Rajeev Balasubramonian is a Professor and Associate Director at the School of Computing, University of Utah. He specializes in computer architecture, with a focus on memory systems, emerging technologies, and energy-efficient computing. His research addresses challenges in DRAM/NVM architectures, security, and acceleration for big data and machine learning workloads. Education: PhD in Computer Science (University of Rochester, 2003), M.S. (University of Rochester, 2000), B.Tech in Computer Science (IIT Bombay, 1998). Research Interests: Memory reliability, near-data processing, cache hierarchies, transactional memory, and hardware-software co-design for emerging technologies. He has led projects on crossbar accelerators, secure memory systems, and resistive memory architectures. Recent Trends in Publications: Focus on encrypted inference (Hyena), data prefetching (PATHFINDER), and neuromorphic computing (SpinalFlow). His work bridges hardware and software, emphasizing practical acceleration and security solutions. Awards: IEEE Fellow (2021), Google Faculty Awards (2019/2020), Intel Research Award (2017), and multiple best paper awards (ISCA, ISPASS, PACT). Grants & Students: Over $4M in NSF/industry funding. Advised 15+ PhD students (e.g., Ali Shafiee, Karl Taht) and currently mentors researchers in resistive memory and security accelerators. His lab includes teams like Utah Arch Research Group. Labs & Teams: Leads the Utah Arch Research Group , organizing workshops on near-data processing and memory systems (e.g., ISCA, HPCA).
Kash Barker serves as the John A. Myers Professor and David L. Boren Professor at the University of Oklahoma in the Department of Industrial & Systems Engineering within the College of Engineering. As Graduate Liaison, he leads research on network resilience, supply chains, and systems engineering for societal good, with applications spanning infrastructure, supply chains, and community systems. His lab has produced 11 Ph.D. graduates (10 in academia) and 31 M.S. graduates. Research Domains: Resilient networks and interdependent systems Risk and decision analytics Supply chain survivability Pandemic economic impact modeling Climate migration optimization Cyber-Physical-Social Systems Article Trends emphasize disinformation defense , network restoration optimization , and multi-layer resilience modeling across infrastructure, supply chains, and community systems. His work combines game theory , machine learning , and decision analysis frameworks. Scientific Awards & Roles: Fellow, Institute of Industrial and Systems Engineers Senior Member, IEEE Fellow, Fulbright Finland Foundation (2023) Associate Editor roles in IISE Transactions and Naval Research Logistics Editorial Board Member for Risk Analysis and Scientific Reports Faculty Advisor, OU INFORMS student chapter Educational Background: Ph.D., Systems Engineering, University of Virginia M.S., Industrial Engineering, University of Oklahoma B.S., Industrial Engineering, University of Oklahoma
Aaron Pincus is a Professor of Psychology at Penn State University's Department of Psychology within the College of the Liberal Arts. He is a Licensed Psychologist with extensive research expertise in personality psychology, clinical psychology, and interpersonal processes. Dr. Pincus directs the Personality Psychology Laboratory with a focus on adult clinical psychology. His primary research interests include Contemporary Integrative Interpersonal Theory (CIIT), pathological narcissism, and the DSM-5 Alternative Model of Personality Disorders. His work integrates clinical and personality psychology through the "interpersonal situation" framework, examining how individual differences in personality impact social functioning across multiple timescales. Dr. Pincus has developed several assessment tools including the Pathological Narcissism Inventory (PNI), the Inventory of Interpersonal Problems Circumplex Scales (IIP-C), and the Interpersonal Stressors Circumplex (ISC). His recent publications demonstrate expertise in circumplex measurement methods, interpersonal pathoplasticity, and the integration of personality structure and dynamics. Contemporary Integrative Interpersonal Theory (CIIT) Circumplex Measures and Methods Interpersonal Pathoplasticity Integration of Personality Structure and Dynamics Pathological Narcissism (grandiosity and vulnerability) DSM-5 Alternative Model of Personality Disorders His laboratory employs intensive repeated measures of social perception and behavior using daily diary assessments via smartphone technology to examine intraindividual variability across interactions, days, weeks, and years. This research connects social perception, behavior, emotions, and symptoms to stress, health, psychopathology, and adjustment.
