Dr. Joshua New is a Distinguished R&D Staff Member at Oak Ridge National Laboratory (ORNL) and holds a Joint Faculty Position at The University of Tennessee since 2012. He leads research in building energy modeling, climate change science, and supercomputing. His work focuses on urban-scale energy systems, AI-driven analytics, and high-performance computing applications. Education: Ph.D. in Computer Science (University of Tennessee, 2009), M.S. in Computer Systems, B.S. in Computer Science and Mathematics (Jacksonville State University). His research interests include optimizing building energy efficiency, simulating climate impacts on urban infrastructure, and developing tools like AutoBEM and ModelAmerica to model 122.9 million U.S. buildings. He has over 150 peer-reviewed publications and led 45+ projects involving supercomputing, visual analytics, and AI for big data. Awards include the R&D 100 Award (2016), ASHRAE Distinguished Service Award (2018), and Lab-Corps (2015). His teams prioritize utility use cases and validate models against real-world data. He is a Senior IEEE member, Certified Energy Manager (CEM), and holds certifications in project management and energy efficiency. Key contributions include the Roof Savings Calculator Suite, AutoGen/AutoSim tools, and the ModelAmerica initiative. His work addresses national energy policy, heatwave resilience, and sustainable city design through interdisciplinary collaborations.
Aspy P. Palia is a Professor of Marketing at the Shidler College of Business, University of Hawaii at Manoa. He has held academic roles since 1984, including visiting professorships at institutions such as Singapore Management University, Chulalongkorn University, and the National University of Singapore. His research focuses on Marketing Decision Support Systems, Countertrade in Asia-Pacific, and experiential learning methodologies. He has published extensively in journals like European Journal of Marketing and Developments in Business Simulation and Experiential Learning , with a recent emphasis on virtual scaffolding and engagement in educational simulations. His work has earned multiple best paper awards and nominations. Dr. Palia's education includes a DBA in International Business from Kent State University (1985), an MBA from the University of Hawaii (1976), and a BS in Mechanical Engineering from the University of Bangalore (1966). His teaching spans global institutions, emphasizing strategic marketing and decision-making tools. Notable awards include the 2017 Marquis Lifetime Achievement Award and the 2011 Fellowship from the Association for Business Simulations and Experiential Learning. His research contributions bridge theoretical marketing frameworks with practical applications, particularly in leveraging technology for pedagogical innovation. Collaborations with organizations like the Japan America Institute of Management Science further underscore his engagement with global academic networks.
Eric Y. Drogin is an Adjunct Professor and Honorary Professor with dual expertise in clinical psychology and law. He is affiliated with Harvard Medical School, serving on the staff of the Forensic Psychiatry Service at Beth Israel Deaconess Medical Center, and is involved in the Harvard Longwood Psychiatry Residency Training Program. He also teaches at the University of New Hampshire School of Law and lectures internationally at Prifysgol Aberystwyth (formerly University of Wales) in both Psychology and Law. His work integrates mental health and legal systems through research, education, and practice. Education: Doctor of Philosophy (Ph.D.) in Clinical Psychology – Hahnemann University Juris Doctor (J.D.) – Villanova University School of Law Dr. Drogin's research interests center on the intersection of psychology, psychiatry, and law, particularly in forensic assessment, expert testimony, ethics, and mental disability law. His work emphasizes multidisciplinary approaches to legal and clinical challenges, supporting both mental health professionals and attorneys. He regularly presents continuing education seminars across North America, Europe, Asia, and Australia. His scholarly publications span criminal and civil law applications of psychological evidence, guardianship evaluation, forensic assessment standards, and the role of mental health professionals in court. These works reflect a consistent focus on improving legal processes through scientific rigor and ethical practice. Scientific Honors and Professional Recognition: Fellow of the American Psychological Association Fellow of the American Academy of Forensic Psychology Diplomate, American Board of Forensic Psychology Diplomate, American Board of Professional Psychology Life Fellow, American Bar Foundation Dr. Drogin has held leadership roles in major professional organizations, including President of the American Board of Forensic Psychology, Chair of APA committees on Legal Issues and Professional Practice, and Co-Chair of the ABA’s Behavioral & Neuroscience Law Committee. He serves as Editor-in-Chief of The Journal of Psychiatry & Law and has co-authored over 250 publications. While formal student advising is not detailed, his instructional roles in residency programs and trial workshops indicate active mentorship. He is a key figure in advancing forensic psychology through education, policy, and interdisciplinary collaboration.
