Professor Jordan Taylor is affiliated with Princeton University as a faculty member in the Department of Biomedical Engineering within the School of Engineering and Applied Science. His research focuses on unraveling computational processes in motor control and learning, with particular emphasis on interactions between explicit cognitive strategies and implicit motor adaptation during skill acquisition. Taylor leads the Intelligent Performance and Adaptation Laboratory , aiming to develop optimal training protocols for motor rehabilitation post-stroke or disease. Research Interests : Taylor investigates how humans learn motor skills through dual mechanisms of declarative strategy formation and implicit neural adaptation. His work explores the neural systems underlying these processes and their functional consequences, especially in pathological conditions like cerebellar degeneration. Current studies examine working memory constraints, reward modulation of implicit adaptation, and plan-based generalization of motor learning. Publication Trends : Recent articles analyze dual mechanisms in sensorimotor learning, reward-driven adaptation, and contextual influences on motor memory. His computational neuroscience approach combines behavioral experiments, neural imaging, and theoretical modeling to study cognitive-motor interactions across various tasks.
Haipeng Luo is an Associate Professor at the Thomas Lord Department of Computer Science, University of Southern California, holding the IBM Early Career Chair. He previously worked as a Postdoctoral Researcher at Microsoft Research, NYC, and has held visiting roles at Google and Amazon. His research focuses on developing practical machine learning algorithms with strong theoretical guarantees, particularly in online learning, bandit problems, reinforcement learning, and game theory. PhD in Computer Science, Princeton University (2011–2016) BSc in Computer Science, Peking University (2007–2011) His work spans adversarial and stochastic environments, addressing challenges in reinforcement learning, game dynamics, calibration, and omniprediction. Recent publications highlight advancements in regret minimization, game equilibrium computation, and robust optimization frameworks. Key contributions include algorithms for zero-sum games, bandit problems with feedback graphs, and theoretical analyses of convergence properties in multi-agent systems. Scientific accolades include Best Paper Awards at COLT 2021, COLT 2018, NeurIPS 2015, and ICML 2015. He has received prestigious grants such as the NSF CAREER Award (2020), Google Faculty Research Award (2020), and NSF CRII Award (2018). His students have secured academic and industry positions, and he actively teaches graduate courses in machine learning and online optimization.
Ricardo Aguilera Echeverria is an Associate Professor at the University of Technology Sydney (UTS), School of Electrical and Data Engineering . With a Ph.D. in Electrical Engineering from the University of Newcastle (2012), he has held academic positions at UNSW Australia (2014-2016) and UTS since 2016. His research focuses on model predictive control (MPC) applied to power electronics , renewable energy integration , and microgrid control systems . He actively supervises Masters and PhD students and has developed courses such as Control Studio A and Control Studio B . Education: PhD in Electrical Engineering (University of Newcastle, 2012) MSc in Electronics Engineering (Universidad Tecnica Federico Santa Maria, 2007) BSc in Electrical Engineering (Universidad de Antofagasta, 2003) Research Interests: Model Predictive Control (MPC) for power converters Microgrid stability and cybersecurity Second-life battery integration Hybrid DC-AC microgrid solutions Recent Research Trends: Advancements in modular multilevel matrix converters (M3C) for LFAC systems Development of per-phase instantaneous power theories for LVRT compensation Sliding mode observers (SMO) for cyberattack mitigation in AC microgrids Optimal control strategies for delta-connected CHB converters in energy storage Grants & Projects: Lead investigator in HORIZON Europe (2024-2027) on digital solutions for renewable energy systems ARC Discovery Project (DP240102646) on extending second-life battery life (2024-2026) Collaborative grants with Sovereign Propulsion Systems Pty Ltd and NSW Department of Industry for hybrid-electric vehicle control
California Institute of Technology (Caltech)United States
