Eric Green is an Adjunct Assistant Professor in the Department of Civil Engineering at the University of Kentucky , affiliated with the Kentucky Transportation Center . He holds a Ph.D., M.S., and B.S. in Civil Engineering from the same institution. Ph.D., Department of Civil Engineering, University of Kentucky M.S., Department of Civil Engineering, University of Kentucky B.S., Department of Civil Engineering, University of Kentucky His research focuses on highway safety , spatial analysis (GIS) , crash modeling , and software development for traffic safety . Recent work includes text mining for secondary crash detection and GPS-based horizontal curve analysis. Publications highlight trends in crash analysis , Highway Safety Manual methodologies , and data integration for asset management . Key subfields include GIS applications, safety modeling, and automated regression techniques.
Thomas Demeester is an Associate Professor at the Internet Technology and Data Science Lab (IDLab), Ghent University - imec, Belgium. Appointed as Assistant Professor in 2019, he leads an AI research group focused on health applications and drug design, co-directing the Text-to-Knowledge research cluster with Prof. Chris Develder. His educational background includes: M.Sc. in Electrical Engineering from Ghent University (2005), completed with thesis work at ETH Zurich Ph.D. in Computational Electromagnetics from Ghent University (2009), funded by Research Foundation - Flanders (FWO) Demeester's research spans artificial intelligence with emphasis on deep learning and neuro-symbolic methods. Current tracks include energy-based models (Hopfield Networks, Deep Equilibrium Models), diffusion models for drug design, and clinical reasoning systems. His work bridges NLP, healthcare informatics, and generative AI with strong industry partnerships. Recent publications (2023-2025) reveal strategic expansion from NLP into health-centric AI: BioLORD biomedical encoders (2023), synthetic medical data frameworks (UAI/NeurIPS 2024), and novel diffusion model guidance (ICLR 2025). This evolution demonstrates convergence of generative modeling, clinical data analysis, and protein design. He actively mentors 24 PhD students across diverse AI domains: Current Research: Conversational agents, emotion analysis, clinical reasoning, antibody design, and diffusion model optimization Recent Graduates: Interpretable language models, biomedical semantics, task-oriented dialogue, and social media knowledge extraction Research is supported by imec funding and collaborations with Flemish biotech companies, building on his post-doctoral experience securing media-sector projects. Within IDLab, he co-leads the Text-to-Knowledge cluster driving NLP innovations for healthcare, legal, and economic applications.
Dr. Karim El-Basyouny is a Killam Laureate Professor and City of Edmonton Urban Traffic Safety Research Chair at the University of Alberta's Faculty of Engineering, where he serves as Associate Dean (Research Infrastructure and Innovation) in the Civil and Environmental Engineering Department. A licensed Professional Engineer in Alberta, he holds advanced degrees in Transportation Engineering from the University of British Columbia and has dedicated his career to advancing road safety through data-driven management frameworks. His academic credentials include: Doctor of Philosophy, Civil Engineering, University of British Columbia, 2011 Engineering Management Sub-specialization, Civil Engineering, University of British Columbia, 2010 Master of Applied Science, Civil Engineering, University of British Columbia, 2006 Bachelor's degree (ABET Equivalent), Civil & Environmental Engineering, United Arab Emirates University, 2003 El-Basyouny's research pioneers the integration of remote sensing, machine learning, and statistical modeling to enhance transportation safety. His work develops automated tools for infrastructure digitization, collision prediction, and speed management, treating safety as a systemic product requiring management frameworks. Key contributions include LiDAR-based road feature extraction, network-level safety evaluations, and frameworks for vision-zero outcomes that address both human-driven and autonomous vehicle contexts. His recent publications demonstrate a cohesive research trajectory centered on leveraging point cloud data and computational intelligence for safety management. Over 15 major publications since 2021 focus on automated infrastructure assessment (light pole detection, clear zone mapping, vertical clearance evaluation), weather-impact modeling, and enforcement resource optimization. This body of work bridges transportation engineering with computer vision and operations research to create scalable safety solutions. His scientific contributions have been recognized with prestigious honors including: 2024 Killam Annual Professorship Award 2024 Road Safety Achievement Award from TAC 2023 Donald Stanley Award for environmental engineering 2022 Faculty of Engineering Graduate Teaching Award 2021 Daniel B. Fambro Student Paper Award As an academic leader, El-Basyouny actively mentors graduate students and secures significant research funding through his endowed chair position. He currently recruits fully-funded PhD and postdoctoral candidates specializing in remote sensing applications, machine learning, and geomatics for road digitization projects. His research group collaborates with national safety committees and municipal agencies to translate findings into policy, while he serves on editorial boards for Transportation Research Record and Analytic Methods in Accident Research. The research group operates at the intersection of transportation engineering and computational science, developing automated frameworks that merge sensor technologies with data processing tools. Current projects focus on semantic segmentation of 3D point clouds, safety implications of infrastructure digitization, and machine learning applications for road feature extraction in both urban and rural environments.
