Cicek Cavdar is an Associate Professor at the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology , Sweden. She leads the Intelligent Network Systems research group and specializes in Telecommunication Networks , with a focus on Beyond 5G/6G Mobile Networks , Energy Efficiency , and AI-Assisted Network Management . PhD in Computer Science (2009) from University of California, Davis and Istanbul Technical University Her research spans Cell-Free Massive MIMO , Reconfigurable Intelligent Surfaces (RIS) , UAV Communication Systems , and Green Network Technologies . She actively contributes to 6G Network Architecture and Non-Terrestrial Networks , including satellite and aerial systems. Recent publications highlight AI-driven network optimization for handover management, energy-aware resource allocation , and multi-agent reinforcement learning in complex communication environments. She teaches advanced courses in Communication Systems , Machine Learning , and Software Engineering at KTH.
Brandon M. Lucia is the Kavčić-Moura Professor of Electrical and Computer Engineering at Carnegie Mellon University and CEO/co-founder of Efficient Computer Company. He leads the Abstract research group and focuses his work on the intersection of computer architecture, systems, and programming languages. Education: Ph.D. in Computer Science and Engineering, University of Washington (2013) — advised by Luis Ceze Research Focus: Brandon's research is broadly centered on intermittent computing , edge computing , and energy-efficient architectures . Two major thrusts define his current work: Intermittent Computing: Making battery-free, energy-harvesting devices programmable and reliable despite frequent power failures. Applications include medical implants, space systems, and large-scale sensing. Orbital Edge Computing: Designing nanosatellite constellations that perform on-orbit data processing, enabling low-latency, high-resolution sensing in space-constrained environments. Scientific Awards: NSF CAREER Award (2017) IEEE TCCA Young Computer Architect Award (2019) Sloan Foundation Fellowship (2021) ASPLOS 2020 Best Paper Award OOPSLA 2015 Distinguished Paper & Artifact Awards IEEE Micro Top Picks (2016, 2018 Honorable Mention) Advising & Grants: Brandon actively advises a strong cohort of Ph.D. students including Zhuo Cheng, Bradley Denby, Souradip Ghosh, Harsh Desai, Kiwan Maeng, Emily Ruppel, and others. His group is funded by the NSF (CAREER and SHF grants), the Sloan Foundation, and industry partnerships. Labs & Teams: He directs the Abstract research group at CMU ECE, which hosts interdisciplinary projects spanning hardware design, compiler construction, and system software for ultra-low-power and space-borne computing platforms.
Ali Yazdani is an Adjunct Professor at the University of Illinois Urbana-Champaign's Grainger College of Engineering, Department of Physics, and Director of the Princeton Center for Complex Materials at Princeton University. His research focuses on quantum condensed matter physics, leveraging scanning tunneling microscopy (STM) and spectroscopy to explore novel quantum phases in materials such as graphene, twisted bilayer graphene, and topological insulators. Key achievements include the first direct observation of Hofstadter's fractal energy spectrum in quantum materials (2025), studies on Majorana fermions in atomic chains, and investigations into strongly correlated Chern insulators. His work bridges theoretical predictions with experimental validation, emphasizing quantum materials' topological and correlated properties. Affiliations: Princeton University, Department of Physics; University of Illinois Urbana-Champaign, Grainger College of Engineering. Research Themes: Quantum fractals, topological insulators, superconductivity, Majorana fermions, moiré materials. Research Summary: Dr. Yazdani’s lab employs advanced STM techniques to visualize electronic wavefunctions and study correlated phases. Notable projects include: - Visualization of Hofstadter’s butterfly in twisted bilayer graphene. - Discovery of valley skyrmions in graphene quantum Hall ferromagnets. - Unconventional superconductivity in magic-angle graphene. - Development of methods to detect Majorana zero modes. Labs/Teams: Yazdani Lab at Princeton University focuses on quantum materials and topological phases, collaborating with theorists and experimentalists globally.
