Find Academic Supervisors

• Materials Science
• Bioelectronics
• Biomedical Engineering
• +5 more

Shuo Li is a Professor in the Department of Macromolecular Engineering at ETH Zürich, Switzerland. His research focuses on innovative biomaterials, bioelectronic systems, and implantable medical devices. Key areas include bioresorbable materials for transient electronics, flexible/stretchable sensors, and soft robotics applications. His work integrates materials science with biomedical engineering to address challenges in tissue integration, real-time diagnostics, and programmable drug delivery. Recent projects emphasize wireless implantable sensors for continuous monitoring of physiological parameters such as blood flow, oxygen saturation, and pH levels in surgical flaps and organ grafts. He has pioneered 3D shape-morphing displays using liquid metal actuators and developed self-healing elastomeric switches for haptic interfaces. His research spans biomaterial synthesis, optoelectronics, and additive manufacturing of soft materials. Publications highlight advancements in bioresorbable platforms for drug delivery, light-controlled actuation systems, and optical probes for in vivo pharmacology. His interdisciplinary approach bridges material design, device fabrication, and clinical applications, with a focus on translating lab innovations into practical medical solutions. Advising and grants: No specific advisees or grant details listed in the provided text. However, his extensive publication record indicates active collaboration with research groups in bioelectronics, soft robotics, and biomedical engineering. Labs/Teams: Likely affiliated with ETH's Macromolecular Engineering lab and collaborate with multidisciplinary teams in materials science, robotics, and medical device development.

Ralf BrownView profile

Researcher

• Machine Translation
• Computational Linguistics
• Digital Forensics
• +5 more

Ralf Brown is a Principal Systems Scientist at Carnegie Mellon University's School of Computer Science, affiliated with the Language Technologies Institute (LTI). His research focuses on machine translation , language identification , and digital forensics , with notable contributions like the open-source CMU-EBMT system and the LTI LangID Corpus for 2000+ languages. Academic Roles: Research faculty since 1993, teaching courses like Coding & Algorithms Bootcamp and MIIS Capstone Project . Research Interests: Multilingual processing, example-based translation, low-resource languages, and text mining. Scientific Contributions: Authored 15+ publications on EBMT, corpus indexing, and context-sensitive translation. Developed tools for digital forensics (corrupted ZIP recovery) and language identification at scale. Awards: IJCAI Distinguished Paper Award Allen Newell Award for Research Excellence Open-Source Contributions: Key developer of darktable image editor Maintainer of the Interrupt List and other technical resources

Mauro RodriguezView profile

Assistant Professor

• Computational Fluid Dynamics
• Multiphase Flows
• Viscoelasticity
• +3 more

Mauro Rodriguez is an Assistant Professor of Engineering at Brown University's School of Engineering. His research focuses on computational fluid dynamics, multiphase flows, viscoelasticity, and high-performance computing. He holds a position at Barus & Holley 441 and can be reached at mauro_rodriguez@brown.edu. His research interests include studying fluid-structure interactions, cavitation bubble dynamics, and inertial effects in complex fluids. Rodriguez explores applications ranging from biomedical engineering (e.g., pulmonary alveolus deformation under ultrasound) to exascale computing for multiphysics simulations. His work often involves numerical modeling and rheometry techniques to characterize material properties. Recent publications highlight advancements in simulating cavitation phenomena, bubble collapse mechanics, and GPU-accelerated algorithms. Rodriguez’s research bridges theoretical fluid dynamics with practical engineering challenges, leveraging high-performance computing resources to tackle multiscale problems. No scientific awards or grants are explicitly listed in the provided materials. His advising activities and lab affiliations remain unspecified.

Joonsik HwangView profile

Assistant Professor

• Compression/Spark Ignition Engines
• Alternative Fuels
• Advanced Combustion
• +3 more

