Mahnoosh Alizadeh is an Associate Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara (UCSB), affiliated with the Institute for Energy Efficiency and the Center for Control, Dynamical Systems and Computation (CCDC). She directs the Smart Infrastructure Systems laboratory and focuses on scalable control frameworks, data analytics, and market mechanisms for sustainable cyber-physical systems in smart grids and electric transportation. PhD in Electrical and Computer Engineering from UC Davis (2014) Recipient of the National Science Foundation CAREER award (2019) Associate Editor for IEEE Transactions on Control of Network Systems and IEEE Open Journal of Control Systems Her research spans theoretical work in networks, optimization, and AI, with applications in smart grids , electric transportation , and resilient infrastructure . She has contributed to safe optimization algorithms, decentralized learning, and game-theoretic approaches in resource allocation. Recent publications highlight advancements in safe optimization (safe linear bandits, conservative linear bandits), decentralized learning (robust federated learning), game theory (General Lotto games, resource allocation), and smart charging (mobility-aware EV scheduling). These works emphasize real-time decision-making under constraints, security, and robustness in cyber-physical systems. NSF Early CAREER Award Northrop Grumman Excellence in Teaching Award Her research group includes PhD students Spencer Hutchinson, Arghavan Zibaei, Nanfei Jiang, and Sajjad Ghiasvand, with alumni placed at institutions like Apple, Toyota, and the University of Colorado.
Gavin Schwarz is a Professor and Head of School at the School of Management and Governance within the UNSW Business School . He specializes in organizational change , organizational failure and inertia , and the dynamics of virtual teams , with a focus on how organizations fail during change processes and how to develop applied strategies for change management . His work spans diverse sectors including healthcare, technology, and education, with publications in leading journals such as Academy of Management Learning and Education and Administrative Science Quarterly . Education : PhD in Management (University of Queensland), MPhil (Hons) in Management (University of Auckland), BA in Management and English (University of Auckland). Grants : 2020 Brock University grant for "University communication in times of COVID-19" , 2020 UNSW Medicine grant for "Translation and change: Embedding effective change management in health" , and earlier Australian Research Council and Gordon J. Samuels Fellowship awards. His research explores the development of knowledge in organizational theories , with an emphasis on collective responses to change , HR management during crises , and technology strategy . He has contributed to understanding organizational communication , team innovation , and structural inertia . His 15 most recent publications cover topics from AI’s role in organizational change to pandemic-driven research adaptation, with keywords spanning management science , behavioral economics , and digital transformation . Scientific Awards 2021-2023 : Outstanding Reviewer Awards (Academy of Management Review) 2017-2020 : Best Reviewer Awards (Journal of Organizational Behavior) 2019, 2013, 2011 : Best Paper Finalist/Awardee (Academy of Management divisions) 2007, 2006 : Editorial Board Excellence (Academy of Management Journal) and Gordon J. Samuels Fellowship As an active supervisor in organizational change and HR development , his work supports healthcare innovation and digital transformation. He serves as Editor-in-Chief for the Journal of Applied Behavioral Science and is on the editorial boards of Academy of Management Review , Journal of Management , and Journal of Organizational Behavior . Contact: g.schwarz@unsw.edu.au
Sean O'Rourke is an Associate Professor in the Department of Mathematics at the University of Colorado Boulder. His research focuses on probability, random matrix theory, and random polynomials. He has organized multiple workshops and minisymposia, including events at the Canadian Discrete and Algorithmic Mathematics Conference (CanaDAM) and ICERM, demonstrating his active role in academic community engagement. His research spans spectral properties of random matrices (e.g., singular values, elliptic matrices, Laplacian matrices), probabilistic behavior of polynomial roots under operations like differentiation and summation, and applications of free probability theory. Recent work includes universal behavior in eigenvalue gaps, non-Hermitian matrix controllability, and asymptotic refinements of classical theorems for random polynomials. Publications highlight collaborations with researchers such as Andrew Campbell, Kyle Luh, David Renfrew, and Van Vu. His work appears in leading journals like Annals of Probability , Electronic Journal of Probability , and Transactions of the American Mathematical Society , covering topics from Gaussian fluctuations to low-rank perturbations and noncommutative harmonic analysis.
