Denizhan Yavas is an Assistant Teaching Professor in the Department of Mechanical Engineering at Rice University, joining in 2024. He holds a Ph.D. in Engineering Mechanics from Iowa State University (2018), an M.Sc. in Aerospace Engineering from Middle East Technical University (METU Ankara, 2013), and a B.S. in Mechanical and Aerospace Engineering (METU Ankara, 2010). Prior to Rice, he served as teaching faculty at the University of Central Florida. His research focuses on experimental and computational solid and fracture mechanics , with emphases on deformation/failure mechanisms in advanced composites and additively manufactured materials, architected materials, interfacial fracture, and ice adhesion. Key areas include bioinspired interfaces, interfacial fracture toughness, and material characterization under dynamic and static loading conditions. Notable recent work explores fracture behavior of 3D-printed thermoplastics, bioinspired soft-hard interfaces, and additive manufacturing techniques for enhancing interlaminar shear strength. These studies highlight cross-cutting themes in materials science, mechanical engineering, and aerospace applications. Awards: Preeminent Postdoctoral Award (University of Central Florida) Research Excellence Award (Iowa State University) Teaching Excellence Award (Iowa State University) Teaching & Advising: No current advisees listed, but actively involved in undergraduate/graduate mechanical engineering education. His work bridges fundamental mechanics research with practical applications in advanced manufacturing, materials design, and aerospace engineering.
Youssef M. Marzouk is the Breene M. Kerr (1951) Professor of Aeronautics and Astronautics at MIT and co-director of the MIT Center for Computational Science and Engineering (CCSE). He is affiliated with the MIT Schwarzman College of Computing, the Statistics and Data Science Center, and the Aerospace Computational Design Laboratory. His research focuses on computational science and engineering, with an emphasis on uncertainty quantification, Bayesian modeling, data assimilation, and machine learning applied to physical systems. He holds a Ph.D. in Mechanical Engineering from MIT (2004), preceded by S.M. (1999) and S.B. (1997) degrees in Aeronautics and Astronautics from the same institution. Marzouk’s work bridges computational mathematics, statistical inference, and fluid dynamics, addressing challenges in energy systems and environmental modeling. He has received numerous awards, including the 2018 AIAA Associate Fellowship and the 2012 MIT Class of 1942 Career Development Chair. His teaching spans computational mathematics, fluid dynamics, and uncertainty quantification. Key collaborations involve the MIT CCSE and external institutions, with funding from DOE and NSF. He advises students on topics like stochastic modeling and inverse problems, and his research lab explores advanced computational methods for high-dimensional systems.
Dr. Debraj Roy is a Visiting Professor at the University of Amsterdam (UvA), affiliated with the Faculty of Science, Mathematics and Computer Science and the Informatics Institute. His research focuses on agent-based modeling, socio-economic dynamics, environmental resilience, and blockchain technology. He investigates complex systems such as urban slums, disaster recovery, and climate adaptation using computational methods like remote sensing and machine learning. His work bridges theory and practice, offering insights into policy design for sustainable development and social equity. Key research interests include slum dynamics, poverty traps, and the application of blockchain oracles for decentralized systems. He employs advanced techniques such as global sensitivity analysis and manifold learning to explore multi-scale socio-environmental challenges. His recent articles highlight trends in carbon pricing, flood risk valuation, and multi-agent systems. Earlier work concentrated on urban inequality in cities like Bangalore and Mexico City, leveraging geospatial and statistical tools. No scientific awards or grants are explicitly listed. His advising and team collaborations are unspecified in the provided text.
Kwan-Wu Chin is a Professor in the School of Electrical, Computer and Telecommunications Engineering at the University of Wollongong, where he also serves as Head of Postgraduate Studies (HPS) and co-directs the Wireless Technologies Lab (WTL). His research focuses on resource allocation problems in Internet of Things (IoT) systems, maritime networks, edge computing platforms, and integrated sensing-communication systems. Chin leads an active research group currently supervising five PhD students working on UAV networks, edge computing, maritime systems, and metaverse resource allocation. He has graduated over 20 PhD students who now hold positions in academia and industry. Chin serves as editor for Elsevier Computer Communications and IEEE Internet of Things Journal. His work develops optimization techniques using graph theory, stochastic processes, and machine learning for next-generation wireless systems.