Sean Lubner is Core Faculty at the Boston University Institute for Global Sustainability (IGS) and Assistant Professor in Mechanical Engineering within the College of Engineering. He holds a PhD from UC Berkeley and BS degrees in Mechanical Engineering and Applied Physics from Carnegie Mellon University. His research focuses on energy transport and storage systems, including thermal energy storage, battery diagnostics, and CO₂ capture technologies. Education: PhD in Mechanical Engineering, UC Berkeley (NSF Fellow) BS in Mechanical Engineering & Applied Physics, Carnegie Mellon University Research Interests: Lubner specializes in grid-scale thermal energy storage, non-invasive sensors for harsh environments, and decarbonization strategies. His work integrates machine learning with materials science to develop advanced energy systems. He collaborates with industry on patents involving battery safety, photonic surfaces, and phase change materials. Article Trends: Recent publications emphasize high-temperature materials, battery failure prediction via thermal signatures, and femtosecond laser processing for photonic surfaces. His work bridges nanoscale phenomena with macro-scale energy systems, leveraging interdisciplinary methods. Awards: Lubner was an NSF Graduate Research Fellow during his PhD. Advising & Grants: While no advisees are listed, his research is supported by industry partnerships and grants focusing on energy storage innovation. He leads the Lubner Group, which develops novel sensing and storage technologies. Labs/Teams: The Lubner Group at BU focuses on sustainable energy solutions, combining experimental and computational approaches to address climate challenges.
Professor Thomas Lukasiewicz is a Full Professor and Head of the Artificial Intelligence Techniques research group at the Faculty of Informatics, Vienna University of Technology (TU Wien). His research focuses on enabling machines to mimic human-like intelligence through techniques spanning deep learning, symbolic reasoning, and predictive coding. Key areas include explainable AI, hybrid neurosymbolic systems, and applications in healthcare and law. He teaches courses such as Deep Learning for Natural Language Processing, Scientific Research and Writing, and multiple seminars in artificial intelligence and knowledge representation. His research projects include Explainable AI in Healthcare (2023–2027) and foundational work on predictive coding networks. His publications (15+ recent articles) address medical image segmentation, neurosymbolic frameworks, and language model evaluation in mathematics. Notable work includes neurosymbolic hybrid models (CCN⁺), reinforcement learning for medical report generation, and theoretical foundations of predictive coding networks.
Yiguang Ju is the Robert Porter Patterson Professor of Mechanical and Aerospace Engineering at Princeton University, affiliated with the HMEI Grand Challenges Program. His research focuses on plasma-assisted combustion, alternative fuels, and nano-material synthesis via flame processes. He investigates energy-efficient systems for microscale energy conversion, catalytic reactions, and low-temperature plasma chemistry. Research interests include non-equilibrium plasma dynamics, ammonia synthesis, and high-pressure oxidation kinetics. He develops advanced diagnostics like hybrid laser spectroscopy and machine learning models to study reaction mechanisms. Recent work explores plasma-enhanced combustion for hydrogen and alternative fuels, with applications in energy storage and emission reduction. His studies address challenges in plasma-chemistry interactions, material synthesis, and high-pressure combustion systems. His articles highlight innovations in plasma catalysis, combustion kinetics, and atmospheric chemistry. Collaborative projects include plasma-based material recycling and supercritical-pressure reactor analysis. He leads initiatives in clean energy technologies and sustainable chemical processes.
