جست‌وجوی استاد و پژوهشگر

Peichen Zhongمشاهده پروفایل

استادیار

• Materials Science
• Machine Learning
• Computational Modeling
• +۵ مورد دیگر

Dr. Peichen Zhong is an Assistant Professor in the Department of Materials Science and Engineering at the National University of Singapore (NUS). He leads the Applied Machine Learning and Materials Modeling (AM³) Group, focused on advancing computational methods for clean energy technologies. His research integrates machine learning with atomistic simulations to tackle challenges in battery materials, disordered materials, and sustainable energy systems. Education: B.S. in Physics from University of Science and Technology of China (2018); Ph.D. in Materials Science from UC Berkeley (2023, advised by Prof. Gerbrand Ceder); Postdoctoral training at Lawrence Berkeley National Lab and BIDMaP, co-advised by Persson, Cheng, and Krishnapriyan. Research Interests: Computational modeling of battery cathodes/electrolytes, AI-driven interatomic potentials, statistical mechanics in disordered materials, and generative models for scientific discovery. Key areas include Li/Na-ion batteries, solid-state reactions, and sustainable energy materials. Awards: BIDMaP Emerging Scholar Fellowship (UC Berkeley CDSS, 202?), 2023 Rising Stars in Materials Science (CMU/MIT/Stanford). Labs/Teams: The AM³ Group at NUS MSE focuses on interdisciplinary research combining theory, computation, and AI4Science. Current openings include PhD students and postdoctoral researchers.

Luca Beniniمشاهده پروفایل

استاد

• Computer Engineering
• Embedded Systems
• VLSI Design
• +۲ مورد دیگر

Luca Benini is a Full Professor at the Department of Information Technology and Electrical Engineering at ETH Zurich. He works at the Institute for Integrated Systems, located at Gloriastrasse 35, 8092 Zurich, Switzerland. His contact number is +41 44 632 66 64. Research Interests: Focus on VLSI design and FPGA-based systems development. Teaching: Offers the course VLSI 1: HDL Based Design for FPGAs in the Fall semester 2025. Contact: lbenini@iis.ee.ethz.ch

Sheng Shenمشاهده پروفایل

استاد

• Nanophotonics
• Nanoscale Energy Transport and Conversion
• Advanced Manufacturing
• +۷ مورد دیگر

Sheng Shen is a Professor in the Mechanical Engineering Department at Carnegie Mellon University (CMU) , with courtesy appointments in the Departments of Electrical and Computer Engineering and Materials Science and Engineering . He earned his Ph.D. in Mechanical Engineering (Minor in Electrical Engineering) from Massachusetts Institute of Technology (MIT) , and his B.S. and M.S. from Huazhong University of Science and Technology in China. Prior to joining CMU in 2011, he conducted postdoctoral research at UC-Berkeley . Education: Ph.D., Mechanical Engineering, MIT (2010) B.S. & M.S., Power Engineering & Engineering Thermophysics, Huazhong University of Science and Technology (2000 & 2003) Research interests include nanophotonics , nanoscale energy transport and conversion , nanofabrication , and advanced manufacturing , with applications in thermal management , light sources and devices , thermal emission control , solar energy conversion , infrared sensing , and multifunctional materials . His work leverages interdisciplinary expertise in thermal and optical measurements , material synthesis , device fabrication , and theoretical modeling . Recent publications highlight advancements in infrared radiation control , thermal interface materials , metasurface engineering , and graphene-based nanosystems . His scientific awards include: NSF CAREER Award DARPA Director's Fellowship DARPA Young Faculty Award Elsevier/JQSRT Raymond Viskanta Award CMU Dean's Early Career Fellowship Philomathia Foundation Research Fellowship Hewlett-Packard Best Paper Award Best Paper Award, Julius Springer Forum Advising spans Ph.D. and postdoctoral researchers in nanoscale energy systems, with alumni contributing to solar energy conversion , infrared sensing , and flexible electronics . His lab receives funding from ARL, DARPA, DOE, DTRA, NASA, NSF, and ONR , and recently secured a DURIP award for instrumentation.

