Mikael Rinne is an Associate Professor in the Department of Civil Engineering at Aalto University's School of Engineering. His research focuses on rock fracture mechanics and its applications in various engineering contexts including nuclear waste repositories, geothermal energy systems, and underground construction. His expertise spans time-dependent rock failure mechanisms, fracture propagation models, and rock mechanics applications in energy storage and disposal systems. His work has direct applications in projects with Posiva Oy (nuclear waste repository), St1 Deepheat (geothermal energy), and mining operations with companies like First Quantum Minerals. Rinne's research integrates advanced numerical modeling with field applications, particularly in Finnish crystalline bedrock conditions. He has contributed significantly to understanding fracture initiation and propagation in rock masses under various stress conditions, with particular emphasis on long-term stability considerations for deep underground structures. His scholarly work demonstrates strong connections between theoretical fracture mechanics and practical engineering applications, with a focus on ensuring safety and reliability in rock engineering projects. His research has evolved from fundamental fracture mechanics studies to application-focused investigations addressing contemporary challenges in energy and waste management. Rinne has supervised doctoral research in rock mechanics and collaborates with researchers specializing in photogrammetry, virtual reality applications, and energy storage systems, creating a multidisciplinary approach to complex rock engineering problems.
Katherine L. Milkman (Katy) is the James G. Dinan Professor at the Wharton School of the University of Pennsylvania, with secondary appointments in Penn's Perelman School of Medicine and School of Arts & Sciences. She co-founded and co-directs the Behavior Change for Good Initiative, a research center dedicated to advancing the science of lasting behavior change. Her work integrates economics and psychology to address challenges like savings, exercise adherence, vaccination rates, and discrimination through large-scale field experiments. Education: PhD in Computer Science and Business from Harvard University; Bachelor's degree (summa cum laude) in Operations Research and Financial Engineering from Princeton University. Research Focus: Milkman's research leverages big data and behavioral science to understand decision-making failures (e.g., self-control, discrimination) and design scalable interventions. Her recent work emphasizes: Nudge-based strategies for education, health, and finance Diversity enhancement in organizational settings Habit formation through incentive structures Publication Trends: Her 15 most recent articles (2022-2025) primarily involve megastudies testing behavioral interventions. Key themes include: leveraging email/reminders to improve math education and vaccination rates; addressing loan delinquency through nudges; and using stereotyping dynamics to increase diversity in hiring. Over 80% employ field experiments across healthcare, finance, and education sectors. Awards & Honors: Thinkers50 Top Management Thinker (2021, 2023) Schmidt Futures Innovation Fellow (2022) Fellow, Association for Psychological Science (2020) Multiple teaching awards from Wharton (2015, 2016) William F. O’Dell Award for impactful research (2017) Advisory & Grants: Milkman has advised major organizations including The White House, Google, Walmart, and the U.S. Department of Defense. Her Behavior Change for Good Initiative secures funding for large-scale social impact research. She hosts Schwab's behavioral economics podcast Choiceology and contributes to policy through op-eds in The New York Times and Scientific American . Labs & Teams: Co-directs the Behavior Change for Good Initiative, collaborating with interdisciplinary researchers (e.g., Angela Duckworth, Sendhil Mullainathan) on longitudinal studies. The initiative designs and tests interventions across health, education, and savings domains.
Herb Winful is a Professor of Optics at the University of Michigan's College of Engineering, Department of Electrical and Computer Engineering. He specializes in nonlinear optics, laser physics, quantum tunneling , and photonics , with a focus on phenomena like superluminal group velocities, frequency comb generation, and light storage via stimulated Brillouin scattering. Research areas span quantum tunneling times , nonlinear photonic materials , and coherent beam combining in fiber laser arrays. His work includes frequency comb spectroscopy using quantum-well diode lasers, ultrafast erbium fiber lasers , and negative group delay engineering in birefringent waveguides. The article list reveals expertise in supercontinuum generation , evanescent wave dynamics , photonic crystals , and nonlinear pulse manipulation . Key subfields include stimulated Brillouin/Raman scattering , parabolic similaritons , and time-domain modeling of optical systems. Award-winning scientific contributions include resolving the Hartman effect paradox and optimizing fiber laser arrays for high-power applications. His research bridges theoretical insights with practical innovations in optical engineering and quantum optics .