Dr. Victoria C. P. Chen is a Professor in the Industrial, Manufacturing, and Systems Engineering (IMSE) department at The University of Texas at Arlington (UTA), where she has served since 2002. She previously held positions at the Georgia Institute of Technology from 1993-2001. Dr. Chen has held several leadership roles at UTA, including Interim Department Chair (2012-2014), Director of the Center on Stochastic Modeling, Optimization, & Statistics (COSMOS) (2008-2012, and again from 2017-present), and Director of Doctoral Studies (2019-present). She was also the George & Elizabeth Pickett Professor from 2015-2017 and was inducted into the UT Arlington Academy of Distinguished Teachers in 2019. Dr. Chen is actively involved with INFORMS (Institute for Operations Research and the Management Science), where she currently serves as Secretary on the Executive Board. Dr. Chen earned her B.S. in Mathematical Sciences from The Johns Hopkins University, and her M.S. and Ph.D. in Operations Research and Industrial Engineering from Cornell University. Her academic journey includes visiting professorships at the University of Genoa, Italy, and Iowa State University. Dr. Chen's research utilizes statistical perspectives to create new methodologies for operations research problems appearing in engineering and science. Her expertise includes the design of experiments, statistical modeling, and data mining, particularly for computer experiments and stochastic optimization. Through her statistics-based approach, she has developed computationally-tractable decision-making methods for many high-dimensional complex systems. Her work spans multiple domains including sustainability, energy, water management, healthcare, and law enforcement. Specific application areas include inventory forecasting, airline optimization, water reservoir networks, wastewater treatment, air quality monitoring, green building design, nurse assignment systems, and pain management programs. Her recent publications demonstrate continued innovation in mixed integer programming for electric vehicle charging stations, vacuum ultraviolet spectroscopy prediction, and sustainable building education. Senior Member, Institute for Operations Research and the Management Sciences (INFORMS) (2024) Data Mining Prize (Lifetime Achievement Award), INFORMS Society on Data Mining (2023) College of Engineering Teaching Award, UT Arlington (2021) Third Place Award, C3.ai COVID-19 Grand Challenge (2020) Academy of Distinguished Teachers, University of Texas at Arlington (2019) George & Elizabeth Pickett Professorship (2015-2017) As an educator and mentor, Dr. Chen has advised over 25 doctoral students across diverse research topics in operations research and systems engineering. She has secured substantial research funding from multiple sources including the National Science Foundation (over $1.5 million in active projects), Environmental Protection Agency, National Institute of Justice, and industry partners like Luminant and Dallas-Fort Worth International Airport. Her current research projects focus on decision analytics for sustainable urban environments, optimization for Texas water management, and statistical methods for pain management programs. She has served as Principal Investigator or Co-PI on more than 20 externally funded research projects totaling over $3 million in funding. Dr. Chen co-founded the Center on Stochastic Modeling, Optimization, & Statistics (COSMOS) at UTA with Dr. H. W. Corley. This research center brings together faculty and students from multiple disciplines to address complex problems through advanced statistical and optimization methods. She also leads interdisciplinary research teams working on projects related to sustainable infrastructure, energy systems, and healthcare optimization, frequently collaborating with researchers from civil engineering, environmental science, and medical fields.
Yolanda Vidal Segui is an Associate Professor in the Department of Mathematics at the Universitat Politècnica de Catalunya (UPC), affiliated with the Escola d'Enginyeria de Barcelona Est (EEBE). Her research focuses on wind energy systems, predictive maintenance, and structural health monitoring of wind turbines. She leads projects in the CoDAlab and WinTurCoM research groups, specializing in data-driven models, condition monitoring, and failure prognosis. Her work integrates machine learning, mathematical modeling, and sensor technology to enhance turbine reliability and energy efficiency. Dr. Vidal holds a PhD in Applied Mathematics and has authored over 350 publications. Her contributions include advancements in SCADA data analysis, vibration-based diagnostics, and AI-driven condition monitoring systems. She has received several accolades, including the WindEurope Technology Workshop recognition and the IFIT Distinction in Mechanism and Machine Science. Her research bridges academia and industry, addressing challenges in offshore wind turbine integrity and maintenance strategies. Active in professional service, she serves on conference committees and editorial boards (e.g., Mechanical Systems and Signal Processing, Wind Energy). Her work emphasizes sustainable energy solutions and has been applied in real-world scenarios like the Alpha Ventus wind farm. She also contributes to educational initiatives, developing innovative teaching materials for engineering students.