Aarti Singhمشاهده پروفایل

استاد

• Reinforcement Learning
• Bandit Algorithms
• Active Learning
• +۷ مورد دیگر

Aarti Singh is a Professor in the Machine Learning Department at Carnegie Mellon University and Director of the NSF AI Institute for Societal Decision Making. She leads research at the intersection of machine learning, statistics, and decision making, with applications to scientific and societal domains. Her work focuses on designing principled interactive algorithms for learning and decision making under uncertainty. Education: Ph.D. in Electrical Engineering, University of Wisconsin-Madison (2008) M.S. in Electrical Engineering, University of Wisconsin-Madison (2003) B.E. in Electronics and Communication Engineering, University of Delhi (2001) Research Interests: Professor Singh's research centers on developing interactive machine learning algorithms that go beyond finding input-output associations to make higher-level decisions about the most informative data and actions. Her work spans autonomous decision making, including active sampling, stochastic optimization, bandits, and reinforcement learning that are statistically optimal, computationally tractable, and robust. She also investigates human factors in decision making, designing algorithms that model and leverage human feedback while accounting for bias, memory effects, and calibration. Her research has applications in material science, cosmology, and peer review systems. Research Trends: Professor Singh's recent publications demonstrate a strong focus on reinforcement learning, particularly in developing more efficient and robust algorithms for decision making under uncertainty. Her work bridges theoretical foundations with practical applications, spanning from fundamental algorithm development to real-world implementation in scientific domains. There's a clear trajectory toward integrating human factors into decision-making algorithms, with significant contributions to peer review systems and preference learning. Scientific Awards: NSF Career Award United States Air Force Young Investigator Award A. Nico Habermann Faculty Chair Award Harold A. Peterson Best Dissertation Award Multiple paper awards Advising and Grants: Professor Singh has advised numerous PhD and master's students, many of whom have gone on to faculty positions or research roles at leading institutions. Her research is supported by prestigious grants from ONR, Simons Foundation, AFRL, ARL, and NSF. She serves as General Chair (2025) and Program Chair (2020) for the International Conference on Machine Learning (ICML) and has held leadership roles in multiple professional organizations. Research Team: Professor Singh leads a vibrant research group within the Machine Learning Department at CMU, with current PhD students working on topics including reinforcement learning, human-AI collaboration, and decision making under uncertainty. She also directs the NSF AI Institute for Societal Decision Making, which brings together researchers from multiple disciplines to develop AI systems that support human decision making in societal contexts.

William Chuehمشاهده پروفایل

استاد

• Energy Storage
• Renewable Energy
• Materials for Carbon-Neutral Energy
• +۳ مورد دیگر

William Chueh is a Professor in the Departments of Materials Science and Engineering and Energy Science & Engineering at Stanford University. He serves as Director of the Precourt Institute for Energy and Faculty Director of the Energy Innovation and Emerging Technologies Program. His research focuses on redox-active materials for energy storage, conversion, and carbon-neutral energy cycles. Education: PhD, Materials Science, Caltech (2010) BS, Applied Physics, Caltech (2005) Research Interests: Energy storage and conversion systems (batteries, fuel cells, electrolyzers) Multi-scale electrochemical and chemical reaction dynamics Materials design rules for redox-active solids Thermodynamic frameworks for sustainable energy Publication Trends: His work spans fundamental materials synthesis, electrochemical characterization, and modeling of redox reactions. Key themes include solar thermochemical cycles, ceria-based systems for CO2/H2O conversion, and advanced battery technologies. Scientific Honors: Outstanding Young Investigator Award (MRS, 2018) Camille Dreyfus Teacher-Scholar Award (2016) Sloan Research Fellowship (2016) CAREER Award (NSF, 2015) Advising: He advises students in energy technologies, materials science, and electrochemistry, including doctoral and master’s candidates. Contact: wchueh@stanford.edu

Tanzeem Choudhuryمشاهده پروفایل

استاد

• Ubiquitous Computing
• Mobile Sensing Systems
• Wearable Technology
• +۴ مورد دیگر