Joonsik Hwang is an Assistant Professor at the Michael W. Hall School of Mechanical Engineering, Mississippi State University (MSU), USA. He holds positions in both the Center for Advanced Vehicular Systems (CAVS) and the Mechanical Engineering department. His research focuses on advanced propulsion systems, spray dynamics, and combustion optimization, particularly in compression ignition engines and alternative fuels. Education: Dr. Hwang earned his Ph.D., M.S., and B.S. in Mechanical Engineering from the Korea Advanced Institute of Science and Technology (KAIST), Republic of Korea, in 2017, 2013, and 2011, respectively. Research Interests: His work emphasizes plasma-assisted ignition, biofuel combustion, soot particle characterization, and high-speed optical diagnostics. He employs CFD simulations and experimental techniques like transmission electron microscopy (TEM) and 3D tomography to study fuel spray behavior and engine performance. Publications: His research spans combustion optimization, fuel injection dynamics, and emission reduction. Recent trends include machine learning for spray modeling, hydrogen jet analysis, and cold-start engine performance. His articles address both fundamental combustion science and practical applications in automotive systems. Awards: 2017 KAUST Travel Fellowship, 2017 ISEES Best Thesis Award, 2016 KSAE Research Award Lab: He directs the Advanced Propulsion & Spray Laboratory at MSU, focusing on innovative propulsion technologies and sustainable fuel systems. His lab collaborates with institutions like Sandia National Laboratories and the Engine Combustion Network (ECN).

• Computer Vision
• Artificial Intelligence
• Machine Learning
• +6 more

Dr. Chris Thomas Conly is an Assistant Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA). He holds a PhD in Computer Engineering and a BS in Computer Science, both from UTA. His roles include serving as Director of Undergraduate Computer Engineering (2025–Present) and Co-Director (2023–2024). He previously held positions such as Senior Lecturer (2016–2020) and GTA/GRA (2012–2016). Education: PhD Computer Engineering (2016), BS Computer Science (2006), both from UTA. Affiliations: Member of Tau Beta Pi, Upsilon Pi Epsilon, ACM, and IEEE. Research interests focus on Computer Vision , AI/Machine Learning , and Gesture Recognition . His work includes developing deep learning methods for HEVC video processing, assistive technologies for sign language interpretation, and gesture recognition systems. Teaching interests include programming, senior design projects, and deep learning. Publications span topics like HEVC optimization, CNN-based classification, and assistive technologies, reflecting contributions to computer vision and signal processing. His 15 most recent articles highlight advancements in video compression, neural networks, and gesture analysis. He has received awards such as the Shining Star Teaching Award (2024) and NSF PETRA Doctoral Consortium Awards (2014–2015). He advises numerous students in doctoral and master’s theses, including Isaac Valdez, Evan Cornish, and Minh Tram. Service roles include leading ABET accreditation efforts, curriculum committees, and student organization advising. He mentors in REU programs and contributes to outreach initiatives, emphasizing broadening participation in computing. Labs/Teams: Involved in CSE capstone design projects, robotics competitions (IEEE Region 5), and assistive technology research through collaborations like PETRA conferences.

Aritra DuttaView profile

Assistant Professor

• Machine Learning
• Federated Learning
• Optimization
• +7 more

Aritra Dutta is an Assistant Professor at the AI Initiative of the University of Central Florida (UCF), primarily affiliated with the Department of Mathematics and secondarily with the Department of Computer Science. He also holds an affiliation with the Pioneer Centre for AI (P1), Denmark. His research focuses on optimization (stochastic/nonconvex), distributed computing (including federated learning), numerical linear algebra, machine learning, low-rank approximation, and computer vision applications such as image/video analysis and object detection/tracking. Dr. Dutta’s work emphasizes interdisciplinary approaches, blending mathematical rigor with computational efficiency. He actively seeks Ph.D. students and postdocs with strong foundations in mathematics (optimization, linear algebra) or computer science (ML, distributed systems), prioritizing candidates with publication records in top-tier venues. His teaching includes courses in applied mathematics and computational methods. His research outputs span communication-efficient distributed learning frameworks, convergence analysis of optimization algorithms, and vision transformer architectures. Notable projects include GAEA (geolocation-aware conversational models) and MAVREC (multi-view aerial visual recognition). Postdoc opportunities: Open for exceptional candidates. Labs/Initiatives: UCF AI Initiative (UCF Aii), UCF Center for Research in Computer Vision (CRCV). Grants: Competitive research assistantships with tuition support.