Ki-Woong Park is a tenure-track full Professor in the Department of Computer and Information Security at Sejong University. He leads the System Security and Computer Engineering Research (SysCore) Lab, which focuses on system security research with numerous ongoing projects funded by major Korean research institutions including IITP, NRF, and KRIT. Sejong University, Department of Computer and Information Security System Security and Computer Engineering Research (SysCore) Lab Leader Member of IEEE, IEEE Computer Society, and ACM Education: Ph.D. in Electrical Engineering & Computer Science, KAIST (Advisor: Prof. Kyu-Ho Park) M.S. in Electrical Engineering & Computer Science, KAIST (Advisor: Prof. Kyu-Ho Park) B.S. in Computer Science, Yonsei University (Summa Cum Laude) Exchange Student at University of California, Los Angeles (UCLA) Professor Park's research focuses on designing, building, and analyzing secure systems, particularly for cloud computing, networked systems, and embedded systems. His work often involves reevaluating existing security mechanisms and actual system implementations with subsequent evaluation in real computing environments. He has made significant contributions to areas including cloud security, IoT security, ransomware detection, moving target defense, and metaverse security. His research approach emphasizes both theoretical foundations and practical implementation, with numerous publications in top-tier security and systems venues. His recent publications (2023-2024) demonstrate a strong focus on emerging security challenges in modern computing environments, particularly in metaverse platforms, UAV systems, and edge computing. These works span both theoretical security frameworks and practical implementations, with an emphasis on visualization techniques, hardware-based security mechanisms, and AI-enhanced security analysis. His research shows a clear progression from traditional cloud and network security toward next-generation security challenges in immersive virtual environments and cyber-physical systems. Scientific Awards: Microsoft Research Fellowship (2009-2010) Best Poster Gold Award at WISA 2020 Best Paper Award at MobiSec'18 Professor Park actively mentors numerous graduate and undergraduate students through the SysCore Lab, with current members including Ph.D. students, MS students, and undergraduate researchers. His research is supported by multiple significant grants, including the NRF Outstanding Researcher-Mid-career Researcher project, IITP Information Security Core Source Technology Development, and Defense Technology Advancement Research Institute projects. These grants total tens of billions of Korean won and address critical national security challenges in cyber defense, cloud security, and metaverse technologies. The SysCore Lab, under Professor Park's leadership, maintains a strong industry and government collaboration network, with part-time researchers from organizations including Hyundai Duty Free, Astron Security, Korea University, and various military cyber commands. This unique structure enables the lab to address both theoretical security challenges and practical implementation issues in real-world systems.
Jean-Pierre Fouque is a Professor in the Department of Statistics and Applied Probability (PSTAT) at the University of California, Santa Barbara. His research focuses on stochastic processes, financial mathematics, systemic risk, and reinforcement learning, with a particular emphasis on mean field games and multi-scale stochastic models. He explores applications in portfolio optimization, risk management, and algorithmic finance. His work combines theoretical advancements in stochastic analysis with practical applications in economics and finance. Notable contributions include developing models for systemic risk in financial networks, analyzing reinforcement learning algorithms in mean-field frameworks, and studying stochastic volatility effects in derivatives pricing. Recent research trends include integrating deep learning techniques for systemic risk quantification, advancing multi-scale asymptotic methods for portfolio optimization, and investigating strategic interactions in financial systems using game-theoretic approaches. His publications frequently address topics such as stochastic volatility calibration, optimal investment strategies under uncertainty, and the dynamics of financial markets under stress scenarios. Dr. Fouque has contributed to foundational textbooks and edited volumes on systemic risk and mean field games. His interdisciplinary work bridges probability theory, mathematical finance, and computational methods, impacting both academic research and practical risk management practices.
Oussama Damen is a Professor at the University of Waterloo's Department of Electrical and Computer Engineering. His research focuses on advanced wireless communication systems, particularly in MIMO (Multiple-Input Multiple-Output) systems, signal processing, and machine learning applications in telecommunications. He is actively involved in developing innovative solutions for beamforming, hybrid precoding, and distributed decoding in massive MIMO and millimeter-wave networks. Damen's work also extends to optical fiber communication, federated learning in wireless systems, and optimization of resource allocation in next-generation networks like 5G/6G. His research interests include wireless communication theory, antenna system design, channel modeling, and algorithm development for improving spectral and energy efficiency. He has contributed extensively to the theoretical foundations of MIMO detection, lattice reduction techniques, and statistical signal processing methods. Notable trends in his publications emphasize bridging theoretical performance limits with practical implementations, particularly in scenarios involving channel impairments, limited backhaul capacity, and multi-core fiber transmission. His work often addresses fairness and optimization in distributed systems, including federated learning frameworks and hybrid beamforming architectures. No scientific awards or grants are explicitly mentioned in the provided information. Damen has advised no listed students, and no specific lab affiliations are noted.