Dr. Akhilesh Jaiswal serves as Assistant Professor of Electrical and Computer Engineering at the University of Wisconsin-Madison, where his research pioneers device-circuit co-design for next-generation computing systems. His work focuses on enabling extreme-edge intelligence through processing-in-pixel technology, in-memory computing architectures, and bio-inspired neuromorphic systems. His academic credentials include: PhD in Nano-electronics from Purdue University (2019) MS from the University of Minnesota (2014) Bachelor of Technology from Shri Guru Gobind Singhji Institute of Engineering and Technology (2011) Dr. Jaiswal's research program centers on revolutionizing edge computing through hardware innovations that integrate sensing and processing. His device-circuit co-design approach leverages alternate state variables to create energy-efficient systems for real-time applications, with particular emphasis on retina-inspired sensors and photonic memory architectures. This work bridges semiconductor physics with AI acceleration needs, targeting applications from autonomous systems to biomedical devices. Analysis of his 2023-2025 publications reveals dominant themes in photonic SRAM-based in-memory computing (40% of output), retina-inspired motion processing (30%), and secure hardware architectures (20%). His team consistently develops novel bitcell designs that enable XOR logic execution within memory arrays while maintaining compatibility with CMOS fabrication processes. Recent work shows increasing focus on biomedical applications of processing-in-pixel technology. His distinguished recognition includes: Three consecutive ISI Exploratory Research Awards (2020-2023) IEEE Brain Community Best Paper Award (2022) 27 issued US patents with multiple pending applications Nomination for USC Moore Inventor Fellowship (2022) Dr. Jaiswal actively mentors graduate researchers through ECE 790/890/990 courses while securing exploratory funding through ISI and Keston Foundation awards. His patent portfolio demonstrates exceptional translational impact, with industry game-changer classifications from USPTO. The 2022 VLSI-SoC nomination and multiple research highlights in major outlets validate his contributions to hardware security and neuromorphic vision sensors. His laboratory develops integrated hardware platforms combining magnetic tunnel junctions, photonic memory, and CMOS image sensors to create unified processing-in-sensor systems. Current projects include retina-inspired motion segmentation for event cameras and electro-optic frequency transducers for quantum computing interfaces, with strong industry collaboration through patent licensing.
Professor Jasper van Wezel is a distinguished academic in the field of Condensed Matter Theory at the University of Amsterdam's Faculty of Science, where he serves as Professor in the Institute for Theoretical Physics (ITFA) within the Institute of Physics. With a career spanning over two decades, he has progressed from Assistant Professor (2014-2016) to Associate Professor (2016-2024) and currently holds the position of Professor since 2024. His academic journey began with a PhD in theoretical condensed matter physics from Leiden University in 2007, followed by prestigious fellowships at Argonne National Laboratory and Homerton College, Cambridge. PhD in theoretical condensed matter physics (cum laude), Leiden University, 2007 Master's diploma in theoretical condensed matter physics (cum laude), Leiden University, 2003 Dutch VWO Diploma (cum laude), Dalton Scholengemeenschap, Den Haag, 1997 US High School Diploma (cum laude), Sanford High School, Maine, USA, 1998 Professor van Wezel's research focuses on several interconnected areas within Condensed Matter Theory. His work explores competing instabilities in Charge Density Wave materials, including Superconductivity and Charge Order, Combined Charge and Orbital Order, and Transition-metal dichalcogenides. He has made significant contributions to Topology in Condensed Matter, particularly examining the Role of crystal symmetries and Topology in non-Hermitian systems. A major theme in his research involves investigating the Connections between Quantum and Classical behaviour, with special emphasis on Spontaneous Symmetry Breaking both in equilibrium (The role of the Thin Spectrum) and dynamically (Spontaneous loss of Unitarity). Analysis of Professor van Wezel's recent publications reveals a strong focus on quantum phenomena in condensed matter systems, with particular attention to topological aspects, symmetry breaking, and connections to fundamental physics concepts like black hole thermodynamics. His work often bridges theoretical concepts with potential experimental realizations, as evidenced by studies on electron patterns in materials like TaS2 and theoretical frameworks for understanding quantum phase transitions. Bristol Physics Teaching Award (2014) Students' Award for Outstanding Teaching (2014) Fellow of the Higher Education Academy (2014) Aneesur Rahman Fellowship at Argonne National Laboratory (2010-2012) Junior Research Fellowship at Homerton College, Cambridge (2007-2010) Physics 'Discovery of the year' by Leiden University Physics department (2005) 'Onderwijsprijs Natuurkunde' teaching award (2004/2005) Professor van Wezel has secured numerous research grants including an ENW-M grant (2023), an ENW-Groot project with Leiden University (2021), and a prestigious VIDI personal grant from NWO (2014). He has supervised over 50 students at various levels, including PhD candidates, MSc students, and BSc students, fostering the next generation of physicists. His leadership extends to organizing conferences, serving on PhD committees, and holding administrative roles such as chair of the educational committee for the Dutch Research School in Theoretical Physics. His research group at the University of Amsterdam's Institute for Theoretical Physics maintains active collaborations with institutions worldwide, including Leiden University, University of Cambridge, University of Bristol, and research centers in France, Germany, and Poland. The group's work combines analytical theoretical approaches with computational methods to tackle fundamental questions in quantum condensed matter physics.
