Yihan Zhang

6 Open Positions under National Elite Foundation grant, AI and Quantum Computing We are excited to announce that our project, “Creating Building Blocks for Hybrid Quantum-Classical Neural Networks,” has been awarded the annual grant from the National Elite Foundation of Iran, one of the highest prestigous and competetive national grants. This project integrates deep learning components (such as residual block, dense block, attention layers, etc.) with quantum computing, targeting complex high-variance problems. Successful candidates will receive support from the National Elite Foundation. Qualification Requirements and Conditions: Two Sub-Groups of Positions: * Sub-Group 1 - Three Positions: Candidates should be distinguished winners of prestigious events, including: - Medalists (gold, silver, bronze) from international or national student Olympiads. - Top 1000 rank holders in Regions 1, 2, or 3 of the National Competitive Examination; i.e. Iran Conquer. - Top 100 rank holders in the the National Competitive Examination of art and foreign languages. - Highest rank achievers in the student, scholar, and technologist sections of the Khawarzmi Youth Festival. - 1st to 3rd rank holders in the Kharazmi International Festival. - 1st to 3rd rank holders in the National Student Olympiads endorsed by the Ministry of Science. - 1st to 3rd rank holders in the Farabi Youth Festival. - 1st to 3rd place winners in the "Business Designers" section of the Sheikh Bahai Festival. - Top 10 rank holders in the National Nanotechnology Competition (Student). - 1st to 3rd place winners in problem-oriented competitive events (Shahid Babaei Project), as per the National Elite Foundation regulations. * Sub-Group 2 - Three Position: Candidates must solve the following task and submit the code via email: “Update a simple residual block using a quantum algorithm.” The choice of Python framework is open. Preferred Selection Criteria: - High motivation and goal orientation with a passion for AI and quantum algorithms. - Proficiency in Pytorch or Tensorflow frameworks. - Familiarity with QNN libraries, such as Qiskit. - Strong analytical skills for tackling complex problems. - Excellent problem-solving abilities and grasp of new theories. - Ability to work both independently and collaboratively. - Competence in conducting and reporting research professionally. Benefits of Joining Our Team: - Certificate opportunity from one of the most prestigious academies in the country. - Exciting job opportunities in one cutting-edge interdisciplinary field. - Professional development opportunities. - High scoring potential from the National Elite Foundation. How to Apply: Interested candidates should send their resumes to: - Head of the Group: Ali A. Kiaei, email: ali.kiaei@sharif.edu - Research Assistant: Fatemeh Aqaei, email: Fatemeh.Aqaei@outlook.com For more information, please contact Fatemeh Aqaei. We look forward to welcoming talented individuals to our innovative team

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Dear Research Community, Exciting news! My research article, "A Tamm-Fano resonance glucose sensor based on Cu and distributed bragg reflector plasmonic coupling interface in the near-infrared regime" has been published in the esteemed Optical and Quantum Electronics journal. https://lnkd.in/gyAHhxPq I'd love to hear your thoughts and insights on this research. Feel free to reach out if you have any questions or would like to discuss potential collaborations. #research #optics #sensor #plasmonics

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Happy to share that our paper "On Optimal Server Allocation for Moldable Jobs with Concave Speed-Up" has been accepted for presentation at ACM Mobihoc 2024, a highly selective single-track conference on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing. It addresses the fundamental question of how to optimally allocate computing resources to parallel tasks including machine learning workloads running on clouds. It uses Stein's method to derive asymptotically optimal algorithms for resource allocation for parallel tasks. The arXiv version can be found in the link given below. https://lnkd.in/dwZeMhzk

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📢 Our new paper "Higher-Order GNNs Meet Efficiency: Sparse Sobolev Graph Neural Networks" has been accepted in IEEE Transactions on Signal and Information Processing over Networks! This work introduces sparse Sobolev graph neural networks (S2-GNN) to address a key challenge in scaling up GNNs. While traditional GNNs typically rely on immediate node neighbors, limiting their effectiveness in capturing complex graph relationships, S2-GNN employs higher-order, sparse filters to efficiently handle large graphs. Our key contributions: Spectral insights: We reveal a spectral similarity between higher-order Laplacians and their sparse approximations, which supports the design of high-performing yet efficient GNNs. Architecture: S2-GNN leverages cascades of sparse filters to enable accurate updates in node embeddings without excessive computational costs. Robustness: We provide stability analysis of S2-GNN, demonstrating robustness to graph perturbations. Extensive benchmarking: S2-GNN consistently outperforms or competes with leading GNN methods across diverse datasets and tasks. We hope this work contributes to advancing scalable, high-efficiency GNN architectures for real-world applications. 🔗 Preprint: https://lnkd.in/eAJJ9SAD 🔗 Code: https://lnkd.in/eWK7XZM2 Thanks to my co-authors Aref Einizade, Andjela Todorovic, Jhon Castro-Correa, Mohsen Badiey, Thierry Bouwmans, and Fragkiskos Malliaros. This work was supported by ANR, the French National Research Agency, Office of Naval Research, Hi! PARIS Center - AI for Science, Business & Society, Télécom Paris, Institut Polytechnique de Paris, University of Delaware, La Rochelle Université, CentraleSupélec, Université Paris-Saclay, and Inria. #GraphNeuralNetworks #SparseGNN

