Ross Thyer is an Assistant Professor in the Department of Chemical and Biomolecular Engineering at Rice University. He holds a BSc (Hons) from the University of Western Australia and a PhD from the Harry Perkins Institute of Medical Research under Drs. Rackham and Filipovska. His postdoctoral training at the University of Texas at Austin with Prof. Andrew Ellington focused on engineered biosynthesis pathways and non-canonical amino acids. He co-founded GRO Biosciences, a Boston-based biotech startup, and leads the Thyer Lab at Rice. His research bridges synthetic biology, protein engineering, and molecular programming to address global challenges. Key areas include expanding genetic codes for therapeutics, engineering biosynthetic pathways via genetic circuitry, and developing microbial systems for environmental bioremediation. Core technologies include deep learning for protein design, modular DNA assembly, and high-throughput selections. The lab also develops tools like MutCompute for enzyme engineering and domesticates non-model bacteria for bioproduction. His work emphasizes technology innovation, with recent advances in selenocysteine incorporation, L-DOPA sensing systems, and actinobacteria toolkits. The Thyer Lab actively collaborates on biocatalyst development and translational applications in healthcare and industry.
Asuman Ozdaglar is the MathWorks Professor of Electrical Engineering and Computer Science and Department Head of EECS at MIT. She also serves as Deputy Dean of Academics for the MIT Stephen A. Schwarzman College of Computing. Her research focuses on large-scale networked systems, including optimization, game theory, social networks, and distributed algorithms. Education: BS in Electrical and Electronics Engineering from Middle East Technical University (1996), SM (1998) and PhD (2003) in Electrical Engineering and Computer Science from MIT. Research emphasizes nonlinear optimization, machine learning, and network economics. She leads work on robust algorithms, misinformation dynamics, and networked systems. Affiliated with the Laboratory for Information and Decision Systems (LIDS) and the Operations Research Center (ORC). Her contributions span theoretical and applied domains, including distributed optimization methods, social network analysis, and privacy-preserving data mechanisms. Active in shaping academic policy through her roles in the College of Computing.
KHOO Siau Cheng is an Associate Professor in the Department of Computer Science at the National University of Singapore (NUS) School of Computing. He also serves as Co-Director of the NUS Business Analytics Centre, established in 2013 in collaboration with the Economic Development Board of Singapore and IBM. His academic journey began with a Ph.D. in Computer Science from Yale University in 1992. Professor Khoo's research focuses on improving software developer productivity through advanced programming language theories and software engineering techniques. His work spans multiple areas including static program analysis, dynamic program optimization, code analytics, and specification mining. He has applied his expertise to develop domain-specific languages for financial data analysis and has pioneered techniques for discovering dynamic program behaviors via data-mining approaches. Research Interests: Programming Languages and Software Engineering Code Analytics and Program Analysis Specification Mining and Bug Signature Discovery Static and Dynamic Program Analysis Program Transformation and Optimization Domain-Specific Languages Professor Khoo's publications demonstrate a consistent focus on improving software quality and developer productivity. His recent work emphasizes scalable approaches to refactoring detection, bug signature mining, and specification inference, showing an evolution from theoretical foundations to practical applications in software maintenance and quality assurance. As an educator and mentor, Professor Khoo has supervised numerous graduate students to completion of their M.Sc. and Ph.D. degrees. His research projects have provided valuable training opportunities for students while addressing important challenges in software development. He has also contributed significantly to academic administration, having served as Vice Dean (Undergraduate Studies) in the School of Computing from 2005 to 2011 and currently as Co-Director of the Master of Science (Business Analytics) Programme.
Paul Ward is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo and a faculty fellow at the IBM Centre for Advanced Studies. He holds a PhD (2002) and MASc (1993) from Waterloo and a BScE (1998) from the University of New Brunswick. His research focuses on distributed systems management, dependable systems, autonomic computing, wireless networks, and IoT. Key areas include fault detection in web services, service-oriented networking, and optimization of wireless mesh networks. Ward's publications span computer networks, cognitive science, and sports analytics, reflecting interdisciplinary applications of computational methods. He holds two patents in mobile web services and fault resolution.
