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Professor
Axel Gandy is a Professor of Statistics at the Department of Mathematics, Imperial College London. He serves as Director of the EPSRC CDT in Modern Statistics and Statistical Machine Learning , overseeing PhD supervision and advanced statistical training.
Professor
Professor Gregor Verbic is a faculty member at the University of Sydney in the School of Electrical and Computer Engineering , where he serves as Director of the Centre for Future Energy Networks . Previously, he held an assistant professor position at the University of Ljubljana and was a NATO-NSERC Postdoctoral Fellow at the University of Waterloo. His career spans academic research, industry leadership as Head of Interenergo's Investment Department, and extensive collaboration with IEEE. PhD in Electrical Engineering (University of Ljubljana) Senior IEEE Member Research Interests focus on transforming power systems to zero-carbon grids through: Aggregation and control of distributed energy resources (DERs) Frequency control with wind generation and electric vehicles Stochastic optimization for multi-energy systems Smart home energy management with phase change materials Notable Contributions include: 2006 IEEE prize paper for voltage instability prediction 2010-2024: 15 recent publications on DER coordination, network tariffs, and low-inertia grid stability Teaching includes courses on: ELEC3203/ELEC9203: Electricity Networks ELEC5213: Engineering Optimisation ELEC5206: Sustainable Energy Systems Labs & Initiatives Centre for Future Energy Networks The Net Zero Institute
Associate Professor
Vera Liao is a Principal Researcher at Microsoft Research, where she is part of the FATE (Fairness, Accountability, Transparency, and Ethics of AI) group. She will join the University of Michigan Computer Science and Engineering department as an Associate Professor in fall 2025. Her work focuses on human-AI interaction, explainable AI, and responsible computing. Liao has made significant contributions to IBM products such as AI Explainability 360 and Uncertainty Quantification 360 during her time at IBM T.J. Watson Research Center. Dr. Liao received her education from the University of Illinois at Urbana-Champaign and Tsinghua University. Her academic journey has positioned her at the intersection of human-computer interaction and artificial intelligence, with a strong emphasis on creating AI systems that are transparent, accountable, and user-centered. Vera Liao's research primarily centers around human-centered AI explainability and transparency. She investigates how to design AI systems that effectively communicate their capabilities, limitations, and decision-making processes to users. Her work examines the intersection of AI transparency with trust, control, and user experience. Liao has pioneered approaches to bridging the socio-technical gap in AI evaluation and has developed frameworks for contextualized evaluation of explainable AI systems. Her research spans multiple domains including conversational interfaces, data storytelling, and creative work with generative AI. Liao's publications reveal a clear trend toward addressing the challenges of large language models and their impact on human-AI interaction. Her recent work focuses on understanding how uncertainty communication affects user trust, how to design for appropriate reliance on AI systems, and how to create authentic co-creation experiences with generative models. She has been examining the risks in AI-infused information ecosystems and developing methods for human-centered evaluation of language technologies. Her scientific contributions have been recognized with multiple honors: Best Paper Award, Honorable Mention at CHI 2025 (two papers) Best Paper Award at CHI 2024 Best Paper Award, Honorable Mention at FAccT 2023 Best Paper Award, Honorable Mention at HCOMP 2022 Best Paper Award, Honorable Mention at CHI 2021 Best Paper Award, Honorable Mention at CHI 2014 Outstanding Paper Award at IUI 2019 Dr. Liao is an active mentor, having guided numerous research interns from top universities including Cornell, Princeton, CMU, Stanford, and MIT. She serves in editorial roles as Co-Editor-in-Chief of the Springer Human-Computer Interaction Book Series and as an Editor for ACM CSCW. Liao has secured research funding through her work at Microsoft Research and previously at IBM, focusing on projects related to AI explainability, transparency, and responsible AI development. As part of Microsoft Research's FATE group, Liao collaborates with a multidisciplinary team of researchers focused on the ethical implications of AI technologies. Her work bridges the gap between technical AI development and human-centered design principles, ensuring that AI systems are developed with user needs and societal impacts in mind.