Dr. Andrew Erwin is an Assistant Professor in Mechanical Engineering at the University of Cincinnati, focusing on robotics, human-robot interaction, and rehabilitation engineering. He holds a PhD and MS from Rice University (2018, 2014) and a BS from the University of Massachusetts Amherst (2012). Prior to UC, he was a postdoc at the University of Southern California and the Jet Propulsion Laboratory. His research explores how forces and movements are executed in healthy individuals, and how robotic devices can assist or restore function post-injury. Key areas include rehabilitation robotics, bio-inspired systems, haptic interfaces, and motor learning. He has received prestigious awards such as the NASA Postdoctoral Program Fellowship (2018) and the IEEE/ASME Transactions on Mechatronics Best Paper Award (2017). Dr. Erwin’s work integrates biomechanics, control systems, and neurophysiology. His lab develops devices like the SE-AssessWrist for wrist assessment and explores planetary seismometers for space missions. He maintains an active Google Scholar profile with over 25 publications. Education: PhD, Mechanical Engineering, Rice University, 2018 MS, Mechanical Engineering, Rice University, 2014 BS, Mechanical Engineering, University of Massachusetts Amherst, 2012 His current research emphasizes curriculum design for robotics learning, human-robot collaboration, and adaptive control systems. He offers a PhD position for Fall 2025 focusing on these areas.
Huazhen Fang is an Associate Professor in the Department of Mechanical Engineering at the University of Kansas School of Engineering, where he joined in 2014. He leads the Information & Smart Systems Laboratory (ISSL) and holds a courtesy appointment in the Department of Electrical Engineering & Computer Science. His research focuses on enabling intelligence for complex systems through information-driven approaches. Dr. Fang received his Ph.D. in Mechanical Engineering from the University of California, San Diego in 2014, following an M.Sc. from the University of Saskatchewan and a B.Sc. in Computer Science & Technology from Northwestern Polytechnic University in China. He was a Visiting Faculty Fellow at Mitsubishi Electric Research Laboratories in 2022. His research interests span Systems and Control, Advanced Battery Management, Energy Storage Systems, and Robotics, with particular focus on system modeling, estimation, control design, machine learning and numerical optimization. Dr. Fang's work has significant applications in energy management, cooperative robotics, and environmental observing systems. His research has been supported by the National Science Foundation, Department of Energy, Army Research Laboratory, and Mitsubishi Electric Research Laboratories. His extensive publication record shows a clear trend toward increasingly sophisticated integration of physics-based modeling with machine learning approaches, particularly in battery management systems and autonomous vehicle control. Recent work demonstrates a growing emphasis on Bayesian inference methods, distributed control architectures, and safety-critical applications of intelligent control systems. Faculty Early Career Award from National Science Foundation (2019) University Scholarly Achievement Award (2024) Miller Professional Development Award (2022) Miller Faculty Scholar Award (2018, 2019, 2023) Wesley G. Cramer Outstanding Mechanical Engineering Faculty Award (2016) Big XII Faculty Fellowship (2015) IEEE Transactions on Transportation Electrification Prize Paper Award (2024) Dr. Fang has successfully mentored numerous graduate students through the Information & Smart Systems Laboratory, with many receiving awards for their research. His research has attracted significant funding from prestigious organizations including the National Science Foundation, Department of Energy, Army Research Laboratory, and Mitsubishi Electric Research Laboratories. He currently serves as an Associate Editor for multiple prestigious journals including Information Sciences, IEEE Transactions on Industrial Electronics, and IEEE Control Systems Letters. The Information & Smart Systems Laboratory (ISSL) under Dr. Fang's leadership has established itself as a center for cutting-edge research in information-driven smart systems. The lab focuses on pushing the frontiers of information extraction, analysis and exploitation for dynamic systems to deal with system complexity and enable system intelligence. The lab actively collaborates with industry partners and local communities, emphasizing research that serves societal needs.
Bo Wu is an Associate Professor in the Department of Computer Science at Colorado School of Mines. His research focuses on compilers and programming systems, particularly program optimizations for heterogeneous computing and emerging architectures, with applications in machine learning and graph processing. He joined Mines in 2014 after earning a Ph.D. from The College of William and Mary and earlier degrees from Central South University in China. Education : B.S. in Computational Science and Technology (Central South University, 2005) M.S. in Computer Science (Central South University, 2008) Ph.D. in Computer Science (The College of William and Mary, 2014) Research Interests : Wu's work emphasizes enhancing data locality in heterogeneous systems, GPU scheduling, and optimizing applications for emerging architectures. His contributions include frameworks like GraphZero for efficient graph mining and FLEP for GPU preemption. Awards & Grants : NSF SPX Award (2018) NSF CAREER Award (2018) Supercomputing Best Paper Award (2015) Multiple NSF grants for GPU-related research Advising & Grants : Wu has led several NSF-funded projects and actively participates in conference program committees (e.g., PPoPP, SC, ICS). His research spans compiler optimizations, parallel computing, and high-performance systems. Labs & Teams : While specific labs aren’t named, his work involves collaborations on GPU-based systems, graph processing frameworks, and compiler toolchains.
