Find Academic Supervisors

Xilin LiuView profile

Assistant Professor

• Integrated Circuits
• Biomedical Engineering
• Brain-Machine Interfaces
• +5 more

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.

Tzu-Chien HsuehView profile

Assistant Professor

• Analog and mixed-signal integrated circuits
• Wireline communication systems
• Data centers
• +5 more

Tzu-Chien Hsueh is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of California, San Diego (UCSD), where he leads the Integrated Communication Circuits Lab. His research focuses on advanced CMOS and heterogeneous process technologies for high-speed, low-power communication systems, including transceiver design, silicon photonics, and data link optimization. He holds a B.S. and M.S. from National Taiwan University (1999-2001) and a Ph.D. from UCLA (2010). Before joining UCSD, he served as a Senior Research Scientist at Intel Labs (2010-2018), leading projects like 200Gb/s SerDes Links and 7nm CMOS Memory I/Os. He currently serves on the Technical Program Committee of IEEE CICC and IEEE SSCS mentoring programs. His honors include the IEEE JSSC Best Paper Award (2015) and multiple Intel recognitions. His lab emphasizes practical design innovations, theoretical analysis, and robust circuit implementations for applications in data centers, cloud computing, and broadband communication networks.

• Machine Learning in Electronics
• Electrostatic Discharge (ESD) Protection
• Circuit Reliability
• +3 more

Elyse Rosenbaum is the Melvin and Anne Louise Hassebrock Professor in Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. She also serves as the Acting Associate Dean for Research at the Grainger College of Engineering. She is the director of the NSF-supported Center for Advanced Electronics through Machine Learning (CAEML), a collaboration between the University of Illinois, North Carolina State University, and Penn State University. Education: Ph.D. in Electrical Engineering, University of California, Berkeley, 1992 M.S. in Electrical Engineering, Stanford University B.S. in Electrical Engineering, Cornell University (with distinction) Research Interests: Her research focuses on machine learning applications in electronics, ESD-robust high-speed I/O circuit design, compact modeling, behavioral modeling of circuits, and CDM-ESD protection for advanced packaging technologies. Scientific Awards: IEEE Fellow for contributions to electrostatic discharge reliability of integrated circuits Best Student Paper Award, IEDM Outstanding and Best Paper Awards, EOS/ESD Symposium Technical Excellence Award, SRC NSF CAREER Award IBM Faculty Award ESD Association’s Industry Pioneer Recognition Award Advising and Grants: She supervises graduate and undergraduate researchers, primarily focusing on those with strong academic records and relevant experience. Her work is supported by NSF and other prominent organizations. Labs and Teams: She leads the CAEML center, which aims to apply machine learning to optimize microelectronic circuits and systems, enhancing design automation and reliability.

John R. LongView profile

Professor

• High Speed Wireline Circuits
• High Frequency Circuits
• Low Power Circuits
• +2 more

John R. Long is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, specializing in high-speed wireline and high-frequency circuit design for integrated wireless communications systems. His work bridges theoretical innovation with practical applications in mobile transceiver technologies. His academic credentials include: Doctorate in Electronics from Carleton University, Canada (1996) Master of Engineering in Electronics from Carleton University, Canada (1992) Bachelor of Science in Electrical Engineering from the University of Calgary, Canada (1984) Dr. Long's research concentrates on energy-efficient circuit architectures for next-generation wireless systems. His expertise spans millimeter-wave frequency doublers, low-voltage RF design, and wideband transceivers, addressing critical challenges in mobile communications bandwidth and power consumption. Recent work demonstrates significant advancements in DC-100 GHz circuit operation and autonomous system integration. Publications from 2015-2016 reveal a consistent focus on pushing the boundaries of solid-state circuit performance, particularly in high-frequency analog design and system-on-chip implementations for wireless applications. No scientific awards were documented in the provided materials. As an active educator, Dr. Long mentors graduate students and teaches core courses including Radio Frequency Integrated Devices (ECE 432) and Integrated Analog Electronics (ECE 444), emphasizing hands-on circuit design principles. His leadership extends to administrative roles, having previously chaired the Electronics Research Laboratory at Delft University of Technology.