Tanzeem Choudhury is a Professor at the Jacobs Technion-Cornell Institute at Cornell Tech, specializing in Information Sciences. She co-founded HealthRhythms Inc. and directs the People-Aware Computing lab, focusing on machine learning for human activity and social network analysis. Her work pioneered the 'Reality Mining' field, leveraging mobile sensors to model social interactions. Choudhury holds a Ph.D. from MIT's Media Lab and B.S. in Electrical Engineering from the University of Rochester. Her research spans wearable tech, mental health interventions, and ubiquitous computing. Notable projects include affective touch wearables for anxiety reduction, stress detection systems (e.g., StressSense), and the BeWell app for well-being monitoring. Choudhury has been recognized as an ACM Fellow and SIGCHI Academy member, with impactful contributions to healthcare technology and sensor-based behavioral analysis. Current lab initiatives include developing closed-loop interventions for mental health, equity-driven sensing systems, and exploring AI integration in psychotherapy. She advises PhD students on topics like digital biomarkers, burnout detection in healthcare workers, and smartphone-based mental health assessment. Education: Ph.D. in Media Arts & Sciences (MIT), M.S. (MIT Media Lab), B.S. in Electrical Engineering (University of Rochester) Lab Team: Includes PhD candidates (Dan Adler, Yiran Zhao) and researchers focusing on health AI, wearable devices, and clinical applications. Major Awards: ACM Fellowship (2020), SIGCHI Academy (2018), Best Paper Awards at Ubicomp and MobileHCI conferences. Key Projects: OptoBeat (skin-tone calibrated blood oxygen sensor), EmotionCheck (anxiety intervention), and Affective Touch devices.

Robert Rohlingمشاهده پروفایل

استاد

• Biomedical Engineering
• Medical Imaging
• Medical Information Systems
• +۳ مورد دیگر

Robert Rohling is a Professor at the University of British Columbia's Faculty of Applied Science, affiliated with the Department of Mechanical Engineering and holding a joint appointment with the Department of Electrical and Computer Engineering. As Director of the Institute of Computing, Information and Cognitive Systems (ICICS), his research focuses on biomedical engineering, medical imaging, robotics, and computational methods. B.A.Sc. (UBC) M.Eng. (McGill) Ph.D. (Cambridge) Rohling's work spans three primary research areas: medical imaging (3D ultrasound, spatial compounding, elasticity reconstruction), medical information systems (radiologist navigation tools for large image datasets), and robotic calibration for surgical applications. His multidisciplinary approach integrates mechanical and electrical engineering principles with clinical needs. Rohling's publications (2020-2022) reveal trends in advanced ultrasound techniques (e.g., shear wave vibro-elastography), AI-driven image processing (cycleGAN translation), and computational optimization for diagnostic accuracy. Keywords across his work include Medical Imaging, Biomedical Engineering, Robotics, and Computational Modeling. As director of the Robotics and Control Laboratory , Rohling leads interdisciplinary collaborations with industry and clinical partners to address practical challenges in medical diagnostics and surgical robotics. His research emphasizes translating engineering innovations into clinical practice.

Francesca Stradoliniمشاهده پروفایل

مدرس

• Bioelectronics
• IoT for Medical Monitoring
• Anesthesia Delivery Systems
• +۳ مورد دیگر

