Andrea Goldsmith is the Dean of the School of Engineering and Applied Science and the Arthur LeGrand Doty Professor of Electrical and Computer Engineering at Princeton University. Previously, she held the Stephen Harris Professorship at Stanford University and remains Harris Professor Emerita there. Her research focuses on information theory, communication theory, signal processing, and their applications to wireless communications, interconnected systems, and neuroscience. She founded Plume WiFi and Quantenna, Inc., and serves on the boards of Medtronic and Crown Castle Inc. Education: B.S., M.S., and Ph.D. in Electrical Engineering, University of California, Berkeley (1986–1994) Research Interests: Her work bridges theoretical foundations with practical applications in wireless systems, including MIMO communications, cognitive radio, and the integration of machine learning in communication protocols. She also explores the intersection of wireless technology with biomedical systems and neuroscience, emphasizing innovations like smart buildings and in-body networks. Key Contributions: Authored seminal textbooks, including Wireless Communications and MIMO Wireless Communications . Inventor on 29 patents, with significant industry impact through startups. Recipient of prestigious awards such as the IEEE Sumner Award, ACM Athena Lecturer Award, and Marconi Prize. Labs & Leadership: Leads the Wireless Systems Lab at Princeton, advancing cutting-edge wireless technologies. Chair of the IEEE Board of Directors Committee on Diversity, Inclusion, and Ethics.
Dr. Joseph Dumpler is a Lecturer at the Department of Health Sciences and Technology at ETH Zürich, specializing in Sustainable Food Processing. He holds a PhD in Dairy Science and Technology from the Technical University of Munich, Weihenstephan, with a focus on UHT treatment of concentrated milk. His work emphasizes advancing food processing technologies, particularly in protein refinement, non-thermal methods, and membrane filtration. Educations: PhD in Dairy Science and Technology, Technical University of Munich, Weihenstephan (2017) MSc Food Engineering, Technical University of Munich, Weihenstephan His research interests include Natural Deep Eutectic Solvents (NADES) for plant protein extraction, microwave vacuum drying of dairy products, and membrane filtration optimization for microalgae and dairy systems. He has pioneered methods to refine rapeseed and pea proteins while minimizing antinutrients, and his work on microfiltration of milk products addresses emerging microbial risks. Key contributions span kinetic modeling of heat-induced protein aggregation, sustainable food processing , and non-thermal concentration techniques . His articles reflect a focus on bridging lab-scale innovations with industrial applications. Awards: J.T.M. Wouters Young Scientist Award Julius Maggi Research Award (2018) Best PhD Thesis Award from the Association of Dairy, Food and Biotechnologists (Weihenstephan) Dr. Dumpler collaborates with industry partners to translate research into scalable processes, such as NADES-based protein extraction and microwave drying systems. His current role at ETH Zürich’s Sustainable Food Processing Lab (Prof. Mathys) focuses on plant-based meat analogs and novel protein refining concepts .