Francesco PicarielloView profile

Researcher

• Electrical and Electronic Measurements
• Data Acquisition Systems
• Compressive Sampling
• +7 more

Francesco Picariello is a Researcher at the Department of Engineering (DING) at the University of Sannio, specializing in Electrical and Electronic Measurements (ING-INF/07). His academic profile shows extensive expertise in measurement systems, data acquisition, and signal processing with applications across multiple domains including biomedical engineering, structural health monitoring, and wireless communications. His research interests focus on the design, implementation, and metrological characterization of mobile measurement systems, wireless sensor networks, data acquisition systems based on compressed sensing, signal processing, and image processing. These research activities have been applied to intelligent transportation systems, aerial photogrammetry using UAVs, indoor navigation, motion tracking, power consumption analysis of processors, wideband data acquisition systems, and physiological monitoring applications. Dr. Picariello's work demonstrates significant trends in compressive sampling techniques, with numerous publications on ECG signal processing, RF emitter localization, and structural health monitoring systems. His research shows a clear progression toward IoT integration, wearable medical devices, and security applications for communication channels. His scientific collaborations include institutions such as the University of Naples 'Parthenope', Military University of Technology of Warsaw, National Institute of Standards and Technology (NIST), Keysight Laboratories, CERN Institute, and the University of Tokyo, reflecting the international recognition of his work. As an active researcher with publications spanning from 2016 to 2025, Dr. Picariello has made substantial contributions to measurement science, particularly in the development of novel techniques for data acquisition, signal processing, and system metrological characterization across multiple application domains.

Thi Thuy Nga DinhView profile

Associate Professor

• IoT connectivity
• Smart home
• Embedded systems
• +7 more

Thi Thuy Nga Dinh is an Associate Professor at the Faculty of Computer Sciences, Department of Computer Science and Communication, Østfold University College, Norway. She holds a Ph.D. from KAIST, Korea, and has previously worked at Bell Labs Seoul, Samsung Electronics, and other academic institutions. Her research focuses on wireless technologies for IoT devices, energy efficiency, network optimization, and experimental development. She contributes to projects like Productive4.0 and Arrowhead Tools within the CPS research group. Research interests include IoT connectivity, smart home systems, energy harvesting, and standards like 5G, LoRaWAN, and Zigbee. Recent work explores machine learning applications in network intrusion detection and indoor air quality forecasting. She collaborates with UiT The Arctic University of Norway and other institutions. Publications highlight contributions to energy-efficient wireless networks, scheduling algorithms, and sensor node performance under LoRaWAN. Awards include Early Bird Patents and First Publication accolades from Bell Labs. Her teaching includes courses on distributed and parallel programming.

• Artificial Intelligence and Optimization
• Energy Transition and Sustainability
• Renewable Energy
• +1 more

Dr. Amir Gharavi serves as a Lecturer (Teaching) in Energy and Data Analytics at University College London's Bartlett School of Environment, Energy, and Resources (BSEER), where he also directs the Energy System Data Analytics (ESDA) program. His academic journey spans petroleum engineering and data science, reflecting his interdisciplinary expertise. Education: PhD in Artificial Intelligence applications for oil reservoirs (University of Portsmouth) MSc in Petroleum Engineering BSc in Petroleum Engineering BSc in Genetics Dr. Gharavi's research bridges traditional energy systems with cutting-edge data analytics, focusing on four interconnected domains: Artificial Intelligence (deep learning for predictive modeling and evolutionary computation), Energy Transition (low-carbon strategies and socio-economic impacts), Renewable Energy (grid integration and lifecycle analysis), and Petroleum Reservoirs (AI-enhanced characterization and enhanced recovery techniques). His work demonstrates how machine learning transforms both conventional and renewable energy sectors. His publication portfolio reveals a clear trajectory from petroleum reservoir analytics toward broader energy system applications, with recent 2024 works expanding into collision avoidance systems - indicating strategic diversification into AI safety applications while maintaining core energy focus. This evolution showcases his ability to transfer domain expertise across technical contexts. As an educator, Dr. Gharavi leads the Energy Data Analytics and Advanced Machine Learning modules, leveraging industry experience from Halliburton and Baker Hughes to create practice-oriented curricula. His multidisciplinary background enables unique pedagogical approaches that connect petroleum engineering fundamentals with contemporary data science techniques. Dr. Gharavi maintains active industry connections through his previous roles as Data Scientist at major energy firms, though current lab affiliations aren't specified in available materials. His research direction suggests growing emphasis on AI applications for energy transition challenges, particularly in system optimization and sustainability metrics.