John S. McCartney is a Professor and Hal Sorenson Endowed Chair in the Department of Structural Engineering at the University of California San Diego (UCSD). He directs the Englekirk Structural Engineering Center and holds editorial roles at journals such as ASCE Journal of Geotechnical and Geoenvironmental Engineering and Computers and Geotechnics. His research focuses on unsaturated soil mechanics, energy geotechnics, and geosynthetics engineering, with applications in thermal energy systems, landfill covers, and seismic response analysis. Education: B.S. and M.S. in Civil Engineering from University of Colorado Boulder (2002), Ph.D. in Civil Engineering from University of Texas at Austin (2007). Research interests include thermo-hydro-mechanical behavior of soils, geothermal energy piles, tire-derived aggregates, and seismic performance of geotechnical systems. His work combines laboratory testing, centrifuge modeling, and numerical simulations to address challenges in sustainable infrastructure and energy systems. Key awards include the Walter L. Huber Research Prize (2016), NSF CAREER Award (2011), and multiple teaching and service recognitions. He actively contributes to ASTM standards and serves as President of the IGS-NA chapter. Lab facilities are located in the Structural and Materials Engineering Building (SME 409). Courses taught include advanced soil mechanics, energy geotechnics, and geotechnical earthquake engineering.
Dr Dongbin Wei is an Associate Professor at the School of Mechanical and Mechatronic Engineering , University of Technology Sydney (UTS), with a career spanning academia and industry. He holds a PhD in Materials Processing Engineering from the University of Science and Technology Beijing (2001) and academic appointments from 2005–2012 at the University of Wollongong (Research Fellow to Lecturer) and 2013–2017 at UTS (Senior Lecturer) before his promotion to Associate Professor in 2018. His research lies at the intersection of Mechanical Engineering , Manufacturing Engineering , and Materials Processing , focusing on: Ultrasonic Additive Manufacturing (UAM) Micro Metal Forming and Size Effects Tribology and Lubrication Numerical Simulations of Material Processing Composite Material Fabrication Key contributions include: Development of the Springback Path–Displacement Adjustment (SP-DA) method for stamping accuracy Advancements in femtosecond laser texturing for silicon wettability control Studies on nanolubrication in hot rolling Optimization of micro-deep drawing parameters He has secured competitive grants from the Australian Research Council (ARC) and industry partners like Weir Minerals Australia Ltd , including projects on: Revolutionizing mineral separation via additive manufacturing Super high-speed grinding technologies Mechanics of micro composite drill fabrication As a lead supervisor, he guided the 2022 thesis 'Creation and Validation of 3D Printable Mineral Separation Spiral' . His work bridges theoretical analysis, computational modeling (FEM/FEA), and practical validation in advanced manufacturing systems.
