Prof. Dr. Tobias Gemmeke is a University Professor at RWTH Aachen University's Faculty of Electrical Engineering and Information Technology, leading the Chair of Integrated Digital Systems and Circuit Design. His work focuses on neuromorphic computing, hardware accelerators, and energy-efficient electronics. He has pioneered advancements in FPGA-based computational neuroscience simulators, neuromorphic processor architectures, and sensor integration for industrial and medical applications. Research interests include time-domain computing, ReRAM reliability, and co-optimization of neural networks with hardware. Notable contributions include the neuroAIx framework for accelerated neuroscience simulations and energy-efficient ASIC designs for post-quantum cryptography. He actively explores memristive devices and domain generalization techniques for edge computing. Recent publications highlight innovations in spiking neural networks, sensor systems for plain bearings, and time-domain compute-in-memory engines. His work bridges theoretical neuroscience with practical hardware implementations, emphasizing scalability and real-time performance.
Rayan Saab is an Assistant Professor in the Department of Mathematics at the University of California, San Diego. His research focuses on the mathematics of information, data science, and signal processing, with an emphasis on quantization, compressed sensing, and machine learning algorithms. Education: Ph.D. in Electrical and Computer Engineering from the University of British Columbia (2010). His work bridges theoretical mathematics and practical applications in data acquisition, digitization, and processing. Recent publications analyze quantization methods for neural networks and compressed sensing systems. He has taught graduate and undergraduate courses in numerical analysis, optimization, and mathematical methods in data science. Research trends include: Developing quantization algorithms with provable guarantees. Applications in high-dimensional data and machine learning. Stochastic frameworks for neural network compression. Scientific awards: Banting Postdoctoral Fellowship (2011-2013). Hellman Fellowship. Advising and grants: Advised students in thesis projects related to signal processing and data science. Received funding for research in mathematical data acquisition. Contact: rsaab@ucsd.edu | Office: AP&M 5157.
Dr. Hillel Adesnik is a Professor in the Department of Neuroscience at the University of California, Berkeley, and a leading researcher in the neural basis of sensory perception. His lab focuses on cortical microcircuits, optogenetics, and neural coding, with emphasis on visual processing and memory formation. Key Research Areas: Cortical Microcircuits Optogenetic Tools Gamma Band Rhythms Neural Coding Mechanisms Dr. Adesnik has pioneered high-speed optical methods like 3D-MAP and 3D-SHOT to manipulate neural activity. His work spans cortical dynamics, synaptic plasticity, and cortical layer interactions, with applications in understanding learning algorithms and sensory inference. Selected Trends from Publications: Recent preprints and papers highlight advancements in cortical VIP neuron function, channelrhodopsin structures, and inter-areal computations. His team utilizes two-photon holography, cryo-EM, and computational modeling to decode perception-related neural codes. Scientific Awards: NIH Director's New Innovator Award (2013) Dr. Adesnik's lab collaborates with institutions like NIH and develops tools for awake animal studies. Funding includes grants from the Beckman Young Investigator Program and NIH.
Jaime Cardenas serves as an Assistant Professor at The Institute of Optics and holds a joint appointment as Assistant Professor of Physics at the University of Rochester. He joined the faculty in July 2016 after earning his Ph.D. in Optical Science and Engineering from the University of Alabama in Huntsville and gaining industry experience as a process engineer followed by research at the Cornell Nanophotonics Group. His educational background includes: Ph.D. in Optical Science and Engineering, University of Alabama in Huntsville Professor Cardenas' research centers on integrated photonics, nanophotonics, and nonlinear photonics, with current projects targeting photonic packaging, 2D materials integration, nonlinear optical phenomena, and on-chip quantum photonics. His group develops nanostructured photonic devices that manipulate light within chip-scale platforms, enabling applications in precision sensing, communications, and quantum technologies. This work bridges fundamental optical physics with practical engineering solutions for real-world implementation. Analysis of his recent publications reveals dominant themes in chip-scale photonic systems, particularly advancements in silicon nitride and lithium niobate platforms. Key research trajectories include weak-value amplification for ultra-precise optical gyroscopes, adiabatic frequency conversion in microring resonators, photonic packaging innovations via laser fusion splicing, and multispectral imaging sensor development. His work consistently emphasizes translating theoretical concepts into manufacturable integrated photonic devices with applications spanning navigation systems, spectroscopy, and quantum information processing. Professor Cardenas leads the Cardenas Lab, which specializes in creating photonic devices that fit on the tip of a needle. The lab's research portfolio spans from fundamental nonlinear optical phenomena to applied educational initiatives, including hands-on photonic kits designed to train the next generation of integrated photonics engineers. Current projects focus on developing robust, manufacturable photonic systems for industrial and defense applications while maintaining strong connections to quantum photonics research.