Joshua D. Bard is an Associate Professor and Associate Head for Design Research at Carnegie Mellon University's School of Architecture. His work bridges traditional craft and cutting-edge robotics, focusing on human-machine collaboration in construction domains. He leads Archolab, an award-winning research group exploring digital fabrication methods like 'Morphfaux' (robotic plaster techniques) and 'Spring Back' (parametric steam bending). Education: M.Arch (Distinction) from University of Michigan; B.A. in Literature & Philosophy from Wheaton College. Professional affiliations include the Manufacturing Futures Institute and rob|arch. Research emphasizes reviving historical crafts through digital tools, such as augmented reality interfaces for architectural education and thermal-tuned concrete panels via robotic processes. His teaching includes generative modeling and architectural robotics labs. Awards: Architect Magazine R+D Award, Canadian Wood Council Merit Award Key Projects: Plaster ReCast AR app, Thermally Informed Robotic Concrete Panels Collaborators: Dana Cupkova, Garth Zeglin, Steven Mankouche Current courses include 62-225 Generative Modeling and 48-555 Introduction to Architectural Robotics. His work is featured in venues like the Carnegie Museum of Art and academic journals like International Journal of Architectural Computing .
Robert D. Gregg, IV is a Professor of Mechanical Engineering, Robotics, and Electrical & Computer Engineering at the University of Michigan. He serves as Associate Director for Graduate Education at Michigan Robotics and directs the Locomotor Control Systems Laboratory. His research focuses on control systems for wearable robots, prosthetics, and orthotics, emphasizing biomimetic principles and nonlinear control theory. Gregg holds a PhD from the University of Illinois at Urbana-Champaign (2010) and prior academic roles at the University of Texas at Dallas and Northwestern University. Education: PhD, Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, 2010 MS, Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, 2007 BS, Electrical Engineering and Computer Sciences, University of California, Berkeley, 2006 Research Interests: Gregg’s work spans control mechanisms for bipedal locomotion, wearable robotics, nonlinear control theory, and rehabilitation engineering. His lab develops high-performance control systems for prosthetic legs and exoskeletons to enhance mobility for individuals with disabilities. Key areas include energy-efficient control strategies, adaptive impedance systems, and biomechanical modeling of human movement. Grant & Award Highlights: $3M NIH R01 Grant (2023): Modeling and control of agile powered prosthetic legs for varied activities. $1.7M NIH R01 Grant (2021): Modular powered orthoses for broad patient populations. NSF CAREER Award (2017) NIH New Innovator Award (2013) Advising & Labs: Gregg mentors PhD students in robotics and biomechanics, emphasizing independent research and collaborative team environments. His lab supports over 15 researchers and has produced notable alumni like Dr. Cara Welker (University of Colorado Boulder faculty). The lab’s work is supported by NIH, NSF, and industry partnerships. Recent Contributions: Recent work includes phase-variable control for stair climbing, energy shaping methods for exoskeletons, and open-source robotic leg platforms. Gregg also chairs major robotics conferences (e.g., IROS 2023) and advises on clinical translation of wearable robotics.
