Vittorio Solina is a Researcher at the Department of Mechanical, Energy and Management Engineering (DIMEG) of the University of Calabria, specializing in industrial systems simulation and digital twin technologies. His work spans Industry 4.0/5.0 applications, supply chain optimization, and human-robot collaboration. Affiliation: University of Calabria Academic Role: Researcher (Legge 240/10) Research Focus: Digital twins, simulation modeling, sustainable manufacturing His research emphasizes advanced simulation tools for decision support, including applications in Industry 4.0 , Smart Manufacturing , and Agri-Food Supply Chains . Recent work explores adaptive simulation optimization for Industry 5.0 and 5G-enabled robotics. Key publication trends include: (1) Digital twin integration for supply chain resilience (2024-2025), (2) Simulation automation methodologies (2025), and (3) Human-robot collaboration frameworks (2024).
Ben Nobbs-Thiessen is an Assistant Professor of History and Chair of Mennonite Studies at the University of Winnipeg. He holds a PhD from Emory University (2016), with prior roles as a research associate at Arizona State University (2016–18) and a teaching fellow at Washington State University (2018–2020). His research focuses on Mennonite migrations across the Americas, environmental change, and indigenous-settler relations. He teaches courses on Latin American Mennonites and Mennonite Studies II. His research combines archival work, field studies, and oral histories from Bolivia, Paraguay, Mexico, Canada, and the U.S. His 2020 book Landscape of Migration examines Bolivia’s lowland colonization and its intersection with Mennonite and indigenous communities. Current projects include a century-long study of Mexico-Canada Mennonite migrations linked to water scarcity and neoliberal reforms. His publications span journals like Journal of Latin American Studies and Agricultural History , addressing topics like frontier development, foodways, and border conflicts. His transnational approach bridges environmental, migration, and colonial histories. He contributes to interdisciplinary initiatives such as the Newberry Consortium in American Indian Studies and the Centre for Rupert's Land Studies.
Minh Q. Phan is an Associate Professor of Engineering at Dartmouth College's Thayer School of Engineering. His expertise spans system identification, iterative learning control, model predictive control, robotic swarm control, and intelligent control systems. He holds a BS from the University of California, Berkeley, and MS/M.Phil/PhD degrees from Columbia University in Mechanical Engineering. Dr. Phan has contributed to over 50 peer-reviewed publications and serves as an Associate Editor for the Journal of Guidance, Control, and Dynamics. His research focuses on advancing control theory applications in robotics, structural health monitoring, and sustainable construction materials. Key contributions include the development of OKID (Observer/Kalman Filter Identification) methods and bilinear system identification frameworks. Education History: Bachelor of Science in Mechanical Engineering, UC Berkeley, 1985 Master of Science in Mechanical Engineering, Columbia University, 1986 Master of Philosophy in Mechanical Engineering, Columbia University, 1988 Doctor of Philosophy in Mechanical Engineering, Columbia University, 1989 Research Interests: Advanced control methodologies for dynamic systems Model-based predictive control strategies Applications in robotics and aerospace engineering Structural health monitoring via system identification Machine learning for materials science Teaching Responsibilities include courses like ENGG 149 (Systems Identification), ENGS 145 (Modern Control Theory), and ENGG 148 (Structural Mechanics). His work bridges theoretical control systems with practical industrial applications, including automation in food processing and sustainable construction practices. Dr. Phan has collaborated on projects addressing viral epidemiology in Vietnam and coastal erosion mitigation strategies.
Amitabha Bagchi is a Professor in the Department of Computer Science and Engineering at IIT Delhi. His research spans data algorithmics, probability, networks, and theoretical computer science, with applications in distributed systems, social networks, and AI-driven platforms. He has published extensively in leading venues such as SIGMOD, VLDB, ICDE, AAAI, and KDD, often collaborating with students and researchers on problems involving graph algorithms, fairness, and large-scale data analysis. Research Interests: His primary research interests include Data Algorithmics, Probability and Networks, Theoretical Computer Science, Distributed Algorithms, Graph Algorithms, and Machine Learning Theory. He investigates algorithmic foundations for real-world problems such as food delivery optimization, social network analysis, and efficient data structures for streaming and large graphs. Publication Trends: Recent publications focus on fairness in gig economy platforms, efficient solvers for graph Laplacians, generalization in neural networks, and temporal graph querying. His work combines theoretical rigor with practical impact, often involving GPU acceleration, distributed computing, and data-aware algorithm design. Scientific Service: Editor, Algorithms (2020–present) Editor, Journal of Discrete Algorithms , Elsevier (2006–2018) Guest Editor, special issue on Algorithms for Shortest Paths in Dynamic and Evolving Networks , Algorithms (2021) Volume Editor for proceedings of ESA, ATMOS, COCOON, and others Conference Leadership: He has served on numerous program committees and as chair for conferences including ESA (Engineering Track, 2016), ATMOS (2020), and ICALP (2019). His involvement spans algorithmic engineering, transportation optimization, and theoretical computer science forums. Teaching: He currently teaches COL863: Special Topics in Theoretical Computer Science on concentration inequalities and their applications. He has previously taught advanced courses in algorithms and data structures.