Associate Professor
Scott McDougall is an Associate Professor in the Department of Earth, Ocean & Atmospheric Sciences at the University of British Columbia (UBC). He specializes in geohazards, particularly landslides and related risks, with a focus on improving risk assessment and mitigation strategies. His work integrates field data, statistical analysis, numerical modeling, and laboratory experiments to understand landslide dynamics. Education: PhD in Geological Engineering, UBC (2006) BASc in Civil Engineering, University of Toronto (1998) Research Interests: Landslide mobility and runout modeling Tailings dam breaches and their impacts Risk evaluation frameworks for geohazards Shoreline erosion and landslide-generated waves Applications of machine learning in hazard prediction Key Contributions: Development of the UBC Geohazards Research Team to advance landslide risk reduction Leadership in the CanBreach project to improve tailings dam breach analysis Pioneering probabilistic runout prediction models for rock avalanches and debris flows Awards: Engineering Geology Best Paper Award 2024 CDA Published Paper Award Advising & Grants: Supervised over 15 graduate students and postdoctoral fellows Active industry collaborations with mining and engineering firms Funded by NSERC, Mitacs, and industry partnerships Labs & Teams: UBC Geohazards Research Team CanBreach Collaborative Research and Development Project
Clinical Professor
Marjorie Corman Aaron serves as Professor of Practice and Director of the Center for Practice at the University of Cincinnati College of Law, where she teaches negotiation, client counseling, trial practice, mediation, and decision analysis. Her leadership oversees the law school's trial practice program and student competition teams in dispute resolution. Her academic foundation includes a BA from Princeton University and JD from Harvard Law School. Professor Aaron's research revolutionizes lawyer-client communication through structured decision-making frameworks. She pioneered methodologies for counseling clients through legal uncertainties, emphasizing decision tree analysis to quantify risks and outcomes. Her seminal works "Client Science" and "Risk and Rigor" bridge psychological insights with practical legal strategy, transforming how attorneys navigate complex negotiations and settlement discussions while maintaining ethical rigor. Her scholarly trajectory reveals consistent innovation in dispute resolution pedagogy, evolving from foundational decision analysis techniques to contemporary explorations of mediator neutrality in politically charged contexts and ethical implications of nondisclosure agreements. Her distinguished recognition includes: Harold C. Schott Scholarship Award (2019) Goldman Prize for Excellence in Teaching (2010) University President’s Excellence Award for Teaching (2006) American College of Civil Trial Mediators Education/Training Achievement Award (1998) CPR Institute for Dispute Resolution Second Prize for Excellence (1995) As an educator, she mentors students through national competition teams while extending her impact via workshops for law firms, corporations, and government entities. Her digital platforms (clientsciencecourse.com and riskandrigor.com) provide global resources for legal educators, complemented by frequent guest lectures at Harvard, Michigan, and international institutions. The Center for Practice functions as an experiential learning hub under her direction, integrating theoretical knowledge with hands-on trial advocacy training and dispute resolution simulations that prepare students for real-world legal practice.
Research Fellow
Ji-Quan Shi is a Research Fellow in the Department of Earth Science & Engineering at Imperial College London's Faculty of Engineering. His affiliations include the Energy Futures Lab, Minerals, Energy and Environmental Engineering, and Petroleum Geoscience and Engineering. His research focuses on geomechanical and coupled THM (thermo-hydro-mechanical) modeling for CO2 storage, geothermal energy systems, and mining-induced seismicity. Key interests include induced seismicity risk assessment, reservoir simulation, and fracture mechanics in subsurface energy systems. Education background not explicitly stated in text, but his expertise spans geoscience, civil engineering, and environmental systems. Research areas emphasize interdisciplinary approaches to subsurface energy challenges, including carbon capture and storage (CCS), geothermal reservoir management, and coal mining hazards. His work combines field observations, numerical modeling, and laboratory experiments to address challenges like CO2 plume tracking, fault activation mechanisms, and microseismic event forecasting. Recent studies focus on Iceland's geothermal fields (Hellisheiði) and North African CO2 storage sites (In Salah). He has pioneered methods for integrating microseismic data with reservoir models to improve safety and efficiency in subsurface operations. Notable contributions include probabilistic frameworks for hazardous microseismicity prediction in coal mines and coupled modeling of thermal effects on induced seismicity. His research also explores innovative monitoring technologies like distributed fiber optic sensing for CO2 plume tracking.
