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2024 BAIR Graduate Directory The Berkeley Artificial Intelligence Research Blog

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Every year, the Berkeley Artificial Intelligence Research (BAIR) Lab graduates some of the most talented and innovative minds in artificial intelligence and machine learning. Our Ph.D. graduates have each expanded the frontiers of AI research and are now ready to embark on new adventures in academia, industry, and beyond.

These fantastic individuals bring with them a wealth of knowledge, fresh ideas, and a drive to continue contributing to the advancement of AI. Their work at BAIR, ranging from deep learning, robotics, and natural language processing to computer vision, security, and much more, has contributed significantly to their fields and has had transformative impacts on society.

This website is dedicated to showcasing our colleagues, making it easier for academic institutions, research organizations, and industry leaders to discover and recruit from the newest generation of AI pioneers. Here, you’ll find detailed profiles, research interests, and contact information for each of our graduates. We invite you to explore the potential collaborations and opportunities these graduates present as they seek to apply their expertise and insights in new environments.

Join us in celebrating the achievements of BAIR’s latest PhD graduates. Their journey is just beginning, and the future they will help build is bright!

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Unveiling the Dynamics of Generative Diffusion Models: A Machine Learning Approach to Understanding Data Structures and Dimensionality Mohammad Arshad Artificial Intelligence Category – MarkTechPost

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​[[{“value”:” The recent advancements in machine learning, particularly in generative models, have been marked by the emergence of diffusion models (DMs) as powerful tools for modeling complex data distributions and generating realistic samples across various domains such as images, videos, audio, and 3D scenes. Despite… Read More »Unveiling the Dynamics of Generative Diffusion Models: A Machine Learning Approach to Understanding Data Structures and Dimensionality Mohammad Arshad Artificial Intelligence Category – MarkTechPost

Enhancing Large Language Model LLM Safety Against Fine-Tuning Threats: A Backdoor Enhanced Alignment Strategy Sana Hassan Artificial Intelligence Category – MarkTechPost

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​[[{“value”:” Despite the impressive capabilities of LLMs like GPT-4 and Llama-2, they require fine-tuning with tailored data for specific business needs, exposing them to safety threats such as the Fine-tuning based Jailbreak Attack (FJAttack). Incorporating even a few harmful examples during fine-tuning can severely compromise… Read More »Enhancing Large Language Model LLM Safety Against Fine-Tuning Threats: A Backdoor Enhanced Alignment Strategy Sana Hassan Artificial Intelligence Category – MarkTechPost

This AI Paper from China Introduces ShortGPT: A Novel Artificial Intelligence Approach to Pruning Large Language Models (LLMs) based on Layer Redundancy Mohammad Asjad Artificial Intelligence Category – MarkTechPost

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​[[{“value”:” Recent advancements in Large Language Models (LLMs) have led to models containing billions or even trillions of parameters, achieving remarkable performance across domains. However, their massive size poses challenges in practical deployment due to stringent hardware requirements. Research has focused on scaling models to… Read More »This AI Paper from China Introduces ShortGPT: A Novel Artificial Intelligence Approach to Pruning Large Language Models (LLMs) based on Layer Redundancy Mohammad Asjad Artificial Intelligence Category – MarkTechPost

This AI Paper from Huawei Introduces DenseSSM: A Novel Machine Learning Approach to Enhance the Flow of Hidden Information between Layers in State Space Models (SSMs) Adnan Hassan Artificial Intelligence Category – MarkTechPost

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​[[{“value”:” Developing efficient and powerful large language models (LLMs) represents a frontier of innovation. These models have relied on the Transformer architecture, celebrated for its ability to understand and generate human-like text. However, as these models scale, they encounter significant hurdles, chiefly their operations’ computational… Read More »This AI Paper from Huawei Introduces DenseSSM: A Novel Machine Learning Approach to Enhance the Flow of Hidden Information between Layers in State Space Models (SSMs) Adnan Hassan Artificial Intelligence Category – MarkTechPost

Meet SafeDecoding: A Novel Safety-Aware Decoding AI Strategy to Defend Against Jailbreak Attacks Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost

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​[[{“value”:” Despite the significant strides in large language models (LLMs) such as ChatGPT, Llama2, Vicuna, and Gemini, they grapple with safety issues. This paper introduces a novel safety-aware decoding technique, SafeDecoding, which aims to protect LLMs from jailbreak attacks, a pressing concern evidenced by LLMs… Read More »Meet SafeDecoding: A Novel Safety-Aware Decoding AI Strategy to Defend Against Jailbreak Attacks Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost

Vision-Based Hand Gesture Customization from a Single Demonstration Apple Machine Learning Research

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​Hand gesture recognition is becoming a more prevalent mode of human-computer interaction, especially as cameras proliferate across everyday devices. Despite continued progress in this field, gesture customization is often underexplored. Customization is crucial since it enables users to define and demonstrate gestures that are more… Read More »Vision-Based Hand Gesture Customization from a Single Demonstration Apple Machine Learning Research

Merge Vision Foundation Models via Multi-Task Distillation Apple Machine Learning Research

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​As the repository of publicly available pre-trained vision foundation models (VFMs) — such as CLIP, DINOv2, and SAM — grows, users face challenges in storage, memory, and computational efficiency when deploying multiple models concurrently. To address these concerns, we introduce a unique approach that merges… Read More »Merge Vision Foundation Models via Multi-Task Distillation Apple Machine Learning Research

Moonwalk: Advancing Gait-Based User Recognition on Wearable Devices with Metric Learning Apple Machine Learning Research

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​[[{“value”:”*=Equal Contributors Personal devices have adopted diverse authentication methods, including biometric recognition and passcodes. In contrast, headphones have limited input mechanisms, depending solely on the authentication of connected devices. We present Moonwalk, a novel method for passive user recognition utilizing the built-in headphone accelerometer. Our… Read More »Moonwalk: Advancing Gait-Based User Recognition on Wearable Devices with Metric Learning Apple Machine Learning Research

This AI Paper from Cornell Proposes Caduceus: Deciphering the Best Tokenization Strategies for Enhanced NLP Models Sana Hassan Artificial Intelligence Category – MarkTechPost

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​[[{“value”:” In the domain of biotechnology, the intersection of machine learning and genomics has sparked a revolutionary paradigm, particularly in the modeling of DNA sequences. This interdisciplinary approach addresses the intricate challenges posed by genomic data, which include understanding long-range interactions within the genome, the… Read More »This AI Paper from Cornell Proposes Caduceus: Deciphering the Best Tokenization Strategies for Enhanced NLP Models Sana Hassan Artificial Intelligence Category – MarkTechPost