Hina Tabassum is an Associate Professor at the Lassonde School of Engineering , York University, Canada, specializing in 5G/6G wireless communications and sensing applications. She was awarded the York Research Chair in 2023 for her work in 5G/6G-enabled mobility and sensing. Areas of Expertise: THz communications, WiFi sensing, and multi-band wireless systems Editorial Roles: Area Editor for IEEE OJCOMS and Associate Editor for multiple IEEE journals Research Focus : Ultra-reliable low-latency communication Reconfigurable intelligent surfaces (STAR-RIS) Machine-type communications Energy-efficient network design Scientific Recognition : Stanford's Top 2% World Researchers (2021-2024) Lassonde Innovation Early-Career Researcher Award (2023) N2Women Rising Stars (2022) Multiple IEEE Exemplary Editor/Reviewer Awards
Biresh Kumar Joardar is an Assistant Professor in the Electrical and Computer Engineering Department at the University of Houston's Cullen College of Engineering. He holds a BE from Jadavpur University (2016) and PhD from Washington State University (2020), with postdoctoral training at Duke University as a Computing Innovation Fellow. His research integrates machine learning with hardware design to develop efficient deep learning accelerators, ReRAM-based architectures, and heterogeneous manycore systems. Current projects focus on enhancing reliability, security, and performance of AI hardware through in-memory computing and 3D integration techniques. Research themes include hardware security (e.g., Rowhammer mitigation), fault-tolerant neural network training, and hardware-software co-design for bioinformatics. Recent articles explore energy-efficient architectures for graph neural networks and cross-layer optimization for AI workloads. Awards: Best Paper Award, International Symposium on Networks-on-Chip (NOCS 2019) Joardar leads the Heterogeneous and In-Memory Computing Lab, seeking PhD students with backgrounds in VLSI, computer architecture, or machine learning. His work has been supported by NSF and industry partnerships.
Fei Liu is an Assistant Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. His research focuses on surgical robotics, medical robotics, and control systems. He holds a PhD in Robotics from the University of Lyon (INSA de Lyon), France, an MSc in Control Systems and Automation Engineering from INSA de Lyon, and a BSc in Control Systems and Automation Engineering from Northwestern Polytechnical University, China. Fei's research interests include autonomous robotic systems, deformable object manipulation, and perception frameworks for surgical applications. His work emphasizes bridging real-world and simulation environments through advanced modeling and control techniques. Recent projects involve optimizing robotic actions using multi-modal demonstrations, improving tool-tissue interaction tracking, and developing frameworks for boundary parameter estimation in surgical settings. His articles highlight contributions to surgical robotics, including real-to-sim matching of deformable tissues, autonomous suturing, and trajectory optimization for wound care. He has also explored applications in haptic training systems and medical telerobotics. Fei's work often combines machine learning, physics-based simulation, and real-time control to address challenges in robotic surgery. Fei is affiliated with the Tickle College of Engineering and maintains an active research profile with collaborations in robotics and medical engineering domains. His lab focuses on advancing robotic autonomy in healthcare environments through interdisciplinary approaches.
Dr. Ali Nazemi is an Associate Professor in the Department of Building, Civil, and Environmental Engineering at Concordia University, where he joined in 2015 as a Strategic Hire in Water Resources. He holds adjunct appointments at the University of Saskatchewan's School of Environment and Sustainability and is an Associate Member of the Global Institute for Water Security. His foundational education includes a PhD from the University of Birmingham (UK), an MSc from Ferdowsi University of Mashhad (Iran), and a BSc from KNT University of Technology (Iran). Dr. Nazemi's research advances methodologies for water security challenges under climate change, focusing on: Hydrological modeling and algorithm development Climate change vulnerability assessments Coupled human-water systems Hydroclimatic data diagnostics His recent publications predominantly explore climate model integration, uncertainty quantification in water systems, and hybrid modeling approaches, with consistent themes of climate adaptation and risk management across Canadian and international contexts. He has been recognized with several awards, including: Dorothy Hodgkin Postgraduate Award (UK government, 2005–2009) Best Presentation Award at IEEE World Congress on Computational Intelligence (2006) Post Graduate Teaching Award (University of Birmingham, 2004–2007) Dr. Nazemi actively supervises graduate students through his Water Security and Climate Change (WSCC) lab, with projects on climate downscaling, vulnerability assessment tools, and coupled human-water systems. He leads research collaborations with the Saskatchewan Water Security Agency, NSERC's Changing Cold Region Network, and the Global Water and Energy Cycle Experiment.