Stanislaw Jarecki is an Associate Professor of Computer Science at the Donald Bren School of Information and Computer Sciences (ICS) at the University of California, Irvine (UCI). He joined UCI in 2003 after earning his Ph.D. in Computer Science from MIT in 2001 under Prof. Shafi Goldwasser. His research focuses on applied and distributed cryptography, with significant contributions to threshold cryptography, secure computation, and password-authenticated key exchange (PAKE) protocols such as OPAQUE, which secures 2 billion WhatsApp users. He also worked at Intertrust’s StarLab and Stanford’s applied cryptography group under Prof. Dan Boneh. Education: Ph.D., Massachusetts Institute of Technology, 2001. Research Interests: Distributed cryptography, secure multi-party computation, privacy-preserving protocols, threshold security, blockchain applications, and efficient cryptographic primitives. His work emphasizes practical solutions for real-world systems, including protocols resilient to server compromises and scalable encryption methods. Awards: 2023 IACR Fellow for contributions to distributed cryptography and efficient secure computation. Advising and Grants: Jarecki’s research has been supported by grants including NSF SaTC programs. He advises students on cryptographic protocol design and security mechanisms. His work on OPAQUE and CHIC protocols exemplifies his focus on bridging theoretical cryptography with practical implementation. Labs/Teams: Leads a research group focused on applied cryptography and security within ICS at UCI, collaborating on projects like secure computation, privacy-preserving data processing, and cryptographic protocol development.
Mohammadreza Karamad is an Assistant Professor in the School of Sustainable Energy Engineering at Simon Fraser University (SFU), with a joint appointment in the Sustainable Energy Engineering department. His research focuses on computational materials discovery, leveraging quantum-mechanical methods (e.g., DFT) and machine learning (ML) to design advanced energy materials for clean technologies like hydrogen storage and catalysis. He holds a Ph.D. from the Technical University of Denmark (DTU) and completed postdoctoral research at Stanford University. His academic background includes leadership roles in the CMD Lab (Computational Materials Discovery), where he explores novel materials for electrochemical energy conversion processes. Key research areas include electrochemistry, heterogeneous catalysis, and material science, with a particular emphasis on CO2 reduction, ammonia synthesis, and sustainable energy storage solutions. Dr. Karamad collaborates with industry and academic partners to advance materials discovery through high-throughput computational screening and AI-driven approaches. He actively seeks motivated students (undergraduate and graduate) to join his research program, focusing on developing next-generation energy materials. His lab is located in room B8220, and he can be reached at mkaramad@sfu.ca. Notable technical contributions include pioneering work on transition metal nitrides for CO2 reduction, single-atom catalysts for ammonia synthesis, and machine learning frameworks for predicting material properties. His research bridges fundamental theory with practical applications, addressing global challenges in sustainable energy and environmental technology.
Dr. Mark Gardner is a Research Fellow in Clinical Imaging at the ACRF Image X Institute, part of the University of Sydney's Sydney School of Health Sciences and Faculty of Medicine and Health. His work focuses on advancing radiation therapy and medical imaging technologies, with particular emphasis on improving treatment accuracy and patient comfort. Gardner holds a PhD from Flinders University, completed in collaboration with the Medical Device Research Institute, and has held research roles at the Cystic Fibrosis Airway Research Group (CFARG). His current projects include the Nano-X radiation therapy device and the Remove the Mask initiative , which aims to eliminate immobilization masks in head and neck cancer treatments. Gardner is affiliated with organizations like the IEEE Engineering in Medicine and Biology Society and the American Association of Physicists in Medicine. Research interests span radiation oncology, translational research in medical imaging, and device innovation. His work integrates advanced imaging techniques (e.g., synchrotron X-rays, cone-beam CT) with machine learning and wearable sensors to address challenges in respiratory therapy and tumor targeting. Notable contributions include developing real-time motion tracking for radiation therapy and improving mucociliary transport measurements. Awards: FameLab 2020 State Finalist, 2018 Medtech e-Challenge Winner, 2017 3MT Runner-Up Grants/Projects: Nano-X radiation therapy development, Remove-the-Mask surface-guided system Collaborations: Industry partnerships, multi-institutional research networks Gardner advises Chen Cheng on real-time head/neck motion monitoring during radiation therapy. His lab contributes to open-source tools and preclinical imaging advancements, bridging engineering and clinical oncology.