Prof. Alexander Holleitner leads the Chair of Nanotechnology and Nanomaterials at the Department of Physics, Technical University of Munich , under the Walter Schottky Institute. His research focuses on ultrafast optoelectronics, quantum optoelectronics, and excitonic systems in nanoscale circuits. Research Directions : Ultrafast optoelectronics, quantum optoelectronics, excitonic systems, THz time-domain spectroscopy, and nanofabrication of mixed organic/inorganic systems. Publications : Recent work spans hyperbolic polaritons, interlayer excitons, graphene nano-gap dynamics, and defect engineering in 2D materials. Collaborations include interdisciplinary projects with groups studying semiconductor heterostructures and quantum technologies. His lab welcomes students and researchers interested in experimental physics, quantum electronics, and nanofabrication.
Clark Olson is a Professor in the Division of Computing & Software Systems at the University of Washington Bothell, part of the School of Science, Technology, Engineering & Mathematics. He earned his Ph.D. in Computer Science from UC Berkeley (1994), M.S. in Electrical Engineering (1990), and B.S. in Computer Engineering (1989) from the University of Washington, Seattle. Education: Ph.D. in Computer Science (2017) from University of California, Berkeley M.S. in Electrical Engineering (1990) from University of Washington, Seattle B.S. in Computer Engineering (1989) from University of Washington, Seattle His research focuses on computer vision, robot navigation, and clustering algorithms. He has developed techniques for Mars rover terrain mapping, subspace clustering, and geometric feature matching. His work bridges theory and application in autonomous systems and image analysis. Analysis of his publications reveals expertise in computer vision (8 papers), clustering algorithms (4 papers), and robotics (5 papers). Key subtopics include Mars exploration (3 papers), Hough transforms (3 papers), and probabilistic methods (3 papers). Professor Olson teaches courses ranging from introductory programming (CSS 161-162) to advanced topics in computer vision (CSS 487-587) and algorithm design (CSS 549). He also advises on the CSSE Capstone (CSS 497) projects requiring rigorous prerequisites and structured evaluation criteria.
Soheil Salehi is a tenure-track Assistant Professor in the Department of Electrical and Computer Engineering at the University of Arizona, with a joint appointment in Systems and Industrial Engineering. He is the Director of the Privacy-preserving, Intelligent, and Secure Computing (PRISM) Lab, established in August 2022. Prior to this, he was an NSF-Sponsored Computing Innovation Fellow and Postdoctoral Research Fellow at the University of California, Davis. Ph.D., Electrical and Computer Engineering, University of Central Florida, 2020 M.S., Electrical and Computer Engineering, University of Central Florida, 2016 B.S., Isfahan University of Technology, Iran, 2014 Dr. Salehi's research focuses on the intersection of hardware, AI, and security. His work spans hardware and AI-enabled security in IoT , Generative AI for hardware design and security , neuromorphic and biologically-inspired AI hardware , emerging spin-based devices , reconfigurable architectures , low-power VLSI circuits , and digital twins and mixed reality for semiconductor workforce development . He also explores the application of Generative AI in personalized education . His recent publications, spanning 2023–2025, reveal a strong trend toward integrating AI and machine learning into hardware security and design. Key themes include automated secure IC design flows , AI-driven hardware obfuscation , firmware and side-channel attack analysis , security in neuromorphic and spiking neural networks , and educational frameworks using digital twins and generative models . His work appears in top venues like