Shari Dunn-NormanView profile

Associate Professor

• Petroleum Engineering
• Hydraulic Fracturing
• Safety Engineering
• +4 more

Dr. Shari Dunn-Norman is an Associate Professor in the Department of Geological and Petroleum Engineering at Missouri University of Science and Technology (Missouri S&T). With over 40 years of experience in the petroleum engineering field, including 28 years at Missouri S&T and 8 years in industry with ARCO Oil & Gas, she specializes in hydraulic fracturing, lost circulation mitigation, and eco-friendly drilling technologies. Her work spans both theoretical and applied research, addressing critical challenges in unconventional reservoirs and sustainable drilling practices. Ph.D. in Petroleum Engineering, Heriot-Watt University (1990) B.S. in Petroleum Engineering, University of Tulsa (1978) Dr. Dunn-Norman's research focuses on: Optimizing hydraulic fracturing fluid systems and proppant transport Developing biodegradable drilling fluid additives from food waste Applying machine learning for lost circulation prediction and mitigation Geomechanical modeling for wellbore stability and fracture gradient estimation Environmental impact analysis of unconventional oil and gas operations Data-driven analytics for drilling efficiency and reservoir characterization Recent publications highlight advancements in: Eco-friendly drilling fluid formulations using peanut shells and mandarin peels Comparative analysis of gel and water fracture stimulation in shale plays Predictive models for equivalent circulation density and rate of penetration CO2 sequestration feasibility in shallow transition zones Statistical and neural network approaches for lost circulation treatment optimization She has served on key advisory committees including the EPA Hydraulic Fracturing Research Advisory Panel and SPE Star Scholarship Selection Committee.

Suihong SongView profile

Research Fellow

• Machine Learning
• Geosciences
• Reservoir Engineering
• +3 more

Suihong Song is a Research Fellow at Stanford University's Energy Resources Engineering department, affiliated with the Stanford Center for Earth Resources Forecast (SCERF). He collaborates closely with Professor Tapan Mukerji, focusing on integrating machine learning with geosciences. His work emphasizes reservoir characterization, geomodelling using generative adversarial networks (GANs), physics-informed neural networks (PINNs), and applications in subsurface flow simulations, CO2 storage, and hydrocarbon exploration. Education includes a Ph.D. from China University of Petroleum (2021), a visiting Ph.D. at Stanford (2020), and earlier degrees from China University of Petroleum-Beijing (B.Eng. 2013, M.Eng. 2017). His research bridges computational methods with geoscience challenges, notably through the GANSim framework, widely adopted by companies like ExxonMobil. Key research areas include machine learning-driven reservoir modeling, porous media flow simulations, decision-making under uncertainty, and geological interpretation of well logs and seismic data. His publications highlight advancements in stochastic modeling, neural operator applications, and interdisciplinary approaches between geophysics and geology. Contact: suihong@stanford.edu .

• Internet of Things
• Neural Networks
• VLSI Design
• +4 more

Professor Tet Him Yeap serves as an Associate Professor and Associate Director of the System Science Program within the School of Electrical Engineering and Computer Science at the University of Ottawa. With a tenure dating back to October 1989, he maintains an active research profile from his office in STE 5025. His academic contributions are anchored in electrical engineering and computer science, particularly in advancing communication systems and neural network applications for real-world problems. Professor Yeap's research program is centered on three interconnected pillars: Internet of Things (IoT) for smart infrastructure, neural network architectures, and VLSI design. Within IoT, he explores applications in smart farming—such as precision agriculture and environmental monitoring—and predictive maintenance for transportation systems. His neural network work emphasizes Restricted Hopfield Networks, focusing on adversarial robustness and associative memory capabilities. Additionally, he investigates VLSI implementations for communication systems, including visible light communication (VLC) under challenging ambient light conditions, and wireline/wireless access technologies. Analysis of Professor Yeap's 15 most recent publications (2023-2025) reveals a strategic shift toward interdisciplinary applications of machine learning in agriculture and robust communication systems. Approximately half of these works address smart farming challenges, leveraging UAV imagery, federated learning, and neural networks to predict crop yields and nitrous oxide emissions. The remainder concentrates on enhancing neural network robustness (particularly Restricted Hopfield Networks) and optimizing visible light communication for outdoor use. This dual focus underscores his commitment to both theoretical advances in AI and practical solutions for sustainability. Professor Yeap mentors graduate students in his research areas, with Kalonji H Kalala currently under his supervision. While specific grant awards are not detailed in the provided materials, his extensive publication record in high-impact areas suggests active involvement in funded projects, likely including collaborations with agricultural technology initiatives and engineering research consortia focused on IoT and communication systems.