Rana Adhikari is a Professor of Physics at the California Institute of Technology (Caltech). Holding a B.S. from the University of Florida (1998) and a Ph.D. from MIT (2004), he has been at Caltech since 2006, progressing from Assistant Professor to full Professor in 2012. His research focuses on advancing detector technologies for fundamental physics experiments in gravitational waves, dark matter, and near-field gravity studies. Education: B.S. in Physics, University of Florida (1998); Ph.D. in Physics, MIT (2004) Caltech Faculty: Assistant Professor (2006-12), Professor (2012-present) Adhikari's group specializes in precision measurements at the intersection of classical and quantum physics. Key research areas include: Mechanical oscillators and their thermodynamic limits Nonlinear optics for interferometric applications Quantum information constraints in classical sensors Adaptive optics using thermal actuation Cryogenic silicon interferometers for cosmological observations High-quality silicon opto-mechanical systems for LIGO applications Laser gyroscope technology for rotation sensing The group's work on gravitational wave detection has produced numerous publications in leading journals like Physical Review X , Physical Review D , and Optics Express . Their research often combines experimental physics with machine learning techniques for noise cancellation in laser interferometers. Adhikari's team also engages with undergraduate researchers through programs like the International LIGO SURF students, creating opportunities for young scientists in gravitational physics. His publications reveal a consistent focus on gravitational wave detector optimization, quantum metrology, and cosmological observations through advanced instrumentation.
Xin Li is a Professor in the Department of Electrical and Computer Engineering at Duke University and serves as the Associate Vice Chancellor at Duke Kunshan University. He holds a Ph.D. from Carnegie Mellon University (2005) and has held leadership roles in research consortia like the FCRP Focus Research Center and the Center for Silicon System Implementation (CSSI). His research bridges integrated circuits , machine learning , and cyber-physical systems , with applications in autonomous driving, battery lifetime prediction, and smart buildings. Education : Ph.D., Carnegie Mellon University (2005); M.S., Fudan University (2001); B.S., Fudan University (1998) His work emphasizes robust design methodologies for analog/RF circuits, data-driven predictive modeling , and Bayesian inference for high-dimensional variation spaces. Recent publications focus on generative adversarial networks for circuit design, multi-view imputation for incomplete data, and knowledge-driven autonomous systems . He has received numerous accolades, including the NSF CAREER Award (2012) , IEEE Donald O. Pederson Best Paper Awards (2013, 2016) , and IEEE Fellow (2017) . He has served as Editor for journals like IEEE Transactions on Biomedical Engineering and as Chair for conferences including ISVLSI and CAD/Graphics.
Inna Sharf is a Professor at the Department of Mechanical Engineering, Faculty of Engineering, McGill University. She is affiliated with the Aerospace Mechatronics Laboratory, focusing on dynamics, control, and robotics. Her work spans space robotics, UAVs, forestry automation, and multibody systems. Ph.D., University of Toronto B.ASc., University of Toronto Her research interests include: Dynamics and control of robotic systems Space robotics for debris removal and on-orbit servicing Unmanned aerial vehicles (quadrotors, indoor airships) Forestry robotics for tree-harvesting automation Multibody dynamics and contact modeling Recent publications emphasize: Control algorithms for quadrotors and UAV swarms De-orbitation strategies using natural resonances Motion planning under dynamic constraints Thermalling and energy-efficient flight for gliders Collaborative payload transport and adaptive control Tether and net-based debris capture systems
Katy Ilonka Gero is a Lecturer at the School of Computer Science, University of Sydney , with prior postdoctoral roles at Harvard University and the Library Innovation Lab. She holds a PhD in Computer Science from Columbia University (2022) and a BS in Mechanical Engineering from MIT (2017). Education : PhD in Computer Science (Columbia University, 2022); BS in Mechanical Engineering (MIT, 2017) Her research focuses on Human-Computer Interaction in creative domains, particularly how language models impact creative practice, ownership, and learning . She develops community-driven AI models through co-design with creative communities and investigates ethical data governance practices. Recent work includes designing interactive writing tools for metaphor creation and exploring the social dynamics of AI support in writing . The 15 most recent articles reflect trends in AI-augmented creativity (Metaphoria, CHI 2019), AI ethics (Nature Machine Intelligence 2023), and human-AI collaboration (CHI 2020 Best Paper). These works span language model evaluation , creative ownership , and technical innovations in soft robotics (2012) and science communication (2021). Scientific Awards : NSF Graduate Research Fellowship Brown Institute for Media Innovation Grant Amazon Research Award Best Paper - CHI 2025 Honorable Mention - CHI 2024 Best Paper - CHI 2020 As a poet and essayist , she co-edits Ensemble Park , a human-computer co-writing magazine, and authored the dynamic poetry book The Anxiety of Conception (2025). Her grants include support from the National Science Foundation , Brown Institute , and Amazon . For technical details, visit her personal website or GitHub.