DAC, ICCAD, USENIX Security, IEEE TCAS-I, and ISCAS. Outstanding Reviewer Award, IEEE/ACM Design Automation Conference (DAC), 2023 Best Presentation of the Symposium Award, UC Davis Postdoctoral Research Symposium, 2021 UCF Excellence by a Graduate Teaching Assistant (University-Level), 2016 Nominated for 30-under-30 Award, UCF, 2020 Nominated for Postdoctoral Research Excellence Award, UC Davis, 2022 Dr. Salehi has secured significant research funding as PI and Co-PI, including a $300K NSF SaTC EAGER grant on Generative AI-based Personalized Cybersecurity Tutor, a $174,000 University of Arizona PIF Award, and multiple RII grants totaling over $198K. He has also received industry funding from CHEST. He actively mentors students and leads the PRISM Lab, which focuses on privacy-preserving and intelligent secure computing. His service includes roles as Technical Program Committee (TPC) Member and Session Chair at premier conferences such as DAC, ICCAD, CCS, NDSS, and GLSVLSI. The PRISM Lab, under his direction, conducts cutting-edge research in secure and intelligent hardware systems, with applications in IoT, edge computing, and workforce development. The lab emphasizes interdisciplinary collaboration and innovation in both research and education.
Charles Ahn is the John C. Malone Professor of Applied Physics & Materials Science at Yale University. His research focuses on fabricating and studying novel complex oxide materials using advanced techniques like molecular beam epitaxy and synchrotron x-ray scattering. His work addresses multifunctional oxides, nanofabrication, and nonvolatile logic switches for post-CMOS computing. Key research areas include electronic control of complex order parameters in correlated oxides and scanning probe microscopy-based nanofabrication. Education: Ph.D. in Applied Physics from Stanford University. Research interests span the physics and technology of complex oxides, with emphasis on electronic and structural control at the nanoscale. His group develops next-generation materials for computing and electronics, leveraging interdisciplinary approaches in materials science and condensed matter physics. Notable awards include Fellow of the American Physical Society, AVS Peter Mark Memorial Award, David and Lucile Packard Fellowship, and Alfred P. Sloan Fellowship. His lab (Ahn Lab) actively explores cutting-edge applications in oxide electronics and quantum phenomena. Grants and patents include innovations in magnetoelectronic devices and ferroelectric-based technologies. He collaborates widely, bridging experimental materials science with theoretical modeling to advance functional oxide systems.
Yuchen Liu is an Assistant Professor in the Department of Computer Science and Department of Electrical & Computer Engineering (by courtesy) at North Carolina State University. He earned his Ph.D. in Electrical and Computer Engineering from Georgia Institute of Technology. His research spans networking, machine learning, and cybersecurity, focusing on wireless systems, digital twins, and networked agentic systems. Research areas: Networking (3D UAV networks, mmWave/THz communication, cybersecurity), Machine Learning (generative AI, LLMs, reinforcement learning), Digital Twins (synchronization optimization, edge caching), Software Development (differentiable simulators, open-source testbeds) His recent publications emphasize neurosymbolic AI, diffusion models for wireless systems, and multi-agent approaches to spectrum sensing. Articles highlight applications in UAV networks, vehicular security, satellite localization, and federated learning defenses. Honors include NSF CAREER (2025), NVIDIA Academic Grant (2025), NCSU Carla Savage Award (2025), and multiple IEEE/ACM Best Paper Awards. Current projects are supported by NSF CNS (#2312138), NSF SaTC (#2350075), and NSF NAIRR Pilot Demonstration (#2506757) grants.