Dr. Mel Marquis serves as Senior Lecturer and Deputy Associate Dean (Engagement) at Monash University's Faculty of Law. An internationally recognized scholar in competition law and policy, Dr. Marquis is also an Advance HE Fellow (FHEA) and actively contributes to global competition law discourse through research, teaching, and international collaborations. Dr. Marquis earned his educational qualifications from prestigious institutions: Doctorate from the Institute of International and European Law at the University of Macerata, Italy LL.M from the European University Institute (Florence) J.D. magna cum laude from Seton Hall University (Newark) B.A. in political science from the University of Washington (Seattle) Dr. Marquis's research focuses on competition law and policy across multiple jurisdictions, with particular expertise in Australian competition law, East Asian regulatory systems, and ASEAN competition frameworks. His work examines the intersection of cultural contexts and competition enforcement, especially Confucian influences on competition law in East Asia. He has made significant contributions to understanding vertical agreements in the ASEAN Economic Community and the evolution of merger control frameworks. Analysis of Dr. Marquis's recent publications reveals a strong emphasis on comparative competition law, with particular focus on Australia, East Asia, and the ASEAN region. His work bridges legal theory and practical enforcement challenges, addressing contemporary issues like digital markets, merger control reforms, and the cultural dimensions of competition enforcement. The publications demonstrate interdisciplinary approaches combining legal analysis with economic perspectives. Dr. Marquis has received numerous scientific awards and recognitions: Dean's Award for Excellence in Research, 2025 Advance HE Fellow (FHEA) since 2024 Grant recipient for Competition Law Principles for Vertical Agreements in the ASEAN Economic Community (2024) Grant recipient for Distribution Agreements research (2023) Antitrust Writing Awards - Named to Jury and Academic Steering Committee (2021-2022) Monash Teaching Award, 2020 academic year Dr. Marquis actively supervises PhD students in competition law and related fields, welcoming research with global, comparative, interdisciplinary, and Australian perspectives. His grant portfolio includes significant projects funded by the Australian Commonwealth and administered by the ACCC, focusing on competition law applications in ASEAN countries. He has served as an International Expert for the Trade Competition Commission of Thailand and worked with international organizations including the United Nations, Asian Development Bank, World Bank, and Italian Ministry of Foreign Affairs. Dr. Marquis is Convenor of the Monash High Academic Achievers Program and a member of the Executive Board of the Centre for Commercial Law and Regulatory Studies (CLARS). He co-organizes inter-faculty events with the Monash Business-Economics Faculty and serves as a University of Oxford Ambassador for the Value of Competition. Additionally, he is a member of the East Asia Academic Network on Competition Policy and Law and the Virtual ASEAN Competition Research Centre.
Heidi Brown is a Professor and Program Director at the University of Arizona , affiliated with multiple programs including the Mel and Enid Zuckerman College of Public Health, Entomology and Insect Science, Remote Sensing and Spatial Analysis, and the School of Geography and Development. PhD in Epidemiology of Microbial Diseases (Yale University) MPH in International Health Promotion (George Washington University) Postdoctoral experience at Oxford University (Zoology) and CDC (Bacterial Diseases Branch) Her research focuses on infectious disease epidemiology , emphasizing the spatial and temporal dynamics of diseases, vector-borne/zoonotic transmission, and environmental determinants of health. She integrates epidemiological and spatial analysis to address climate change impacts on disease patterns. Recent publications highlight her work linking climate variability to infectious diseases like Campylobacter, stakeholder engagement in climate adaptation, and dual hazard response strategies during the 2020 heat-COVID overlap. Her 2019 Fulbright in Brazil and 2023 sabbatical at Heidelberg Institute of Global Health underscore her international expertise. Scientific Awards: Fulbright (2019) She leads projects such as Adaptation Mal-Adaptation Assessment , Climate and Health Adaptation Monitoring Program (CHAMP) , and initiatives on heat health resilience. Her work bridges academic research with practical implementation for state and local health departments.
Sean Andersson is a Professor in Mechanical Engineering and Systems Engineering at the College of Engineering, Boston University, and serves as Director of the BU Robotics Lab. His research bridges systems and control theory with applications in nanotechnology , atomic force microscopy , and robotics . His work in nanobioscience focuses on single molecule tracking and high-speed imaging in atomic force and fluorescence microscopy, leveraging control theory to enhance imaging capabilities. In robotics, he develops stochastic control methods for autonomous systems operating in complex environments, emphasizing multi-agent systems , sparsely sampled data , and symbolic control frameworks . Recent publications highlight trends in receding horizon control , persistent monitoring , neural style transfer for imaging , and stochastic policy optimization . The Andersson Lab also explores compressive sensing and optimal control for sensor networks and nanoscale fluid dynamics.