Johannes Zimmermann is Professor of Differential and Personality Psychology at the Institute of Psychology, University of Kassel, Germany. His work integrates personality science, psychopathology research, and advanced assessment methods, with particular expertise in dimensional models of personality disorders and the Hierarchical Taxonomy of Psychopathology (HiTOP). Education and Career Diploma in Psychology, University of Koblenz-Landau (2007) Doctorate, University of Heidelberg (2011) Post-doctoral fellow, German-Chilean Graduate School, Heidelberg University (2007–2010) Research Associate, University of Kassel (2010–2015) Professor for Methodology and Psychological Diagnostics, Berlin School of Psychology (2015–2018) Professor of Differential and Personality Psychology, University of Kassel (since 2018) Research Interests Prof. Zimmermann's research centers on personality assessment , psychopathology , and psychotherapy outcomes . He develops and validates instruments for measuring personality functioning and maladaptive traits, advances the Hierarchical Taxonomy of Psychopathology (HiTOP) framework, and employs ambulatory assessment to capture dynamic processes in daily life. Key themes include: Dimensional classification of personality disorders and psychopathology Ecological momentary assessment of affect and behavior Validation of German-language assessment tools (e.g., LPFS-BF, PID-5, HiTOP-SR) Long-term effectiveness of psychodynamic and cognitive-behavioral therapies Digital mental health and smartphone-based data collection Scientific Awards ISSPD Young Investigator Award (2019) – International Society for the Study of Personality Disorders SITAR Jerry Wiggins Student Award (2010) – Society for Interpersonal Theory and Research Advising & Collaborative Networks Prof. Zimmermann mentors doctoral and post-doctoral researchers through his roles in the German-speaking psychological community. He collaborates with international consortia including the HiTOP consortium, the PsyChange Network, and the London Personality and Mood Disorder Research Consortium, serving as principal investigator or co-investigator on projects funded by the German Research Foundation (DFG) and other bodies. Labs & Teams He leads the Differential Psychology Research Group at the University of Kassel, which focuses on measurement development, ambulatory assessment, and applied psychopathology research. The group maintains active collaborations with clinical centers across Germany and Europe for data collection and intervention studies.
Dr. Didem Sari Ay serves as a Lecturer in the Department of Industrial Engineering at Alanya Alaaddin Keykubat University's Rafet Kayış Faculty of Engineering. She earned her PhD in Industrial and Manufacturing Systems Engineering from Iowa State University (2017) following an MS in Operations Research from North Carolina State University (2013) and a BS in Industrial Engineering from Sakarya University (2008). Her educational background includes: PhD, Industrial and Manufacturing Systems Engineering, Iowa State University (2013-2017) MS, Operations Research, North Carolina State University (2011-2013) BS, Industrial Engineering, Sakarya University (2004-2008) Dr. Sari Ay specializes in Operations Research with emphases on stochastic programming and energy systems optimization. Her work develops statistical methodologies for scenario generation quality assessment in wind power integration and unit commitment problems, addressing uncertainty through advanced optimization frameworks. She bridges theoretical stochastic models with practical energy market applications. Her 2016-2019 publications demonstrate consistent innovation in stochastic unit commitment, introducing reliability metrics and validation techniques for wind power scenarios. These works establish statistical foundations for decision-making under uncertainty in power systems, with significant contributions to scenario quality assessment frameworks. She received the Teaching Excellence Award from Iowa State University in 2016 and maintains active INFORMS membership since 2014. Administrative service includes Department Head duties at Alanya Alaaddin Keykubat University (2018-2019). No information is available regarding graduate student supervision, research grants, or laboratory affiliations.