Nadia Shardt is an Associate Professor in the Department of Chemical Engineering at the Norwegian University of Science and Technology (NTNU). Her research focuses on interfacial thermodynamics, particularly in systems with nanoscale curvature, with applications spanning atmospheric science, biomedical cryopreservation, and industrial process optimization. She contributes to teaching courses such as TKP4580 - Chemical Engineering Specialization Project and KP3100 - Chemical Engineering . PhD in Chemical Engineering (University of Alberta, 2019) BSc in Chemical Engineering (University of Alberta, 2015) Postdoctoral researcher at ETH Zurich (2020-2022) Her work addresses fundamental challenges in phase behavior under curvature constraints, combining microfluidic experimentation , Gibbsian thermodynamic modeling , and machine learning techniques to study systems like CO 2 storage media, cloud microphysics, and food emulsions. Recent publications emphasize surface tension modeling for complex multi-component systems and cryoprotectant loading efficiency. Scientific awards include the ETH Postdoctoral Fellowship Natural Sciences and Engineering Research Council of Canada (NSERC) Postdoctoral Fellowship Outstanding Academic Fellows Programme 2024-2028
Børge Rokseth is an Associate Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU). His work focuses on maritime systems, autonomous vessel control, and safety verification. He actively supervises Master's students and contributes to research on risk-informed control systems, hybrid power systems, and systems-theoretic process analysis (STPA). Research Interests: Rokseth's research spans autonomous ship systems, dynamic risk assessment, and safety verification. He explores risk-based decision-making for maritime autonomy, hazard identification in hybrid propulsion systems, and control function allocation in dynamic positioning. His work integrates systems theory, machine learning, and regulatory compliance (e.g., COLREGS) to enhance safety and environmental performance in marine operations. Publications: His recent work includes probabilistic trajectory prediction frameworks for autonomous ships, STPA-based safety analyses, and studies on decarbonization barriers in the maritime industry. These publications emphasize risk modeling, systems-theoretic approaches, and simulation-based verification. Teaching: Rokseth teaches courses such as TTK4130 - Modelling and Simulation, contributing to the education of future engineers and researchers in cybernetics and maritime systems.
Stefano Grivet Talocia is a Full Professor in the Department of Electronics and Telecommunications at Polytechnic University of Turin. He serves as Director of the Doctoral School, is a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory, and holds positions on the University Committee for Research and the Commission for the Promotion of Library, Archive and Museum Heritage. He is also President of the Doctoral School Council. His educational background includes a Laurea degree (summa cum laude) in Electronic Engineering (1994) and a Ph.D. in Electronic and Communication Engineering (1998), both from Polytechnic University of Torino. From 1994 to 1996, he worked at NASA/Goddard Space Flight Center in Greenbelt, MD, USA. Professor Grivet Talocia's research focuses on passive macro-modeling of concentrated and distributed interconnect structures for Signal/Power Integrity, order reduction techniques, and modeling and simulation of fields, circuits, and their interactions. His work spans several key areas including fast simulation of transmission lines (TOPLine technique), macromodeling and model order reduction, simulation methods for fields and circuits, passivity enforcement of lumped macromodels, waveform relaxation techniques, and wavelet applications. His research has significant applications in electromagnetic compatibility and signal integrity verification of complex electronic systems. His recent publications demonstrate strong trends in model order reduction techniques applied to power integrity verification, advanced macromodeling for electromagnetic compatibility, nonlinear circuit analysis, uncertainty quantification in PCB design, and power electronics modeling. These works consistently address practical engineering challenges in high-speed electronic design with emphasis on computational efficiency and accuracy. URSI Young Scientist Award (1999) Best symposium paper (2006) Three IBM Shared University Research Awards (2007-2009) IEEE Transactions on Advanced Packaging Best Paper Award (2007) Best EPEP conference paper awards (2007, 2008) Best Associate Editor Award - IEEE Transactions (2020) Best Conference Paper Award (2020) Three Intel SRS Grants (2022-2024) IEEE Fellow (2018) Professor Grivet Talocia actively supervises PhD students working on cutting-edge topics including machine learning applications in signal integrity, model reduction techniques, and electromagnetic compatibility. He has secured significant research funding through competitive grants including PRIN projects and multiple industry-sponsored research contracts with major technology companies such as IBM, Intel, Nokia, Hitachi, and Infineon. His technology transfer activities include co-founding the spin-off IdemWorks (acquired by CST in 2016) and maintaining active collaborations with industry partners. He leads the EMC Group (Electromagnetic Compatibility) within the Department of Electronics and Telecommunications and has developed the autoCircuits web service for automated generation of circuit theory problems. His research has been recognized by inclusion in the top 2% worldwide researcher catalog (Stanford) since 2019.