Plamen Atanassov is a Chancellor’s Professor in the Department of Chemical and Biomolecular Engineering with a joint appointment in Materials Science and Engineering at the Samueli School of Engineering, University of California, Irvine . His work focuses on developing advanced electrocatalysts for energy conversion and storage systems. Department: Chemical and Biomolecular Engineering, Materials Science and Engineering Academic Rank: Professor (Chancellor’s Professor honorific) Research Themes: Electrocatalysis, Bio-electrocatalysis, Fuel Cells, Energy Harvesting Research Interests: Prof. Atanassov specializes in non-platinum and platinum-based electrocatalysts for fuel cells, bio-inspired energy systems , and carbon dioxide valorization technologies . His group has pioneered: Atomically dispersed metal-nitrogen-carbon catalysts Novel synthesis methods for durable electrocatalysts Machine learning-guided fuel cell optimization Electrochemical ammonia and urea production Hydrogen evolution reaction with non-precious metals Scientific Contributions: With over 380 peer-reviewed papers (101 h-index), 50 issued US patents , and 35+ PhD students advised , his work bridges fundamental electrochemistry and industrial-scale energy solutions. Recent publications emphasize catalyst durability under realistic conditions, CO2 reduction, and sustainable manufacturing practices.
Stefano Grivet-Talocia is a Full Professor at the Department of Electronics and Telecommunications at the Polytechnic University of Turin, where he also serves as Director of the Doctoral School and President of the Doctoral School Council. He is a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory, the University Committee for Research, Technology Transfer and Services to the Territory, and the Commission for the Promotion of Library, Archive and Museum Heritage. His academic career spans over two decades at Politecnico di Torino, where he has established himself as a leading researcher in electromagnetic modeling and signal integrity. Grivet-Talocia earned his Laurea degree (summa cum laude) in Electronic Engineering in 1994 and his Ph.D. in Electronic and Communication Engineering in 1998, both from the Polytechnic University of Turin. Between 1994 and 1996, he conducted research at NASA/Goddard Space Flight Center in Greenbelt, Maryland. His educational background laid the foundation for his expertise in electromagnetic modeling, wavelet analysis, and signal processing. His research focuses on behavioral modeling, electromagnetic compatibility, macromodeling, model order reduction, numerical modeling, passivity, power integrity, signal integrity, transmission lines, and wavelets . Grivet-Talocia is particularly renowned for his work on passive macromodeling of interconnect structures, development of the TOPLine technique for transmission line simulation, and pioneering contributions to passivity enforcement algorithms. He has co-authored the first book entirely dedicated to Macromodeling (2016) and developed innovative approaches to waveform relaxation and wavelet-based signal processing. His recent publications (2024-2025) demonstrate continued leadership in model order reduction, with significant contributions to data-driven modeling of linear and nonlinear systems, power integrity analysis, and electromagnetic compatibility. His work spans both theoretical advances in numerical methods and practical applications in circuit design, with strong industry relevance particularly for semiconductor and electronic design automation companies. IEEE Fellow (2018-present) Three Intel SRS Grants (2022-2024) Three IBM SUR Grant Awards (2007-2009) Best Associate Editor Award - IEEE Transactions on Components, Packaging and Manufacturing Technology (2020) Multiple Best Conference Paper Awards (2006-2020) URSI Young Scientist Awards (1999) Ranked among the "top 2% worldwide researchers" (Stanford) since 2019 Grivet-Talocia actively supervises doctoral students including Michele Cusano, Sara Paknezhad Panahi, Antonio Carlucci, and Kun Zhao. He has secured numerous research grants from competitive national calls (PRIN) and commercial contracts with industry partners including Intel, IBM, Nokia, Hitachi, Infineon, and Cadence. His technology transfer activities include co-founding the spin-off IdemWorks (2007-2016), which was acquired by CST in 2016. He also developed the autoCircuits web service for automated circuit problem generation, widely used in electrical engineering education. He leads the EMC Group (Electromagnetic Compatibility) at DET and has been instrumental in establishing the Compact Dynamical Modeling research area. His work has practical applications in high-speed electronics design, with algorithms embedded in commercial tools like IBM PowerSPICE. Grivet-Talocia maintains strong industry connections through his research projects and serves as Associate Editor for IEEE Transactions on Components, Packaging and Manufacturing Technology.