Daniel Epstein is an Associate Professor in the Department of Informatics at the University of California, Irvine (UCI), where he leads the Personal Informatics Everyday (PIE) Lab. He holds a courtesy appointment in the Department of Computer Science and is affiliated with the Connected Learning Lab, Institute for Future Health, and Accessibility Research Collective. Epstein earned his Ph.D. in Computer Science & Engineering from the University of Washington and a B.S. in Computer Science from the University of Virginia. His research focuses on how personal tracking technology can better serve diverse users' needs, emphasizing ethical design, health equity, and long-term user engagement. Notable areas include pregnancy tracking, wearable technology for children and ADHD management, and AI-driven health chatbots. Epstein has received grants from NSF, Snap Inc., and UCI programs, totaling over $650k in funding. Epstein's work bridges HCI and health informatics, with over 60 peer-reviewed publications. He advises six Ph.D. students and has mentored numerous undergraduates. Awards include the ACM Senior Member distinction (2025), CHI Best Paper (2023), and NSF CAREER Award (2023). Professional service includes program committees for CHI, CSCW, and UbiComp, plus leadership roles in workshops like the Workgroup on Interactive Systems in Healthcare (WISH). Beyond academia, Epstein enjoys hiking, collecting turtle figures, and advocating for accessible technology.
Tanzeem Choudhury is a Professor at the Jacobs Technion-Cornell Institute at Cornell Tech, specializing in Information Sciences. She co-founded HealthRhythms Inc. and directs the People-Aware Computing lab, focusing on machine learning for human activity and social network analysis. Her work pioneered the 'Reality Mining' field, leveraging mobile sensors to model social interactions. Choudhury holds a Ph.D. from MIT's Media Lab and B.S. in Electrical Engineering from the University of Rochester. Her research spans wearable tech, mental health interventions, and ubiquitous computing. Notable projects include affective touch wearables for anxiety reduction, stress detection systems (e.g., StressSense), and the BeWell app for well-being monitoring. Choudhury has been recognized as an ACM Fellow and SIGCHI Academy member, with impactful contributions to healthcare technology and sensor-based behavioral analysis. Current lab initiatives include developing closed-loop interventions for mental health, equity-driven sensing systems, and exploring AI integration in psychotherapy. She advises PhD students on topics like digital biomarkers, burnout detection in healthcare workers, and smartphone-based mental health assessment. Education: Ph.D. in Media Arts & Sciences (MIT), M.S. (MIT Media Lab), B.S. in Electrical Engineering (University of Rochester) Lab Team: Includes PhD candidates (Dan Adler, Yiran Zhao) and researchers focusing on health AI, wearable devices, and clinical applications. Major Awards: ACM Fellowship (2020), SIGCHI Academy (2018), Best Paper Awards at Ubicomp and MobileHCI conferences. Key Projects: OptoBeat (skin-tone calibrated blood oxygen sensor), EmotionCheck (anxiety intervention), and Affective Touch devices.
Alice Reznickova is an Associate Teaching Professor and Capstone Lead in the Masters of Environment (MENV) Program at the University of Colorado. She holds a PhD in Environment and Resources from the University of Wisconsin-Madison, an MS in Chemistry from the same institution, and a BA in Chemistry from Smith College. Her work focuses on sustainability education, food systems, urban design, and policy, with a strong emphasis on equity and community engagement. Previously served as Director of Sustainable Urban Environments at NYU Tandon School of Engineering Worked as Campus Sustainability Director at Ripon College Interdisciplinary Scientist at Union of Concerned Scientists, focusing on sustainable food policy Reznickova pioneered NYC's first urban agriculture census and developed experiential learning programs such as the C40 Students Reinvent Cities competition and Ripon's Sustainability Fellowships. Her research addresses food justice, nutrition security, and inclusive sustainability practices.