Francesca Stradolini is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Electrical Engineering Doctoral School (EDEE). She holds a PhD in Electrical Engineering from EPFL (2018), with prior education in Bio Engineering and Neuroscience from the University of Genoa (2012, 2015). Her current work focuses on R&D engineering for medical devices, emphasizing system lifecycle management, human factors engineering, and compliance with CE/FDA regulations. As an invited guest, she contributes to usability engineering education and interdisciplinary research at EPFL. PhD: IoT Bio-Electronic Multi-Panel Device for On-line Monitoring of Anaesthesia Delivery (EPFL, 2018) MSc: Bio Engineering (University of Genoa, 2015) BSc: Bio Engineering (University of Genoa, 2012) Her research spans bioelectronics, IoT-enabled medical monitoring, and electrochemical sensor development for clinical applications. Key areas include anesthesia delivery systems, wearable diagnostics, implantable devices, and human factors engineering. Her work integrates fluidic systems, noble metal nanostructures, and open-source hardware (e.g., Raspberry Pi) to address challenges in traceability, fouling mitigation, and pharmacokinetic modeling. Scientific recognition includes the Ville de Lausanne Award (2019) , EPFL Outstanding PhD Thesis Distinction (2018) , and a Best Paper Award at MOBIHEALTH (2016) . She has published extensively in journals like IEEE TBIOCAS, Sensors and Actuators B, and IEEE Sensors Journal, with collaborative studies on propofol monitoring, carbon nanoallotrope drug detection, and implantable sensor validation. Her technical expertise includes managing medical device development lifecycles, designing IoT architectures for intensive care, and optimizing electrochemical sensors for stability in undiluted biological fluids. She has contributed to conferences such as IEEE BioCAS, ISCAS, and MOBIHEALTH, focusing on translational studies in personalized medicine and 3D-printed medical systems.

Gary Fedderمشاهده پروفایل

استاد

• Microelectromechanical Systems (MEMS)
• Advanced Manufacturing
• Nanomanufacturing
• +۶ مورد دیگر

Gary Fedder is the Howard M. Wilkoff Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU), with courtesy appointments in Biomedical Engineering, Mechanical Engineering, and Robotics. He serves as Faculty Director of the Manufacturing Futures Institute (MFI) and previously held roles such as Vice Provost for Research and Interim CEO of the Advanced Robotics for Manufacturing (ARM) Institute. Fedder’s research focuses on MEMS, advanced manufacturing, and implantable microsystems. He earned his B.S., M.S., and Ph.D. in EECS from MIT and UC Berkeley, respectively. Education: Ph.D., Electrical Engineering and Computer Science, UC Berkeley (1994) M.S., Electrical Engineering and Computer Science, MIT (1984) B.S., Electrical Engineering and Computer Science, MIT (1982) Research Interests: Microelectromechanical systems (MEMS), digital twins, aerosol jet printing, stretchable electronics, and manufacturing innovation. His work integrates MEMS with CMOS processes, emphasizing low-cost, high-performance systems. Key Contributions: Co-founded the ARM Institute; developed MEMS-based sensors and actuators; pioneered methods for manufacturing innovation through projects like America Makes. His research spans over 300 publications and 21 patents. Awards: IEEE Fellow (2007), Ross Tucker Award (1993), NSF CAREER Award (1996), and leadership roles in Manufacturing USA initiatives. Leadership & Outreach: Directed the Institute for Complex Engineered Systems and led national initiatives to advance U.S. manufacturing competitiveness. Active in editorial roles for journals like IoP Journal of Micromechanics .

• Semiconductor Physics
• Gallium Nitride (GaN) Electronics
• Two-Dimensional Materials
• +۶ مورد دیگر

Tomas Palacios is a Professor of Electrical Engineering at the Massachusetts Institute of Technology (MIT) , where he directs the Center for Graphene Devices and 2D Systems and leads the Microsystems Technology Laboratories (MTL). His research focuses on pushing the boundaries of microelectronics through novel semiconductor materials and device architectures, including Gallium Nitride (GaN) and 2D materials like graphene and molybdenum disulfide (MoS2). Professor, MIT Electrical Engineering and Computer Science Director, MIT Center for Graphene Devices and 2D Systems Clarence J. LeBel Professor, MIT Faculty Director, Northeast Microelectronics Internship Program (NMIP) Research Interests span multiple cutting-edge domains: High-frequency electronics (>300 GHz) for 6G and quantum applications High-voltage power devices (600V–10kV) for energy conversion Post-silicon logic devices using 2D materials High-temperature electronics (e.g., Venus rover applications) Distributed neural networks on large-area 2D materials Graphene-based biosensors and chemical detection systems Scientific Contributions include: Double recipient of the IEEE George Smith Award for groundbreaking GaN transistor work Co-invented first MoS2 electronic circuits Developed world’s first Wi-Fi-to-electricity conversion antenna Led MIT’s Microsystems Technology Laboratories since 2021 Advising Philosophy emphasizes cross-layer expertise, with students gaining experience from materials synthesis to system-level prototyping. His lab has incubated startups like Vertical Horizons , focused on GaN power devices for AI and EVs.