Aldeida Aleti is a Professor in the Department of Software Systems & Cybersecurity at Monash University. Her research focuses on Automated Software Engineering, leveraging AI and optimization techniques for tasks like software design, testing, and repair. She has held roles including Chief Examiner for units like FIT4002 and FIT5136, and has contributed to teaching FIT3077 and FIT1008. Education: PhD in Software Engineering (Swinburne University of Technology, 2012), Master of Computer Engineering (Polytechnic University of Tirana, 2008), and Bachelor Honours in Computer Engineering (Yildiz Technical University, 2005). Research Interests: Automated software engineering, fitness landscape analysis, optimization, and search-based techniques. She leads projects like RAISE (Responsible AI Software Engineering) and collaborates on quantum computing and healthcare AI initiatives. Awards include the FIT Dean's Award (2016), Best Paper Awards (2015, 2011), and the Heidelberg Laureate Forum invitation (2014). She has been a grant assessor for the Australian Research Council since 2015. Advising: Accepting PhD students in AI-driven software engineering, optimization, and automated testing. Active in committees like the Faculty Research Committee and Early Career Researcher committee.
Nathan Young is an Assistant Professor in the Department of Sustainable Resources Management at SUNY College of Environmental Science and Forestry (ESF). His research focuses on physical hydrology and environmental science, with a particular emphasis on understanding how heat, water, and solutes move through the environment in response to climate change and landscape alterations such as permafrost thaw, sea-level rise, and environmental hazards like wildfires and landslides. He integrates fieldwork, numerical modeling, and quantitative analysis to improve predictive models for sustainable water management. Education includes a Ph.D. in Geology and Environmental Science (2019, Iowa State University), an M.S. in Earth and Environmental Science (2014, Wright State University), and a B.A. in Geology and Sociology/Anthropology (2012, Earlham College). Research interests span hydrogeology, climate change impacts, permafrost dynamics, and groundwater flow modeling. His work often involves advanced tools like MATLAB, R, Python, and numerical models such as MODFLOW and HydroGeoSphere. He emphasizes fieldwork in student mentorship, valuing hands-on experience and quantitative skills. Teaching includes courses like Watershed Hydrology (FOR 340) and Watershed Ecology and Management (FOR 442). Current advisee Ford Ford is pursuing an MS in Environmental Science. Awarded funded graduate assistantships and undergraduate research positions are available to qualified candidates with academic excellence, research experience, and enthusiasm for hydrological studies. His lab focuses on cryohydrogeological systems, particularly in Arctic regions like Nunavik, Québec, and investigates the interplay between thermal regimes, permafrost degradation, and subsurface hydrology.
Susan D. Rozelle is a Professor of Law at Stetson University College of Law , where she has taught since 2009. She holds a B.A. from the University of Central Florida and a J.D. from Duke University , and her expertise lies in criminal responsibility, death penalty jurisprudence , and legal reform . Her courses include Criminal Law, Evidence, Criminal Adjudication , and the Criminal Responsibility Seminar . Prior academic appointments: University of Oregon, Seattle University, Capital University Clerkships: Massachusetts Appeals Court, Supreme Judicial Court of Massachusetts Pro bono work: Co-authoring amicus briefs in Uttecht v. Brown , Flores-Figueroa v. US , and Blueford v. Arkansas Leadership roles: Past chair of AALS Criminal Justice Section, current chair of AALS New Law Professors Teaching Materials Network Her research focuses on cognitive bias in intentional homicide law , gender dynamics in criminal cases , and structural flaws in capital punishment . Her work has been cited by the New York Governor's Council on Capital Punishment and the Law Commission for England and Wales . Five of her articles were among the top 10 downloads from SSRN in Criminal Law, Criminal Procedure , and Evidence Law categories. She has presented at conferences at Georgetown University Law Center , University of California at Berkeley , and others. Media appearances include ABC, CBS, NBC, FOX news and NPR , where she discusses legal issues as a commentator.