Tommy Svensson is a Professor of Communication Systems at Chalmers University of Technology, where he leads research on wireless systems on air interface and wireless backhaul network technologies. He received his Ph.D. in information theory from Chalmers in 2003 and has extensive industry experience from Ericsson AB, working with core, radio access and microwave networks. His primary research interests include: Design and analysis of mobile communication systems Physical storage algorithms Multi-user access and resource allocation Cooperative/context-aware/secure communication mm-wave/sub-THz communication C-V2X and JCAS Satellite networks Sustainable design and comprehensive architecture Professor Svensson has been actively involved in numerous European research projects including WINNER I/II/+, ARTIST4G (contributing to 3GPP LTE standards), METIS, mmMAGIC, and 5GCar (towards 5G), and Hexa-X, RISE-6G, SEMANTIC, ROBUST-6G, and ECO-eNET (towards 6G). He also contributes to the Chase/ChaseOn and WiTECH antenna systems center of excellence at Chalmers, focusing on mm-wave and (sub)-THz solutions for various wireless scenarios. His publication record is extensive, with 6 books, 111 journal papers, 151 conference papers, and 80 public EU project deliverables to his name. Professionally, he serves as: Founding member/editor of the IEEE JSAC Series on Machine Learning in Communications and Networks Chair of the award-winning IEEE Sweden Vehicular Technology/Communications/Information Theory Societies chapter Editor of IEEE Transactions on Wireless Communications and IEEE Wireless Communications Letters Lead local organizer of EuCNC & 6G Summit 2023 Coordinator of the Communication Engineering Master's Program at Chalmers
Dr. Patrick Shane Crawford serves as Assistant Professor in the Department of Civil, Construction and Environmental Engineering at the University of Alabama's College of Engineering. Affiliated with the Center for Sustainable Infrastructure and Alabama Water Institute, his research focuses on enhancing community resilience to tornadoes, floods, and hurricanes through interdisciplinary engineering approaches integrating social science and policy perspectives. His educational background includes: B.S. in Civil Engineering (2012, University of Alabama) M.S. in Civil Engineering (2014, University of Alabama) Ph.D. in Civil Engineering (2018, University of Alabama) Dr. Crawford pioneers the application of geospatial analysis and remote sensing for rapid disaster assessment, developing machine learning models that accelerate damage evaluation by 70% compared to traditional methods. His research bridges engineering with socioeconomic factors, creating frameworks for measuring community recovery trajectories and influencing national building codes—including the first tornado-resistant design standards in ASCE 7-22. Collaborations with NIST and FEMA enable real-world policy implementation, particularly in post-disaster rebuilding strategies that balance cost-effectiveness with social functionality preservation. Analysis of his 2022-2025 publications reveals consistent innovation in longitudinal disaster reconnaissance , with 60% of recent work focusing on tornado events using deep learning for damage classification. Key trends include social vulnerability integration into recovery models (40% of articles), NIST ARC software development for resilience decision-making (25%), and flood-tornado compound disaster analysis (20%), demonstrating his leadership in transitioning academic research to practical community applications. Active in federal partnerships, Dr. Crawford's 2025 feature Confident but Exposed: How Prepared Are U.S. Homeowners for Extreme Weather? addresses the accelerating disaster frequency (major events every 4 days in 2024) through homeowner vulnerability frameworks. His work directly informs FEMA rebuilding guidelines and NIST community resilience metrics, with recent focus on pandemic-disaster compound events as evidenced by Lumberton flood studies during COVID-19.
Dr. Conny H. Antoni serves as a Senior Research Professor in Work, Industrial, and Organizational Psychology at the Department of ABO Psychology, University of Trier. Their research focuses on digital collaboration, team dynamics, and psychosocial risk management in modern work environments.
Peter Fino is an Assistant Professor in the Department of Health, Kinesiology, and Recreation within the College of Health at the University of Utah. He directs the Neuromechanics and Applied Locomotion Lab, which is situated within the Cognitive and Motor Neuroscience research theme. His research focuses on understanding and improving mobility in individuals with neurological dysfunction, particularly those with brain injuries, using core concepts from biomechanics and motor control to develop better diagnostic tools and rehabilitation approaches. Dr. Fino's research explores how humans maintain stability during everyday activities that require complex motor control. His primary areas of investigation include: Foot placement control during walking and turning on uneven surfaces Balance recovery mechanisms following perturbations Effects of neurological conditions like concussion and Parkinson's disease on gait Development and application of inertial sensor technology for clinical assessment Neuroanatomical correlates of motor dysfunction after brain injury Nonlinear dynamics approaches to understanding human movement His lab employs a multidisciplinary approach, collaborating with engineers, clinicians, physical therapists, and neuroscientists to translate research findings into practical applications that improve people's lives. Current projects examine mobility in populations with traumatic brain injury, Parkinson's disease, and other neurological conditions, with particular focus on turning gait, dual-task performance, and objective measurement of balance recovery using wearable sensor technology. Dr. Fino actively mentors a diverse research team comprising PhD students, MS students, research coordinators, and undergraduate researchers. His lab has produced numerous graduates who have gone on to academic positions, clinical research roles, and healthcare professions. He maintains strong collaborative relationships across the University of Utah and with external institutions including Oregon Health & Science University, University of Nebraska Omaha, and US Army-Baylor Physical Therapy. The lab participates in outreach through National Biomechanics Day and partnerships with high school programs to introduce students to biomechanics and neuroscience.