Bobak Mortazavi is an Associate Professor in the Department of Computer Science & Engineering at Texas A&M University. His research focuses on medical analytics, machine learning, wearable sensors, and cyber-physical systems. He leads interdisciplinary projects in healthcare technology, including AI-driven diagnostics and predictive modeling for cardiovascular diseases. He has received notable awards such as the Best Demonstration Award at IEEE EMBS 2012 and the Best Paper Award at the Fourth International Conference on Data Analytics 2015. His work bridges machine learning with clinical applications, emphasizing practical solutions for healthcare challenges. Recent research includes developing AI tools for aortic stenosis detection, electrolyte estimation via ECG, and real-time patient monitoring systems. He collaborates with industry and academic partners to advance telemedicine and wearable health technologies. Key contributions include the SMART-LV project for smartphone-based cardiac diagnostics and the ArterialNet framework for blood pressure reconstruction using wearable sensors. His work is published in top journals like IEEE Journal of Biomedical and Health Informatics and Elsevier's Pervasive and Mobile Computing.
Hauke Smidt is a Personal Professor at Wageningen University & Research, affiliated with the Molecular Ecology group within the Faculty of Veterinary Medicine and Animal Sciences. His research focuses on microbial ecology, particularly gut microbiota dynamics, antibiotic resistance, environmental microbiology, and the One Health framework. Smidt has supervised over 50 PhD students and contributed to 519 publications, including studies on microbiome resilience, fermented food impacts, and bioremediation strategies. Education and affiliations include leadership roles in projects addressing antimicrobial resistance, pig health, and drinking water microbiology. His work spans collaborations across Europe and Asia, with notable contributions to understanding microbial community responses to environmental stressors and dietary interventions. Smidt is actively involved in public outreach, as seen in media contributions discussing microbiome research and health benefits. Key projects include aquifer treatment for drinking water, microbiome simulation models, and combating plant pathogens. His awards and recognition are not explicitly listed, but his extensive publication record and project leadership highlight significant academic impact.
Tim Althoff is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington, specializing in Artificial Intelligence and Human-Centered Computing. His research focuses on behavioral data science, combining Data Science Natural Language Processing Social Computing Human-Centered AI Ethics & Fairness to extract insights about health and well-being. Recent publications highlight advancements in: Mental health support through AI Wearable sensor health monitoring Online community analysis Reproducibility in data science Public health interventions with notable papers in ACL, Nature Machine Intelligence, and NeurIPS. Scientific recognition includes: ACL 2023 Outstanding Paper Award WWW 2021 Best Paper Award Double ICWSM 2021 Best Paper Awards SIGKDD Dissertation Award 2019 Fulbright Scholarship German National Merit Foundation Actively mentoring postdoctoral researchers and seeking PhD students in areas like neural representation learning, NLP applications to psychology, and mobile health technologies through his Behavioral Data Science Group .
Hugo Jose Herrera de Leon is a Research Fellow at the Department of Geography, University of Bergen. His work focuses on socio-ecological resilience, system dynamics modeling, and policy planning for climate change adaptation. He is affiliated with the System Dynamics research group and has presented at major international conferences like the European Conference on Operational Research and the International System Dynamics Conference. His research explores bioeconomy transitions in Norwegian agriculture, resilience in farming systems, and participatory policy design. He has been funded by the Research Council of Norway (2021-2025) for projects investigating sustainable transitions in rural systems. Notable contributions include developing frameworks for assessing resilience through hybrid simulations and collaborative modeling approaches. Publications span topics like microworld simulations for policymaking, organizational impacts of group model-building, and system dynamics applications in food security. His doctoral thesis (2018) addressed public policy strategies for enhancing resilience in socio-ecological systems under climate change.
Brian Y. Lim is an Associate Professor in the Department of Computer Science at the National University of Singapore (NUS), leading the NUS Ubicomp Lab. His research integrates Explainable AI (XAI) , Human-Computer Interaction (HCI) , and Ubiquitous Computing to address societal challenges in healthcare , wellness , and urban sustainability . He holds a Ph.D. and M.S. in Human-Computer Interaction from Carnegie Mellon University and a B.S. in Engineering Physics from Cornell University. Education: B.S., Engineering Physics, Cornell University M.S., Human-Computer Interaction, Carnegie Mellon University Ph.D., Human-Computer Interaction, Carnegie Mellon University His research focuses on designing human-centric AI systems that prioritize transparency and user trust. Key areas include explainable AI algorithms , context-aware computing , data visualization , and trustworthy machine learning . Projects like RexNet and Directed Diversity demonstrate his work in creating relatable AI explanations and enhancing crowd creativity through algorithmic prompting. Recent publications (2022–2025) highlight advancements in faithful visual explanations , user-centric XAI frameworks , and healthcare analytics . Notable awards include the CHI'22 Best Paper Award , IMWUT Distinguished Paper Award , and MOE Outstanding Mentor Award . He has advised numerous PhD and Masters students and leads the NUS Ubicomp Lab, which explores applications like TasteHealthy (food recommendation app) and Food Loves Fellowship (narrative data visualization).