Norbert HanikView profile

Associate Professor

• Optical Communications
• Signal Processing
• Information Theory
• +3 more

Prof. Norbert Hanik is an Associate Professor at the Technical University of Munich (TUM), leading the Chair of Wired Communications. His research focuses on modeling, design, and optimization of optical and copper-based communication systems, integrating methods from communications engineering, information theory, and quantum physics. He has held roles at Deutsche Telekom Technology Center and was a visiting professor at DTU Photonics in Denmark. Education: Diploma in Electrical Engineering (TUM, 1989), Dr.-Ing. in Communications Engineering (TUM, 1995). Research Interests: Optical communication system optimization, nonlinear fiber effects, polymer optical fibers, short-reach copper communications, and advanced modulation schemes. His work includes developing high-capacity optical transmission systems and investigating novel materials like silicon nitride waveguides for wavelength conversion. Grants and Collaborations: Funded by BMBF, EU, and industry partners. Projects include SENDATE (secure data center networks) and FICUS (flexible infrastructure for secure communication). Labs/Teams: Active in the Wireline Transmission Technology group, focusing on physical modeling, simulation, and optimization of optical systems. Collaborates with industry leaders like ADVA Optical Networking.

• Electrical Measurements
• Electronic Measurements
• Data Acquisition Systems
• +9 more

Eulalia Balestrieri is an Associate Professor in the Department of Engineering at the University of Sannio, specializing in Electrical and Electronic Measurements (ING-INF/07). She teaches courses including Data Acquisition Systems for Biomedical Applications and Fundamentals of Measurement across various engineering programs, with office hours held Tuesdays from 10:00 to 12:00 at the Bosco Lucarelli Palace. Her research spans several key areas in electrical and electronic engineering: Measurement theory and instrumentation Analog-to-Digital and Digital-to-Analog converter technologies Compressive sampling and signal processing techniques Biomedical measurement systems, particularly for ECG monitoring RF emitter localization and spectrum analysis Biomedical image analysis including morphometric parameter measurements Professor Balestrieri's recent publications demonstrate a strong focus on applying advanced measurement techniques to biomedical applications and communication systems. Her work on compressive sampling for ECG monitoring represents innovative approaches to reducing data requirements in healthcare technology. She has made significant contributions to IEEE standards for measurement devices, establishing herself as an authority in measurement methodology. Her 2023 research on fish blood cell analysis shows expansion into biomedical image processing applications. Her extensive collaborative research shows strong partnerships with colleagues including De Vito, Daponte, Picariello, Rapuano, and Tudosa, resulting in numerous joint publications across multiple research areas. The 2025 publication on cable diagnosis using machine learning demonstrates her ongoing innovation in measurement techniques.