Byron Boots is the Amazon Professor of Machine Learning in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, where he directs the UW Robot Learning Laboratory. He also serves as a Principal Research Scientist in the Seattle Robotics Lab at NVIDIA Research and co-chairs the IEEE Robotics and Automation Society Technical Committee on Robot Learning. Dr. Boots received his Ph.D. from the Machine Learning Department in the School of Computer Science at Carnegie Mellon University, where he was a member of the Sense, Learn, Act (SELECT) Lab co-directed by Carlos Guestrin and his advisor Geoff Gordon. Prior to joining the University of Washington faculty, he was an Assistant Professor in the School of Interactive Computing within the College of Computing at Georgia Tech, and before that, he completed a post-doc in the Robotics and State Estimation Lab directed by Dieter Fox at the University of Washington. Professor Boots' research focuses on the intersection of machine learning, artificial intelligence, and robotics, with particular emphasis on developing theory and systems that tightly integrate perception, learning, and control. His work spans computer vision, state estimation, localization and mapping, high-speed navigation, motion planning, and robotic manipulation. His group develops algorithms drawing from deep learning and neural networks, nonparametric statistics, graphical models, nonconvex optimization, quantum physics, online learning, reinforcement learning, and optimal control. The research demonstrates a strong theoretical foundation while maintaining practical relevance to real-world robotic systems. His recent publications reveal a clear trend toward integrating advanced machine learning techniques with robotics, particularly in model predictive control, motion planning, and learning-based approaches to robot control. His work shows increasing focus on developing theoretically grounded methods that can handle the complex, nonlinear dynamics of real-world robotic systems while maintaining computational efficiency. The publications span top venues including ICRA, CoRL, IROS, and NeurIPS, demonstrating broad impact across multiple subfields of robotics and AI. Finalist for Best Systems Paper at Conference on Robot Learning (CoRL-2021) Multiple papers selected for oral presentations at top robotics conferences Work recognized for theoretical contributions and practical applications in robot learning As director of the UW Robot Learning Laboratory, Boots leads a vibrant research group focused on fundamental and applied research in robot learning. The lab maintains strong collaborations with NVIDIA Research and has produced numerous high-impact publications that bridge theory and practice. Professor Boots teaches courses in autonomous robotics, machine learning, and reinforcement learning, contributing to both undergraduate and graduate education at the University of Washington.
Dushan Boroyevich is a University Distinguished Professor at Virginia Tech's Bradley Department of Electrical and Computer Engineering and serves as Deputy Director of the Center for Power Electronics Systems (CPES). He holds adjunct roles at Tsinghua, Xi'an Jiaotong, Zhejiang, and National Cheng-Kung Universities. His research focuses on power electronics systems, including multi-phase power conversion, electronic power distribution, and modular multilevel converters. He pioneered the geometric modeling approach for high-frequency converters and has led over 200 students in generating 1000+ publications and 20 patents. Education: Dipl. Ing. (University of Belgrade, 1976), M.S. (University of Novi Sad, 1982), Ph.D. (Virginia Tech, 1986). Awards include IEEE Fellow, IEEE William E. Newell Award, and election to the U.S. National Academy of Engineering (2014). His CPES leadership has driven global advancements in power electronics integration and modularization. Research emphasizes high-power density, EMI mitigation, and next-gen SiC-based converters. Recent work includes medium-voltage PEBB designs, common-mode noise reduction, and grid-interface systems. He collaborates closely with industry through CPES's 80+ member consortium. Awards: IEEE Fellowships, Owen Distinguished Service Award, European Power Electronics Association Awards Labs/Teams: CPES, Virginia Tech Power Electronics Research Group Grants/Projects: NSF National Engineering Research Center funding, Industry Consortium projects
Professor Line Roald is a faculty member in the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison. Her research focuses on power system optimization, renewable energy integration, grid resilience, and wildfire risk mitigation using stochastic optimization and data-driven methods. Education : PhD (2016), MS (2012), BS (2009) from ETH Zurich Key Research Areas : Power Systems Optimization, Renewable Energy Integration, Wildfire Risk Mitigation, Stochastic Programming, Grid Decarbonization Her work addresses critical challenges in sustainable energy systems, including balancing grid efficiency and risk, optimizing electrolyzer scheduling for flexibility, and predicting cascading blackout severity using graph neural networks. She has developed frameworks for carbon intensity comparison and wildfire risk assessment in power systems. Scientific Awards : 2024 Inclusion, Equity and Diversity in Engineering Award 2024 Vilas Faculty Early Career Investigator Award 2023 IEEE Power Tech Best Student Paper Award 2021 NSF CAREER Award 2019 MTLE Fellow Professor Roald mentors graduate students and teaches courses including Introduction to Optimization and On-Line Control of Power Systems . Her publications highlight innovative approaches to grid security, carbon-efficient energy markets, and climate resilience in infrastructure systems.