Jiatao Guمشاهده پروفایل

استادیار

• Machine Learning
• Generative Models
• 3D Generation
• +۳ مورد دیگر

Jiatao Gu is an Assistant Professor in the Department of Computer and Information Science (CIS) at the University of Pennsylvania, with a part-time role as Staff Research Scientist at Apple (MLR). He holds a Ph.D. in Electrical and Electronic Engineering from the University of Hong Kong (2018) and a B.Eng. in Electronic Engineering from Tsinghua University (2014). His research focuses on generative machine learning and AI agent interaction with the physical world, emphasizing multi-modal systems spanning language, images, videos, and 3D. Key themes include efficient modeling , flexible architecture design , and scalable decision-making frameworks . 2025: ICLR paper on DART framework 2024: TMLR work on GFlowNet alignment 2023: NeurIPS research on diffusion stability 2022: ACL papers on speech translation Recent publications explore diffusion models for text-to-image synthesis, 3D reconstruction, and efficient sampling techniques. His work addresses fundamental challenges in attention mechanisms, entropy collapse, and multi-stage distillation while advancing non-autoregressive translation and vision-language reasoning . Prospective students can apply through his recruitment process at UPenn. Prior affiliations include Meta AI (FAIR Labs) and academic collaborations with institutions like New York University's CILVR Lab.

Michael A Osborneمشاهده پروفایل

استاد

• Machine Learning
• Bayesian Optimization
• Gaussian Processes
• +۳ مورد دیگر

Michael A Osborne is Professor of Machine Learning at the University of Oxford and leads the Bayesian Exploration Lab . He serves as Director of the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems and co-directs the Oxford Martin AI Governance Initiative. His research focuses on Bayesian optimization, Gaussian processes, and probabilistic numerics with applications in quantum devices, battery modeling, and AI governance. Key Positions: Professor of Machine Learning, University of Oxford Official Fellow, Exeter College Co-founder of Mind Foundry Lead Researcher, Oxford Martin Programme on Technology and Employment Research Themes: Probabilistic modeling for quantum systems Uncertainty quantification in energy storage AI safety and societal impact analysis Automated experimental design Quantum device calibration Probabilistic numerical methods Technical Contributions: Bridging reality gap in quantum devices Efficient Bayesian quadrature techniques Personalized neurostimulation algorithms Automated measurement protocols Quantum-classical hybrid ML

• Bioelectronics
• Nanotechnology
• CMOS Technology
• +۱۲ مورد دیگر

Sandro Carrara is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he leads the Bio/CMOS Interfaces (BCI) laboratory. He is affiliated with the School of Engineering (STI), the Institute of Electrical Engineering (SCI-STI-SC), and the Integrated Systems Laboratory (LSI). His academic leadership spans teaching, doctoral supervision, and editorial roles in major journals including IEEE Sensors Journal and BioNanoScience. Education: Diploma in Electronics, National Technical Institute of Albenga, Italy Master in Physics, University of Genoa, Italy PhD in Biochemistry and Biophysics, University of Padua, Italy His research focuses on the integration of biological systems with CMOS technology, particularly in the development of nanoscale biosensors for health monitoring. Key areas include memristive biosensors, wearable and implantable sensors, electrochemical detection, and therapeutic drug monitoring. His work bridges electronics, nanotechnology, and biomedicine to enable point-of-care diagnostics and personalized medicine. His recent publications (2023–2025) show a strong trend toward sustainable printed electronics, machine learning for biosensing, in-memory computing for cancer diagnostics, and remote health monitoring. These works appear in high-impact journals such as IEEE Sensors Journal , Nanoscale , and Biosensors and Bioelectronics: X . Scientific Awards: IEEE Fellow (2015) IEEE Sensors Council Technical Achievement Award (2016) Distinguished Lecturer, IEEE Sensors Council (2017) Best Paper Award, IEEE MeMeA Symposium (2020) Multiple Gold and Bronze Leaf Prizes at PRIME and IEEE conferences Best Poster Awards at EMBEC and Nano-Tera meetings He actively advises PhD students and leads research projects involving CMOS-based biosensors, wireless implants, and smart sensor systems. His lab collaborates widely across disciplines and institutions, focusing on real-world applications in oncology, neurology, and environmental health. He has also contributed to the development of battery-free wearable devices, optical power transfer systems, and IoT-enabled telemedicine platforms. Laboratories and Teams: Bio/CMOS Interfaces (BCI) Laboratory, EPFL Integrated Systems Laboratory (LSI), EPFL Collaborations with IEEE Sensors Council and Circuits and Systems Society Editorial leadership in IEEE Sensors Journal and BioNanoScience