Wafi Danesh is an Assistant Professor in the Department of Engineering Programs at SUNY New Paltz, part of the School of Science & Engineering. He holds a PhD in Electrical and Computer Engineering from the University of Missouri Kansas City (2022). Prior to academia, he served as a Senior Engineer I - Design at Microchip Technology Inc. (2022-2023). His research focuses on hardware security, leveraging machine learning for FPGA Trojan detection and secure 3D IC design. Teaching interests include System-on-Chip Design, Digital Logic Fundamentals, and Computer Architecture. Education: PhD in Electrical and Computer Engineering, University of Missouri Kansas City, 2022 Research Interests: Dr. Danesh explores cutting-edge methods to enhance hardware security, including AI-driven approaches for IoT device protection and thermal management in 3D integrated circuits. His work bridges machine learning and physical hardware vulnerabilities, emphasizing FPGA security and PUF-based solutions for wireless systems. Publications Trends: His articles span FPGA Trojan detection via NLP and unsupervised learning, thermal challenges in 3D ICs, and neuromorphic computing innovations. Recent work highlights automated security tools and multi-valued computing for energy efficiency. Awards: None explicitly listed in the provided materials. Advising & Grants: No formal advisees or grants are mentioned. His professional activities center on research and teaching.
Roland N. Horne is the Thomas Davies Barrow Professor of Earth Sciences at Stanford University and Senior Fellow at the Precourt Institute for Energy. He holds positions in the Department of Energy Science & Engineering and is an Affiliate at the Stanford Woods Institute for the Environment. With degrees from the University of Auckland (BE, PhD, DSc), Horne has established himself as a leading expert in geothermal reservoir engineering and energy production optimization. His research focuses on inverse problems in reservoir modeling, including tracer analysis of fractures, computer-aided well test analysis, production schedule optimization, and automated history matching. Horne has made significant contributions to understanding geothermal reservoir engineering and multiphase flow of boiling fluids through porous materials and fractures. The analysis of his recent publications (2023-2025) reveals a strong emphasis on enhanced geothermal systems (EGS), with particular focus on flexible operations, economic modeling, and advanced characterization techniques. His work increasingly incorporates machine learning approaches for reservoir analysis and has expanded into microbial tracing methods for interwell connectivity assessment. There's also significant attention to US geothermal resource potential and integration into the broader energy transition. Honorary Member of the Society of Petroleum Engineers Member of the US National Academy of Engineering Multiple SPE Distinguished Lecturer appointments (1998, 2009, 2020) John Franklin Carl Award recipient Five Best Paper awards from Geothermal Resources Council Patricius Medal from German Geothermal Society Core Values Award from Women in Geothermal (2023) Horne has supervised 60 PhD and 135 MS students throughout his career. His current teaching includes undergraduate and graduate courses in Fundamentals of Energy Processes, Geothermal Reservoir Engineering, Mass and Energy Transport in Porous Media, and Well Test Analysis. He previously served as President of the International Geothermal Association (2010-2013) and Technical Program Chair for multiple World Geothermal Congress events. Horne maintains active research collaborations worldwide, including with the University of Tokyo (where he was a Fellow of the School of Engineering in 2016) and China University of Petroleum. His current research group focuses on advancing EGS technologies and developing more accurate reservoir characterization methods for geothermal applications.