John Stolz is Professor of Environmental Microbiology and Director of Environmental and Energy Engineering at Duquesne University's School of Science and Engineering. His NASA-trained research examines microbial metal reduction, modern stromatolite ecosystems, and shale gas extraction impacts. His lab investigates arsenic/selenium biotransformation pathways, microbial communities in Shark Bay stromatolites, and environmental impacts of unconventional energy development. Recognized with multiple excellence awards including the Nobel J. Dick Endowed Chair. Over 150 publications explore microbial respiration mechanisms, biogeochemical cycling, and environmental bioremediation. Current grants support water quality testing near shale gas operations.
Dr. Karthika Mohan is an Assistant Professor of Computer Science in the College of Engineering at Oregon State University, affiliated with the School of Electrical Engineering and Computer Science. Her research bridges artificial intelligence and causal inference, focusing on graphical models, missing data, and non-IID data challenges. Her work has been recognized with the Google Outstanding Graduate Research Award. She serves as an associate editor for the Journal of Causal Inference and has secured NSF funding for research on incomplete data. Dr. Mohan mentors students in causal inference methods and maintains collaborations with institutions like UC Berkeley and UCLA. Her laboratory develops innovative approaches for causal reasoning in AI systems.
Essa Yacoub is a Professor in the Department of Radiology at the University of Minnesota, affiliated with the PhD Program in Medical Physics and the Center for Magnetic Resonance Research. His work focuses on advancing MRI and fMRI technologies, particularly at ultrahigh magnetic fields (e.g., 10.5 T), to achieve unprecedented spatial and temporal resolution in brain imaging. He leads projects in RF coil design, noise reduction algorithms, and developmental neuroimaging. Roles: Professor, Medical Physics Program Faculty Affiliations: Center for Magnetic Resonance Research, Department of Radiology Research emphasizes high-resolution fMRI applications, including layer-specific brain mapping, pediatric neurodevelopment studies (e.g., Baby Connectome Project), and translational tools like BIBSNet for infant brain segmentation. His innovations bridge hardware engineering (RF coils) and software (denoising pipelines) to tackle challenges in mesoscopic-scale imaging. Key contributions include optimizing imaging protocols at 7T/10.5T, developing NORDIC denoising for submillimeter data, and advancing understanding of brain networks in aging and neurological disorders. His work is foundational for large-scale initiatives like the Human Connectome Project and non-human primate neuroimaging collaborations. Grants and collaborations focus on translational imaging technologies, while educational contributions include training through the Medical Physics PhD Program. Ongoing efforts aim to refine ultra-high field MRI applications for clinical and basic neuroscience research.