Associate Professor
Dr. Vikas Srivastava is an Associate Professor of Engineering and Director of the Graduate Program in Biomedical Engineering at Brown University's School of Engineering. His research focuses on solid mechanics, continuum biomechanics, and cell mechanics, with applications in materials under extreme environments and biomedical science. He leads the Srivastava Lab for Solid Mechanics and Biomechanics, which integrates computational models with experimental techniques to address interdisciplinary challenges. Dr. Srivastava holds a Ph.D. in Mechanical Engineering from MIT (2010) and previously held senior roles at ExxonMobil, including leadership in materials mechanics and deepwater drilling engineering. His academic career at Brown began in 2018, during which he has directed over 15 graduate students and secured notable funding. His research interests span mechanobiology, hydrogel-based drug delivery systems, AI-driven predictive modeling, and biomaterial innovations for cancer therapies. He has pioneered physics-informed neural networks for material characterization and developed novel hydrogels to enhance chemotherapy efficacy. Recent articles highlight advancements in polymer fracture modeling, machine learning for non-destructive evaluation, and predictive epidemiological modeling for pandemics. Dr. Srivastava has received the Dean’s Award in Bioengineering and was promoted to tenured Associate Professor in 2023. He actively mentors students through grants like the NSF Graduate Research Fellowship and leads initiatives in biomedical technology translation. The Srivastava Lab collaborates extensively across engineering, biology, and medicine to advance translational research in materials science and clinical applications.
Assistant Professor
Ziran Wang is an Assistant Professor in the Department of Civil Engineering at Purdue University's College of Engineering, appointed as new faculty in 2022. His research bridges digital twin technologies, autonomous driving systems, and human-machine interaction to advance intelligent transportation solutions. Ph.D. in Mechanical Engineering, University of California, Riverside Prior role: Principal Researcher at Toyota North America His work focuses on creating personalized autonomous driving experiences through machine learning, emphasizing safety and efficiency in real-world applications. Key areas include multimodal large language model integration, federated learning for privacy-preserving data sharing, and cooperative perception frameworks. He develops novel approaches for digital twin-based traffic simulation, medical emergency detection in vehicles, and human behavior modeling in complex urban environments. Analysis of his 2024-2025 publications reveals a dominant trend toward generative AI applications in autonomous driving, particularly for perception-prediction-planning integration and real-world validation. His research increasingly incorporates digital twins for safety-critical testing and explores medical applications through in-vehicle health monitoring systems. Dr. Wang advises graduate students including Wenhui Huang and leads the Purdue Digital Twin Lab, which develops advanced simulation and testing platforms for autonomous systems. His lab maintains strong industry partnerships with Toyota for real-world deployment and validation of research成果.
Professor
Fabio Sigrist is a Professor of Applied Statistics and Data Science at the Institute of Financial Services Zug (IFZ) , part of the Lucerne University of Applied Sciences and Arts . He also holds a Senior Scientist and Lecturer position at the Seminar for Statistics, ETH Zurich . His career spans academic research, industry consulting, and project leadership in finance and data science. PhD in Statistics (2013), ETH Zurich MSc in Mathematics with distinction (2008), ETH Zurich MEd in Mathematics Education (2008), ETH Zurich Sigrist’s research focuses on integrating Machine Learning with Spatial Statistics for applications in Financial Econometrics and Credit Risk . His work includes developing novel algorithms like GPBoost and KTBoost , advancing spatio-temporal modeling , and applying tree-based boosting to financial problems. Projects such as CreHos (credit risk in hospitality) and NISMO (interpretable real estate modeling) highlight his interdisciplinary approach. His publications address challenges in large-scale spatial data , loss given default modeling , and stock volatility prediction . He contributes to software development with tools like spate (R package) and varycoef (spatially varying coefficients).