David A. Muller serves as the Samuel B. Eckert Professor of Engineering in the School of Applied and Engineering Physics at Cornell University and co-directs the Kavli Institute at Cornell for Nanoscale Science. His research group focuses on developing quantitative electron microscopy methods to understand materials properties at the atomic scale, with particular emphasis on sustainable energy applications and quantum materials. Muller's laboratory utilizes some of the world's highest resolution electron microscopes housed in specially designed, environmentally isolated rooms. Muller received his undergraduate education at the University of Sydney and earned his Ph.D. in Physics from Cornell University in 1996. Between 1997 and 2003, he was a member of the technical staff at Bell Laboratories, where he applied his expertise in imaging single atoms and atomic-scale spectroscopy to determine the physical limits of transistor miniaturization. In 2003, he returned to Cornell as a faculty member, where he has since established himself as a leader in advanced electron microscopy techniques. Muller's research spans multiple frontiers in materials science, with particular focus on understanding how electronic-structure changes at the atomic scale control macroscopic behavior in diverse systems like turbine blades, fuel cells, and transistors. His current work emphasizes the physics of renewable energy materials, atomic-scale control of materials to create electronic phases that cannot exist in bulk, and developing hardware and algorithms for 'big data' acquisition from high-bandwidth pixelated electron microscope detectors. His group's work bridges theoretical physics and experimental techniques, requiring researchers who can think in both real and reciprocal space while considering both fundamental principles and practical applications. Analysis of Muller's recent publications reveals a strong trend toward advancing electron ptychography and 4D-STEM techniques for atomic-scale imaging. His group has pioneered methods for 3D atomic-scale metrology, strain mapping, and imaging of radiation-sensitive materials. The research spans applications from semiconductor technology to quantum materials and energy storage systems, demonstrating the versatility of his microscopy approaches across multiple scientific domains. Top 100 Young Innovator by Tech Review Magazine (2003) Burton Medal from Microscopy Society of America (2006) Ernst Ruska Prize of German Society for Electron Microscopy (2021) John Cowley Medal from International Federation of Societies for Microscopy (2023) Fellow of American Physical Society Fellow of American Association for the Advancement of Science Fellow of Microscopy Society of America Muller has mentored an extensive group of students and postdocs who have gone on to successful careers in academia and industry. His former students hold faculty positions at institutions including Rice University, University of Southern California, Seoul National University, Colorado School of Mines, and the University of Michigan, among others. His research has been supported by substantial grants, including a $22.5M NSF grant that accelerates materials discovery. The Muller lab maintains close collaborations with the Kavli Institute at Cornell and PARADIM (Platform for the Accelerated Realization, Analysis, and Discovery of Interface Materials). The Muller lab operates at the forefront of electron microscopy, housing specialized instrumentation including high-resolution transmission electron microscopes in environmentally isolated rooms. The group collaborates extensively with other research teams at Cornell and worldwide, focusing on understanding materials atom by atom. Current research directions include applying machine learning to electron microscopy data analysis, developing cryogenic techniques for studying low-melting-point materials, and exploring quantum phenomena in engineered materials systems.
Erhan Kutanoglu is an Associate Professor in the Operations Research and Industrial Engineering Graduate Program at The University of Texas at Austin's Cockrell School of Engineering. He joined the faculty in 2002 and received a National Science Foundation Early Career Development Award that year. His research focuses on integrating predictive models with stochastic optimization to address challenges in disaster resilience, humanitarian logistics, and semiconductor manufacturing. Key areas include hurricane mitigation, power grid resilience, and supply chain optimization. Education: PhD in Industrial Engineering from Lehigh University (1999). Research Interests: Applied operations research for manufacturing/service logistics, disaster resilience decision-making, semiconductor cycle time optimization, and inventory modeling. Recent work emphasizes hurricane evacuation planning, flood mitigation for critical infrastructure, and equity considerations in grid resilience. Publications: Over 50 peer-reviewed articles in journals like IEEE Transactions, European Journal of Operational Research, and Annals of Operations Research. Notable work includes models for power grid resilience, patient evacuation strategies, and semiconductor manufacturing efficiency. Awards: NSF CAREER Award (2002), recognized for contributions to service logistics optimization and stochastic modeling. Advising & Grants: Advised graduate students on projects involving hurricane preparedness and semiconductor scheduling. Active in collaborative research with industry partners to streamline manufacturing processes and enhance disaster response systems. Labs/Teams: Engaged with the Cockrell School's infrastructure resilience research groups and interdisciplinary teams addressing climate adaptation challenges.