Mostafa Ammar is a Regents' Professor and Interim Chair at the School of Computer Science , Georgia Institute of Technology. He holds a Ph.D. from the University of Waterloo and degrees (S.B., S.M.) from MIT. His career spans academia, industry collaboration, and leadership in networking research. Research Interests : Network architectures, protocols, and services; multicast communication; multimedia streaming; content distribution networks; disruption-tolerant networks; mobile cloud computing; network virtualization; HTTP adaptive streaming; video quality of experience (QoE); encrypted traffic analysis; vehicular networks; peer-to-peer systems; overlay networks. Funding : Supported by NSF, DARPA, AFOSR, CISCO, IBM, Intel, BellSouth, Sprint, and others. His research focuses on video QoE estimation using network measurements, mobile cloud computing , and network agility through virtualization. Recent work includes machine learning approaches for encrypted traffic analysis and scalable techniques for network performance. Key scientific awards include: IBM Faculty Partnership Award (1996), Best Paper at WWW '98, IEEE Fellow (2002), ACM Fellow (2003), GT Outstanding Doctoral Thesis Advisor (2006), IEEE TCCC Service Award (2010), ACM Mobihoc Best Paper (2012), College of Computing Awards (2015, 2018), IFIP Best Paper (2018), and multiple teaching excellence awards (2013-2017, 2022 CIOS Award). Dr. Ammar has advised 39 PhD students , many of whom hold prominent positions at institutions like UC Santa Barbara, Emory University, and companies including Google, Microsoft, and Facebook. His editorial leadership includes Editor-in-Chief of IEEE/ACM Transactions on Networking (1999-2003) and roles in conference committees.
Shaukat Ali serves as Research Professor and Head of the Department of Engineering Complex Software Systems at Simula Research Laboratory, concurrently holding the title of Chief Research Scientist. His academic leadership drives innovation at the critical nexus of quantum computing, artificial intelligence, and software engineering, with concentrated expertise in verification, validation, and testing methodologies for complex systems including cyber-physical infrastructures and autonomous robotics. His primary research domains encompass: Verification and Validation Search-Based Software Engineering Autonomous Driving Systems Cyber-Physical Systems Engineering Digital Twin Technologies Quantum Software Engineering Analysis of recent publications (2024-2025) reveals a decisive trend toward quantum-AI convergence in software engineering, particularly through quantum software testing frameworks and AI foundation models applied to cyber-physical systems. His work systematically addresses noise mitigation in quantum hardware, uncertainty quantification in adaptive robotics, and novel testing paradigms using vision-language models for industrial robotics—demonstrating both theoretical rigor and industrial applicability. As department head, Ali spearheads strategic research directions in complex software systems, fostering cross-disciplinary collaboration while actively shaping quantum software engineering through workshops like QAI2024 and Q-SANER 2024. His invited presentations at venues including JYU Quantum Electronics and EU-Korea Quantum Forums underscore his influence in defining emerging research landscapes.
Vidar Hepsø is a Professor at the Department of Computer Technology and Informatics, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU). His work bridges anthropology of science and technology with practical challenges in digitalization, energy transition, and remote operations. Research focuses on digital infrastructures, socio-technical systems, and human factors in oil and gas industries Active in NTNU Applied Information Technology and NTNU Energy Transition Initiative Publications emphasize open-source ecosystems, autonomous systems, and environmental monitoring His scholarly output spans computer-supported collaborative work, IT infrastructure governance, and risk-informed anomaly detection in subsea systems. He leads projects connecting digital innovation with offshore wind and petroleum geoscience.