Professor
J. Eric Bickel is a Professor at The University of Texas at Austin, serving as Director of the Operations Research & Industrial Engineering (ORIE) and Engineering Management programs. He holds a courtesy appointment in the Department of Petroleum and Geosystems Engineering and directs the Center for Engineering & Decision Analytics (CEDA). His academic background includes a PhD and MS in Engineering-Economic Systems from Stanford University and a BS in Mechanical Engineering from New Mexico State University. His research focuses on decision analysis under uncertainty, addressing topics like probabilistic modeling, climate engineering, risk management, and applications in sports and energy sectors. His work has been featured in major media including The New York Times and Wall Street Journal , and his climate engineering research was endorsed by Nobel Laureates as a top climate change response strategy. Professor Bickel has extensive industry experience, having previously served as Senior Engagement Manager and Co-Director of Client Education at Strategic Decisions Group (SDG), where he remains on the Board of Directors. His consulting spans oil/gas, energy trading, and financial services sectors. He has received recognition as a Fellow of the Society of Decision Professionals and contributed to the Copenhagen Consensus on Climate Project. His teaching extends to executive education through Texas Executive Education and McCombs School of Business. Research highlights include novel methods for probabilistic dependence modeling, value-of-information analysis in shale reservoirs, and critiques of risk assessment tools like heat maps. His climate engineering work emphasizes economically viable solar radiation management strategies.
Associate Professor
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.
Associate Professor
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.
Professor
Prof. Dr.-Ing. Gerhard Müller is a Full Professor at the Chair of Structural Mechanics within the TUM School of Engineering and Design at Technical University of Munich (TUM). Since 2004, he has held this distinguished position, and since 2014, he has served as Executive Vice President for Academic and Student Affairs at TUM. His research focuses on structural dynamics and vibroacoustics, with specific expertise in dynamic soil-structure interaction, sound radiation analysis, and seismic risk assessment. Professorship: Structural Mechanics University: Technical University of Munich School: TUM School of Engineering and Design Department: Chair of Structural Mechanics in Civil Engineering Prof. Müller's research spans multiple domains, including: Structural Dynamics : Examining building and vehicle vibrations, seismic soil-structure interaction, and advanced model order reduction techniques Vibroacoustics : Investigating sound radiation from vibrating structures and developing acoustic metamaterials for noise control Computational Methods : Pioneering hybrid deterministic-statistical approaches, Wave Based Methods (WBM) for saturated elastodynamic structures, and parametric model order reduction His recent publications demonstrate expertise in: Wave propagation analysis in poroelastic media Bayesian parameter updating for structural models Acoustic metamaterials for vibration control Advanced numerical methods for seismic risk assessment Hybrid ITM-FEM approaches for soil-structure interaction Energy flow analysis in timber structures Awarded the Spindler Prize in 1984 , Prof. Müller also holds significant academic leadership roles: President of European Association for Structural Dynamics (EASD) Chairman of Bavarian-French University Center (BayFrance) Active member of ASIIN accreditation agency and Bavarian Chamber of Engineers Previously served as Dean of Civil Engineering and Surveying at TUM (2010-2014) He leads the Structural Dynamic Lab (formerly Vibroacoustics Lab) and has developed interactive web apps for engineering education. His work bridges theoretical advancements with practical applications in construction acoustics, transportation noise control, and geothermal energy infrastructure analysis.