Andrea Mason is a Professor and Department Chair in the Department of Kinesiology at the University of Wisconsin-Madison. Her research focuses on motor control, particularly in autism spectrum disorder, aging, and virtual environments. She holds the Conway Professorship of Kinesiology (2021) and has received the NSF Career Award (2004). Education: Ph.D. in Human Motor Systems from Simon Fraser University (under Dr. Christine MacKenzie). Her work examines bimanual coordination, gait analysis, and balance training. She leads a lab (visit lab webpage) and collaborates on projects involving robotic-assisted motor assessments and VR feedback for clinical populations. Key research themes include: Motor control in autism Age-related changes in locomotion and grasping Virtual environment interaction Developmental coordination disorders Recent studies explore gait variability in dual-task scenarios, sensory feedback effects in VR, and biofeedback-based balance training for children with autism. Her work bridges kinesiology, neuroscience, and clinical applications. Awards: Conway Professorship (2021), Best Paper (2013), NSF Career (2004) Lab: Accessible via dedicated webpage CV: Downloadable from her portal
Iris D. Tommelein serves as the Roy W. Carlson Distinguished Professor in the Civil and Environmental Engineering Department at the University of California, Berkeley's College of Engineering, where she directs the Project Production Systems Laboratory (P2SL). A globally recognized pioneer in Lean Construction, she has revolutionized architecture-engineering-construction (AEC) practices through research, industry workshops, and leadership since co-founding the Lean Construction Institute in 1997. Her educational foundation spans multiple disciplines: Ph.D. in Civil Engineering (Construction Engineering and Management), Stanford University, 1989 M.S. in Computer Science (Artificial Intelligence), Stanford University, 1989 M.S. in Civil Engineering (Construction Engineering and Management), Stanford University, 1985 B.S. (5-year degree) in Civil Engineer-Architect, Vrije Universiteit Brussel, Belgium, 1984 Professor Tommelein's research centers on transforming construction processes through Lean principles and digital innovation . Her work pioneers takt planning for workflow reliability, industrialized construction for labor and sustainability challenges, and mistakeproofing to eliminate errors. She integrates digital twins , AI , and optimization to develop practical decision-support systems for supply chains, logistics, and production management. Recent focus includes modular offsite construction and Industry 4.0 applications. Analysis of her 2023-2025 publications reveals intensifying research on takt planning maturity models and industrialized construction feasibility , with growing emphasis on mass timber automation and visual management systems. Her work consistently bridges lean theory with practical implementation across megaprojects, subcontracting networks, and heavy civil engineering. Her exceptional contributions have earned: Lean Pioneer Award (Lean Construction Institute, 2015) National Academy of Construction induction (2019) PPI Technical Achievement Award (2022) Robert B. Harris Award (University of Michigan, 2024) ASCE Construction Management Award (2024) - first woman recipient in 51 years Through the P2SL, she leads industry-collaborative research on production system design, mistakeproofing frameworks, and digital transformation. Her grant-funded projects develop assessment tools for industrialized construction adoption and takt planning methods adaptable to diverse project types. She actively mentors graduate students and drives knowledge transfer via workshops and the annual Construction Innovation Day. The Project Production Systems Laboratory (P2SL) operates as a global hub for construction innovation, partnering with owners, contractors, and suppliers to implement lean production systems. Current initiatives include developing serious games for mistakeproofing training, optimizing work density methods for heavy civil projects, and creating digital twins for real-time construction management.
Dr. Teresa Puthussery is an Associate Professor in the School of Optometry & Vision Science at the University of California, Berkeley. Her research focuses on retinal neurobiology and neurophysiology, investigating how visual signals are encoded in healthy retinas and disrupted during degeneration. She uses advanced techniques like patch-clamp electrophysiology, immunohistochemistry, and microscopy to study retinal circuits, neurotransmitter receptors, and ion channels. Dr. Puthussery teaches courses on vision science anatomy, physiology, and problem-based learning, including VISION SCIENCE 206B/C and 260C. Her research explores questions such as how retinal neurons extract motion/spatial details, how photoreceptor mutations cause degeneration, and how inner retinal circuits adapt post-photoreceptor loss. Recent work includes studies on ON-type direction-selective ganglion cells, optogenetic therapy for vision restoration, and calcium dynamics in foveal ganglion cells post-degeneration. She collaborates on projects involving primate and rodent models, contributing to understanding retinal disease mechanisms and therapeutic targets. Dr. Puthussery’s lab (retinalab.berkeley.edu) emphasizes translational research, bridging basic science and clinical applications. Her work has been published in journals like Nature and Cell Reports , with a focus on retinal degeneration, synaptic plasticity, and optogenetic interventions. She actively participates in training future vision scientists through Berkeley’s Optometry program and oversees GSI affairs as a faculty advisor.