John Folkesson is an Associate Professor at the Department of Robotics, Perception and Learning at KTH Royal Institute of Technology. His research focuses on mobile robotics, underwater autonomous vehicles (AUVs), and Simultaneous Localization and Mapping (SLAM), particularly addressing challenges in dynamic underwater environments. He leads the AUV group within the Swedish Maritime Robotics Centre (SMaRC2.0) and supervises multiple PhD projects, including those funded by Ocean Infinity and Vinnova. Folkesson has pioneered work on sonar-based SLAM, bathymetric mapping, and autonomous underwater navigation without human intervention. He teaches courses such as Probabilistic Graphical Models (DD2420) and Applied Estimation (EL2320). Recent projects include developing neural rendering techniques for sidescan SLAM and automatic launch systems for AUVs in collaboration with Purdue University and SAAB. His research emphasizes long-term autonomy, environmental ambiguity, and sensor data interpretation in unstructured underwater scenarios. Education: PhD in Robotics (2005, KTH Royal Institute of Technology) Recent Funding: 2024 projects include ALARS (Vinnova), WASP WARA-PS, and industrial collaborations. Research Interests Folkesson's work spans underwater robotics, SLAM algorithms, and sensor fusion. Key areas include: Underwater SLAM and sonar modeling Bathymetric reconstruction using neural networks Autonomous decision-making in AUV missions Real-time terrain modeling and localization Articles Trends Recent publications emphasize neural networks for SLAM optimization, sonar data processing, and autonomous underwater systems. Themes include real-time bathymetric mapping, sensor fusion in dynamic environments, and neural rendering techniques for improving navigation accuracy. Folkesson's work bridges theory and practice, with applications in marine robotics and industrial surveys. Advising & Grants PhD supervision: AUV perception (2024), SLAM with Ocean Infinity, event-response AUV systems. Collaborations: Purdue University, SAAB, Ocean Infinity. Course responsibilities: Over 10 advanced robotics and engineering courses at KTH. Labs & Teams Lead of SMaRC2.0, KTH's official research center for maritime robotics. Active in developing AUV systems for long-duration missions, including ice-covered and deep-sea exploration.
Orly Linovski is an Associate Professor in the Department of City Planning at the University of Manitoba’s Faculty of Architecture. Her work bridges professional planning practice, political processes, and urban space, emphasizing equity and justice. She holds a PhD from UCLA and advanced degrees from the University of Toronto and McGill University. Education: PhD (Urban Planning), University of California, Los Angeles M.Sc. (Urban Planning), University of Toronto B.A. (Geography/Urban Systems), McGill University Research focuses on transportation equity, private sector consulting roles, and municipal governance. Notable themes include equity in transit planning, professional practice dynamics in publicly traded firms, and participatory engagement strategies. Her work has been funded by SSHRC, MITACS, and the Centre for Professional and Applied Ethics. Teaching includes courses like Transportation and Urban Form (ARCG 7080) and Planning Research Methods (CITY 7020). She supervises students exploring transportation equity, professional planning practice, or privatization in planning, particularly with GIS or qualitative methods. Grants and Awards: Her research has been supported by SSHRC, MITACS, and other institutions. Collaborations include projects like the SSHRC-funded study on transit equity for marginalized groups in Canada. Labs/Teams: Active in the Manitoba Professional Planners Institute and as a Registered Professional Planner. Engaged in community-based research through partnerships highlighted in her SSHRC-funded projects.
Dr. Sajid Alavi is a Professor in the Department of Grain Science and Industry at Kansas State University. He joined the faculty in 2002 after earning his Ph.D. in Food Science/Food Engineering from Cornell University (2002), M.S. in Agricultural and Biological Engineering from Penn State (1997), and B.S. in Agricultural Engineering from IIT (1995). His research focuses on extrusion processing in food, pet food, and feed applications, with expertise in rheology, food microstructure imaging, and process sustainability. He leads global projects in Africa, Brazil, India, and beyond, emphasizing sustainable food technologies and AI-driven processing innovations. Dr. Alavi is a recipient of the 2010 Young Research Scientist Award from the Cereals & Grains Association. He teaches GRSC 620 (Intro to Extrusion Processing) and GRSC 820 (Advanced Extrusion Processing), and has trained over 1,000 industry leaders through his renowned 'Extrusion Processing: Technology and Commercialization' short course. His work bridges food science and engineering, addressing challenges in plant-based meat analogs, nutrient bioavailability, and food aid product development. Key facilities associated with his work include the BIVAP Feed Quality Assurance Lab and Hal Ross Flour Mill. His research spans sensory analysis of meat alternatives, fiber utilization in pet food, and sustainability assessments of novel crops like intermediate wheatgrass. Recent studies explore insect protein in pet food, AI-driven extrusion optimization, and iron bioavailability in fortified foods. Dr. Alavi’s contributions span academic, industrial, and global food security domains, reflecting a commitment to innovative, scalable food solutions.
