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Jeff ZhangView profile

Assistant Professor

• Deep learning
• Computer architecture
• Embedded systems
• +5 more

Jeff Zhang is an Assistant Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University. He joined ASU in January 2023 after completing a postdoctoral fellowship at Harvard University. His research spans deep learning, computer architecture, embedded systems, and EDA, with particular emphasis on energy-efficient and fault-tolerant design for AI/ML systems and hardware accelerators. Education: Ph.D., New York University M.Eng., B.Eng., Hunan University Dr. Zhang's research bridges theoretical machine learning with practical hardware implementation, developing novel architectures that optimize performance, power consumption, and reliability. He has pioneered approaches for hardware acceleration of large language models, efficient sparse matrix operations, and novel memory technologies for AI workloads. His work has received multiple awards including IEEE Top Picks in Test and Reliability (2023) and IEEE Micro Best Paper Award (2022). His recent publications demonstrate a strong trend toward heterogeneous computing, with significant work in chiplet-based AI accelerators, photonic computing for AI, and 2.5D/3D integration techniques. The research spans from high-level compiler frameworks to circuit-level innovations, with a consistent theme of co-designing algorithms and hardware for optimal AI performance. His work on the SODA toolchain has been particularly influential in bridging Python to silicon. Scientific Awards: IEEE Top Picks in Test and Reliability, IEEE ITC, 2023 Best Paper Award, IEEE Micro, 2022 Best Paper Award Candidate, IEEE DATE, 2022 Best Presentation Award Nomination, ACM SIGDA DATE PhD Forum, 2020 Best Paper Award Nomination, IEEE VLSI Test Symposium, 2018 Ernst Weber Ph.D. Fellowship, New York University, 2015, 2016 Dr. Zhang actively mentors a diverse group of graduate and undergraduate students, with several alumni now working at leading technology companies including Apple, TSMC, and Ansys. His research is supported by prestigious grants from NSF, Sandia National Labs, and industry partners. He serves on technical program committees of numerous top conferences and has organized special sessions on emerging topics like Gen AI for Chip Design and LLM-Aided Design. Dr. Zhang leads a vibrant research group that collaborates extensively with industry partners and national laboratories. Current projects focus on next-generation AI hardware, including chiplet-based systems, photonic accelerators, and novel memory technologies for large language models. His group has developed several open-source tools and frameworks, including the SODA toolchain for bridging Python to silicon.

• Biomedical Engineering
• Biomechanics
• Tissue Engineering
• +3 more

Peyman Karami is a Postdoctoral Researcher at the Laboratory of Biomechanical Orthopedics (LBO) within École Polytechnique Fédérale de Lausanne (EPFL)'s College of Engineering . Research focuses on adhesive hydrogels for cartilage repair and orthopedic applications Investigates biomimetic stimuli (hydrostatic pressure, temperature) in chondrocyte homeostasis Develops ligin-based multifunctional hydrogels for sustainable biomedical applications Expertise in mechanobiology and thermomechanical regulation of tissue-engineered constructs Scientific Contributions: Leads 15+ publications on hydrogel technologies for cartilage regeneration, thermomechanical stimulation effects, and lignin functionalization, including breakthrough work in NIR-light photocuring , malacic trachea repair , and biomimetic temperature gradients . Current Research Trends: Prioritizes injectable adhesive hydrogels , noninvasive tissue repair , and multi-functional biomaterials that couple mechanical and biochemical cues for enhanced regeneration.

• Industrial Photonics
• Laser Engineering
• Materials Processing
• +3 more

Professor Bill O'Neill is a Fellow in Engineering at Downing College and holds the Professor of Laser Engineering position at the University of Cambridge. He leads the Centre of Industrial Photonics and focuses on cutting-edge laser-based manufacturing technologies. BSc (Essex) MSc (Essex) MA PhD (Imperial) His research spans high-power laser applications in materials processing, including aerospace alloys, medical alloys, ceramics, and polymers. He investigates ultra-short laser-matter interactions (femtosecond pulses) for thermal-free machining and nanofabrication techniques aimed at developing personal factory systems. Key projects include electron backscattered diffraction analysis (EBSD) validation of material responses and £10M UK research council-funded innovations. Publications highlight advancements in laser microstructuring, x-ray collimation, and cold gas dynamic spray technologies. His work trends toward precision manufacturing, materials science, and photonics applications. As head of the Centre of Industrial Photonics, O'Neill's team explores nanoparticle manipulation for on-demand fabrication of complex products, envisioning a future where desktop printers create micro/nano devices.