Carl Henrik Ek is a Professor of Statistical Learning at the Department of Computer Science and Technology (Computer Laboratory) at the University of Cambridge. He is also a fellow and Director of Studies at Pembroke College, and holds visiting positions at Karolinska Institute in Stockholm and the Royal Institute of Technology. He serves as co-Director for the UKRI AI Centre for Doctoral Training in Decision Making for Complex Systems, a collaboration between Cambridge and Manchester universities, and is involved with the Accelerate Program in the Computer Laboratory. Dr. Ek's educational background includes a MEng degree in Vehicle Engineering from the Royal Institute of Technology in Stockholm, followed by a PhD from Oxford Brookes University. During his PhD, he spent time at the University of Manchester and the University of Sheffield. His PhD supervisors were Professor Neil Lawrence and Professor Phil Torr, and his postdoctoral research was conducted at UC Berkeley with Professor Trevor Darrell and Professor Raquel Urtasun. Professor Ek's research focuses on statistical learning, particularly on developing data-efficient and interpretable machine learning methods. His work spans modeling and inference in machine learning, with special emphasis on Bayesian non-parametric methods and Gaussian processes. He explores how to specify assumptions that allow learning from small amounts of data, bridging theoretical foundations with practical applications in various domains. His recent publications demonstrate a strong trend toward applying machine learning to healthcare, drug discovery, and engineering design. There's significant work on Gaussian processes, reinforcement learning, and generative models, with applications ranging from medical diagnostics to structural engineering. His research shows an increasing interdisciplinary focus, connecting machine learning with fields like cardiology, pharmacology, and computational geometry. Professor Ek has received numerous teaching awards throughout his career: Pilkington Price for Teaching Excellence (2024) Teacher of the year in Computer Science at University of Bristol (2016) Docent in Machine Learning at Royal Institute of Technology (2016) Teacher of the year at Royal Institute of Technology, Sweden (2015) Teacher of the year from Student chapter in Industrial Economics at Royal Institute of Technology (2015) Teacher of the year in Computer Science at Royal Institute of Technology (2012) Professor Ek teaches Advanced Data Science, Advanced topics in machine learning, and Machine Learning and the Physical World. He has supervised PhD students throughout his career but is not currently accepting new PhD students for 2025/26 or 2026/27. His research is supported by various grants, including his role as co-Director of the UKRI AI Centre for Doctoral Training. He is an active member of the ml@cl research group at Cambridge and has previously been involved with research groups at University of Bristol and Royal Institute of Technology. His work connects with several interdisciplinary initiatives, particularly in healthcare AI and engineering applications of machine learning.
Vicente Grau Colomer is a Professor of Engineering Science and Biomedical Image Analysis at the University of Oxford, affiliated with the Institute of Biomedical Engineering. He serves as Director of the Centre for Doctoral Training in Healthcare Innovation and a Professorial Fellow at Mansfield College. His work bridges biomedical engineering, medical imaging, and artificial intelligence. Education: PhD in medical image analysis from Universidad Politécnica de Valencia, Spain. Postdoctoral research at Harvard University and LSU Health Sciences Center. Joined Oxford in 2004, awarded full professorship in 2015. Research focuses on medical image analysis, AI-driven diagnostics, and collaborations between academia, industry, and clinicians. Notable tools include the MSP-tracker software for cellular analysis and advancements in bone marrow fibrosis quantification. Recent articles highlight interdisciplinary efforts in AI applications, MPN pathophysiology, and biomedical software development. Awards include recognition as a Professor at Oxford. Advising: Leads the Healthcare Innovation CDT and mentors students in biomedical engineering. Active in Oxford’s e-Research Centre and Systems Approaches to Biomedical Sciences CDT. Collaborates globally to translate research into clinical solutions.