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In this new paper we explore the use of classical programming techniques in implementing the quantum lattice Boltzmann method in the Intel Quantum SDK. https://lnkd.in/gpDSFe6b

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The off-line lectures of my Computational Microelectronics course this semester have concluded. This year, I have introduced several new features: 1) Simple instruction set (L1) 2) Compact model for MOSFETs (L2~L5) 3) Neural network (L8 & L9) 4) Level-set method (L20 & L21) 5) Monte Carlo method for ion implantation (L22) 6) Thermal expansion (L23) The entire playlist of live-streamed (or pre-recorded) videos is available on YouTube: https://lnkd.in/g-CmNeu6 The lecture slides and materials can be downloaded from my GitHub repository: GitHub - hi2ska2/cm2024f Some of newly introduced topics have been covered briefly, and I plan to improve their treatment in the coming years. Additionally, I will continue to expand the course coverage to stay aligned with the advancements in computation microelectronics.

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Here is our new paper accepted in Neurocomputing journal. SITU: Stochastic input encoding and weight update thresholding for efficient memristive neural network in-situ training The Analog-to-Digital Converter (ADC) sensing and conductance update are the most power-demanding processes in the in-situ training of memristive neural networks. In this work, we propose a new thresholded weight update method in conjunction with stochastic input encoding to reduce the ADC sensing requirement and weight updates. This leads to better power and area efficiency for simple neural network models developed in situ on memristive crossbars. This is an interesting research work by Xuening Dong and collaboration between University of Toronto, James Cook University , and York University. https://lnkd.in/gY4-GgtC

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I'm happy to share that our paper titled “Gegenbauer Graph Neural Networks for Time-Varying Signal Reconstruction” has been accepted for publication in IEEE Transactions on Neural Networks and Learning Systems. You can access the paper here: https://lnkd.in/eGNfPc_a and ArXiv link: https://lnkd.in/e-mqmqgp. This work is part of our recent efforts in the broader field of spatiotemporal graph analysis. In our study, we introduce two significant contributions. Firstly, we propose a specialized Graph Neural Network (GNN) architecture designed specifically for addressing the task of time-varying graph signals recovery. Secondly, we present a novel graph convolution approach based on Gegenbauer polynomials, which generalizes the well-known Chebyshev convolutions in GNNs. Our research marks a notable advancement in the domain of time-varying graph signals, a field predominantly explored through the lens of graph signal processing. Through experimentation, we discovered that utilizing traditional methods like the Graph Convolutional Network (GCN) for this task is inadequate. Instead, we developed an autoencoder-type architecture that achieves competitive performance. This paper is the result of a fruitful collaboration with Jhon Castro-Correa (first author), Mohsen Badiey, and Fragkiskos Malliaros. If you're interested in delving deeper into this topic, feel free to explore our GitHub repository https://lnkd.in/evmRdRxU. Special thanks to all the institutions involved in supporting this research Télécom Paris, Institut Polytechnique de Paris, University of Delaware, CentraleSupélec, Université Paris-Saclay, Inria. I welcome any discussions and look forward to sharing more insights in the future. Stay tuned for further developments in our exploration of spatiotemporal graphs! #gnn, #graph, #spatiotemporalgraphs, #gnnupdates

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If you're interested in GNNs and spatiotemporal graph prediction tasks, check out our recent IEEE TNNLS paper!

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UCLA researchers, led by prof/ Aydogan Ozcan, have conducted an in-depth analysis of nonlinear information encoding strategies for diffractive optical processors, offering new insights into their performance and utility. Their study, published in Light: Science & Applications, a journal of the Springer Nature Group, shed light on their advantages and limitations in the optical processing of visual information. https://lnkd.in/gHPvj7ai

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Our paper "Novel design of cryptographic architecture of nanorouter using quantum‑dot cellular automata nanotechnology" is accepted in Nature Scientific Reports.

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Joint work with Dhrumil Patel and Patrick Coles now published in Quantum - the open journal for quantum science: https://lnkd.in/g7vUGDjn Popular Summary: Many real-world problems in science and industry can be expressed as optimization problems, which involve finding the best solution while meeting specific constraints. Among these, a special class of optimization problems called semidefinite programming holds significance. They are widely used to model or approximate problems arising in various fields such as operations research, combinatorial optimization, control theory, and quantum information theory. For solving these programs, quantum algorithms have been proven to provide a quadratic speedup over classical algorithms. However, these quantum algorithms are not well-suited for current quantum devices, which are noisy and limited in their capabilities. In this work, we propose three quantum algorithms designed to run on these noisy devices. Our algorithms are hybrid quantum-classical algorithms that have a classical computer available for optimization, only calling a quantum computer for tasks that are not efficiently solvable by it. We rigorously analyze the performance of one of our algorithms, quantifying how rapidly it converges to the optimal value. Finally, to demonstrate their practicality, we numerically simulate our quantum algorithms for problems like MaxCut, a prominent graph theoretic problem. Our simulations showcase their effectiveness even in the presence of noise.