Associate Professor
Brad Knox is a Research Associate Professor in the Department of Computer Science at the University of Texas at Austin . His work bridges machine learning, human-computer interaction, and computational cognitive science, with a focus on developing systems that learn from human feedback. Key research areas: Reinforcement Learning, Human-AI Interaction, Reward Design, Autonomous Systems Notable contributions: TAMER framework for human-guided learning, empirical studies on reward misdesign, and human preference modeling for autonomous agents Research Trends : His recent work (2023-2025) emphasizes reward alignment, safety in autonomous systems, and preference-based learning frameworks. Earlier studies (2012-2020) established foundational methods for integrating human feedback into reinforcement learning architectures and exploring behavioral signatures in decision-making. Scientific Honors : Bert Kay Dissertation Award (2013) Victor Lesser Distinguished Dissertation Award (IFAAMAS, Runner-up, 2013) NSF SBIR Grant (PI, 2016) NSF Graduate Research Fellowship (2008-2011) IEEE Intelligent Systems AI 10 to Watch (2013) Teaching & Leadership : Knox served as Principal Lecturer for MIT's Interactive Machine Learning course (2013) and held organizational roles at major conferences including Reinforcement Learning Conference (Scheduling Chair, 2025) and RLDM workshop (Co-chair, 2022).
Assistant Professor
Karthik Menon serves as an Assistant Professor with a joint appointment in the Woodruff School at Georgia Institute of Technology and the Coulter Department of Biomedical Engineering. His research integrates fluid mechanics, computational modeling, and data-driven methodologies to address critical challenges in healthcare, renewable energy, and bio-inspired engineering systems. His academic credentials include: Ph.D. in Mechanical Engineering, Johns Hopkins University (2021) M.S. in Mechanical Engineering, Johns Hopkins University (2019) B.E. in Mechanical Engineering, Birla Institute of Technology and Science, Pilani, India (2015) Menon's research program centers on three interconnected domains: cardiovascular flows for personalized treatment of heart disease, fluid-structure interactions in biological systems like heart valves and bio-mimetic robots, and vortex-dominated flows for renewable energy applications. His approach combines high-fidelity computational modeling with machine learning to uncover fundamental physics and develop clinical solutions, such as cardiovascular digital twins for non-invasive risk assessment. Current projects focus on patient-specific hemodynamics using CT imaging and uncertainty quantification to improve surgical planning. Analysis of his 15 most recent publications (2023-2025) reveals a dominant focus on advancing multi-fidelity computational frameworks for cardiovascular applications. Key trends include Bayesian uncertainty quantification, zero-dimensional solver development, and integration of clinical imaging data to create predictive digital twins. His work bridges fluid dynamics with clinical cardiology, targeting improved outcomes in coronary artery disease and Kawasaki-related complications through physics-informed machine learning. Menon's scholarly contributions have been recognized through competitive awards: WCCM-PANACM 2024 Travel Award, U.S. Association for Computational Mechanics (2024) Future Faculty Symposium Travel Award, Society of Engineering Science Conference (2023) Mark O. Robbins Prize in High-performance Computing, Johns Hopkins University (2021) Corrsin-Kovasznay Outstanding Paper Award, Johns Hopkins University (2020) Prosperetti Travel Award, Johns Hopkins University (2017) Mechanical Engineering Departmental Fellowship, Johns Hopkins University (2016) As principal investigator of the ComBiNE Fluid Dynamics Lab, Menon mentors graduate students in developing computational tools for fluid-structure interaction problems. His collaborative projects with cardiologists at Stanford and Emory hospitals translate engineering principles into clinical applications for cardiovascular disease management. Current grant activities focus on NSF and NIH-funded initiatives for uncertainty-aware cardiovascular modeling and bio-inspired flow energy harvesting. The ComBiNE Fluid Dynamics Lab operates as an interdisciplinary hub where engineers, clinicians, and data scientists collaborate on fluid mechanics challenges. Current lab initiatives include developing real-time hemodynamic simulators for surgical planning, creating reduced-order models for cardiac device optimization, and investigating vortex dynamics in fish schooling for underwater vehicle design. The lab maintains strong partnerships with Children's Healthcare of Atlanta and the Parker H. Petit Institute for Bioengineering and Bioscience.
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