Dr. Qian Zhang serves as Assistant Professor in the Robert M. Buchan Department of Mining at Queen's University's Smith Engineering, leading the Green Mining Value Chain (GreeMVC) Lab. His research develops strategic frameworks for sustainability and resilience throughout mining value chains, with emphasis on climate change mitigation and resource efficiency in global mineral systems. His academic foundation includes a Ph.D. in Urban Engineering from the University of Tokyo (awarded Japanese Government MEXT Scholarship), complemented by MSc and BSc degrees in Environmental Science plus a Minor in Economics from Peking University. Prior to his current role, he conducted postdoctoral research at the University of Victoria and University of Tokyo while consulting for the World Resources Institute on climate-energy initiatives. Dr. Zhang's expertise spans carbon footprint analysis , life-cycle assessment , and industrial ecology applied to mining systems. He employs advanced methodologies including input-output analysis and material flow accounting to model environmental pressures across urban infrastructure and mineral supply chains. His work specifically addresses greenhouse gas accounting, water-energy nexus challenges, and circular economy implementation in resource-intensive sectors. Recent publications reveal strong methodological convergence between artificial intelligence and environmental assessment, particularly in optimizing mining operations through reinforcement learning and geospatial analysis. Key thematic clusters include carbon accounting standardization, critical mineral sustainability, and policy-oriented modeling of environmental pressures throughout mineral value chains. His research program is supported by major competitive grants: NSERC Discovery Grant (2022-2027) SSHRC Institutional Grant (2023, 2025) NSERC Alliance Missions Grant (2023, 2024) Mitacs Accelerate Grant (2023, 2025) NFRF Exploration Grant (2025-2027) NRCan Energy Innovation Program (2025) Dr. Zhang actively mentors a dynamic research group comprising 10+ graduate students and postdocs, securing collaborative funding through institutional and federal channels. His GreeMVC Lab maintains active partnerships with industry leaders and government agencies to translate research into practical sustainability solutions for the mining sector, with current projects focusing on AI-driven fleet management and life-cycle assessment of mineral supply chains. The GreeMVC Lab operates as a multidisciplinary hub with structured mentorship programs, regular industry engagement events, and international collaborations including the COM symposium on sustainable circularity. The lab's physical space in Goodwin Hall supports advanced computational analysis of mining value chains while fostering innovation in green mining technologies through student-led research initiatives.
Dr. Barbara E. Jones serves as an Associate Professor in the Department of Internal Medicine at the University of Utah School of Medicine, with dual appointments in Pulmonary and Critical Care Medicine. Her clinical practice spans diverse healthcare settings within the Veterans Affairs system and academic medical centers, focusing on evidence-based adaptation of care to varied patient populations. Her educational background includes: M.D. from University of Washington School of Medicine B.A. in Philosophy from Dartmouth College Master of Science in Clinical Investigation (M.S.C.I) from University of Utah Postdoctoral Fellowship in Pulmonary and Critical Care Medicine at University of Utah Residency in Internal Medicine at University of Utah Dr. Jones' research centers on decision-making processes in pneumonia diagnosis and treatment, employing a tripartite informatics approach combining population analytics, cognitive behavior analysis, and clinical decision support systems. Her work specifically targets reducing diagnostic uncertainty and treatment variation across healthcare systems, with emphasis on equitable care delivery for diverse patient populations. Current projects investigate diagnostic discordance in community-acquired pneumonia, electronic surveillance for hospital-acquired infections, and machine learning applications for diagnostic error detection. Analysis of her 15 most recent publications reveals consistent focus on pneumonia management systems, with emerging emphasis on pandemic impacts on diagnostic practices and AI-driven quality improvement. Her work predominantly utilizes large VA healthcare datasets spanning 100+ medical centers, featuring mixed-methods approaches that integrate quantitative analytics with qualitative clinician experience assessment. Dr. Jones actively contributes to clinical guideline development and medical education through editorial work in major journals including Chest and Annals of Internal Medicine , where she frequently addresses controversies in pneumonia diagnosis and antibiotic stewardship. Her research program operates at the intersection of the University of Utah Health system and the Veterans Affairs national healthcare network, leveraging electronic clinical decision support implementations across diverse hospital settings including rural and critical access facilities. Current initiatives focus on real-time feedback systems for diagnostic performance improvement and automated surveillance for healthcare-associated infections.