Chad DutyView profile

Professor

• Additive Manufacturing
• Polymer Composites
• Material Extrusion
• +7 more

Chad Duty is a Professor in the Department of Mechanical, Aerospace, and Biomedical Engineering at the University of Tennessee (2021-present), previously serving as Associate Professor (2015-2021). His research focuses on Additive Manufacturing (AM), particularly polymer and composite structures, with emphasis on Big Area Additive Manufacturing (BAAM). He leads initiatives in large-scale AM tooling, material development, and process optimization. Duty has held roles at Oak Ridge National Laboratory (ORNL), including Group Leader of Deposition Science & Technology (2013-2015) and Senior Research Scientist at the Manufacturing Demonstration Facility (2011-2015). He earned a PhD from Georgia Tech (2001) and BSME from Virginia Tech (1997). Research interests include anisotropic mechanical behavior, melt flow characterization, and recycling of composite materials. Notable contributions include the development of BAAM for multi-meter structures and novel coating materials. His work bridges academia and industry, with applications in aerospace, automotive, and renewable energy sectors. Awards include the ORNL Director's Award (2014), R&D 100 Awards (2009, 2011), and the Eugene P. Wigner Fellowship (2004). His lab focuses on advancing AM technologies through material innovation and process scalability, collaborating with institutions like ORNL and industry partners.

Paul SavaView profile

Professor

• Seismic Imaging
• Planetary Geophysics
• Wavefield Tomography
• +5 more

Paul Sava is a Professor and C.H. Green Chair of Exploration Geophysics at the Colorado School of Mines, serving as Department Head of Geophysics since 2019. He holds a PhD in Geophysics from Stanford University (2005), an MSc from Stanford (1998), and an Engineer Geophysicist degree from the University of Bucharest (1995). His research focuses on seismic and radar wavefield imaging, planetary geophysics, and infrastructure monitoring. Notable contributions include advancements in wave-equation migration and planetary radar tomography. Education: PhD in Geophysics, Stanford University, 2005 MSc in Geophysics, Stanford University, 1998 Engineer Geophysicist, University of Bucharest, 1995 Research interests emphasize wavefield-based methods for subsurface imaging, planetary resource exploration, and autonomous data acquisition. Recent work explores seismic imaging of asteroids and Mars, leveraging orbital radar systems. His publications span seismic inversion, microseismic monitoring, and elastic wavefield tomography. Awards include the Reginald Fessenden Award (2007), T.K. Young Leadership Award (2020), and multiple Top 30 presentations at SEG Annual Meetings. He leads the Center for Wave Phenomena and actively collaborates with NASA and industry consortia on grants totaling over $6 million. Advising and grants involve mentoring over 30 students and postdocs, with grants focused on planetary radar imaging, seismic FWI, and Mars subsurface analysis. Key projects include the Planetary Orbital Radar Processing and Simulation System (PORPASS) and autonomous seismic exploration systems. Labs/Teams: Directly leads the Center for Wave Phenomena and the Seismic Imaging Team at CWP, advancing open-source tools like Madagascar for reproducible geophysical research.

Laura BalzanoView profile

Associate Professor

• Machine Learning
• Optimization
• Signal Processing
• +7 more

Laura Balzano is an Associate Professor of Electrical Engineering and Computer Science at the University of Michigan, with a courtesy appointment in the Department of Statistics within the College of Literature, Science, and the Arts. Her research focuses on optimization, machine learning, and signal processing, particularly in high-dimensional data analysis, subspace learning, and low-rank structures. She holds affiliations with the Department of Statistics and is located at 323 West Hall, Ann Arbor, MI. Contact information includes girasole@umich.edu . Her work bridges theoretical foundations and practical applications, addressing challenges in heterogeneous data, online learning, and robust algorithms. Key research areas include subspace clustering, tensor decomposition, and model compression for deep learning systems. Recent contributions explore dynamic networks, control theory integration, and energy-efficient algorithms for large-scale models. Laura Balzano's publications span topics like PCA variants for noisy data, optimization on Riemannian manifolds, and neural collapse analysis. Her methods emphasize scalable solutions for real-time applications, such as energy disaggregation and medical imaging. While no explicit awards are listed, her extensive publication record reflects recognition in her field. Advising and grant details are not provided in the available text.

Outi SupponenView profile

Assistant Professor

• Multiphase Fluid Dynamics
• Biomedical Engineering
• Material Science
• +3 more