Yashar Ganjali is Professor in the Department of Computer Science at the University of Toronto, where he leads research on computer networks and distributed systems. His work focuses on improving efficiency in data center operations through innovative algorithmic approaches. Research areas include: Machine learning applications for network optimization SDN controller architectures and load migration Congestion control mechanisms for high-speed networks Data center traffic engineering and resource allocation Recent projects explore joint time-space scheduling for distributed ML training, network-aware transport protocols, and reinforcement learning for congestion management. His team collaborates with industry partners including Google to develop practical solutions for cloud infrastructure challenges.
Professor Antonio Griffo holds the position of Professor of Power Electronics and Electric Drives at the University of Sheffield's School of Electrical and Electronic Engineering. He leads the Electrical Machines and Drives Research Group and is involved in the High Reliability Drives Group. His academic journey includes a MSc (2003) and PhD (2007) in Electrical Engineering from the University of Naples, followed by research roles at Bristol and Sheffield Universities before becoming a Lecturer in 2013 and later a Professor. His research focuses on advanced control of electric drives, SiC-based power electronics for aerospace/renewables, fault detection in machines, and thermal management. Key projects include modeling hybrid AC/DC power systems for 'More Electric Aircraft', sensorless control techniques, and real-time simulation methodologies. He has pioneered work on SiC converter reliability, insulation monitoring, and condition-based maintenance systems. Publications (15+ in top journals like IEEE Transactions) emphasize innovative solutions for power electronics challenges, including voltage stress mitigation, thermal modeling, and fault tolerance. His work bridges theory and application, addressing critical issues in aerospace, renewable energy, and electric vehicle systems. Griffo also contributes to educational advancements through modular training platforms for power electronics education. Labs/Teams: Active in the Electrical Machines and Drives Research Group, focusing on high-reliability drive systems and sustainable energy technologies. Collaborates with industry on projects like the EPSRC Offshore Wind Prosperity Partnership.
Xiaoming Li is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Delaware , focusing on compiler optimization, GPU computing, and hardware-software interaction. His work bridges machine learning with code generation to enhance program efficiency. B.S. and M.E. from Nanjing University (1998, 2001) Ph.D. in Computer Science from University of Illinois at Urbana-Champaign (2006) Research interests include: Compiler optimizations for static and dynamic code transformation Machine learning-driven code generation techniques FFT algorithms for sparse and hybrid systems Non-traditional compilers for SAT solvers and virtual machines GPU acceleration for large-scale computational problems His publications span 15 years , emphasizing: FFT optimization across GPU/CPU architectures Compiler techniques for heterogeneous systems Adaptive scheduling and error resilience Integration of empirical and model-driven approaches Notable awards: NSF CAREER Award (2008) Best Paper Award at ADAPT Workshop (2013) Advising highlights: Current students: Ryan Taylor, Sha Li, Shuo Chen, Yuanfang Chen, Chao Yang, Chaoyu Chen Graduates: Liang Gu (FFT Libraries), Jakob Siegel (GPGPU Frameworks), Murat Bolat (Context-Aware Compilation)
Stefan Krastanov is an Assistant Professor at the University of Massachusetts Amherst, focusing on quantum hardware design, control, and optimization across multiple layers of quantum computing and networking technologies. His work bridges physical hardware descriptions with logical circuit compilation, emphasizing resilience in noisy quantum systems. Research Interests include Quantum Hardware Design, Entanglement-Based Networking, Quantum Error Correction, and Modeling Software for Quantum Systems. His primary lab is the Quantum Information Lab , with affiliations to the Advanced Classical and Quantum Information Research Lab. Recent work trends highlight advancements in quantum repeater networks, error-corrected compilation, and photonic neural networks. His publications span topics like non-Markovian dynamics simulation, NP-hard optimization in quantum dot arrays, and scalable spin quantum memory control. Labs and Teams: Quantum Information Lab (leading experimental/theoretical work) and collaborations through the Advanced Classical and Quantum Information Research Lab.