R. Michael Alvarez , Flintridge Foundation Professor of Political and Computational Social Science at Caltech, is a leading scholar in election technology, political methodology, and machine learning applications in social science. Affiliated with the Caltech/MIT Voting Technology Project , the Social and Decision Neuroscience Program , and the Resnick Sustainability Institute , his work bridges technology and democracy. Education: B.A. from Carleton College, Ph.D. from Duke University Academic Career: Caltech faculty since 1992 His research spans: Election Integrity : Monitoring election security, fraud detection, and ballot systems Computational Social Science : Applying machine learning to voter behavior and policy analysis Climate Policy : Examining public attitudes and behavioral interventions for sustainability Online Behavior : Analyzing toxicity in gaming and social media dynamics Key article trends show focus on election forensics (2025 Nature Climate Change study), game toxicity analysis (2025 CHI Play paper), and LLM applications in social science. His students include Jacob Morrier, Mitchell Linegar, and teams of postdocs and undergraduates in Caltech's SURF program. Scientific recognition includes: Google Cloud Research Innovators Class of 2022 Co-editor of multiple academic series including Cambridge Elements in Quantitative Methods
Pietro Liò is Full Professor in the Department of Computer Science and Technology at the University of Cambridge, where he leads research in Artificial Intelligence and Computational Biology as part of the AI group and the Cambridge Centre for AI in Medicine. He holds additional affiliations as Fellow and Council member of Clare Hall College, member of Ellis (European Lab for Learning & Intelligent Systems), and member of Academia Europaea. Professor Liò earned dual PhDs in Complex Systems and Non Linear Dynamics from the University of Florence and in Theoretical Genetics from the University of Pavia, Italy. His educational background bridges theoretical computer science with biological sciences, forming the foundation for his interdisciplinary research approach. His research focuses on developing Artificial Intelligence and Computational Biology models to understand disease complexity and advance personalized medicine. Current work emphasizes Graph Neural Network modeling for integrating multi-scale, multi-omics, and multi-physics data; combining deep learning with mechanistic approaches; explainability in medical AI; and developing AI-based medical digital twins and personal decision support systems. His work spans from fundamental algorithm development to clinical applications, with particular emphasis on translating computational advances into medical solutions. Analysis of his recent publications reveals strong activity in geometric deep learning , explainable AI for healthcare , and multi-omics integration , with increasing focus on clinically applicable tools that maintain both predictive power and interpretability. Member of Academia Europaea Listed among Top Italian Scientists by VIA-Academy Professor Liò has mentored over 40 PhD students and postdoctoral researchers, including notable names such as Petar Velickovic, David Buterez, and Chaitanya Joshi. His research is supported through collaborations with the Cambridge Centre for AI in Medicine and various international partnerships. He serves on departmental committees including Student Complaints and Postdoc Mentoring, and has completed equality and diversity training essentials. He leads research within the Artificial Intelligence group at Cambridge, focusing on creating computational frameworks that bridge biological complexity with clinical applications through advanced machine learning techniques.
Subhabrata Sen is an Assistant Professor of Statistics at Harvard University, located in Science Center 713, Cambridge. His research focuses on Applied Probability, Statistics of Networks, Signal Detection, and Machine Learning. He holds a PhD from Stanford University (2017), advised by Amir Dembo and Andrea Montanari, and prior degrees from the Indian Statistical Institute, Kolkata. His work bridges statistical theory, high-dimensional data analysis, and applications in networks and physics-inspired methods. Key contributions include foundational studies on spin glasses, community detection, and causal inference in complex systems. His research often employs mean-field techniques and explores universality principles in estimation problems. Selected awards and recognition are not explicitly mentioned in the provided text. His advising and grants include postdoctoral mentoring at Microsoft Research and MIT (2017-19). He collaborates on projects involving spectral methods, random matrix theory, and multi-layer network analysis. Labs/teams: Active in Harvard's Statistics Department research groups focused on statistical theory and network science. Maintains an academic website with preprints and resources.
Pardis Pishdad is an Associate Professor and Graduate Program Director in the School of Building Construction at Georgia Institute of Technology’s College of Design. She directs the Smart Built Environment Eco-System (Smart Bees) Laboratory, focusing on integrating cyber-physical systems, digital twins, and innovative project delivery methods (e.g., IPD, Flash Tracking) for sustainable built environments. Her research bridges technology adoption, trust-building in construction contracts, and supply chain optimization. Education: PhD, Environmental Design and Planning (Virginia Tech) Master’s Degrees: Civil Engineering (Virginia Tech), Design Studies in Project Management (Harvard), Architecture (University of Tehran) Bachelor’s in Architectural Engineering (Azad University of Shiraz) Research Interests: Her work emphasizes sustainable construction practices using IoT, BIM, and blockchain. Key areas include lifecycle cost analysis, lean construction, and smart building technologies. She explores trust dynamics and collaboration in construction projects through game theory and process optimization. Recognition: 2018 ENR Top 20 Under 40 Professionals 2016 CII National Outstanding Researcher Award 2020-2022 Georgia Tech Provost Teaching Learning Fellow Advisory Roles: Academic Advisor for CII’s Supply Chain Management Community, Vice Chair of BuildingSMART’s BIM Forum 5D Taskforce. Formerly advised the Construction Management Association of America’s Board (2016–2018). Industry Collaboration: Partnerships with Turner Construction, GDOT, and VDOT. Research on Flash Tracking and blockchain has been integrated into industry practices. Labs & Teams: The Smart Bees Lab pioneers cyber-physical systems for smart buildings, exploring AI-driven solutions and sustainable construction frameworks.