• Integrated Circuit Design
• Biomedical Microsystems
• Low-Power Sensor Interfaces
• +۳ مورد دیگر

Dr. Jiamin Li is an Associate Professor at the School of Microelectronics, Southern University of Science and Technology (SUSTech), Shenzhen, China. She holds a Ph.D. and B.Eng. in Electrical and Computer Engineering from the National University of Singapore (NUS). Her research focuses on biomedical integrated circuits, energy harvesting, and wireless body-area networks. Education: Ph.D. and B.Eng. in Electrical and Computer Engineering from NUS Employment: Associate Professor (2024–Present), Assistant Professor (2022–2024) at SUSTech; Research Fellow at NUS Dr. Li's research addresses wireless body area powering , low-power biosignal interfaces , and intelligent biosignal processors . Her work leverages microsystem design for applications in biomedical engineering , particularly in energy-autonomous medical devices and human body-coupled communication . Her recent publications highlight advancements in concurrent power/data transmission , subharmonic pulse injection , and triboelectric energy harvesting for biomedical systems. Key venues include IEEE JSSC , Nature Electronics , and conferences like ISSCC and BioCAS. Awards: IEEE SSCS Predoctoral Achievement Award, ISSCC Best Demo Award, ASSCC SDC Best Design Award Committees: IEEE SSCS Women-in-Circuit (WiC) Committee, ISSCC Student Research Preview Committee Opportunities: Positions available for Ph.D., Master's, Postdoctoral Fellows, and Visiting Scholars

Benjamin Carrison-Schaferمشاهده پروفایل

دانشیار

• Reconfigurable Computing
• FPGAs
• Electronic Design Automation
• +۷ مورد دیگر

Benjamin Carrison-Schafer is an Associate Professor and Assistant Dean for Graduate Student Success at the Department of Electrical and Computer Engineering, Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. He leads the Design Automation and Reconfigurable Computing Laboratory (DARClab), focusing on systems, high-level design, and programming methodologies for VLSI computing systems and FPGAs. Education: MBA from McGill University, Canada (2012) PhD in Electrical and Electronic Engineering from University of Birmingham, UK (2003) Research Interests: His work spans modeling, analysis, synthesis, optimization, and implementation of VLSI systems. Current projects include: Approximate Computing, High-Level Synthesis Design Space Exploration, Hardware Security, Behavioral MPSoC Optimization, and Automatic Fault-Tolerant System Generation. His research blends theory and practice, using analytical and experimental techniques to solve real-world problems. Publication Trends: Recent publications demonstrate strong focus on hardware security, FPGA optimization, and automated design methodologies. Key themes include High-Level Synthesis innovations, hardware acceleration techniques, and cross-disciplinary applications of reconfigurable computing. His work frequently addresses challenges in hardware trustworthiness, energy efficiency, and design automation scalability. Professional Activities: Associate Editor: IEEE Transactions on Sustainable Computing (2022-present) Conference Chair: ICCD 2024, DCAS 2024 Program Committee: ASP-DAC, DATE, FCCM, GLSVLSI Advising & Labs: Supervises multiple PhD and Master's students at DARClab. Current research includes domain-specific architecture design, hardware security, sustainable computing, and ML for VLSI design. The lab collaborates with industry partners like Renesas Electronics and develops commercial tools through spin-off company highX Technologies.