Jiro Katto is a Professor at Waseda University's School of Fundamental Science and Engineering, where he has been conducting research and teaching since 1999. He received his Ph.D. from the University of Tokyo and has established himself as a leading researcher in multimedia signal processing and computer networks. His academic journey includes positions as Associate Professor (1999-2004), Professor (2004-present), and Director at NEDO (2004-2008), along with research experience at NEC C&C Laboratories (1992-1999) and a Visiting Scholar position at Princeton University (1996-1997). Professor Katto's research interests focus on Multimedia Signal Processing and Computer Networks, with particular expertise in video compression, 5G network performance, and learned image compression techniques. His work bridges theoretical advancements with practical implementations, as evidenced by his extensive publications in top-tier conferences and journals. His research group has made significant contributions to point cloud compression, latency compensation in remote systems, and hardware-accelerated video encoding for UHD streaming. His publication record is impressive, with 276 papers cited 3,323 times in Scopus and 6,169 times in Google Scholar, reflecting his substantial impact in the field. His recent work shows a strong trend toward applying deep learning techniques to traditional signal processing problems, particularly in the areas of image and video compression, where his team has developed novel approaches to improve compression efficiency while reducing computational complexity. Electric Telecommunications Promotion Foundation Telecommunications System Technology Award (2023) Takayanagi Kenjiro Foundation Takayanagi Kenjiro Achievement Award (2020) Institute of Image Information and Television Engineers Fellow (2020) Institute of Electronics, Information and Communication Engineers Fellow (2015) IEICE Communications Society Activity Contribution Award (2006) IEICE Academic Encouragement Award (1995) SPIE VCIP 1991, Best Student Paper Award (1991) Professor Katto has served on numerous prestigious committees including IEEE ComSoC Tech News Editorial Board, IEEE Technical Program Committees for major conferences (Globecom, ICC, ICIP), and editorial boards for several academic journals. His leadership in the academic community extends to chairing conferences like IWAIT 2011 and serving as Editor-in-Chief for journals in his field. His research has practical applications in commercial 5G networks, video streaming services, and remote monitoring systems, demonstrating the real-world impact of his work.
Juergen Schmidhuber is Associate Professor at the Faculty of Informatics of Università della Svizzera italiana and a leading researcher at the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI). He is also Chief Scientist at NNAISENSE, a company dedicated to building practical general-purpose AI. His work has profoundly influenced modern artificial intelligence, particularly through the development of Long Short-Term Memory (LSTM) networks in 1991, now deployed across billions of devices for speech recognition, machine translation, and virtual assistants. His research interests span Artificial Intelligence, Deep Learning, Recurrent Neural Networks, Universal AI, Meta-Learning, Algorithmic Information Theory, Artificial Curiosity, Robotics , and Low-Complexity Art . He has pioneered mathematically rigorous frameworks for self-improving AI systems and formal theories of creativity and beauty. His work bridges theoretical foundations with real-world applications in computer vision, natural language processing, and autonomous robotics. The recent articles reflect a consistent trajectory of innovation, combining deep theoretical insights with scalable machine learning architectures. His publications emphasize sequence modeling, universal learning, intrinsic motivation, and computational creativity , demonstrating both foundational contributions and industrial impact. From LSTM to Goedel machines, his work consistently targets the long-term goal of self-improving general AI. Scientific Awards: Numerous awards in AI and machine learning (specific names not listed) Schmidhuber leads a research group at IDSIA, where he mentors students and researchers in advancing the frontiers of AI. His lab has secured significant recognition and industrial collaboration, though specific grants are not detailed. He promotes the 'New AI'—general, sound, and relevant to physics—and continues to explore the convergence of intelligence, computation, and the universe. Labs and Teams: Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI) NNAISENSE (as Chief Scientist)
Nicholas Antipa is an Assistant Professor at the University of California San Diego's Jacobs School of Engineering, in the Electrical and Computer Engineering department. His research focuses on the co-design of optical systems and algorithms to develop advanced computational imaging systems, leveraging innovations in 3D printing, sensors, machine learning, and AI. He holds a PhD in Computational Imaging from UC Berkeley and previously worked at the Lawrence Livermore National Lab on optical metrology for the National Ignition Facility. His work includes pioneering projects like the DiffuserCam and Miniscope3D, which enable high-dimensional optical signal capture and 3D microscopy. Education: PhD in Computational Imaging, UC Berkeley (2020) MS in Optics, University of Rochester Institute of Optics BS in Optical Science and Engineering, UC Davis Research Interests: Computational imaging systems, single-shot high-dimensional optical capture, lensless imaging, and applications in neuroscience and marine science. His lab explores novel optical designs, compressed sensing, and AI-driven imaging techniques to push the boundaries of conventional systems. Scientific Awards: Best Paper at ICCP 2019, 2016 Best Demo at ICCP 2017 No. 2 in Optica 15 Top-Cited Articles (2020) Affiliations: Director of the Computational Imaging Systems Lab at UCSD. Collaborates with institutions like Lawrence Livermore National Lab and the Scripps Institution of Oceanography for projects in marine sediment mapping and underwater object detection. His lab emphasizes open-source tools, such as the DiffuserCam Raspberry Pi tutorial.