Dr. Alastair Key serves as Director of Studies in Archaeology and Official Fellow in Archaeology at Queens' College, University of Cambridge. His research bridges Paleolithic archaeology, stone tool technology, and hominin behavioral evolution through experimental and computational approaches. Director of Studies and Official Fellow at Queens' College, Cambridge Specializes in Paleolithic stone tool analysis, Acheulean technology, and hominin adaptation Conducts experimental archaeology and computational modelling to assess tool functionality Key's research focuses on Acheulean handaxe production , lithic microwear patterns , and ergonomic constraints in prehistoric tool use . He has extensively published on topics including glacial-stage hominin occupations , Oldowan toolmakers , and machine learning applications to archaeological analysis . His recent publications (2025-2023) span diverse subfields: Acheulean chronology , hominin tool use biomechanics , experimental projectile testing , and computational morphometric methods . The work often integrates multidisciplinary datasets and open-source analytical tools to address fundamental questions about human technological evolution. Current research directions include stone tool sharpness quantification , handaxe social signaling potential , and cross-species tool use comparisons through primate studies.
Jonathan Külz is a Researcher at the Technical University of Munich (TUM) , affiliated with the Department of Informatics 6 - Chair for Cyber Physical Systems under Prof. Matthias Althoff. His research focuses on Modular Robotics , Reinforcement Learning , Cyber-Physical Systems , and Control Systems . His work includes algorithmic synthesis of modular robot compositions, model-based manipulator co-design, and unifying benchmarks for robotics. He has supervised multiple Master’s theses Bachelor’s theses Practical courses on topics like Robot Workspace Representation and Dynamic Model Identification . Notable supervised projects include Autonomous Navigation of Reachbot and Task-Based Modular Robot Configuration Synthesis . His recent publications span Robotics , Benchmarking , and Computational Social Science . Key trends include Computationally efficient assessment of robot capabilities Deep reinforcement learning for robotics Analysis of political discourse polarization Jonathan emphasizes structured thesis supervision, requiring exposés, shared folders, and protocol-driven meetings. He advocates for LaTeX in scientific writing and tools like NotebookLM and Zettlr for research documentation.
Elyse Rosenbaum is the Melvin and Anne Louise Hassebrock Professor in Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. She also serves as the Acting Associate Dean for Research at the Grainger College of Engineering. She is the director of the NSF-supported Center for Advanced Electronics through Machine Learning (CAEML), a collaboration between the University of Illinois, North Carolina State University, and Penn State University. Education: Ph.D. in Electrical Engineering, University of California, Berkeley, 1992 M.S. in Electrical Engineering, Stanford University B.S. in Electrical Engineering, Cornell University (with distinction) Research Interests: Her research focuses on machine learning applications in electronics, ESD-robust high-speed I/O circuit design, compact modeling, behavioral modeling of circuits, and CDM-ESD protection for advanced packaging technologies. Scientific Awards: IEEE Fellow for contributions to electrostatic discharge reliability of integrated circuits Best Student Paper Award, IEDM Outstanding and Best Paper Awards, EOS/ESD Symposium Technical Excellence Award, SRC NSF CAREER Award IBM Faculty Award ESD Association’s Industry Pioneer Recognition Award Advising and Grants: She supervises graduate and undergraduate researchers, primarily focusing on those with strong academic records and relevant experience. Her work is supported by NSF and other prominent organizations. Labs and Teams: She leads the CAEML center, which aims to apply machine learning to optimize microelectronic circuits and systems, enhancing design automation and reliability.