Outi Supponen is an Assistant Professor of Multiphase Fluid Dynamics at ETH Zurich's Department of Mechanical and Process Engineering, leading the Institute of Fluid Dynamics since 2020. She holds a MEng in Aeronautical Engineering from Imperial College London (2013) and a DSc in Mechanics from EPFL (2017). Prior to ETH, she was a Postdoctoral Fellow at the University of Colorado (2018–2019) and an Assistant Professor at McGill University (2019). Education: MEng, Aeronautical Engineering, Imperial College London (2013) DSc, Mechanics, Ecole Polytechnique Fédérale de Lausanne (2017) Her research focuses on experimental investigations of high-speed multiphase fluid phenomena, with applications in biomedical engineering , material science , and hydraulic machinery . Key areas include cavitation bubble dynamics, ultrasound-driven microbubble behavior, and fluid-structure interactions in medical and industrial contexts. Her work bridges fundamental fluid dynamics with practical applications, such as targeted drug delivery via microbubble jetting and kidney stone fragmentation mechanisms. She collaborates extensively with biomedical and engineering communities, leveraging advanced imaging techniques like X-ray phase-contrast and high-speed visualization. Grants & Advising: Supervises research on multiphase systems and collaborates on EU-funded projects on medical fluid dynamics. Labs/Teams: Leads the Fluid Dynamics Group at ETH, specializing in advanced experimental setups for high-speed fluid phenomena.

Tenio PopmintchevView profile

Assistant Professor

• Extreme Nonlinear Optics
• Attosecond Science
• X-ray Lasers
• +3 more

Tenio Popmintchev is an Assistant Professor at the University of California, San Diego (UCSD), previously affiliated with the JILA institute at the University of Colorado Boulder. His research focuses on extreme nonlinear optics and attosecond science, particularly in developing tabletop X-ray lasers and high-harmonic generation techniques. He holds a Ph.D. in Physics from the University of Colorado Boulder (2010). Key research areas include phase-matched generation of coherent X-rays, ultrafast imaging of quantum materials, and applications in magnetic circular dichroism spectroscopy. His work has led to breakthroughs in generating high-brightness X-ray pulses using mid-infrared and ultraviolet lasers, enabling compact and cost-effective X-ray sources. Notable achievements include the Science News Young Scientist award and multiple patents on X-ray generation methods. His research has been published in top journals like Science and Nature Photonics , with contributions to ultrafast laser technology, coherent X-ray sources, and quantum design of light. Awards: Science News Young Scientist award (2016), U.S. Patents 8,462,824 (2013) and 61873794 (2015). Labs/Teams: JILA (Boulder), UCSD Physics Department labs. Future Work: Expanding tabletop X-ray laser applications, improving laser efficiency, and exploring quantum materials dynamics.

Wong Kok SheikView profile

Associate Professor

• Multimedia Signal Processing
• Cybersecurity
• Digital Health
• +3 more

Associate Professor Wong Kok Sheik is the Deputy Head (Research) at the School of Information Technology, Monash University Malaysia. He holds a Doctor of Engineering from Shinshu University (Japan) and advanced degrees in Computer Science and Mathematics from Utah State University (USA). His research focuses on multimedia signal processing, cybersecurity, and digital health, with contributions to data hiding, encryption, and smart grid security. He leads a EU-funded WAge project on post-pandemic workplace health interventions. Education: PhD in Engineering, Shinshu University, Japan (2009) Masters in Computer Science & Mathematics, Utah State University, USA (2005-2004) Bachelor of Science, Utah State University, USA (2002) Professional Roles: Associate Editor, IEEE Signal Processing Letters Member, IEEE Signal Processing Society’s IFS Technical Committee Board Member, APSIPA Multimedia Security and Forensics (MSF) Committee His research interests span multimedia forensics, encrypted domain processing, and cybersecurity frameworks. Notable projects include recovery of missing coefficients in compression standards and HDR imaging enhancement. Recent work integrates digital health, addressing workplace mental/physical health in post-pandemic environments. Publications reflect expertise in watermarking, encryption, and biometric security. Key contributions include encryption-resistant data embedding and secure smart grid analytics. Awards include IEEE CES Service Awards (2015, 2016) and Best Paper recognitions. He supervises over 20 PhD/MSc students, focusing on topics like biometric recognition, data hiding, and anomaly detection. Grants include a €2M EU Horizon 2020 project (IDENTITY) and a RM146k smart grid initiative. His teaching emphasizes foundational IT research methods and theoretical computer science.

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +3 more

Dr. Jiayan Qiu is a Lecturer (Assistant Professor) at the University of Leicester's College of Computing and Mathematical Science. Previously, he was a postdoctoral research fellow collaborating with Prof. Zhou Wang at the University of Waterloo's Department of Electrical & Computer Engineering. He holds a Ph.D. from the University of Sydney (USYD), advised by Prof. Dacheng Tao, and completed his MPhil and Honorable B.S. at the Australian National University (ANU). His research focuses on computer vision, machine learning, and artificial intelligence, with notable contributions to visual relationship modeling, image outpainting, depth estimation, and generative models. His work has been published in top-tier venues like IEEE TPAMI, CVPR, ECCV, and ACM KDD. Professional service activities include serving as a reviewer for prestigious journals (e.g., IEEE T-PAMI, T-IP) and conferences (CVPR, ICCV, NeurIPS), as well as a member of program committees for leading AI conferences. He also contributes to academic leadership as a Guest Editor for Frontiers in Signal Processing and MDPI-Electronics .