Piotr Indyk is the Thomas D. and Virginia W. Cabot Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT. He is co-director of the Foundations of Data Science Institute (FODSI) and a member of MIT's Theory of Computation Group, Computer Science and Artificial Intelligence Lab (CSAIL), and multiple research initiatives like Wireless@MIT and Big Data@CSAIL. Education: Magister (MA) in Computer Science, University of Warsaw (1995) Ph.D. in Computer Science, Stanford University (2000), advised by Rajeev Motwani Research Interests: Focuses on high-dimensional computational geometry, data stream algorithms, sparse recovery, compressive sensing, and machine learning. His work includes foundational contributions like locality-sensitive hashing (LSH), the Sparse Fourier Transform, and efficient similarity search algorithms. Key Contributions: Known for developing FALCONN (Fast Approximate Nearest Neighbor Search library), and for pioneering work in sub-linear algorithms, streaming algorithms, and geometric computing. Awards: ACM Paris Kanellakis Award (2012) ACM Fellow (2015) Simons Investigator (2013) Member, National Academy of Sciences (2024) Member, American Academy of Arts and Sciences (2023) Teaching & Mentorship: Advised numerous PhD/MSc students and postdocs, and taught courses on geometric computation, streaming algorithms, and algorithmic aspects of embeddings. Labs & Teams: Leads research in areas like FODSI, geometric algorithms, and data science at MIT's CSAIL.
Xilin Liu is an Assistant Professor at the Edward S. Rogers Sr. Department of Electrical & Computer Engineering (University of Toronto) and the Center for Advancing Neurotechnological Innovation to Application (CRANIA) . He obtained his PhD from the University of Pennsylvania and previously worked at Qualcomm Inc. in California. Expertise in integrated circuits and systems for brain-machine interfaces , neuromodulation , and edge AI Published in top venues including Nature Electronics , IEEE JSSC , and ISSCC Recipient of multiple best paper awards and IEEE Senior Member His research spans three main themes: High-speed data converters for wireless/wireline communication IC design for neural interfacing Accelerating machine learning via hardware Recent publications focus on closed-loop neuromodulation , ultra-wideband transceivers , and flexible biomedical sensors . These works integrate analog IC design , edge AI , and real-time neural interfacing across medical rehabilitation , parkinson's monitoring , and memory research . Awards include: IEEE Solid-State Circuits Society Predoctoral Achievement Award (2016) Best Paper Award at BioCAS (2015) ECE Department Teaching Award (2022) Multiple conference best paper finalists His lab collaborates with UHN , EMBS , and global institutions while maintaining strong commitments to equity, diversity, and inclusion (EDI) in research practices.
