texas at Austin (2002)

Experience

  • Associate Professor, MIT CSAIL (2015-2022)
  • Senior Research Scientist, DeepMind (2012-2015)
  • Research Scientist, OpenAI (2008-2012)

Research Interests

Multi-agent reinforcement learningHierarchical reinforcement learningSafe and robust RLTransfer learning in RL

Notable Publications

  • HARMONY: Hierarchical Agent Reinforcement Learning with Memory and Ontology (ICML 2023)
  • Safe Reinforcement Learning via Shielding (AAAI 2022)
  • Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments (NeurIPS 2019)

Awards & Recognition

  • AAAI Outstanding Paper Award (2022)
  • NSF CAREER Award (2018)
  • AAMAS Best Paper Award (2016)

Dr. Aisha Patel

Senior Research Scientist

Natural Language Processing

Biography

Dr. Patel specializes in large language models and conversational AI. Her work on transformer architectures has been widely cited and implemented in production systems worldwide. She leads our NLP research team and collaborates closely with industry partners.

Education

  • PhD Computational Linguistics, Carnegie Mellon University (2014)
  • MS Computer Science, University of Washington (2010)
  • BA Linguistics and Computer Science, Harvard University (2008)

Experience

  • Lead Research Scientist, AI2 (2018-2022)
  • NLP Researcher, Facebook AI Research (2014-2018)
  • Research Intern, IBM Research (2013)

Research Interests

Large language modelsConversational AIMultilingual NLPKnowledge grounding in language models

Notable Publications

  • KnowledgeGPT: Enhancing Large Language Models with Structured Knowledge (ACL 2023)
  • Multilingual Transformers for Low-Resource Languages (EMNLP 2022)
  • Dialogue State Tracking with Hierarchical Attention (NAACL 2020)

Awards & Recognition

  • ACL Outstanding Paper Award (2023)
  • Google Faculty Research Award (2020)
  • EMNLP Best Demo Paper (2019)

Dr. James Liu

Machine Learning Engineer

Distributed Systems & MLOps

Biography

Dr. Liu focuses on scaling AI systems and developing robust training infrastructure. He has extensive experience in distributed computing and has built several large-scale ML platforms that power some of the world's most advanced AI systems.

Education

  • PhD Computer Systems, UC Berkeley (2016)
  • MS Computer Engineering, Georgia Tech (2012)
  • BS Electrical Engineering, University of Michigan (2010)

Experience

  • Senior Staff Engineer, Google AI (2019-2022)
  • Systems Researcher, Microsoft Research (2016-2019)
  • Software Engineer, NVIDIA (2012-2013)

Research Interests

Distributed training systemsML compiler optimizationHardware-software co-design for AIEnergy-efficient ML

Notable Publications

  • ScaleML: Distributed Training Framework for Trillion-Parameter Models (OSDI 2023)
  • Efficient Memory Management for Large Language Model Serving (MLSys 2022)
  • Compiler Optimization for Neural Network Acceleration (ASPLOS 2020)

Awards & Recognition

  • USENIX Best Paper Award (2023)
  • ACM SIGOPS Hall of Fame Award (2021)
  • IEEE Micro Top Picks (2019)

Dr. Elena Kowalski

Research Scientist

Cognitive Science & Human-AI Interaction

Biography

Dr. Kowalski bridges the gap between AI systems and human cognition. Her interdisciplinary research combines insights from psychology, neuroscience, and AI to create more intuitive agent interactions and improve human-AI collaboration.

Education

  • PhD Cognitive Science, Harvard University (2017)
  • MS Psychology, University of Cambridge (2013)
  • BA Neuroscience, Yale University (2011)

Experience

  • Research Scientist, Allen Institute for AI (2020-2022)
  • Postdoctoral Researcher, Stanford HAI (2017-2020)
  • Research Assistant, MIT Media Lab (2012-2013)

Research Interests

Human-AI collaborationCognitive models of AI systemsExplainable AIAI alignment with human values

Notable Publications

  • Mental Models in Human-AI Collaboration (CHI 2023)
  • Cognitive Foundations of Explainable AI (Trends in Cognitive Sciences 2022)
  • Aligning AI Systems with Human Mental Models (CogSci 2021)

Awards & Recognition

  • CHI Best Paper Award (2023)
  • Cognitive Science Society Young Investigator Award (2021)
  • NSF Early Career Development Award (2019)

Dr. Raj Sharma

Applied Research Lead

Robotics & Embodied AI

Biography

Dr. Sharma leads our efforts in embodied AI and robotics applications. His work focuses on developing agents that can interact with the physical world through robotic platforms, with applications in manufacturing, healthcare, and autonomous systems.

Education

  • PhD Robotics, Georgia Tech (2015)
  • MS Mechanical Engineering, University of Michigan (2011)
  • BTech Mechanical Engineering, IIT Delhi (2009)

Experience

  • Principal Robotics Engineer, Boston Dynamics (2018-2022)
  • Research Scientist, Toyota Research Institute (2015-2018)
  • Robotics Intern, NASA JPL (2014)

Research Interests

Robot learning from demonstrationSim-to-real transferDexterous manipulationHuman-robot collaboration

Notable Publications

  • Learning Dexterous Manipulation from Human Demonstrations (ICRA 2023)
  • Sim-to-Real Transfer for Complex Manipulation Tasks (CoRL 2022)
  • Hierarchical Planning for Long-Horizon Robot Tasks (RSS 2020)

Awards & Recognition

  • ICRA Best Paper Award in Robot Manipulation (2023)
  • Amazon Research Award (2021)
  • IEEE RAS Early Career Award (2019)
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