Yuzhe YangView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Healthcare Technology
• +3 more

Yuzhe Yang is an Assistant Professor of Computational Medicine and Computer Science at UCLA, with a visiting research scientist role at Google Health. He holds a PhD in Computer Science from MIT (2024), advised by Dina Katabi, and a B.S. with honors from Peking University. Research Focus: Machine learning for healthcare, medical AI fairness, and AI-driven biomedical discovery Key Contributions: Ten Notable Advances (Nature Medicine) and Ten Crucial Advances (The Lancet Neurology) His lab develops Trustworthy Learning Algorithms and Generalist Health Models that integrate Multimodal Data for personalized health coaching. Notable projects include AI-based Parkinson's Disease Biomarkers via nocturnal breathing and Foundation Models for Equitable Medicine . Recent publications at ICLR 2025 (wearable foundation models), Nature Medicine 2024 (medical AI fairness), and Science Advances 2025 (vision-language medical bias) highlight his interdisciplinary work. He serves on ML4H workshops and reviews for top conferences like NeurIPS and ICML. Awards include Forbes 30 Under 30 , Takeda Fellowship , and Baidu PhD Fellowship . Advising opportunities: Recruiting PhD students (CS/CompMed) and postdocs in AI for health. Lab: Health Intelligence Lab (HAIL)

• Energy Efficiency in Communication Networks
• Quality of Service (QoS) and Quality of Experience (QoE)
• Network and Service Management
• +5 more

Dr. Frank Loh is a researcher at the Department of Computer Science III, University of Würzburg, specializing in energy efficiency, network performance, and Quality of Experience (QoE) in communication networks. His work focuses on optimizing LoRaWAN deployments, serverless computing, and edge-cloud environments, with an emphasis on reducing message collisions and improving resource utilization. He actively contributes to methodologies for gateway placement, traffic modeling, and energy consumption metrics. Research Areas Energy Efficiency in Communication Networks Quality of Service (QoS) and Quality of Experience (QoE) LoRaWAN Network Planning Edge and Serverless Computing Network Resource Analysis Recent Publications 2025: Energy modeling for 6G base stations 2025: Server cluster resilience via Markov models 2024: Serverless computing in edge-cloud environments 2024: LoRaWAN channel access optimization

Songbin GongView profile

Associate Professor

• RF and microwave photonics
• Antennas for communication and wireless sensing
• Microwave acoustics
• +7 more

Songbin Gong is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana Champaign, where he has been a faculty member since August 2013. He was promoted from Assistant Professor to Associate Professor in August 2019 and holds the Intel Alumni Fellowship. His research is centered at the Micro and Nanotechnology Lab, where he leads the Integrated RF Microsystems research group. Professor Gong's research focuses on RF and microwave photonics, microwave acoustics, and Micro-Electro-Mechanical Systems, with particular expertise in lithium niobate-based devices. His work spans the development of acoustic resonators, filters, and transducers operating from VHF to Sub-THz frequencies. Recent publications demonstrate significant advances in high-frequency acoustic devices, including GHz resonators with high electromechanical coupling and low loss characteristics. His research has direct applications in 5G communications, wireless sensing, and imaging systems. Gong has established himself as a leader in the field of RF MEMS and acoustic devices, with numerous high-impact publications in top journals including IEEE Transactions on Microwave Theory and Techniques, Journal of Microelectromechanical Systems, and Optics Express. His work shows a clear progression toward higher frequency operation, improved device performance, and novel integration approaches for next-generation communication systems. Among his notable achievements is the development of thin-film lithium niobate devices that overcome traditional frequency limitations of MEMS resonators, enabling operation beyond 10 GHz. This work addresses critical challenges in 5G and future wireless technologies where conventional approaches face scaling limitations. IEEE Ultrasonics Early Career Investigator Award DARPA Young Faculty Award 2014 NASA Early Career Faculty Award 2017 Intel Alumni Fellow 2017-present Multiple Best Paper Awards at major conferences including International Ultrasonic Symposium and International Microwave Symposium Professor Gong actively mentors graduate and undergraduate students, with several of his PhD students achieving notable success, including Ruochen Lu who joined UT Austin as a tenure-track assistant professor. His research group has secured significant funding from agencies including DARPA and NASA, supporting cutting-edge work in RF microsystems. The group maintains strong industry connections, particularly with Intel, reflecting the practical relevance of their research to commercial communication technologies. The Gong Research Group leverages micro/nano electro mechanical systems (N/MEMS), integrated photonic, and compound semiconductor technologies to develop chip-scale hybrid microsystems for RF communication, sensing, and imaging applications. Their current work focuses on pushing the boundaries of acoustic device performance while maintaining compatibility with standard semiconductor manufacturing processes.