Grégoire DANOY is a Researcher at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability, and Trust (SnT) and Head of the Parallel Computing and Optimization Group (PCOG). He specializes in artificial intelligence, with a focus on optimization algorithms, machine learning, and swarm intelligence. His work addresses challenges in cloud computing, high-performance computing, smart mobility, and unmanned autonomous systems like drone swarms. He has authored over 150 publications, including articles in IEEE Transactions and conferences like NeurIPS and GECCO. He currently leads major projects such as UltraBO (€1.019M), ADHOC (€1.291M), and SERENITY (€1.228M), collaborating with institutions in France and Poland. Education: PhD in Computer Science (2008) from École Nationale Supérieure des Mines de Saint-Étienne, Master’s in Computer Science (2004), and Industrial Engineering Degree (2003) from Luxembourg University of Applied Sciences. Research Interests: Developing novel AI techniques for solving large-scale optimization problems, with applications in distributed systems, autonomous robotics, and federated learning. He emphasizes scalable solutions for combinatorial challenges using parallel computing and swarm intelligence. Grants & Projects: Principal Investigator for EU-funded initiatives like ADARS (2021–2024) and FNR PoC/SIMMS (2019–2021). His work bridges academia and industry, with technology transfer projects in autonomous robot swarms. Awards: Recognitions include the Best Student Paper Nomination (2022), IEEE CybConf Best Paper Award (2017), and ACM GECCO nominations (2016, 2009). He serves on the editorial board of Engineering Applications of Artificial Intelligence (EAAI). Labs & Teams: Leads the Parallel Computing and Optimization Group (PCOG), focusing on interdisciplinary research in AI and distributed systems. He also contributes to outreach programs like FNR's Researchers at School.
Professor Vishnu Pareek is the John Curtin Distinguished Professor at Curtin University, leading the Western Australian School of Mines (WASM) within the Faculty of Science and Engineering. He has held academic roles including Dean of Engineering, Head of School, and various professorships since 2002. His research focuses on multiphase flow modeling, computational fluid dynamics, and reactor engineering, with applications in energy and chemical processes. He holds a BE (Hons) from MNIT, MTech from IIT Delhi, and a PhD from UNSW. Key research interests include LNG process modeling, erosion modeling, and granular flow dynamics. He has authored over 200 peer-reviewed publications, with recent work emphasizing structured packing design, biomass gasification, and additive manufacturing for process intensification. Notable projects include CFD-ANN hybrid models for fluidized beds and experimental studies on 3D-printed structured packings. His expertise spans industrial collaborations in LNG safety, fluid catalytic cracking, and biofuel production. Teaching areas include chemical engineering fundamentals and process systems engineering. He advises on energy policy and leads research teams in multiphase flow and reactor design.
Dr. Natalie Simpson is Professor and Associate Dean for Graduate Programs at the University at Buffalo's School of Management, Department of Operations Management and Strategy. She holds a PhD and MBA from the University of Florida, and a BFA from North Carolina School of the Arts. Her research explores emergency response systems , supply chain logistics , and educational technology , with particular focus on operational challenges in crisis management. She investigates hyper-project coordination in emergency contexts and resource allocation frameworks for incident commanders. Simpson's scholarly contributions show strong emphasis on: Modeling emergency response operations and supply chain vulnerabilities Developing pedagogical innovations for operations management education Analyzing healthcare workflow efficiency and disaster management systems Her extensive recognition includes: Decision Sciences Institute's Best Case Studies Award (2005) National Instructional Innovation Award (2004) SUNY Chancellor's Award for Excellence in Teaching (2002) Grinter Fellowship and Matherly Scholarship As Academic Director of Digital Access Education, she leads technology-enhanced learning initiatives and advises graduate programs. Administrative responsibilities include heading the Digital Access Working Group and serving on editorial boards for Decision Sciences.