Akshay Narayan is a Senior Lecturer (Educator Track) at the School of Computing, National University of Singapore (NUS), where he teaches senior undergraduate and graduate-level courses in AI Planning and Decision Making, as well as introductory and intermediate-level Software Engineering courses. Education: Ph.D. in Computer Science from National University of Singapore (completed in 2020) M.Tech. in Information Technology from International Institute of Information Technology Bangalore, India B.E. in Computer Science & Engineering from Visveswaraya Technological University, India Research Interests: Dr. Narayan's research spans multiple domains within computer science with a primary focus on artificial intelligence and its applications. His current research centers on transfer learning in reinforcement learning, multi-agent decision making, and AI planning. He has also made significant contributions to cloud computing research, particularly in areas such as smart metering, chargeback systems, power-aware cloud metering, and workload analysis for virtual machine sizing. His work bridges theoretical foundations with practical applications, addressing real-world challenges in computing systems. He has recently expanded his research to include technology in education, exploring how AI can be integrated into teaching and learning processes. Publication Trends: Dr. Narayan's publication record demonstrates a clear evolution from foundational work in cloud computing to more recent explorations in reinforcement learning and AI education. His early work focused on practical applications in cloud systems, including smart metering and QoS monitoring. More recently, his research has shifted toward AI planning, decision making, and the educational applications of AI. This progression shows his ability to adapt to emerging fields while maintaining a strong foundation in systems research. Awards and Recognition: Teaching and Mentoring: Dr. Narayan teaches a variety of courses at NUS including CS2113 Software Engineering & Object-Oriented Programming, CS3219 Software Engineering Principles and Patterns, CS3268 Responsible AI: From Algorithms to Impact, and IT5100F Industry Readiness: Data Analytics and AI in Practice. He has also taught CS4246/CS5446 AI Planning and Decision Making. His teaching approach integrates his research expertise with practical applications, providing students with both theoretical foundations and hands-on experience. He has taught these courses across multiple academic years from AY-2013/14 through AY-2020/21. Research Groups and Collaborations: Dr. Narayan has collaborated with researchers across multiple institutions, including work with Prof. Tze Yun Leong at NUS (his PhD advisor), Shrisha Rao, Zhuoru Li, and others. His research has often involved interdisciplinary collaborations that bridge theoretical computer science with practical system implementations.
Taylor Johnson is an Associate Professor of Computer Science and Electrical and Computer Engineering at Vanderbilt University's School of Engineering. He directs the Verification and Validation for Intelligent and Trustworthy Autonomy Laboratory (VeriVITAL) and serves as a Senior Research Scientist in the Institute for Software Integrated Systems. Previously, he was an Assistant Professor at the University of Texas at Arlington from 2013 to 2016. His research focuses on formal verification techniques for cyber-physical systems (CPS), emphasizing safety, reliability, and security through hybrid systems, formal methods, and control theory. He has published extensively on neural network verification, earning best paper awards and recognition from IEEE, IFIP, and ACM. Education: Ph.D., Electrical and Computer Engineering (University of Illinois at Urbana-Champaign, 2013) M.Sc., Electrical and Computer Engineering (University of Illinois at Urbana-Champaign, 2010) B.S.E.E., Electrical and Computer Engineering (Rice University, 2008) Research Interests: Formal verification of neural networks and CPS, safety-critical systems, autonomous systems, and AI/ML security. His work bridges theoretical foundations (e.g., hybrid systems) with practical applications in aerospace, energy systems, and robotics. Key Contributions: Developed the NNV tool for neural network verification, led the Verification of Neural Networks Competition (VNN-COMP), and pioneered techniques for robust federated learning and malware detection. Awards: AFOSR YIP Award (2016), NSF CRII Award (2015), and multiple best paper honors. His research is funded by AFRL, NSF, Intel, NVIDIA, and industry partners. Labs & Collaborations: VeriVITAL Lab (Vanderbilt), collaborations with United Technologies Research Center, Boeing, and Toyota.
Dr. Farhad Maleki is an Assistant Professor in the Department of Computer Science at the University of Calgary, Faculty of Science. He holds a PhD in Computer Science from the University of Saskatchewan (2019). His postdoctoral research at McGill University’s Augmented Intelligence & Precision Health Laboratory focused on machine learning for medical image analysis. He has held leadership roles, including President of the Association of Postdoctoral Fellows at McGill and President of the Computer Science Graduate Council at the University of Saskatchewan. Currently, he serves on the Machine Learning Education Sub-Committee of the Society for Imaging Informatics in Medicine and as a guest editor for journals in medical data analysis. Dr. Maleki’s research spans Artificial Intelligence , Machine Learning , Biomedical Data Analysis , and Computer Vision . His work emphasizes medical applications, including tumor segmentation, clinical outcome prediction, and AI-driven diagnostics in oncology and cardiology. He also explores agricultural challenges, such as wheat head segmentation using generative models and domain adaptation. Key contributions include developing robust medical imaging tools (e.g., Rel-UNet for tumor segmentation) and frameworks for evaluating AI model reliability ( RIDGE ). His work bridges clinical needs with computational innovation, addressing issues like reproducibility, generalizability, and low-annotation learning across healthcare and agriculture domains. Dr. Maleki’s articles focus on advancing AI methods for precision health and agriculture. His recent work highlights interdisciplinary applications, such as integrating clinical and pathology data for cancer survival prediction, optimizing radiation therapy using Bayesian methods, and leveraging synthetic data for crop phenotyping. These studies emphasize practical deployment and ethical considerations in AI adoption.