Yinzhi CaoView profile

Associate Professor

• Security and privacy of machine learning
• Web security and privacy
• Mobile security and privacy
• +1 more

Yinzhi Cao is an Associate Professor at the Johns Hopkins University Department of Computer Science . He serves as Technical Director of the Johns Hopkins Information Security Institute and is affiliated with the Data Science and Artificial Intelligence Institute and the Institute for Assured Autonomy . Cao joined JHU in 2018 from Lehigh University, where he was an Assistant Professor. Doctor of Philosophy (PhD) in Computer Science, Northwestern University (2014) Bachelor of Engineering (BE) in Electronic Engineering, Tsinghua University (2008) Research Interests focus on security and privacy of web, mobile, and machine learning systems . Key projects include Vulnerability Analysis of Web Applications and Security, Privacy, and Fairness Analysis of ML Systems . His work addresses prototype pollution in JavaScript, node.js vulnerabilities, browser fingerprinting, federated learning privacy, and automated exploit generation. Scientific Recognition includes the NSF CAREER Award (2021) DARPA Young Faculty Award (2022) & Director's Fellowship (2024) Amazon Research Awards (2022, 2017) IEEE Security & Privacy Test of Time Award (2025) Distinguished Paper Awards at IEEE S&P 2025, CCS 2023, USENIX Security 2022 Advising & Grants highlight mentorship of 20+ PhD and Master’s students across institutions. Major grants include $1.2M collaborative CICI TCR grant (2024-2026) with Dr. John Aucott $750K DARPA YFA grant (2022-2025) $500K NSF SaTC grant (2022-2025) NSF EAGER grant (2016-2017) Labs & Teams : Affiliated with Johns Hopkins Information Security Institute , Data Science AI Institute , and Institute for Assured Autonomy . Collaborates with institutions like Columbia, UC Santa Barbara, and SRI International. His group investigates real-world vulnerabilities in over 2,500 websites and NPM packages, uncovering 80+ zero-day issues.

Xiaofeng LiuView profile

Assistant Professor

• Artificial Intelligence
• Deep Learning
• Medical Imaging
• +5 more

Xiaofeng Liu is an Assistant Professor at Yale University School of Medicine in the Departments of Radiology & Biomedical Imaging and Biomedical Informatics & Data Science. He is also an Associate Member at the Broad Institute of MIT and Harvard. Previously, he held faculty positions at Harvard Medical School and research roles at Massachusetts General Hospital and Beth Israel Deaconess Medical Center. PhD in Mechatronics from University of Chinese Academy of Sciences Dual Bachelor's degrees in Automation (Wang-Daheng Elite Class) and Communication from University of Science and Technology of China His research integrates trustworthy AI, medical imaging, and data science to improve diagnosis, prognosis, and treatment monitoring for neurological disorders, cancer, and cardiovascular diseases. Key focus areas include domain adaptation techniques, diffusion models, and interpretable AI systems. Led special issues in IEEE Transactions on Pattern Analysis and Medical Image Analysis Developed novel frameworks like Ordinal UDA and Memory-Consistent Adaptation Scientific accolades include the Trailblazer R21 Award (NIBIB), OpenAI Research Award, and National Artificial Intelligence Research Resource Pilot Award. He serves as Associate Editor for IEEE Transactions on Neural Networks and Learning Systems and actively contributes to MICCAI and NIH review panels. His lab at Yale (XLiu Lab) investigates neural basis of intelligence to inspire AI development, with applications in brain tumor segmentation, cardiac imaging, and cross-modal medical diagnostics.

Chris FullerView profile

Professor

• Acoustics
• Materials Design
• Medical Devices
• +5 more

Chris Fuller, Ph.D., is the Samuel Langley Distinguished Professor of Engineering at the College of Engineering , Virginia Tech. He leads the Vibrations and Acoustics Laboratory (VAL) , focusing on active/passive noise control systems, metamaterials, and their application to aerospace, medical devices, and industrial machinery. Education: Ph.D. (1979) and B.E. (1974) from the University of Adelaide, Australia. Research Interests: Structural acoustics, adaptive materials, machine learning in noise prediction, and biomedical acoustics (e.g., neonatal incubators). Awards: ASME Rayleigh Award (2017), NASA Team Achievement Award (1996), and Fellow of the Acoustical Society of America. Recent Publications: Highlight advancements in drone noise reduction using neural networks, metamaterials for HVAC systems, and poro-elastic materials for low-frequency noise control.