Dr. Sajedul Talukder is an Assistant Professor in the Department of Computer Science at The University of Texas at El Paso (UTEP), directing the SUPREME Lab. He holds a Ph.D. in Computer Science from Florida International University (2019) and has held prior faculty positions at Southern Illinois University (2021-2024) and Pennsylvania Western University (2019-2021). Education: Ph.D. in Computer Science, Florida International University (2019) M.S. in Computer Science, Florida International University (2018) B.S. in Computer Science and Engineering, Bangladesh University of Engineering and Technology (2014) Research Interests: Focuses on cybersecurity, privacy-enhanced machine learning, and AI-driven solutions for social good. Key areas include: Security and privacy in online systems Abuse detection in social networks Quantum security and distributed systems Federated learning for healthcare and industrial IoT His work emphasizes practical applications like AI for nuclear plant cybersecurity and mitigating sockpuppet attacks. Recent Article Trends: Recent publications highlight advancements in federated learning frameworks (e.g., SAFARI, FLASH), context-aware emotion detection (CAMERA), and AI-driven nuclear facility security (ContextGPT, AML-TIN). These contributions address privacy, scalability, and real-time threat monitoring. Awards & Grants: $500K NRC grant (2024) for AI-driven nuclear plant cybersecurity NSF CISE CRII Award ($157K) for sockpuppet defense IMEC/NIST grant ($99K) for industrial IoT security Best Paper Awards (ICEEICT 2014, ACM SAC 2022) Advising & Labs: Mentored over 40 students (K-12 to Ph.D.), including 2 recent M.S. graduates. Leads SUPREME Lab and affiliated with UTEP AI Institute and NSF IDEAS Center. Active in program committees for ASONAM, ICWSM, and CHI.
Sean McGinnis serves as a Professor of Practice and Director of the Green Engineering Program within the Department of Materials Science and Engineering at Virginia Tech's College of Engineering. His academic and professional endeavors focus on advancing sustainable engineering practices through education, research, and industry collaboration. Dr. McGinnis earned his educational qualifications from prestigious institutions: a B.S. in Chemical Engineering and Materials Science from the University of Minnesota, followed by a Ph.D. in Materials Science and Engineering from Stanford University. His research program is centered on Green Engineering principles, with specific expertise in Life Cycle Assessment, Sustainable Manufacturing Processes, Design for Environment, Carbon Footprint Analysis, Renewable Energy systems, and Interdisciplinary Education methodologies. He has pioneered courses such as Introduction to Green Engineering (ENGR 3124) and Environmental Life Cycle Analysis (ENGR 4134) that equip students with tools to evaluate environmental impacts across product lifecycles. His Earth Sustainability course (UCCS 2984) further extends this interdisciplinary approach to broader societal challenges. Analysis of his publication record reveals a consistent trajectory toward sustainable technology innovation, particularly in the life cycle assessment of nanomaterials and waste streams. Recent work examines circular economy models for nanowaste recycling, environmental implications of nanoparticle synthesis, and the development of optical systems that mitigate harmful blue light exposure while maintaining visual performance. This research bridges materials science, environmental engineering, and human health considerations. Professional recognition for Dr. McGinnis includes: LEED AP BD&C certification Life Cycle Assessment Certified Professional designation Johnson & Johnson Standards of Leadership Award (2003) In his leadership capacity as Director of the Green Engineering Program, Dr. McGinnis oversees curriculum development that integrates sustainability across engineering disciplines. He has published on educational methodologies for assessing interdisciplinary integration of green engineering knowledge and has organized introductory green engineering courses for undergraduates. His work extends to operational phase life cycle assessment of facilities and sustainable provision of food and water systems.