René Jr Landry is a full Professor in the Department of Electrical Engineering at École de technologie supérieure (ETS), Université du Québec, specializing in Global Navigation Satellite Systems (GNSS), avionics, and wireless communication technologies. His academic journey includes a B.Ing. from Polytechnique Montréal, M.Sc. from University of Surrey (UK), and Ph.D. from SupAréo in Toulouse. He maintains active research leadership through two key laboratories: LASSENA (Laboratory of Space Technologies, Embedded Systems, Navigation and Avionics) and LACIME (Communications and Microelectronic Integration Laboratory). His research spans critical aerospace navigation domains including GNSS signal processing, inertial navigation systems, software-defined radio for avionics, radio frequency interference mitigation, and indoor positioning technologies. Landry's work addresses real-world challenges in satellite navigation robustness, precision positioning in urban/denied environments, and next-generation avionic system security. His current projects focus on blockchain-enhanced IoT security, AI-driven GNSS disruption analysis, and adaptive RF front-ends for multi-band avionics applications. Analysis of his recent publications reveals strong emphasis on resilient positioning systems through multi-constellation integration (particularly Iridium-NEXT), blockchain applications for navigation security, and explainable AI techniques for GNSS signal quality assessment. His work increasingly bridges traditional navigation engineering with cutting-edge security and machine learning paradigms. 2014 Prix d'excellence du c.a. pour les services à la collectivité Landry has supervised over 100 graduate students across doctoral, master's, and research projects since 2005, with current supervision extending through Summer 2025. His research funding supports multiple industry partnerships focused on avionics certification, software-defined radio implementations, and next-generation navigation systems. The LASSENA laboratory under his leadership develops certified avionic products from open-source SDR platforms and advances multi-sensor fusion techniques for challenging navigation environments. His research infrastructure includes specialized facilities for GNSS signal simulation, avionics hardware testing, and multi-sensor integration. Current work emphasizes flight-tested validation of RF front-end technologies, blockchain-secured navigation data, and real-time interference mitigation systems for aviation applications.
Ayse Coskun is a Professor in the Electrical and Computer Engineering Department at Boston University's College of Engineering. She serves as Director of the Center for Information and Systems Engineering (CISE) and as interim Associate Dean for Research and Faculty Development. Her research focuses on the intersection of computer systems, energy efficiency, and AI. Dr. Coskun received her PhD from the University of California, San Diego in 2009. Prior to joining academia, she worked at Sun Microsystems (now Oracle). Her research spans energy-efficient computing, cloud computing, high performance computing, computer architecture, and embedded systems, with recent work focusing on AI's impact on data center energy demands. Her publication record shows consistent innovation across multiple domains, with recent work emphasizing AI applications for improving cloud security (through frameworks like DeltaSherlock and Praxi) and transforming data centers into grid-responsive assets (Emerald AI project). Her research bridges theoretical advances with practical applications, resulting in tools adopted by industry partners including IBM. IBM Faculty Award (2020) Ernest S. Kuh Early Career Award (2017) NSF CAREER Award (2012-2017) Multiple best paper and artifact awards at top conferences As an educator, Dr. Coskun teaches courses including EC327 Introduction to Software Engineering, EC535 Introduction to Embedded Systems, and EC713 Advanced Computing Systems and Architecture. She has advised numerous PhD students including Mert Toslali, Anthony Byrne, and Burak Aksar. Her lab maintains strong industry partnerships with IBM, Intel, AMD, and Oracle, and collaborates with academic institutions worldwide including Brown University, MIT, EPFL, and CEA-Tech in France. Dr. Coskun leads the Coskun Lab, which secured a $500K grant from Sandia National Labs for AI-based analytics in high performance computing systems, demonstrating the practical impact of her research on critical computing infrastructure.