Indranil Chowdhury is an Assistant Professor in the Department of Mathematics and Statistics at the Indian Institute of Technology Kanpur. He holds a Ph.D. from Tata Institute of Fundamental Research, Centre for Applicable Mathematics in Bengaluru (2017) and has previously served as a Postdoctoral Researcher at University of Zagreb, Croatia (2020-2022) and Norwegian University of Science and Technology, Trondheim, Norway (2018-2020). Ph.D: Tata Institute of Fundamental Research, Centre for Applicable Mathematics, Bengaluru, India (2017) PG: Tata Institute of Fundamental Research, Centre for Applicable Mathematics, Bengaluru, India (2012) UG: St. Xavier's College, Kolkata, India (2010) Dr. Chowdhury's research focuses on the theory and numerical analysis of partial differential equations, with particular expertise in nonlocal and fractional order problems and fully nonlinear equations. His work bridges theoretical mathematics with practical applications in areas such as mean field games, optimal control, and mathematical modeling. His research program demonstrates a consistent trajectory of advancing the mathematical understanding of complex nonlocal phenomena through rigorous analytical techniques and innovative numerical methods. His publication record reveals a strong focus on fractional calculus, nonlocal diffusion processes, and mean field games. The research shows progression from foundational work on fractional Poincaré inequalities to increasingly sophisticated studies of fully nonlinear mean field games with both local and nonlocal diffusions. His recent work (2023-2025) demonstrates continued innovation in the field, particularly in addressing strongly degenerate cases and establishing precise error bounds for numerical approximations. Dr. Chowdhury maintains an active research program with consistent publication output in high-impact journals such as Foundations of Computational Mathematics, SIAM Journal on Numerical Analysis, and Discrete and Continuous Dynamical Systems. His collaborative work with researchers across international institutions reflects the global significance of his contributions to the field of nonlocal partial differential equations.
Gururaj Mirle Vishwanath is an Assistant Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur. His research focuses on the integration of renewable energy sources into power systems, with particular emphasis on grid stability and control mechanisms. His educational background includes a PhD from IIT Roorkee (2020), an M.Tech from NITK Surathkal (2015), and a B.Tech from Visveswariah Technological University Belgaum (2009). Dr. Vishwanath's research interests span renewable energy integration challenges, machine learning applications to power systems, power converters for electric vehicles, power electronics applications to power systems, and microgrid control including black out scenarios, islanded operation, and energy management systems. His work addresses critical challenges in modern power systems as they transition toward greater renewable penetration. His recent publications demonstrate a strong focus on microgrid control architectures, voltage regulation schemes, and integration of wind energy systems with the grid. The research spans both theoretical control algorithms and practical implementation challenges for power system stability and reliability in the presence of renewable energy sources. Dr. Vishwanath works within the Advanced Centre for Electronic Systems (ACES) at IIT Kanpur, where his research contributes to advancing power system technologies for the evolving energy landscape.
Alton Russell is an Assistant Professor at the Department of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine and Health Sciences, McGill University. He serves as an Affiliate Investigator at the Research Institute of the McGill University Health Centre (RI-MUHC) and is affiliated with the Quantitative Life Sciences program. His research focuses on data-driven decision modeling to optimize healthcare resource allocation through methods in decision analysis, simulation, health economics, and machine learning. PhD in Management Science and Engineering (2021), Stanford University MSc in Management Science and Engineering (2018), Stanford University BSc in Industrial Engineering (Health Systems concentration) and Interdisciplinary Studies (Global Health and Sustainability concentration) (2014), North Carolina State University Russell's research program develops advanced models for blood safety, pediatric kidney disease management, opioid crisis interventions, and infectious disease surveillance. His lab (D3Mod) integrates individual-level data with machine learning and Bayesian statistics to address heterogeneity in patient populations and policy impacts. His work emphasizes open science practices, with publications and code archived via DOIs. Current research themes include personalized donor risk assessment, emergency service optimization, and harmonization of serosurveillance data. Russell teaches advanced decision modeling (EPIB 676) and economic evaluation of health programs (PPHS 528) at McGill.