David BrainardView profile

Professor

• Behavioral Neuroscience
• Cognitive Neuroscience
• Sensation and Perception
• +3 more

David Brainard is the RRL Professor of Psychology at the University of Pennsylvania. He leads the Brainard Lab, which investigates human vision through experimental and computational approaches, focusing on how the visual system interprets object properties from light signals. His research integrates psychophysics, computational modeling, and machine learning to understand color perception, visual processing, and neural mechanisms. Education: BS in Physics from Harvard University; PhD in Psychology from Stanford University. Research interests include human vision, visual neuroscience, and computational modeling of visual processing. Specific areas: color appearance under varying illumination, object identification via color, and development of machine vision systems mimicking human performance. Recent work explores retinal physiology, chromatic aberration correction, and evolutionary constraints on color naming systems. Notable contributions include studies on retinal ganglion cell physiology in primates, image reconstruction frameworks, and quadratic models of visual cortex responses. Collaborations span neurobiology, optics, and cognitive science. Awards: None explicitly listed. Active advising of graduate students and postdocs including Callista Dyer, Semin Oh, and Raymond Warner. Labs/Teams: The Brainard Lab at Penn, with ongoing projects on retinal imaging, computational models of vision, and cross-disciplinary applications in machine learning.

Krishna Teja Chitty-VenkataView profile

Researcher

• Deep Learning
• Systems and Machine Learning
• Neural Network Optimization
• +7 more

Krishna Teja Chitty-Venkata is a Postdoctoral Researcher at the Argonne Leadership Computing Facility (ALCF), Argonne National Laboratory, USA, where he works in the AI/ML team (formerly Data Science group). His research lies at the intersection of systems and machine learning, focusing on optimizing neural network training, finetuning, and inference on general-purpose and AI-specific hardware platforms. He is actively involved in AI for science applications and high-performance computing for AI (HPC for AI). Education: PhD in Computer Engineering, Iowa State University, 2017–2023 Bachelor of Engineering in Electronics and Communication Engineering, University College of Engineering, Osmania University, Hyderabad, India, 2013–2017 Research Interests: Krishna's research spans hardware-aware inference optimization of deep neural networks, enhancing training and finetuning of large language models (LLMs) and vision-language models (VLMs), neural architecture search (AutoML), pruning and quantization techniques, performance modeling, and AI for science. He is particularly interested in efficient adaptation methods such as LoRA-NAS integration, structured pruning (e.g., WActiGrad), and KV cache optimization (e.g., Paged Compression). Publication Trends: His recent work emphasizes benchmarking and optimization of LLMs on diverse AI accelerators (e.g., LLM-Inference-Bench), developing scalable frameworks for CNN and ViT evaluation (ConVision Benchmark), and advancing structured pruning and mixed-precision search methods. His publications span high-impact journals and conferences in computer science, AI, and systems, reflecting a strong focus on practical, hardware-aware solutions for deep learning efficiency. Scientific Contributions: Developed LLM-Inference-Bench for evaluating LLM performance across hardware and frameworks. Created ConVision Benchmark for standardized evaluation of CNNs and Vision Transformers. Proposed WActiGrad, a structured pruning method for efficient LLM finetuning and inference. Introduced Paged Compression for efficient KV cache management in vLLM. Designed LangVision-LoRA-NAS for optimizing VLMs via NAS-integrated adapters. Professional Experience and Advising: Krishna has been mentored by Prof. Arun K. Somani (Iowa State) and supervisors Murali Emani and Venkatram Vishwanath at Argonne. He has interned at AMD, Intel, and Argonne, contributing to deep learning optimization projects. While no formal students are listed, he has co-authored multiple papers with researchers and students, indicating collaborative advising. He has no publicly listed grants, but his work at Argonne is likely supported by institutional and DOE funding. Labs and Teams: He is part of the AI/ML team within the Argonne Leadership Computing Facility, a premier HPC and AI research division. His work involves close collaboration with teams developing AI accelerators and scientific applications, positioning him at the forefront of AI for science initiatives.