Daniele Ielmini is a Professor at the Department of Electronics, Information and Bioengineering at Politecnico di Milano, Italy, where he leads research in non-volatile memory technologies and neuromorphic computing. He received his Laurea (with merit) and Ph.D. in Nuclear Engineering from Politecnico di Milano in 1995 and 2000, respectively, and has held visiting positions at Intel Corporation (2006), Stanford University (2006), and the University of Illinois at Urbana-Champaign (2010). His research focuses on the modeling and characterization of non-volatile memories, including nanocrystal memory, charge trap memory, phase change memory (PCM), resistive switching memory (RRAM), and spin-transfer torque magnetic memory (STT-MRAM). He has co-edited the book 'Resistive switching – from fundamental redox-processes to device applications' and published over 300 papers with more than 10,000 citations and an H-index of 69 (Scopus, September 2023). Prof. Ielmini's recent publications demonstrate a strong trend toward in-memory computing and neuromorphic applications, with particular emphasis on closed-loop analog computing architectures, reservoir computing with 2D materials, and hardware security implementations using emerging memory technologies. His work bridges fundamental device physics with practical computing applications, especially for energy-efficient AI acceleration. Intel Outstanding Researcher Award (2013) ERC Consolidator Grant (2014) IEEE-EDS Paul Rappaport Award (2015) Fellow of the IEEE Prof. Ielmini leads multiple ERC-funded projects including SHANNON (Secure Hardware with Advanced Nonvolatile memories), NEURO2D (neuromorphic systems based on reservoir computing in MoS2), and ANIMATE (closed-loop in-memory computing). His research group includes post-doctoral researchers, PhD students, and M.Sc. students working on various aspects of emerging memory technologies and their applications. He serves as Associate Editor for IEEE Trans. Nanotechnology and Semiconductor Science and Technology (IOP), and has served in several Technical Subcommittees of international conferences including IEEE-IEDM, IEEE-IRPS, and IEEE-ISCAS. His laboratory at Politecnico di Milano is equipped with advanced semiconductor device testing equipment including probe-stations, semiconductor parameter analyzers, high-speed waveform generators, and other specialized instruments for nano-electronic research. The lab collaborates with major semiconductor companies including Micron Technology Inc. and STMicroelectronics, as well as participating in national and international research projects.
Cecilia Mascolo is a Professor of Mobile Systems at the University of Cambridge , specifically in the Department of Computer Science and Technology . She co-directs the Centre for Mobile, Wearable System and Augmented Intelligence and is a Fellow of Jesus College, Cambridge . Her research focuses on mobile systems , machine learning for mobile health , and earable technology . She has been awarded prestigious grants such as the ERC Advanced Research Grant (2019-2025) and the EPSRC Open Research Fellowship (2025-2030). Currently on sabbatical at Harvard University , her work bridges systems and machine learning for health applications. Education: PhD in Computer Science from the University of Bologna, Italy. Previous Affiliation: Faculty at University College London before 2008. Her research spans mobile and wearable systems for health and behavior monitoring, focusing on on-device machine learning , uncertainty-aware models , and audio-based diagnostics . Key areas include federated learning , edge computing , and respiratory disease progression analysis via wearables. She explores earable technology for physiological monitoring, gait analysis, and even toothbrushing tracking using in-ear sensors. Her recent publications highlight advancements in earable-based health monitoring , including heart rate estimation , respiratory rate detection , and ECG analysis using machine learning. She emphasizes longitudinal health data from consumer devices, advocating for scalable diagnostics beyond traditional clinical standards. Scientific Awards: ERC Advanced Research Grant EPSRC Open Research Fellowship Best Paper Award - IEEE Percom 10-Year Impact Award - ACM Ubicomp Computer Laboratory Ring Hall of Fame Best Paper Award Student: Andrea Ferlini - ACM SIGMOBILE Doctoral Dissertation Runner-up She leads the Mobile Systems Research Laboratory , mentoring a team of 15 researchers (postdocs and PhD students), and has graduated over 25 PhD students. Her teaching includes Mobile Health courses at the University of Cambridge, and she serves as Director of Studies for Computer Science at Jesus College.
Tariq Iqbal is an Assistant Professor at the University of Virginia , with joint appointments in the Department of Systems and Information Engineering and Department of Computer Science . He leads the Collaborative Robotics Lab (CRL) , specializing in human-robot teams and embodied AI . Previously, he was a Postdoctoral Associate at MIT's CSAIL , advised by Prof. Julie Shah , and earned his Ph.D. in Computer Science from University of California San Diego (UCSD) under Prof. Laurel Riek . Ph.D. in Computer Science, University of California San Diego (2017) M.S. in Computer Science, University of Texas at El Paso (2012) B.S. in Computer Science and Engineering, Bangladesh University of Engineering and Technology (2007) His research lies at the intersection of artificial intelligence and robotics , focusing on human-robot collaboration in dynamic environments. Key areas include motion prediction , multimodal fusion , trust modeling , and collaborative learning . His work integrates cognitive science and deep learning to enhance robotic fluency in naturalistic settings. Recent publications (2023–2025) highlight advancements in human-robot team dynamics , multimodal dataset creation , and motion prediction algorithms . Notable works include Energy-Based Transformers for scalable AI, PoseTron for motion prediction, and Accessible Navigation Mapping for assistive robotics. These contributions span trust modeling , cloud robotic infrastructure , and safety in close-proximity collaboration . National Science Foundation (NSF) CAREER Award Air Force Office of Scientific Research (AFOSR) Young Investigator Program (YIP) Award Commonwealth Center for Advanced Manufacturing (CCAM) Innovation Award As faculty, he has secured grants from NSF and AFOSR , mentored research students, and taught courses like Stochastic Modeling I (SYS 6005) and Robots and Humans (SYS 4582/6465, ECE 4502/6465, CS 6465) . His prior industry roles at IBM Watson Lab and Grameenphone Ltd. inform his applied research in telecom infrastructure and cognitive robotics . He leads the Collaborative Robotics Lab (CRL) at UVA, which develops multimodal datasets , real-time coordination algorithms , and adaptive pathfinding systems . Current projects explore human motion prediction , team synchrony , and embodied question-answering , reflecting his commitment to advancing human-robot fluency and contextual AI .