Benjamin D. Piekut is a Professor in the Department of Music at Cornell University, New York. His academic journey includes a Ph.D. in historical musicology from Columbia University, M.A. in composition from Mills College, and B.A. in music and philosophy from Hampshire College. Research Focus: Music and performance after 1950, critical improvisation studies, sound art, and intersections of music with race/gender politics Key Publications: Experimentalism Otherwise (2011), Henry Cow: The World Is a Problem (2019), and The Oxford Handbook of Critical Improvisation Studies (2016) Recognitions: Whiting Foundation Fellow, MIT Press '50 Most Influential Articles' selection, and funding from NEH and Arts and Humanities Research Council Editorial Roles: Co-editor of Oxford Handbook, editor of Tomorrow is the Question, and special issues for Contemporary Music Review and Third Text Contemporary Engagement: Curatorial advisory board member at Blank Forms (NYC), contributor to Flash Art and Texte zur Kunst His recent work explores the 'vernacular avant-garde' concept, analyzing experimental music's relationship with commercial institutions and mass audiences. Research spans from 1960s New York avant-garde to 1970s European experimental rock band Henry Cow. Current projects include sound art preservation challenges and media-specific music analysis.
Fabio Furini is an Associate Professor at the Department of Computer Science, Automatics, and Management (DIAG) at Sapienza University of Rome since September 2021. Prior to this position, he served as a CNR researcher at IASI-CNR in Rome (2020-2021), Maître de Conférences at Université Paris-Dauphine, France (2013-2019), postdoctoral researcher at Université Paris-13, France (2012-2013), and research fellow at the University of Bologna (2011-2012). His educational background includes a Ph.D. in Control Engineering and Operations Research from the University of Bologna in 2011. He further obtained the Habilitation à Diriger des Recherches (HDR) in France in 2017 and the National Scientific Qualification for Full Professor in Operations Research in Italy in 2019. Fabio Furini conducts theoretical and methodological research on Combinatorial Optimization and Operations Research. His primary focus is on developing exact algorithms based on decomposition and reformulation techniques for integer linear programming problems. His research spans various applications including network optimization, graph theory, and combinatorial problems such as the maximum clique problem, bin packing problem, and vertex separator problem. His work often bridges theoretical developments with practical applications in transportation, logistics, and network security. His recent publications demonstrate a strong focus on exact algorithms for combinatorial optimization problems, particularly in network interdiction, bin packing with temporal constraints, and graph-based problems. His work consistently combines integer programming techniques with combinatorial search methods to develop novel formulations and efficient solution approaches that advance the state-of-the-art in these domains. Among his notable scientific awards are the Prime d'encadrement doctoral et de recherche (PEDR), which he received annually from 2014 to 2020, recognizing him among the top 15% of researchers in the French university system. He also holds the prestigious Habilitation à Diriger des Recherches from France (2017) and the National Scientific Qualification for Full Professor in Operations Research from Italy (2019). Fabio Furini has been actively involved in supervising PhD students and has served as principal investigator for numerous national and international research projects. His extensive network includes over 60 co-authors across European and American universities. He is also a member of the editorial boards for three prestigious international journals: Omega, Annals of Operations Research, and Discrete Applied Mathematics. His research activities include collaborations with various institutions across Europe and the United States, including Imperial College London and the University of Colorado. These collaborations have resulted in a robust research program focused on advancing the theoretical foundations and practical applications of combinatorial optimization.