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I am delighted to share that our latest paper titled: A Numerical Study on the Novel Composition of Porous and Solid Ribs to Augment the Thermal Performance of a Hybrid Nanofluid-Based Microchannel Heat Sink with External Fins is accepted for publication in Iranian Journal of Science and Technology, Transactions of Mechanical Engineering. https://lnkd.in/dQGvQ4Ph #microchannel #heatsink #Porous #Rib #Fin #Heat_Transfer

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https://lnkd.in/dFjq4DxS It is great to see that our recently published paper @NanoLetters is the one most read in the past 30 days. #quantumphotonics UNAM Institute of Materials Science and Nanotechnology Bilkent University

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We're happy to share the finalized PhD thesis of Abdullah Giray Yağlıkçı, now online! "Enabling Efficient and Scalable DRAM Read Disturbance Mitigation via New Experimental Insights into Modern DRAM Chips", PhD Thesis on arXiv: https://lnkd.in/eDXEB3xK Giray's thesis work has made significant contributions to hardware security with a special focus on DRAM robustness and performance, in particular, on better understanding and solving the #RowHammer vulnerability, with papers at HPCA, MICRO, ISCA, DSN, and SIGMETRICS. His work on #BlockHammer was named as a finalist by Intel in 2021 for the Intel Hardware Security Academic Award, and his work on Svärd received first place in the ACM SRC at PACT 2023. His dissertation was recently selected as one of the five finalists in the #HOST2024 PhD competition. Read more on Giray's thesis: https://lnkd.in/eZwzUu_K We recently interviewed Giray, he discusses his PhD work, RowHammer and future plans: https://lnkd.in/eEddkmXz Giray's website: https://lnkd.in/ewDEGhHD ETH Zurich, Department of Information Technology and Electrical Engineering #computing #hardware #design #computerarchitecture #security #reliability #RowHammer #memory #DRAM #DDR4 #voltage #wordline #refresh #parallelism #subarray #controller #realchips #bitflips

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Department Head of Sensory and Sensorimotor Systems, Zhaoping Li: "I am extremely delighted that my Ph.D. supervisor John J. Hopfield is being awarded the 2024 Nobel Prize in Physics for foundational discoveries that enable machine learning in artificial neural networks! It is heart-warming that the physics community is recognizing his pioneering contributions that are fundamentally changing the world. His Hopfield network has shaped many physicists to combine physics, biology, psychology, and neuroscience approaches to understand the brain. I am immensely grateful for his priceless mentoring, and for his uplifting encouragement to my V1 Saliency Hypothesis many years before its recognition. Here is an interesting anecdote: during my Ph.D. study in 1980s, he suggested that I look into the olfactory system, and said that it was interesting that olfactory neurons had been shown to exhibit oscillatory activities. This was remarkable for two reasons, firstly, the Hopfield network does not display oscillatory neural activities, secondly, this was way before olfaction became a popular research topic." #nobelprize #physics #artificialintelligence #machinelearning

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[PAPER UPDATE] Excited to announce that our paper "When In-Memory Computing Meets Spiking Neural Networks: From Applications to Circuits" has been published in #AppliedPhysicsReviews! 🔥Curious about the trade-offs that shape cutting-edge designs? How do neuro-inspired spiking neural networks (SNNs) algorithms and In-memory Computing (IMC) revolutionize the future of AI? This paper dives deep into their synergies, investigating the complex interactions between accuracy, energy, latency, and area. Major Contributions: - Comprehensive overview of SNNs + IMC applications & circuits powering neuromorphic computing. - Exploiting SNN temporal sparsity for analog IMC with massive PPA gains—featuring #SpikeSim, an SNN-specific IMC architecture. - Tackling NVM non-idealities in IMC crossbars with solutions like noise-aware batchnorm, ensuring robust SNN inference. - Challenging the myth: "More NVM precision = better," highlighting trade-offs in energy efficiency, and area. -Future-forward insights on online learning, emerging neuronal devices, and co-exploration to drive energy-efficient IMC-SNN in next-gen AI hardware. Authors: Abhishek Moitra Abhiroop Bhattacharjee Yuhang Li Youngeun Kim Paper Link: https://lnkd.in/eDtFavrg SpikeSim Code: https://lnkd.in/eBz5ZnRD #NeuromorphicComputing #InMemoryComputing #SpikingNeuralNetworks #APR #CoDesign Center for the Co-Design of Cognitive Systems

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And now we are doing several experiments with the new smart phenotyping system developed for the first time in the controlled environment agriculture center, College of Agricultural technology, University of Tehran. The video is played in 10x speed in order to see how the system work. The protocol here used is to detect the maximum quantum yield of photosystem II, which should be done in dark.

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Just published today, our paper on DigiSpan. Two things I am pleased about: 1. DigiSpan is more accurate than the live-voice digit span memory testing that has been used for decades, that it hopefully replaces. 2. It is being used clinically in several countries even before the paper was published. The median time between research showing that a clinical tool is useful and it being taken up clinically is 17 years! https://lnkd.in/gjFKKHYc

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