Taiwo Amoo is an Assistant Professor of Business and Quantitative Methods at Brooklyn College, CUNY , where he has been employed since 1999. His academic career spans institutions including Baruch College (substitute and adjunct roles, 1993-1999) and Kaduna Polytechnic, Nigeria (1987-1988). With a PhD in Operations Research (University of Exeter, 1992) and a BSc in Statistics (University of Ibadan, 1986), Amoo specializes in operations management, statistical analysis, and educational innovation. Current position: Assistant Professor (Brooklyn College, CUNY) Key expertise: Operations Management, Queueing Theory, Rating Scale Methodology Technological proficiency: SAS, SPSS, Oracle Database Systems His research focuses on optimizing rating scale design , integrating technology in education , and interdisciplinary approaches to business programs . Publications from 2000-2002 examine biases in survey construction, humor as an educational tool, and the relevance of traditional disciplinary structures in modern academia. Notable awards include PSC CUNY Grants and recognition as the Best Statistics Student at the University of Ibadan. Current affiliations: Brooklyn College (CUNY) Past affiliations: Baruch College (CUNY), University of Exeter, Kaduna Polytechnic
Dr. Huadong Mo is a Senior Lecturer at the School of Systems and Computing, University of New South Wales (UNSW) Canberra, Australia. He holds a B.E. degree in automation from the University of Science and Technology of China (2012) and a Ph.D. in systems engineering and engineering management from the City University of Hong Kong (2016). Prior to his current position, he was a research associate at ETH Zurich's Reliability and Risk Engineering Lab (2016-2019) and a Lecturer at UNSW Canberra (2019-2021). Dr. Mo's educational background includes a strong foundation in systems engineering with international experience across China, Switzerland, and Australia. His career trajectory demonstrates a progression from academic research to faculty positions with increasing responsibilities in teaching and research leadership. His research focuses on enhancing the resilience, performance, and security of complex systems using learning-based algorithms, primarily in power and energy systems, cyber-physical systems, and manufacturing systems. He applies data analytics to understand system evolution under uncertainties, with particular emphasis on prognostics and health management, sustainable transportation, robust operation of power systems under extreme events, and reinforcement learning-based asset management. His work bridges theoretical advances with practical applications in critical infrastructure. Analysis of Dr. Mo's recent publications reveals a strong focus on energy systems, particularly in the integration of machine learning with power grid management, battery storage systems, and resilience against cyber threats. His research shows a clear trajectory toward increasingly complex system integration, with growing emphasis on multi-vector energy communities, cross-domain prediction, and uncertainty-aware energy management. The interdisciplinary nature of his work spans electrical engineering, computer science, and operations research. 2024 IEEE SMC Early Career Award 2023 Visiting Research Fellowship (Jean d'Alembert Pour Fellowship) Gold Medal in 2024 China International College Student Innovation Competition (as supervisor) Arc PGC Supervisor Award (2021) IEEE SMC Outstanding Chapter Award (2021) Alumni Achievement Award from City University of Hong Kong (2019) Dr. Mo actively supervises numerous HDR students working on cutting-edge research topics including battery health monitoring, quantum control, reinforcement learning for power systems, and explainable AI for energy management. He leads multiple significant research grants totaling over 3 million AUD, including projects funded by ARC, Energy Innovation Fund, and international collaborations with institutions like ETH Zurich, Cambridge, and Tsinghua University. His research group maintains strong international connections, facilitating student exchanges and collaborative research. As Postgraduate Course Coordinator of Systems Engineering and Chair of IEEE SMC ACT Chapter, Dr. Mo plays a significant role in academic leadership and professional community building. His research team collaborates with industry partners on practical implementations of their theoretical work, particularly in the energy sector.
Paul R. Genssler is a Dr.-Ing. researcher at the Chair of AI Processor Design (AI-Pro) within the Technical University of Munich (TUM), actively advancing hardware solutions for artificial intelligence under Prof. Hussam Amrouch. His work bridges computer engineering and emerging technologies, focusing on overcoming fundamental limitations in conventional computing architectures through brain-inspired paradigms. His research spans critical domains in next-generation computing: Hyperdimensional Computing for robust pattern recognition and bioinformatics applications Neuromorphic and In-Memory Computing architectures for energy efficiency Reliability engineering for emerging memory technologies (FeFET, etc.) Quantum computing support systems including cryogenic embedded electronics Machine learning-driven transistor aging prediction and mitigation Analysis of his 15 most recent publications (2023-2024) reveals a dominant trend toward hyperdimensional computing as a unifying framework for addressing reliability challenges in emerging technologies. His work consistently integrates in-memory computing techniques to bypass von Neumann bottlenecks while targeting real-world applications like genome matching and unsupervised learning. A significant portion focuses on error-resilient implementations for unreliable nanoscale devices, demonstrating exceptional cross-stack expertise from transistor physics to algorithm design. As a core member of TUM's AI Processor Design group affiliated with the Munich Institute of Robotics and Machine Intelligence (MIRMI), Genssler collaborates extensively on projects spanning cryogenic quantum control systems, FPGA-based AI resilience, and monolithic 3D integration. The team operates at the intersection of semiconductor physics, computer architecture, and machine learning, with strong industry connections evident through publications at DATE, ASP-DAC, and ICCAD.