Hamid Krim is a Professor in the Department of Electrical and Computer Engineering at North Carolina State University. He leads the Vision, Information and Statistical Signal Theories and Applications (VISSTA) group, focusing on statistical signal/image analysis, data science, and machine learning. His prior roles include Research Scientist at MIT’s Laboratory for Information and Decision Systems and Member of Technical Staff at AT&T Bell Labs. He holds a Ph.D. in Electrical Engineering from Northeastern University, and degrees from the University of Washington and University of Southern California. Education: Ph.D., Electrical Engineering, Northeastern University (MA), 1990s Master's, Electrical Engineering, University of Washington Bachelor's, Electrical Engineering, University of Southern California and University of Washington Research Interests: Machine Learning, AI, Signal Processing, Communications, and Control Systems . His work bridges formal mathematical frameworks with applied problems, emphasizing generative AI, adversarial robustness, and subspace-driven data analysis. Recent innovations include Volterra neural networks and expansive synthesis techniques for data generation. Awards & Recognition: 2000 NSF CAREER Award 2008 IEEE Fellow 2019 IEEE SPS Sustained Impact Paper Award Multiple extended research invitations at top institutions globally Grants & Advising: Leads the VISSTA Lab, collaborating on projects like medical algorithm development (e.g., lung wheeze analysis) and hurricane activity prediction. His work spans interdisciplinary applications in healthcare, robotics, and defense systems. Labs & Teams: Director of the VISSTA Lab, fostering research in signal theory and machine intelligence. Collaborates with academia and industry on cutting-edge AI and sensor fusion technologies.
Jonathan Hauenstein is the Robert and Sara Lumpkins Collegiate Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame, serving as Department Chair. He holds a Ph.D. from Notre Dame (2009) and M.S. from Miami University (2005). His research focuses on numerical algebraic geometry and computational methods for solving nonlinear equations, implemented in the Bertini software package. Applications span engineering, ecology, sports science, and machine learning. Education: Ph.D., Applied and Computational Mathematics, University of Notre Dame (2009) M.S., Mathematics, Miami University (2005) Research Interests: Development of numerical algorithms for polynomial systems, real algebraic geometry, and scientific computing. Key areas include homotopy continuation methods, parameter space decomposition, and applications in mechanism design, ecological modeling, and sports biomechanics. His work bridges theoretical mathematics with practical computational tools. Awards: Sloan Research Fellowship DARPA Young Faculty Award Army Research Office Young Investigator Award Office of Naval Research Young Investigator Award College of Science Research Award Advising & Grants: Advised numerous undergraduates, graduate students, and postdoctoral researchers. Active in securing grants for computational mathematics projects, including NSF-funded initiatives. His work emphasizes interdisciplinary collaboration between mathematics and engineering. Labs/Teams: Leads computational algebraic geometry research groups at Notre Dame, focusing on software development (e.g., Bertini) and numerical methods innovation.
Stephen L. Bearne is a Professor in the Departments of Biochemistry and Molecular Biology and Chemistry at Dalhousie University, affiliated with the Faculty of Medicine. He has been a department member since 1996 and served as Department Head from 2012 to 2022. His research focuses on enzymology, enzyme catalysis, and protein engineering, with a particular emphasis on transition state analogues, enzyme inhibition mechanisms, and the chemical basis of disease-associated enzymes. His work integrates organic synthesis, biophysical techniques, and computational modeling to explore enzyme function and design inhibitors for therapeutic applications. Dr. Bearne holds a PhD from the University of Toronto and an MDCM from McGill University. His lab is part of the Protein Assembly Research Team and the BioActives CREATE Training Program. Current research themes include understanding carbon acid substrate catalysis in mandelate racemase, developing inhibitors for CTP synthase and racemases involved in diseases like cancer and neglected tropical infections, and proteomic tools for enzymatic activity profiling. His research leverages advanced techniques such as site-directed mutagenesis, isothermal titration calorimetry, NMR spectroscopy, and macroion mobility spectrometry. His lab supports equity, diversity, and inclusivity and has been funded by NSERC, CIHR, and other agencies. Recent publications highlight advancements in enzyme inhibition strategies, allosteric regulation mechanisms, and enzyme filamentation roles in metabolic pathways.