Li HuiView profile

Professor

• Cryptographic Information Security
• Privacy Computing
• Information Theory
• +5 more

Professor Li Hui serves as the executive dean of the School of Network and Information Security at Xidian University, where he holds the position of second-level professor and doctoral supervisor. He is nationally recognized as a distinguished teacher and serves in multiple prestigious roles including member of the National Steering Committee for Postgraduate Education in Cryptography, inaugural president of ACM SIGSAC CHINA, and director of several major academic societies related to cryptography and information security. Professor Li's research spans cryptographic information security, privacy computing, information theory, and coding theory, with significant contributions to network and cyberspace security. His work demonstrates a strong focus on both theoretical foundations and practical applications, particularly in developing security protocols for emerging technologies like blockchain, federated learning systems, and IoT environments. His research output shows consistent innovation in balancing security requirements with computational efficiency across diverse application domains. With over 300 publications and more than 15,000 Google Scholar citations (H-index 60), Professor Li's scholarly impact is substantial. His recent publications demonstrate increasing emphasis on privacy-preserving machine learning, secure multi-party computation, and cryptographic protocols for distributed systems, reflecting the evolving security challenges in the AI era. Three second-class national teaching achievement awards Special prize and first-class national teaching achievement awards Four first-class provincial and ministerial science and technology progress awards Privacy Computing Theory award (Qian Weichang Chinese Information Processing Science and Technology Award) Multiple patents with over 80 granted inventions Professor Li leads the Cyber Changan Team and serves as head of the Shaanxi Provincial Innovation Team for Mobile Internet Security. He has successfully supervised numerous doctoral and master's students who have gone on to win prestigious competitions like the National College Student Information Security Competition. His research is supported by major national grants including a National Key R&D Program project and key projects from the National Natural Science Foundation of China.

Omobolanle OgunseijuView profile

Assistant Professor

• Workforce Development
• Construction Safety
• Artificial Intelligence
• +7 more

Omobolanle Ogunseiju is an Assistant Professor in the School of Building Construction at Georgia Institute of Technology . She holds a Ph.D. in Environmental Design and Planning from the Department of Building Construction at Virginia Tech. Education: Ph.D. in Environmental Design and Planning, Virginia Tech Current Role: Assistant Professor, Georgia Tech School of Building Construction Her research focuses on integrating wearable robotics and Artificial Intelligence (via digital twin , cyber-physical systems , and data sensing ) to improve construction workforce safety, health, and well-being . She explores ethical implications of automation in construction, particularly in human-technological dynamics. Key research trends include: Advancing smart communities through robotics and AI Exoskeleton evaluation for ergonomic risk reduction Mixed reality environments for construction education Data analytics for cognitive and physical risk assessment Professional identity development in construction engineering students Industry-academia alignment for sensing technology integration Scientific awards: Outstanding Doctoral Candidate, Myers-Lawson School of Construction Outstanding Doctoral Student, College of Architecture and Urban Studies at Virginia Tech Teaching philosophy emphasizes experiential learning , engagement techniques , and hierarchical assessments . She developed the Construction Cost Management course at Georgia Tech and will lead Construction Technology courses. Previously, she taught Smart Construction , Building Systems Technology , and Wireless Sensing in Construction Management at Virginia Tech.