Maria Leonilde Rocha Varela is an Associate Professor with Habilitation at the School of Engineering, University of Minho, Portugal, where she also serves as a Senior Researcher at the Algoritmi Research Centre. She has been an integrated member of the Algoritmi Research Centre since 2012 and works in the Department of Production and Systems. Dr. Varela earned her degree in Production Engineering from the University of Minho in 1994, completed a Master's in Computer Integrated Production at DPS-UMinho in 1999, and received her Ph.D. in Production and Systems from the University of Minho in 2007. Her primary research focuses on Manufacturing Management, particularly Production Planning, Control and Optimization, and Collaborative Paradigms, Networks and Decision Making Models. She maintains extensive international collaborations with institutions worldwide including the National Institute of Industrial Engineering, VSB-Technick Univerzita Ostrava, University of Belgrade, and others. Her research spans Web Applications and Services for supporting Engineering and Production Management, with increasing emphasis on Artificial Intelligence, Robotic Process Automation, and Industry 4.0/5.0 applications. She has made significant contributions to scheduling algorithms, optimization techniques, and decision support systems for manufacturing environments. Analysis of her recent publications reveals a strong trend toward integrating Artificial Intelligence with traditional manufacturing processes, particularly in Robotic Process Automation applications. Her research increasingly focuses on sustainable manufacturing practices, with numerous publications addressing energy efficiency, environmental sustainability, and resource optimization. There is a clear emphasis on multi-objective optimization approaches to solve complex manufacturing problems, particularly in distributed job shop scheduling. Her work demonstrates an evolution from traditional production planning methods to more advanced AI-driven approaches for Industry 4.0 and 5.0 environments. Dr. Varela has held significant academic leadership roles, currently serving as the director of the master's course in Engineering and Quality Management at DPS-UMinho. She previously coordinated the industrial management and systems subgroup from 2012 to 2021 and was part of the steering committee for the master's course in systems engineering between 2016 and 2019. She has successfully supervised more than 70 MSc projects, with over 15 currently ongoing, focusing on Production and Systems Engineering. Her supervision encompasses collaborative management models, traditional decision approaches, and web-based platforms incorporating AI techniques. She coordinates research projects including 2 concluded Ph.D. projects and 6 ongoing ones. She collaborates as a research member in several R&D projects with national and international industrial enterprises and institutions, and in international Erasmus projects. Dr. Varela is an active participant in the academic community, serving on editorial boards of several international journals and as a member of organizing and scientific committees for numerous international conferences. She is a member of several prestigious research networks including the Euro Working Group of Decision Support Systems (EWG-DSS), Institute of Electrical and Electronics Engineers (IEEE), Industrial Engineering Network, and the Institute of Industrial and Systems Engineers (IISE).
Marisa Exter serves as Associate Professor of Learning Design and Technology within Purdue University's Department of Curriculum and Instruction since 2020, following promotion from Assistant Professor (2013-2020). With 15+ years of software design and development experience, she holds dual expertise in Computer Science (BS/MS) and Instructional Systems Technology (PhD). Her educational foundation includes: PhD in Instructional Systems Technology, Indiana University (2011) MS in Computer Science, Illinois Institute of Technology (2003) BS in Computer Science, Elmhurst College (1999) Dr. Exter's research pioneers transdisciplinary educational innovation , focusing on how design processes transform technology-creation fields like Instructional Design, Computing, and Engineering. She investigates formal and non-formal learning experiences to enhance undergraduate education through interdisciplinary programs, while simultaneously examining online graduate program structures that support adult learners' connection to faculty and peers. Her work integrates heutagogical principles for lifelong learning and rigorously documents design successes/failures through scholarly design cases. Analysis of her 15 most recent publications reveals accelerating focus on dispositional development in computing professions , competency-based curriculum models, and transdisciplinary experience design. Her methodological evolution includes increased use of Q methodology and collaborative autoethnography to capture diverse perspectives in educational innovation. As Purdue PI for a $3M multi-institutional computing competencies grant and co-coordinator of AECT's Summer Research Symposium, she drives national curriculum reform. Her mentorship approach cultivates the Exploring Disruptive Education research family group, which investigates interdisciplinary design processes across technology-rich learning environments. Professional service spans Computing Curriculum 2020 Task Force leadership and ACM/AERA membership. The Exploring Disruptive Education research collective she co-leads operates as an interdisciplinary incubator for educational innovation. This team examines how design thinking transforms learning experiences through technology integration, with particular emphasis on creating connections between formal education and industry practices in computing and engineering fields.