Professor Tim Dodwell holds a personal chair in Machine Learning at the University of Exeter, spanning the Department of Mechanical Engineering and the Institute of Data Science and AI. He leads the Data Centric Engineering Group and serves as co-founder and CTO of digiLab, a deep tech startup. His prestigious appointments include a 5-year Turing AI Fellowship from the Alan Turing Institute and the Romberg Visiting Professorship at Heidelberg University in Scientific Computing. His academic foundation includes a 1st class BSc in Mathematics from the University of Bath (2004-2008) and a PhD in Applied Mathematics from the Bath Institute of Complex Systems (2009-2012), where he researched variational models for complex materials under Professors Giles Hunt and Mark Peletier. Dodwell's research pioneers the intersection of applied mathematics, probabilistic machine learning, and high-performance computing, with signature contributions to Multilevel Methods in Bayesian Inverse Problems , Generative Hybrid Modelling , and Machine Learning in Safety Critical Engineering . His work bridges theoretical data science with industrial applications across nuclear fusion, aerospace materials, air traffic control, nuclear decommissioning, water treatment, and urban solar energy systems. His major recognitions include: Turing AI Fellowship (2019-2024) Romberg Visiting Professorship at Heidelberg University Visiting Professorship at MIT Prize Fellowship in Engineering Mathematics (2013-2015) Pro Vice Chancellors Fellowship (2015-2018) Through competitive fellowships and digiLab initiatives, Dodwell secures funding for uncertainty quantification research while driving real-world impact in sustainability sectors. His dual academic-industry roles enable rapid translation of theoretical advances into engineering solutions, particularly through digiLab's twinLab platform which delivers 60,000x acceleration in simulation workflows. He directs the Data Centric Engineering Group at Exeter and co-founded digiLab's multidisciplinary team comprising AI specialists, domain experts, and educators. The organization operates through three synergistic pillars: developing AI solutions for critical infrastructure, building the twinLab platform for industrial ML deployment, and running an ML academy for practitioner training through datacamps, internships, and specialized courses.
Abhijit Sarkar is a Professor in the Department of Civil and Environmental Engineering at Carleton University, Ottawa. His work centers on computational dynamics and probabilistic modeling, with office MC 3076 in the Minto Centre for Advanced Studies in Engineering and contact details including phone (613) 520-2600 x6320 and email abhijit_sarkar@carleton.ca . Education: D.Phil. from University of Oxford M.Sc. from Indian Institute of Science (IISc) B.E. from Calcutta University Professional Engineer (P.Eng.) designation His research drives innovation in uncertainty quantification for complex engineering systems. Core interests include dynamics of nonlinear structures, probabilistic mechanics for stochastic finite element methods, and Bayesian inference frameworks for parameter estimation. He pioneers scalable high-performance computing solvers for large-scale systems and sparse learning algorithms to address overfitting in statistical modeling. Recent publications (2022-2024) reveal three dominant trends: (1) Bayesian model calibration for stochastic compartmental systems applied to epidemiology and aerospace, (2) domain decomposition techniques for scalable uncertainty quantification in stochastic PDEs, and (3) sparse learning methods for nonlinear aerodynamic encoding. Key applications span wind turbine vibration analysis, flutter margin prediction, MEMS resonator optimization, and geospatial pandemic modeling. Scientific awards: No awards, fellowships, or medals listed in the source material Graduate supervision includes 6 current students (Ajay Kumar, John Clarabut, Nastaran Dabiran, Sakhi Mittal, Michael Pantano, Brandon Robinson) and 18 graduated students across 17 years (2006-2023). His research leverages high-performance computing for projects in structural dynamics, aeroelasticity, and computational epidemiology, frequently co-supervised with Dominique Poirel and Chris Pettit. Notable grants focus on wind tunnel validation for nonlinear systems and pandemic spread modeling. Based in the Minto Centre for Advanced Studies in Engineering, his computational mechanics group develops algorithms for stochastic dynamics using Carleton University's high-performance computing infrastructure. Collaborations span aerospace engineering (flutter analysis), civil infrastructure (seismic wave propagation), and public health (Covid-19 modeling).