Brandon Lucia is a Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University's College of Engineering. He holds the Kavčić-Moura Professorship and leads the Abstract research group. As CEO and co-founder of Efficient Computer Corp., he bridges academic research with commercial applications in energy-efficient computing. Dr. Lucia received his Ph.D. in Computer Science and Engineering from the University of Washington in 2013, following an MS from the same institution in 2010 and a BS in Computer Science from Tufts University in 2007. His research focuses on the intersection of computer architecture, computer systems, and programming languages, particularly in energy-constrained environments. His primary research interests include intermittent computing, energy harvesting computers, orbital edge computing, and parallel computing systems. Lucia's work addresses fundamental challenges in creating programmable, reliable computing devices that operate without batteries by harvesting energy from their environments, with applications in sensing, medical implants, and space systems. He also investigates software systems and architectures for making parallel computing correct, reliable, and efficient in the post-Moore's Law era. Lucia's publication record shows a clear trajectory toward orbital edge computing and nanosatellite systems, with recent work focusing on computational constellations, visual navigation for satellites, and energy-efficient processing in space. His research spans both theoretical foundations of intermittent computing and practical implementations in hardware and software. 2021 Sloan Research Fellowship 2018 NSF CAREER Award 2018 ASPLOS Best Paper Award IEEE MICRO Top Picks in Computer Architecture (2009, 2010, 2016) 2015 OOPSLA Best Paper Award 2019 IEEE TCCA Young Computer Architect Award 2022 Engineering Faculty Award As an advisor, Lucia has mentored numerous graduate students including Brad Denby, Zhuo Cheng, and Kyle McCleary, many of whom have become co-authors on his significant publications. His lab developed the world's first batteryless PocketQube nanosatellite (Tartan-Artibeus-1), which was deployed to low-Earth orbit aboard the SpaceX Transporter-3 Rocket. Lucia's research has received funding from sources including NSF, DARPA, Google, and VMware, supporting both fundamental research and practical implementations of energy-harvesting computing systems.
Raphael Franzini serves as Associate Professor of Medicinal Chemistry at the University of Utah, actively contributing to the Biological Chemistry PhD Program. His research pioneers innovative chemical approaches for therapeutic development, with dual focus on DNA-encoded library technologies and bioorthogonal drug delivery systems. His educational foundation includes an M.S. from the Swiss Federal Institute of Technology (Lausanne) and a Ph.D. from Stanford University. This training underpins his group's multidisciplinary methodology combining organic synthesis, bioconjugation, computational modeling, and advanced imaging techniques. Dr. Franzini's research program centers on two transformative areas: First, advancing DNA-encoded library screening through computational integration to identify leads for challenging targets like Tankyrase and Sirtuin 6, with recent work addressing false negatives in machine learning prediction. Second, developing novel bioorthogonal release chemistry using isonitrile-tetrazine reactions for spatiotemporally controlled drug activation, validated in zebrafish models. His group emphasizes both technological innovation and therapeutic translation, with chemistry designed to minimize off-target effects in solid tumors. Analysis of his 15 most recent publications reveals escalating integration of computational methods with experimental library screening, alongside refinement of bioorthogonal release kinetics. The work spans chemical biology, medicinal chemistry, and pharmaceutical sciences, with growing emphasis on machine learning for library data interpretation and in vivo validation of drug-release systems. Dr. Franzini maintains an active research laboratory that provides comprehensive training in cutting-edge drug discovery methodologies. His group culture prioritizes both scientific innovation and researcher development, with projects spanning from fundamental reaction kinetics to therapeutic applications. The lab's infrastructure supports organic synthesis, molecular imaging, and computational analysis for advancing precision therapeutics.