• Channel coding
• Error-correcting codes
• Optical communication
• +6 more

Frank R. Kschischang is a Distinguished Professor of Digital Communication in the Edward S. Rogers Sr. Department of Electrical and Computer Engineering at the University of Toronto, where he has been a faculty member since 1991. He also served as the Associate Chair for Graduate Studies from 2015 to 2018, and is a registered Professional Engineer in Ontario, Canada. Education: B.A.Sc. (Honours) in Electrical Engineering, University of British Columbia, 1985 M.A.Sc. in Electrical Engineering, University of Toronto, 1988 Ph.D. in Electrical Engineering, University of Toronto, 1991 Research Interests: Professor Kschischang’s research is centered on channel coding techniques and their application to wireline , wireless , and optical communication systems . His work spans error-correcting codes , LDPC codes , network coding , and iterative decoding algorithms . He has contributed foundational results in factor graphs , staircase codes , and nonlinear Fourier transform-based communication . Scientific Awards: IEEE Fellow (2006) Fellow of the Royal Society of Canada (2012) Fellow of the Canadian Academy of Engineering (2018) Canada Research Chair (Tier I, 2001–renewed 2008) Killam Research Fellow (2010) IEEE Information Theory Society Paper Award (2018) IEEE Communications Society & IT Society Joint Paper Award (2010) Canadian Award in Telecommunications Research (2012) University of Toronto Distinguished Professor of Digital Communication (2015) Aaron D. Wyner Distinguished Service Award, IEEE ITSoc (2016) Advising & Grants: Professor Kschischang has supervised a large cohort of graduate students and postdoctoral fellows. His recent PhD graduates include Masoud Barakatain, Lei M. Zhang, Christopher G. Blake, Siddarth Hari, and Siyu Liu. Current and former group members are actively supported by NSERC, Canada Foundation for Innovation, and industrial partners. Labs & Teams: He leads an active research group within the Communications Group at the University of Toronto. The group focuses on theoretical and experimental aspects of coding and communication, with strong ties to the Optical Networks Laboratory and Institute for Optical Sciences . The lab maintains open-source software tools such as ZipperSim and ZMG for research and education.

Sebastian HoyosView profile

Professor

• Wireless and wireline communication systems
• Biomedical sensing and imaging
• Analog and mixed-signal circuit design
• +4 more

Sebastian Hoyos is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He leads the Advanced Mixed-Signal Interfaces Laboratory (AMSI Lab), focusing on analog and mixed-signal systems for communication and biomedical applications. Education: Ph.D., Electrical Engineering, University of Delaware (2004) M.S., Electrical Engineering, University of Delaware (2002) B.S., Electrical Engineering, Pontificia Universidad Javeriana (2000) Research Interests: Wireless/wireline communication systems Biomedical sensing and imaging Compressive sensing and ADC architectures Multi-channel filter-bank receivers Recent Trends in Articles: Articles emphasize high-speed serial links, compressive sensing for medical imaging, and ADC-based transceivers. Recent work includes 56Gbps transceivers, mmWave beamforming, and machine learning-driven nonlinearity mitigation. Awards: 2012 SRC TECHCON Best Paper Award 2009 IEEE Colombian Chapter STSIVA Best Paper Award Multiple fellowships and academic accolades Advising & Labs: Mentored over 20 graduate students. AMSILab focuses on low-power mixed-signal systems for biomedical and communication applications. Collaborates with industry partners like Qualcomm and Texas Instruments.

• Signal Processing
• Communication Systems
• Machine Learning algorithms and systems
• +7 more

Andrew C. Singer is a Professor at the University of Illinois at Urbana-Champaign , with joint appointments in the College of Engineering (Electrical and Computer Engineering, Industrial and Enterprise Systems Engineering) and the College of Business (Business Administration). Since 1998, he has held roles including the Fox Family Endowed Professorship , Associate Dean for Innovation and Entrepreneurship (2018–present), and Director of the Technology Entrepreneur Center (2005–2017). He co-founded Intersymbol Communications, Inc. (2000, acquired by Finisar) and OceanComm (2015, underwater video communication). Education: Ph.D. in Electrical Engineering and Computer Science, MIT (1996). Research Interests span statistical signal processing , acoustic communication systems , machine learning , and low-power VLSI design , with applications in underwater acoustics, through-tissue communication, and augmented listening. His work integrates theoretical signal processing with practical hardware implementations. Recent Publications focus on acoustic communication under nonlinear conditions , low-complexity ADC design , and biomedical applications of signal processing . Articles from 2023–2020 highlight innovations in underwater navigation, face mask acoustics, and subsurface data transmission. Scientific Awards include: Hughes Aircraft Masters Fellowship Harold L. Hazen Memorial Award (1991) NSF CAREER Award (2000) Xerox Outstanding Faculty Award (2001) IEEE Fellow IEEE Signal Processing Society Distinguished Lecturer (2014) Best Paper Awards (2006, 2008) Leadership roles include Associate Editor for IEEE Transactions on Signal Processing and service on the MIT Educational Council . He directs the Coordinated Science Lab and advises on expert witness cases in audio and communication industries.