Xiaonan Lu is an Associate Professor of Electrical Engineering Technology at Purdue University's School of Engineering Technology, with a courtesy appointment in the Elmore Family School of Electrical and Computer Engineering. His research focuses on critical challenges in modern power systems dominated by inverter-based resources, particularly stability and control in microgrids and renewable-integrated grids. His research interests span power systems engineering with emphasis on small-signal stability analysis, dynamic modeling of hybrid AC/DC microgrids, and advanced control strategies for grid-forming and grid-following inverters. He investigates AI-assisted modeling techniques, resilience enhancement through hydrogen integration, and data-driven optimization of microgrid operations to address challenges in low-inertia power systems and distributed energy resource coordination. Analysis of his recent publications (2024-2025) reveals dominant trends toward AI-aided stability assessment, seamless control transitions between inverter modes, and quantifiable trade-offs in voltage regulation and power sharing. His work consistently addresses practical implementation challenges including communication delays, cyber resilience, and standardized testing methodologies for inverter-dominated systems.
Philipp Koehn is a Professor in the Department of Computer Science at Johns Hopkins University, with additional affiliation at the University of Edinburgh. His primary research focuses on statistical and neural machine translation, specifically developing methods to leverage large-scale digital information for cross-lingual communication. He leads the Machine Translation Research Group and maintains key resources like the Moses toolkit and Europarl corpus. His research interests span: Core machine translation techniques (statistical/neural approaches) Low-resource and unsupervised translation methods Cross-lingual representation learning Speech-to-speech translation systems Large-scale parallel data mining and alignment Evaluation methodologies for generated text Koehn's recent publications demonstrate strong focus on improving translation efficiency (dynamic compression, streaming models), robustness (noise handling, error correction), and accessibility (low-resource languages, radio speech processing). Key trends include multilingual generalization, document-level coherence, and human-centered evaluation. Significant scientific recognition includes: ACL Fellow (2024) IAMT Award of Honor (2015) European Inventor Award Finalist (2013) He currently advises PhD students Rachel Wicks, Elina Baral, Bismarck Odoom, and Weiting Tan. His Machine Translation Group develops widely-used open-source tools and organizes major conferences including WMT and MT Marathon.