Prof. Dr. Jörg E. Drewes is a Full Professor and Chair at the Research Institute for Urban Water Management, Technical University of Munich (TUM), Germany. He also serves as Academic Program Director for Environmental Engineering at TUM and is a co-founder of Waterloop Solutions GmbH. His work spans advanced water treatment technologies, wastewater reuse, and environmental health surveillance. Affiliations : TUM, Colorado School of Mines, King Abdullah University of Science and Technology, UNSW Water Research Centre Research focuses on advanced oxidation processes , membrane fouling mitigation , micropollutant removal , and wastewater epidemiology for public health. His team develops integrated systems for water-energy-food nexus applications and graphene oxide-based treatment solutions. Recent publications address PFAS remediation , UVC-LED pre-treatment for biofouling control, and GIS-based wastewater surveillance models . Awards include the Bayerische Staatsmedaille (2023) and William Dunbar Medal (2022). 2023 : Bayerische Staatsmedaille 2022 : William Dunbar Medal 2018 : IWA Fellow He leads the Nexus@TUM initiative and contributes to EU water resilience strategies. Collaborations include projects in Germany, China, India, and Spain, focusing on climate-resilient water systems and resource recovery .
Benjamin Carrison-Schafer is an Associate Professor and Assistant Dean for Graduate Student Success at the Department of Electrical and Computer Engineering, Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. He leads the Design Automation and Reconfigurable Computing Laboratory (DARClab), focusing on systems, high-level design, and programming methodologies for VLSI computing systems and FPGAs. Education: MBA from McGill University, Canada (2012) PhD in Electrical and Electronic Engineering from University of Birmingham, UK (2003) Research Interests: His work spans modeling, analysis, synthesis, optimization, and implementation of VLSI systems. Current projects include: Approximate Computing, High-Level Synthesis Design Space Exploration, Hardware Security, Behavioral MPSoC Optimization, and Automatic Fault-Tolerant System Generation. His research blends theory and practice, using analytical and experimental techniques to solve real-world problems. Publication Trends: Recent publications demonstrate strong focus on hardware security, FPGA optimization, and automated design methodologies. Key themes include High-Level Synthesis innovations, hardware acceleration techniques, and cross-disciplinary applications of reconfigurable computing. His work frequently addresses challenges in hardware trustworthiness, energy efficiency, and design automation scalability. Professional Activities: Associate Editor: IEEE Transactions on Sustainable Computing (2022-present) Conference Chair: ICCD 2024, DCAS 2024 Program Committee: ASP-DAC, DATE, FCCM, GLSVLSI Advising & Labs: Supervises multiple PhD and Master's students at DARClab. Current research includes domain-specific architecture design, hardware security, sustainable computing, and ML for VLSI design. The lab collaborates with industry partners like Renesas Electronics and develops commercial tools through spin-off company highX Technologies.
Brendan Russo serves as an Associate Professor in the Department of Civil Engineering, Construction Management, and Environmental Engineering at Northern Arizona University, where he conducts influential research in transportation safety and traffic engineering. His work focuses on improving safety outcomes for vulnerable road users through rigorous analysis of crash data, traffic operations, and emerging mobility technologies, with significant contributions to Arizona-specific transportation challenges and national safety practices. Russo's research program centers on bicycle and pedestrian safety, crash severity analysis, and the integration of autonomous systems into transportation networks. He employs advanced methodologies including spatial analysis, statistical modeling (e.g., random parameters bivariate probit models), and observational studies to investigate traffic stress levels, intersection safety, and the impacts of infrastructure treatments. His work consistently bridges theoretical transportation engineering with practical applications for safer community design. Analysis of Russo's recent publications reveals a strong emphasis on emerging transportation technologies and their safety implications, particularly regarding autonomous delivery robots and vehicle-pedestrian interactions, while maintaining core focus on traditional safety concerns like bicycle crash frequency and severity. His research demonstrates increasing integration of spatiotemporal analysis and scenario-based testing methodologies, with a clear geographic concentration on Arizona metropolitan regions that provides valuable localized insights applicable to broader transportation contexts. No scientific awards were mentioned in the provided text. No specific information about advising responsibilities or grant funding was provided in the text, though his extensive publication record and dataset contributions indicate active research leadership. Russo collaborates within a robust research network centered on transportation safety, frequently partnering with colleagues including Gehrke, Smaglik, and Holliday on projects involving field data collection, bicycle infrastructure evaluation, and safety performance metrics. His work leverages both observational studies and simulation approaches to develop data-driven guidance for transportation practitioners, with particular attention to Arizona's unique transportation environment and metropolitan planning challenges.