Juan-Pablo Correa-BaenaView profile

Associate Professor

• Materials Science
• Photovoltaics
• Semiconductor Technology
• +3 more

Juan-Pablo Correa-Baena is an Associate Professor at the Georgia Institute of Technology , holding the Goizueta Early Career Faculty Chair in the School of Materials Science and Engineering. He leads the Materials for Solar Energy Harvesting and Conversion research initiative at the Institute for Materials (IMat) and Strategic Energy Institute, aiming to consolidate Georgia Tech's expertise in photovoltaics and interdisciplinary energy research. Education: PhD in Environmental Engineering, University of Connecticut (2014) MS in Environmental Engineering, University of Connecticut (2011) BS in Management and Engineering for Manufacturing, University of Connecticut (2008) His research focuses on the chemistry-structure-property relationships of low-cost semiconductors for optoelectronic applications. Key areas include halide perovskites , nanoscale control , and advanced deposition/characterization techniques . He develops atomic layer deposition and synchrotron-based imaging to address metastable material behavior. Recent publications highlight innovations in dimensional control , machine learning for thermal stability , and flexible photovoltaic devices . His work integrates materials synthesis , quantum phenomena , and industrial scalability . Scientific recognition: Highly Cited Researcher (Web of Science, 2019–2021) Nature Index Leading Early Career Researcher in Materials Science (2019) NSF, DoE, and industry-funded projects Students and team: He advises 14 graduate students and postdocs, including Sanggyun Kim, Diana LaFollette, and Leonardo Josué Lugo Salas, fostering interdisciplinary collaboration through workshops and symposia.

Marco BernardiView profile

Professor

• Theoretical Materials Physics
• Computational Materials Science
• Electron Transport
• +7 more

Marco Bernardi is a Professor of Applied Physics, Physics and Materials Science at the California Institute of Technology (Caltech). His research focuses on theoretical and computational materials physics , developing first-principles methods to investigate electron transport, ultrafast dynamics, and light-matter interactions in materials. His work has applications in electronics, optoelectronics, ultrafast spectroscopy, energy technologies, and quantum devices. Education : Ph.D. in Materials Science from MIT (2013), M.S. from University of Rome Tor Vergata (2008), B.S. from University of Rome La Sapienza (2004). Research Interests : Electron-phonon interactions, polarons, spin relaxation and decoherence, nonequilibrium electron dynamics, quantum materials, and software development for materials simulations ( PERTURBO code). Scientific Awards : NSF CAREER Award (2018) AFOSR Young Investigator Award (2017) Psi-K Volker Heine Young Investigator Award (2015) Intel Ph.D. Fellowship (2013) Franco Strazzabosco Award (2020) Teaching : Offers graduate courses at Caltech including Structure and Bonding in Materials (MS 131) , Computational Solid State Physics (APh/MS 256) , and Introduction to Computational Methods (APh/MS 141) . Group Members : Mentors current graduate students and postdocs in developing advanced computational techniques for materials research, with former advisees now in academic and industry positions.

Ali YaparView profile

Professor

• Electromagnetics
• Microwave Engineering
• Antenna Technologies
• +3 more

Professor Ali Yapar is a faculty member at Istanbul Technical University in the Electronics and Communication Engineering department. His research focuses on Electromagnetics , Microwave Engineering , and Antenna Technologies , with a particular emphasis on inverse scattering problems and microwave imaging for biomedical applications. He has supervised numerous graduate students and led projects related to breast cancer treatment and rough surface imaging. PhD in Electronics and Communication Engineering from Istanbul Technical University (1997) MSc in Electronics and Communication Engineering (1995) His recent publications analyze advanced techniques for microwave hyperthermia systems, reverse time migration methods, and Newton-based solutions for electromagnetic inverse scattering. Key projects include TUBITAK-funded initiatives on microwave tomography and brain stroke imaging. He serves as a project investigator and executive for electromagnetic research programs. Research areas span Electromagnetic Wave Propagation , Green's Function Applications , and Dielectric Material Analysis . Collaborations include IEEE members and international researchers in computational electromagnetics.

• high resolution MRI
• visual perception
• functional imaging
• +6 more

Reza Farivar-Mohseni is an Associate Professor at McGill University , affiliated with the Faculty of Medicine and Health Sciences and the Department of Ophthalmology and Visual Sciences . He serves as a Scientist at the RI-MUHC (Montreal General Hospital site), contributing to the Brain Repair and Integrative Neuroscience (BRaIN) Program and the Centre for Translational Biology . Research Interests: Dr. Farivar-Mohseni’s work focuses on cortico-cortical communication, information processing in the brain, and disruptions in neurological disorders like traumatic brain injury. He specializes in advancing non-invasive brain imaging (MRI) for both fundamental and clinical applications, particularly improving concussion detection and diagnosis. Publications: His research spans high-resolution MRI, visual perception, and functional imaging. Key themes include depth-cue invariance in object recognition, gamma-band neural representations, and cortical deficits in amblyopia. Recent studies (2025–2022) address computational neuroscience, vision screening tools, and neural imaging techniques. Labs & Collaborations: He collaborates with the MGH-MRI Research Platform and works within the Centre for Translational Biology , focusing on translating imaging advancements into clinical tools.