Torgeir Dingsøyr is an Adjunct Chief Research Scientist and Research Professor in the Department of IT Management at Simula Metropolitan, a leading Norwegian research institute specializing in software engineering and digital technologies. His role encompasses both strategic research leadership and active empirical investigation into large-scale agile software development and software process improvement. Research Interests Large-scale agile software development methods and governance. Coordination and communication challenges in very large development programmes. Software process improvement (SPI) with emphasis on practical, evidence-based approaches. Teamwork effectiveness and autonomous teams in continuous deployment environments. Digital transformation project organization, particularly within Scandinavian contexts. Across more than two decades, Dingsøyr has produced influential handbooks and empirical studies that bridge the gap between SPI theory and industrial practice. His work increasingly addresses second-generation agile methods, emphasizing safety nets for high-risk development and autonomy at scale. Scientific Awards No specific awards listed in the provided text. Advising & Grants Dingsøyr collaborates extensively with industry and academic partners to secure funding for longitudinal case studies and improvement initiatives. While individual student names are not provided, his publications demonstrate active supervision and mentoring within multi-partner projects. Laboratory & Teams He operates within Simula Metropolitan’s research environment, contributing to interdisciplinary teams that unite software engineering researchers, data scientists, and industrial practitioners to advance empirical software engineering and agile transformation.
Lakshmi Balasubramanyan is an Associate Professor in the Department of Banking & Finance at Case Western Reserve University's Weatherhead School of Management, joining in 2017. She holds a PhD and MS from Penn State University in quantitative banking analytics, alongside BA/MA degrees from the National University of Singapore. Specializes in AI integration in commercial banking Focuses on credit/operational risk modeling Expert in regulatory frameworks (Dodd-Frank Act) Active media commentator on financial trends Her research bridges traditional banking practices with emerging technologies, particularly through RegTech and FinTech innovations. Key contributions include analyzing bank balance sheet dynamics under liquidity regulations and developing entropy-based approaches to risk heterogeneity. Recent publications explore causal relationships between risk oversight and bank stability, syndicated loan market information asymmetry, and credit market feedback mechanisms. Current projects involve NSF-funded AI-enabled financial ecosystems. Recipient of two CSWEP Research Fellowships Multiple teaching award nominations (2020-2023) 2021 Weatherhead Intramural Grant recipient 2019 UCITE NORD Grant awardee Active in institutional service as Faculty Senate Committee member and Women in Finance advisor. Collaborates with Federal Reserve institutions on banking supervision research.
NAKAJIMA, Tatsuo serves as a Professor at Waseda University's School of Fundamental Science and Engineering, Department of Computer Network Engineering. Holding a Doctor of Engineering from Keio University, he has been affiliated with Waseda since 1999 after positions at Japan Advanced Institute of Science and Technology (1993-1999), Cambridge University, and Carnegie Mellon University. His academic profile shows substantial research output with 433 papers and 3,338 citations on Scopus, and 7,604 citations with an h-index of 42 on Google Scholar. Dr. Nakajima's research focuses on Distributed Systems, Embedded Systems, and Ubiquitous Computing, with particular emphasis on virtualization architectures for embedded environments. His work bridges theoretical computer science with practical applications in information appliances, operating systems, and persuasive computing technologies. He has developed innovative systems including SPUMONE (a composition kernel for multi-OS environments), SIGMA System, and SPLiT (a performance optimization library for multicore processors). Analysis of his 15 most recent publications reveals a consistent research trajectory centered on enhancing reliability, security, and performance of embedded and pervasive computing systems. His work shows increasing integration of human factors, particularly in sustainable behavior applications through persuasive technology. The research spans from low-level system architecture to user-centered applications, demonstrating both technical depth and practical relevance. Nokia Research Center, Visiting Research Fellow (2005.04) Dr. Nakajima's research has produced numerous practical frameworks including SPUMONE for multi-OS environments, SPLiT for performance optimization, and persuasive applications like EcoIsland for sustainable behavior. His work on kernel monitoring, anomaly detection, and self-healing systems demonstrates strong focus on system dependability. Current research appears directed toward integrating human factors with embedded systems, particularly in environmental sustainability applications. His laboratory work centers around the SPUMONE project, a virtualization layer for multi-core embedded systems that enables multiple operating systems to coexist with minimal engineering cost. This research environment supports exploration of resource management, security monitoring, and performance optimization in embedded contexts. The work has practical applications in information appliances, smart homes, and pervasive computing environments.