Laura Albert is a Professor of Industrial & Systems Engineering at the University of Wisconsin-Madison and former David H. Gustafson Department Chair (2021-24). She serves as a Fellow of AAAS and IISE, and previously held the INFORMS presidency. Her research focuses on optimizing public-sector systems, with applications in critical infrastructure protection, emergency response, and cybersecurity. She advocates for public engagement through op-eds and blogs like 'Punk Rock Operations Research.' Education: PhD in Industrial Engineering, University of Illinois at Urbana-Champaign (2006) MS in Industrial Engineering, University of Illinois at Urbana-Champaign (2001) BS in Industrial Engineering, University of Illinois at Urbana-Champaign (2000) Research Interests: Dr. Albert’s work bridges operations research and public policy, addressing challenges in emergency medical services, voting system security, law enforcement strategies, and opioid treatment diversion programs. She employs mathematical optimization, game theory, and stochastic modeling to design resilient systems. Awards: INFORMS Impact Prize (2018) NSF CAREER Award (2011) Fulbright Award (2020) AAAS Fellow (2020) Advising & Grants: While explicit student names aren’t listed, her research has been supported by grants from the NSF, Army Research Office, and other agencies. She advises on interdisciplinary projects involving public health, cybersecurity, and disaster management. Labs/Teams: Engaged in collaborative efforts across the College of Engineering, including affiliations with the Electrical & Computer Engineering department. Leads initiatives on resilient infrastructure and emergency response systems.
Dongwoo Kim is a researcher affiliated with Hanyang University, ERICA Campus (Department of Electronics and Communication Engineering) and has previously collaborated with institutions like POSTECH , Chungnam National University , and Microsoft . His work spans interdisciplinary domains in Computer Science and Engineering . Hanyang University, ERICA Campus - Department of Electronics and Communication Engineering POSTECH - Power Analog Electronics & Semiconductor Devices Lab Microsoft Chungnam National University Kim's research focuses on formal verification of automotive control software, deep learning applications in environmental monitoring, 3D modeling for indoor positioning, and machine learning for signal processing. His recent publications highlight advancements in graph neural networks (GNNs), including analyzing oversmoothing and gradient dynamics, as well as developing geometric vision-language models with domain-agnostic encoders. His 15 most recent articles (2023-2025) address topics like: Optimizing hybrid electric vehicle engine performance 3D modeling for indoor localization GNN training stability UAV-based environmental monitoring Algorithm difficulty prediction for programming problems Millimeter-wave antenna design Kim collaborates with researchers in software engineering , signal processing , and environmental science domains. His work intersects formal methods , applied machine learning , and embedded systems research.
Thomas Winkler is an Associate Professor at the Division of Micro and Nanosystems, KTH Royal Institute of Technology, Sweden, and collaborates with TU Braunschweig, Germany. His research focuses on solving life science challenges using microsystems tools, particularly in neuropsychiatric disorders like schizophrenia. He develops organ-on-chip models, engineered microfluidic platforms, and biosensors for point-of-care diagnostics. Winkler leads an interdisciplinary ERC-funded team addressing metabolic coupling in neurovascular units and oxidative stress biomarkers. Key achievements include the ERC Starting Grant (2023) and work on electrochemical sensors for clozapine monitoring. He teaches courses such as Microsystem Technology (EK2350) and supervises PhD and postdoctoral researchers. Current projects include machine learning-guided robotic organoid maturation and electrochemical technology development for the CHIPzophrenia initiative. His lab actively seeks talent through open positions in Stockholm and Braunschweig. Scientific awards include the ERC Starting Grant and Marie Skłodowska-Curie Actions Fellowship. Research spans sensor development, microfabrication, and biomaterials, with a focus on translating lab technologies to clinical applications. Collaborations bridge engineering and life sciences, emphasizing personalized mental healthcare solutions.
