AvivBick.github.io/index.html at master · AvivBick/AvivBick.github.io · GitHub

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        <p style="margin-top:3%; text-align:justify;font-family: 'Oswald:wght@300&display=swap'; font-size: 18px;">
          I’m Aviv Bick, a Ph.D. student in the Computer Science Department at Carnegie Mellon University, where I work under the guidance of
          <a href="https://goombalab.github.io" target="_blank" rel="noopener noreferrer">Prof. Albert Gu</a>
          and
          <a href="http://www.cs.cmu.edu/~epxing/" target="_blank" rel="noopener noreferrer">Prof. Eric Xing</a>.
          My research focuses on representation learning for sequential data and developing efficient computational methods for it.
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        <div style="font-family: 'Oswald', sans-serif; font-size: 32px;"><b>Aviv Bick</b></div><br>
        <p><b>first_name [at] cmu [dot] edu</b><br>
        <p>Computer Science Department<br>
          Carnegie Mellon University<br>
          9003 Gates and Hillman Centers<br>
          Pittsburgh, USA <br>
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              <h4>Ph.D., Computer Science</h4>
              <p>Carnegie Mellon University, 2022 - Present</p>
            </li>
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              <h4>M.S., Computer Science</h4>
              <p>Tel Aviv University, 2019 - 2021</p>
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              <h4>B.S., Computer Science</h4>
              <p>The Hebrew University of Jerusalem, 2016 - 2019</p>
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          <h2>Publications</h2>
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            <li>
              <h4>Retrieval-Aware Distillation for Transformer-SSM Hybrids</h4>
              <p><strong>Authors:</strong> Aviv Bick, Eric P. Xing, Albert Gu</p>
              <p><a href="https://arxiv.org/abs/2602.11374" target="_blank">[Read Paper]</a></p>
            </li>
            <li>
              <h4>Understanding the Skill Gap in Recurrent Language Models: The Role of the Gather-and-Aggregate Mechanism</h4>
              <p><strong>Authors:</strong> Aviv Bick, Eric P. Xing, Albert Gu</p>
              <p><strong>Conference:</strong> International Conference on Machine Learning, ICML 2025</p>
              <p>
                  <a href="https://arxiv.org/abs/2504.18574" target="_blank">[Read Paper]</a>
                  <a href="https://www.youtube.com/watch?v=aNgg6McW558" target="_blank">[YouTube]</a>
              </p>
            </li>
            <li>
              <h4>Thinking Slow, Fast: Scaling Inference Compute with Distilled Reasoners</h4>
              <p><strong>Authors:</strong> Daniele Paliotta<sup>*</sup>, Junxiong Wang<sup>*</sup>, Matteo Pagliardini<sup>*</sup>, Kevin Y. Li<sup>*</sup>, Aviv Bick, J. Zico Kolter, Albert Gu, François Fleuret, Tri Dao</p>
              <p><a href="https://arxiv.org/abs/2502.20339" target="_blank">[Read Paper]</a></p>
            </li>
            <li>
              <h4>Llamba: Scaling Distilled Recurrent Models for Efficient Language Processing</h4>
              <p><strong>Authors:</strong> Aviv Bick, Tobias Katsch, Nimit Sohoni, Arjun Desai, Albert Gu</p>
              <p><strong>Conference:</strong> ICLR 2025 Workshop on Scalable Optimization for Efficient and Adaptive Foundation Models</p>
              <p><a href="https://arxiv.org/abs/2502.14458" target="_blank">[Read Paper]</a></p>
            </li>
            <li>
              <h4>Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models</h4>
              <p><strong>Authors:</strong> Aviv Bick<sup>*</sup>, Kevin Y. Li<sup>*</sup>, Eric P. Xing, J. Zico Kolter, Albert Gu</p>
              <p><strong>Conference:</strong> Advances in Neural Information Processing Systems, NeurIPS 2024</p>
              <p><a href="https://arxiv.org/abs/2408.10189" target="_blank">[Read Paper]</a></p>
            </li>
            <li>
              <h4>Distributed Zero-Knowledge Proofs Over Networks</h4>
              <p><strong>Authors:</strong> Aviv Bick, Gillat Kol, Rotem Oshman</p>
              <p><strong>Conference:</strong> ACM-SIAM Symposium on Discrete Algorithms, SODA 2022</p>
              <p></p>
              <p>
                  <a href="https://epubs.siam.org/doi/10.1137/1.9781611977073.97" target="_blank">[Read Paper]</a>
                  <a href=https://www.youtube.com/watch?v=nmIfuFJxPwI" target="_blank">[YouTube]</a>
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