Prof. Joaquin GARCIA ALFARO is a Professor at Telecom SudParis, affiliated with the SCN department. His research focuses on cybersecurity, network security, quantum computing applications, and resilience engineering in cyber-physical systems. He has contributed to advancements in intrusion detection systems, blockchain integration in cellular networks, and privacy-preserving frameworks for IoT and healthcare. University: Telecom SudParis Key Research Areas: Cybersecurity, Quantum Computing, IoT Security, Resilience Engineering Labs: SAMOVAR laboratory His work emphasizes practical solutions for real-world challenges, including secure data provenance, digital twin implementations, and energy-efficient edge computing. Recent research explores quantum-resistant protocols and collaborative drone systems.
Wiebke Meesenburg is an Assistant Professor in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU), specializing in Thermal Energy. She is actively involved in research on large-scale heat pump systems, district heating integration, and digital twin applications for energy optimization. Her research focuses on sustainable thermal energy systems, particularly the design, monitoring, and optimization of heat pumps in district heating networks. Key areas include dynamic modeling, real-time adaptation, fouling mitigation, and the integration of renewable energy sources. She contributes to advancing energy efficiency and sustainability in urban infrastructure. The recent publications highlight a strong trend toward digitalization and optimization of thermal systems, with an emphasis on model-based monitoring, digital twins, and operation scheduling using advanced algorithms. Her work bridges mechanical engineering, energy systems, and computational modeling to improve system performance and reliability. She has supervised PhD research and contributed to major projects such as the implementation of digital twins for heat pump systems and EnergyLab Nordhavn. Collaborations involve key figures in energy research at DTU, including Professor Brian Elmegaard. While no formal awards are listed, her active participation in conferences and project leadership demonstrates recognition in her field. Wiebke Meesenburg has been involved in organizing and presenting at international events, including the 35th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems and workshops on Modelica and flexible heat supply. Her work is embedded in interdisciplinary teams focused on future energy infrastructures and smart urban energy systems.
Bruce Stephen is a Senior Lecturer and Strathclyde Chancellor's Fellow in the Department of Electronic and Electrical Engineering at the University of Strathclyde, where he has been since 1999. His work lies at the intersection of data science and power systems engineering, with a strong focus on real-world industrial applications. His educational background includes a BSc in Aeronautical Engineering from the University of Glasgow (1997), an MSc from the University of Strathclyde (1998), and a PhD in Electronic and Electrical Engineering (2005) from the University of Strathclyde. Dr. Stephen's research centers on data-driven methodologies for solving complex engineering challenges in power systems, particularly under conditions of limited data or domain knowledge. His applications span the entire energy value chain—from generation (nuclear, wind, solar) to transmission, distribution, and end-use. He develops software solutions for condition assessment, anomaly detection, and predictive modeling to support asset management and future grid planning. Notably, he co-founded Silent Herdsman Ltd, a spin-out company applying intelligent systems to precision livestock farming. His recent publications highlight a strong trend toward advanced machine learning techniques such as transfer learning, surrogate modeling, and synthetic data generation (e.g., using CTGANs) to improve reliability and decision-making in power systems. These works emphasize explainability, uncertainty quantification, and scalability, particularly in renewable-rich and data-scarce environments. Dr. Stephen is currently the Principal Investigator on the EPSRC-funded Analytical Middleware for Informed Distribution Networks (AMIDiNe) project, aiming to identify barriers to Net Zero through improved data modeling of unmonitored networks. He has also contributed to major projects including EU FP7 ORIGIN, EPSRC APAtSCHE, AGILE, and Transactive Energy Supply Arrangements. He actively advises students and collaborates on interdisciplinary research. His professional activities include organizing the QFF Quarterly Forecasting Forum (2018) and delivering invited talks at industry workshops. He has supervised datasets and research involving structural health monitoring and industrial diagnostics. His work supports UN Sustainable Development Goals related to affordable and clean energy, industry innovation, and climate action.