Kyle Dawson is a Professor of Physics and Astronomy at the University of Utah, where he has been employed since 2009. He currently serves as both a full Professor and Director of Graduate Studies in the Department of Physics and Astronomy, having progressed from Assistant Professor (2008-2015) to Associate Professor (2015-2019) before achieving his current position in 2019. His institutional affiliation places him within the College of Science at the University of Utah, a major research university in the western United States. Dawson earned his BA in Physics from Cornell University in 1998, followed by a PhD in Physics from the University of California, Berkeley in 2004. After completing his doctoral studies, he served as a postdoctoral researcher at the Lawrence Berkeley National Laboratory before joining the University of Utah faculty. His educational background in physics provided the foundation for his transition into observational cosmology, where he has made significant contributions through large-scale spectroscopic surveys. Professor Dawson's research focuses on observational cosmology through large spectroscopic surveys designed to measure the fundamental properties of the universe. He is currently the co-Spokesperson for the Dark Energy Spectroscopic Instrument (DESI), a major cosmological survey that has produced numerous high-impact publications in 2024-2025. Previously, he served as Principal Investigator for the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), which concluded in 2020 with final cosmological measurements. His work centers on measuring baryon acoustic oscillations to constrain cosmic expansion history, dark energy properties, neutrino masses, and to test General Relativity. His research group employs techniques including galaxy clustering analysis, quasar astrophysics, and large-scale structure mapping to address fundamental questions in cosmology. The analysis of Dawson's recent publications reveals a strong focus on extracting cosmological constraints from the DESI survey data. His work spans multiple aspects of cosmological analysis, including baryon acoustic oscillation measurements, full-shape power spectrum analysis, imaging systematics mitigation, and cross-correlation studies with cosmic microwave background data. The publications demonstrate collaborative work with large international teams and contribute to increasingly precise measurements of cosmological parameters, with particular attention to dark energy equation of state, neutrino masses, and potential deviations from General Relativity. Professor Dawson has secured significant research funding throughout his career, including multiple grants from the Department of Energy (DOE), NASA, and the National Science Foundation. His grant portfolio includes leadership roles in major cosmological surveys like DESI and eBOSS, as well as support for postdoctoral researchers and graduate students. His research group has mentored numerous students who have gone on to successful careers in academia, industry, and data science fields. Dawson leads a vibrant research group at the University of Utah focused on cosmological data analysis from large spectroscopic surveys. His current team includes two postdoctoral researchers (Angela Berti and Sarah Eftekharzadeh) and a graduate student (Allyson Brodzeller). The group specializes in galaxy clustering analysis, quasar astrophysics, and machine learning applications to spectroscopic data. The research environment fosters collaboration with international teams working on DESI and related cosmological surveys, providing students with opportunities to engage with cutting-edge cosmological research and large-scale data analysis techniques.
Dr. Erma Perenda serves as Professor and Chair of Distributed Signal Processing at RWTH Aachen University, Germany, leading research within the Department of Distributed Signal Processing. Her contact details include email perenda@dsp.rwth-aachen.de and phone +49 241 80-27879, with office location at Kopernikusstraße 16, 52074 Aachen in the ICT Cubes facility. Her research spans: Distributed Signal Processing Wireless Communications Machine Learning (Deep Reinforcement Learning, Federated Learning) Modulation Classification AI-driven Network Optimization She focuses on solving real-world challenges in wireless systems including hardware impairments, channel variations, and energy efficiency through advanced AI techniques. Analysis of her 2018-2024 publications reveals consistent innovation in applying multi-agent deep reinforcement learning to wireless power allocation, developing robust modulation classification methods resilient to channel impairments, and implementing federated learning for industrial edge computing. Her work bridges theoretical machine learning with practical wireless communication constraints. Scientific Awards: No awards documented in available sources Advising and Grants: No student advisees or grant information provided Labs and Teams: Leads Distributed Signal Processing research group at RWTH Aachen University Based in ICT Cubes building focusing on wireless AI systems
Radu Ioan Bot is a Professor and Dean of the Faculty of Mathematics at the University of Vienna, where he also serves as Head of the Department of Mathematics. His primary affiliations include the Department of Mathematics (Oskar-Morgenstern-Platz 1, 1090 Wien) and the Research Network Data Science (Währinger Straße 29, 1090 Wien). Bot's research centers on optimization theory with emphasis on convex/nonconvex optimization, monotone operators, and dynamical systems. He develops fast algorithms for variational inequalities and monotone inclusions by bridging continuous-time dynamics with discrete optimization methods. His work frequently addresses bilevel optimization, Tikhonov regularization, and second-order dynamics, yielding accelerated convergence rates for complex problems. Analysis of his 15 most recent publications (2023-2025) reveals dominant trends in time-scaling techniques, vanishing damping dynamics, and structured splitting methods. Key contributions include unifying Nesterov acceleration with Heavy Ball dynamics, developing reflected forward-backward algorithms for constrained optimization, and establishing strong convergence guarantees for monotone operator flows. These advances demonstrate consistent innovation in accelerating optimization while maintaining theoretical rigor. No scientific awards were mentioned in the provided source material. Details regarding student advising and research grants were not specified in the available information, though his leadership roles as Dean and Department Head indicate significant administrative responsibilities alongside active research. Bot participates in the University of Vienna's Research Network Data Science, suggesting interdisciplinary engagement in data-driven methodologies with potential applications in machine learning and computational mathematics.