Reza Ghabcheloo is a Professor at Tampere University, affiliated with the Faculty of Engineering and Natural Sciences and the Department of Automation Technology and Mechanical Engineering. He leads the Robotics major and the international Automation Engineering program. His research focuses on autonomous mobile machines, robotics, control systems, and safety engineering, with specific interests in construction robotics, sensor fusion, and hydraulic systems. He co-leads the Autonomous Mobile Machines Group and is associated with the Robotics and Intelligent Machines Lab and the Innovative Hydraulics and Automation Lab. His research emphasizes developing autonomous systems for off-road machinery, safe control strategies, and energy-efficient automation. He has published extensively on topics such as reinforcement learning for crane control, radar-based perception, and safety architectures for autonomous systems. His work bridges robotics, control theory, and industrial automation, addressing challenges in heavy-duty machinery and real-world robotic applications. Research Group: Autonomous Mobile Machines Group Labs: Robotics and Intelligent Machines Lab, Innovative Hydraulics and Automation Lab Key Projects: Safety of automated off-road machinery, machine learning for autonomous loading, and trajectory optimization
Professor Graham Sander is a Professor of Hydrology at Loughborough University, affiliated with the School of Civil and Building Engineering. His research focuses on mathematical modeling of soil erosion, unsaturated soil flow, contaminant transport dynamics, and nonlinear diffusion-convection equations. He leads the NERC-funded project on multi-dimensional soil erosion and chemical transport, collaborating with Lancaster University’s Department of Environmental Science. His academic qualifications include a BSc (Hons) and PhD. Recent research emphasizes integrating particle size-selective models to predict sediment and contaminant delivery to water bodies, supported by lab and field experiments. He has also pioneered pseudospectral methods for infiltration modeling and explored flood risk management through nature-based solutions like leaky barriers. Key contributions include advancing understanding of rainfall-driven erosion dynamics, including splash effects and rock fragment coverage impacts. His work spans experimental hydrology, numerical simulation, and interdisciplinary approaches to environmental challenges. He co-edits hydrology journals and advocates for rigorous scientific communication in the field. Grants: NERC-funded soil erosion project, Australian Research Council project on unsaturated soil flow. Labs/Teams: Collaborator with Lancaster University’s Environmental Science Department.
Louis Hickman is an Assistant Professor of Industrial-Organizational Psychology at Virginia Tech’s Department of Psychology. He also serves as a Visiting Academic at Amazon and holds a Senior Fellow position at Wharton People Analytics, University of Pennsylvania. His research bridges technology and work, focusing on machine learning applications in organizational science, particularly automated interviews and algorithmic fairness. He leads the Workplace Assessment and Social Perceptions (WASP) Lab, exploring how biases influence hiring and using AI to reduce algorithmic bias. Hickman holds a Ph.D. in Industrial-Organizational Psychology from Purdue University (2021), alongside advanced degrees in Computer Science and Creative Writing. His work emphasizes interdisciplinary collaboration, spanning psychology, computer science, and management. Research Interests: Automated personnel assessment via AI Algorithmic bias mitigation in hiring Machine learning applications in HR and education Interpersonal perception dynamics Unproctored testing in the AI era Publications: Recent work examines automated interview validity, LLM impacts on testing, and recruitment algorithm ethics. His 2025 studies highlight risks of unproctored testing and bias in automated systems. Earlier research (2023–2022) explores text mining for personality assessment and fairness in AI-driven selection. Awards: None explicitly mentioned, though his work has been widely cited in organizational psychology and AI ethics domains. Advising & Labs: Currently not accepting graduate students for 2026, but oversees the WASP Lab. Past research collaborations include projects on LLM competencies, bias simulation, and algorithmic fairness frameworks. Grants and funding sources are unspecified in provided text.