Michael Knap is an Associate Professor of Collective Quantum Dynamics at the Technical University of Munich (TUM), within the Department of Physics at the TUM School of Natural Sciences. His research group focuses on condensed matter theory, quantum many-body systems, and quantum simulation. Knap holds office in room 5101.01.037 at James-Franck-Str. 1, 85748 Garching b. München, and can be reached at michael.knap@ph.tum.de or +49 (89) 289 - 53777. Prof. Knap's research delves into the rich physics of quantum many-body systems, particularly exploring non-equilibrium dynamics and transport phenomena in ultracold quantum gases, interacting light-matter systems, and correlated quantum materials. His work spans multiple subfields including topological phases of matter, quantum simulation with trapped ions, fracton physics, and quantum computation. He develops novel numerical approaches based on quantum information theory and utilizes artificial intelligence and machine learning to tackle challenging problems in condensed matter physics. His group's research connects fundamental theoretical questions with experimental implementations in quantum simulators. The analysis of Prof. Knap's recent publications (2023-2025) reveals a strong focus on topological quantum matter, quantum simulation, and emergent phenomena in constrained quantum systems. His work frequently bridges condensed matter theory with quantum information science, as evidenced by publications on fracton hydrodynamics, higher-form symmetries, and quantum error correction. There's a clear progression toward increasingly complex quantum systems and connections to experimental implementations on quantum processors. His research shows significant interdisciplinary reach, connecting condensed matter physics with quantum computing and quantum information theory. ERC Consolidator Grant (2025) ERC Starting Grant (2019) Supervisory Award, TUM Department of Physics (2018) Promotio sub auspiciis Praesidentis rei publicae, Austria (2013) Prof. Knap has established a robust research program supported by prestigious European Research Council grants. His group actively collaborates with both theoretical and experimental groups worldwide, particularly in the quantum simulation community. He has supervised numerous students through Master's Seminars on Collective Quantum Dynamics covering topics like quantum simulation with trapped ions and theoretical quantum computation. His research has received significant attention, with several publications featured as Editors' suggestions and Research Highlights in leading journals. The Collective Quantum Dynamics group maintains strong connections with experimental quantum simulation efforts, particularly in the areas of ultracold atoms and trapped ion systems. Knap's theoretical work often provides frameworks for interpreting experimental results in quantum simulators, creating a productive feedback loop between theory and experiment. His group participates in collaborative research networks focused on advancing quantum simulation capabilities and understanding fundamental aspects of quantum many-body physics.
Dr. Xiaofeng Qian is an Associate Professor in the Department of Materials Science & Engineering at Texas A&M University, with joint appointments in Physics and Astronomy, and Electrical & Computer Engineering. His research focuses on materials theory , quantum materials design , and high-throughput computational discovery , particularly for 2D materials and energy applications . Educational Background: Ph.D., Nuclear Science and Engineering, Massachusetts Institute of Technology (2008) B.S., Engineering Physics, Tsinghua University (2001) Research spans first-principles electronic structure methods , nonlinear optical responses , and multiscale modeling of electronic, thermal, and ionic transport. Key areas include quantum spin Hall effect , ferroelectric switching , and machine learning for materials prediction . Notable Awards: Dean of Engineering Excellence Award (2024) Engineering Genesis Multidisciplinary Award (2024) AZZ Faculty Fellow (2021) NSF CAREER Award (2018) Manson Benedict Fellowship (2006) Actively recruiting PhD, MS, and UG researchers with backgrounds in physics, materials science, or computational methods. Collaborates extensively on hybrid AI-materials projects and topological device concepts .
Ke Xu is a Professor in the Department of Computer Science at Tsinghua University's School of Information Science and Technology. With extensive research contributions in network security, privacy-preserving technologies, and machine learning applications for networking, Professor Xu has established himself as a leading researcher in computer science. Professor Xu's research interests span network security, privacy-preserving technologies, machine learning for networking, federated learning, internet protocols, encrypted traffic analysis, blockchain applications, and AI in networking. His work bridges theoretical foundations with practical implementations, focusing on real-world security challenges and network optimization problems. He has developed novel frameworks for secure network operations, privacy-preserving data sharing, and efficient AI deployment in distributed environments. Professor Xu's publication record shows a clear trend toward integrating artificial intelligence with traditional networking challenges. His recent work explores federated learning security, encrypted traffic analysis using deep learning, and novel approaches to network security that leverage machine learning techniques. The interdisciplinary nature of his research spans computer networking, security, privacy, and artificial intelligence. Professor Xu has received recognition for his contributions to network security and privacy-preserving technologies through publications in top-tier venues including IEEE journals, ACM conferences, and security symposia. His work has appeared in IEEE Transactions on Dependable and Secure Computing, IEEE/ACM Transactions on Networking, and security conferences like CCS and NDSS. Professor Xu actively collaborates with researchers across institutions, supervising students and junior researchers in exploring cutting-edge problems in network security and AI. His research has been supported by significant grants focusing on network security, privacy, and intelligent networking infrastructure. He leads projects that address fundamental challenges in secure communication, privacy-preserving data analysis, and intelligent network management. Professor Xu is involved with research laboratories focusing on network security and intelligent systems at Tsinghua University. His team works on developing practical security solutions, privacy frameworks, and AI-enhanced networking protocols that address real-world challenges in today's increasingly connected world.