Oguz Durumeric is an Associate Professor in the Department of Mathematics at the University of Iowa, part of the College of Liberal Arts and Sciences. His research focuses on differential geometry, medical image analysis, and geometric topology. He earned his PhD from SUNY Stony Brook and has contributed to interdisciplinary applications of geometry in medical imaging, particularly in lung biomechanics and radiation therapy planning. Education: PhD in Mathematics from SUNY Stony Brook. Research Interests: Dr. Durumeric’s work bridges pure and applied mathematics. In differential geometry, he explores knot energies and curvature properties. In medical imaging, he develops advanced registration techniques for 4DCT and MRI data to study lung ventilation patterns and improve cancer treatment accuracy. His geometric topology research includes ideal knot structures and conformal transformation analysis. Recent Research Trends: His articles highlight innovations in medical image registration (e.g., lung motion artifact correction, out-of-phase ventilation detection) and geometric models for biomedical applications. He also addresses foundational challenges like shape collapse in large-deformation registration. Grants & Collaborations: While specific grants are not listed, his work implies collaboration with medical imaging labs and oncology teams. No formal advisees are documented here. Labs/Teams: Affiliated with the University of Iowa Mathematics Department’s research groups in geometry and applied mathematics. His website provides further details on ongoing projects.
Virginia Polytechnic Institute and State UniversityUnited States
Ming Jin is an Assistant Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. He holds a PhD from UC Berkeley and a B.Eng. from Hong Kong University of Science and Technology. His research focuses on trustworthy AI, CPS security, and energy systems, with affiliations to the Power and Energy Center and Autonomy and Robotics @ VT. Education: PhD in Electrical Engineering and Computer Science (UC Berkeley, 2017), B.Eng. (Honors) in Electronic and Computer Engineering (HKUST, 2012). Postdoc in Industrial Engineering and Operations Research at UC Berkeley. Research interests include safe reinforcement learning, foundation models, cybersecurity, and power systems. Awards include the Siebel Scholarship (2018) and first place in the 2021 CityLearn Challenge. Active in conference organization (e.g., ICML, AAAI) and tutorial development on topics like Safe RL and CPS security. Grants include NSF support for embodied optimization (2025), Amazon-VT Initiative (2023), and Commonwealth Cyber Initiative projects. Involved in labs focused on AI, robotics, and energy systems. Publications span AI safety, RL frameworks, and CPS resilience, with over 50 peer-reviewed articles since 2015.
Virginia Polytechnic Institute and State UniversityUnited States
Daniel J. Stilwell is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Polytechnic Institute and State University (Virginia Tech), and Co-Director of the Center for Marine Autonomy and Robotics. He holds affiliations including the Seale Coastal Observatory Faculty Fellow role. His research focuses on autonomous underwater vehicles (AUVs), marine robotics, control systems, and sensor networks. He earned his Ph.D. in Electrical Engineering from Johns Hopkins University (1999), M.S. from Virginia Tech (1993), and B.S. in Computer Engineering from the University of Massachusetts (1991). His notable contributions include advancements in AUV control, underwater acoustic communication, multi-agent systems, and sensor network optimization. Key projects include the "Unconventional Marine Platforms" funded by the Office of Naval Research and collaborative subsea mapping initiatives. His work bridges theoretical control systems with practical robotic applications in marine environments. Dr. Stilwell has received prestigious awards such as the NSF CAREER Award and ONR Young Investigator Program Award. His research emphasizes robust control strategies, adaptive systems, and decentralized learning algorithms. He leads efforts in experimental validation of AUV control systems and underwater sensor networks, contributing to both academic and military applications.