Roy Dong is an Assistant Professor at the University of Illinois at Urbana-Champaign, affiliated with the Coordinated Science Laboratory. His research bridges Control Theory Economics Statistics Optimization to address challenges in cyber-physical systems and the Internet of Things, focusing on data manipulation, privacy, and strategic behavior in interconnected systems. His academic journey includes a Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley (2017) and dual B.S. degrees in Economics and Computer Engineering from Michigan State University (2010). At Illinois, he teaches courses ranging from Control Systems to Convex Optimization , with multiple teaching excellence awards. Roy's research explores Closed-loop effects of machine learning Causality in decision systems Incentive design for strategic agents Privacy-utility tradeoff optimization Human behavior modeling with applications in smart grids, transportation networks, and semi-autonomous vehicles. His work formulates privacy-preserving mechanisms as optimization problems, balancing data utility against user privacy in dynamic systems. Article trends show expertise in Game theory for strategic data sources Energy disaggregation techniques Nonlinear basis pursuit algorithms Privacy-aware control systems with a focus on cyber-physical systems and human-in-the-loop applications. Scientific recognition includes 'Teacher Ranked as Excellent' awards (ECE 120, ECE 486, ECE 515) Contributions to smartSDH building control and CPRL compressive sensing Roy leads the Privacy-aware Control Systems research group, collaborating with institutions like UC Berkeley and Michigan State University , and directs projects funded by grants including the New USDA NIFA grant for agricultural robot autonomy .
Professor Paul Midgley is a leading academic in Materials Science at the University of Cambridge's Department of Materials Science and Metallurgy, serving as Professor since 2007 and Head of Department from 2018–2020. He is a Fellow of Peterhouse College and holds multiple prestigious awards, including the Royal Society Fellowship and the Ernst Ruska Prize. Education: PhD in Physics (University of Bristol, 1991), MSc (Distinction) in Semiconductor Materials (1988), BSc (Hons) Physics (1987). Administration: Director of the Wolfson Electron Microscopy Suite, and active on various University committees including Research, Teaching, and REF. His research focuses on advanced electron microscopy techniques such as convergent beam diffraction, electron tomography, and nanostructure analysis, with applications in nanoscale materials science and 3D reconstruction using compressed sensing. He has pioneered methods like precession electron diffraction and multi-dimensional electron microscopy, contributing to fields like plasmonic nanoparticles and catalytic materials. Key Research Themes: Electron crystallography, nanomaterial characterization, energy materials, and defect analysis in perovskites. Midgley has delivered over 20 invited/plenary lectures globally, including at EUROMAT, the Welch Symposium, and the John Cowley Memorial Lecture. His grant income exceeds £16M as Principal Investigator. Labs/Teams: Leads the Wolfson Electron Microscopy Suite and collaborates internationally on microscopy advancements and materials innovation.
Jun-Yan Zhu is an Assistant Professor at Carnegie Mellon University's School of Computer Science, affiliated with the Robotics Institute and Computer Science Department. His research focuses on generative models, computer vision, and graphics. He holds a B.E. from Tsinghua University and a Ph.D. from UC Berkeley, with postdoctoral work at MIT CSAIL. Zhu leads the Generative Intelligence Lab, exploring human-creator collaboration with generative models. Affiliations: Robotics Institute, CMU Graphics Lab, CMU Computer Vision Group Education: B.E. (Tsinghua), Ph.D. (UC Berkeley) Research Interests: Generative AI, image/video synthesis, neural rendering, tactile sensing integration Notable contributions include CycleGAN, pix2pix, and GAN compression techniques. His work has been commercialized in Adobe's Firefly and NVIDIA's Canvas tools. Awards: ACM SIGGRAPH Dissertation Award, David J. Sakrison Prize, CVPR Best Paper Finalist Lab Members: 10+ PhD students and researchers Current projects include LEGO design synthesis, tactile-driven 3D generation, and generative model personalization.
