Alexandros G. Dimakis is a Professor at the University of California, Berkeley's Department of Electrical Engineering and Computer Sciences (EECS), College of Engineering. He is also Co-Director of the National AI Institute for Foundations of Machine Learning and Co-Founder of BespokeLabs.ai. PhD (2008) and Diploma (2003) in Electrical Engineering His research focuses on Generative AI , Information Theory , and Machine Learning . Recent work includes advancements in diffusion models, compressed sensing, and causal inference. His publications (150+) emphasize inverse problems, neural network verification, and generative model optimization. Recent publications highlight trends in Diffusion Models for inverse problems, Language Model Scaling , and 3D-Aware Generative Systems . Collaborative projects span biomedical applications, large-scale dataset curation (Datacomp-LM), and parameter-efficient model fine-tuning. Scientific Awards : IEEE Fellow (2022) James Massey Award (2018) NSF CAREER Award (2011) Google Research Faculty Award Best Paper awards at UAI workshops Eli Jury Dissertation Award (UC Berkeley) He advises PhD students in generative modeling, compressed sensing, and information theory. His research group collaborates with institutions like MIT, NYU, and IBM Research. Former students hold positions at Google, Amazon, and academic institutions like Purdue University.
Gregory Paradis is an Assistant Professor in the Department of Forest Resources Management at the University of British Columbia (UBC) Faculty of Forestry. His research focuses on sustainable forest management, integrating operations research, mathematical optimization, and systems modeling to address complex interactions between ecosystems, industries, and society. He works with the FRESH Lab and collaborates with the Integrated Remote Sensing Studio, emphasizing ecological and economic integration in forest planning. Sustainable Forest Management Operations Research Forest Economics Data Science Risk Assessment GIS-based Methods His research spans forest inventory optimization, climate change adaptation strategies, wildfire risk modeling, and decision support systems for invasive species. He develops computational frameworks to enhance wood supply planning, carbon management, and ecological resilience. Recent work includes machine learning applications for fire safety in timber structures and automated road planning tools for wildlife conservation. Paradis’s publications highlight trends in applying optimization methods to sustainable forestry, with a focus on biodiversity, climate adaptation, and value chain innovation. He advocates for interdisciplinary approaches that bridge silviculture, industrial engineering, and data science to tackle emerging challenges in forest ecosystems. As an educator, he seeks motivated students with quantitative and creative problem-solving skills. His lab collaborates on remote sensing integration, risk assessment models, and policy-relevant forest management strategies, ensuring plans account for uncertainties like insect infestations or windthrow events.
Haryadi S. Gunawi is a Professor in the Department of Computer Science at the University of Chicago where he leads the UCARE research group (UChicago systems research on Availability, Reliability, and Efficiency). His work focuses on improving the dependability of storage and cloud computing systems, with a particular emphasis on addressing performance stability, reliability, and scalability challenges in modern computing environments. Dr. Gunawi received his Ph.D. in Computer Science from the University of Wisconsin, Madison in 2009. Following his doctoral studies, he was a postdoctoral fellow at the University of California, Berkeley from 2010 to 2012 before joining the University of Chicago faculty. His research focuses on three main areas: (1) performance stability, where he builds storage and distributed systems robust to latency tails and "limping" hardware; (2) reliability and scalability, where he addresses concurrency and scalability bugs in cloud-scale distributed systems; and (3) the intersection of machine learning and systems, exploring how machine learning techniques can solve operating and storage system problems. His work often combines theoretical insights with practical system implementations that address real-world challenges in cloud and storage infrastructure. Dr. Gunawi's publication record shows a consistent focus on storage and cloud system reliability, with recent work increasingly incorporating machine learning techniques to address traditional systems challenges. His research spans the full stack from hardware interfaces to distributed system design, with a strong emphasis on practical solutions that can be deployed in production environments. His work often involves close collaboration with industry partners to ensure real-world relevance and impact. Dr. Gunawi has received numerous prestigious awards including the NSF CAREER award, NSF Computing Innovation Fellowship, Google Faculty Research Award, multiple NetApp Faculty Fellowships, and an Honorable Mention for the 2009 ACM Doctoral Dissertation Award. He has also received the Provost's Global Faculty Award and Facebook Faculty Research Award, highlighting the broad recognition of his contributions to the field. As an advisor, Dr. Gunawi has mentored several PhD students including Ruidan Li, Ray Andrew, Rani Ayu Putri, and William Nixon. His research has been supported by major grants from NSF, Google, Facebook, and NetApp, enabling his team to pursue ambitious research projects at the intersection of systems, storage, and machine learning. Dr. Gunawi leads the UCARE research group at UChicago, which focuses on improving the dependability of storage and cloud-scale distributed systems. He is also involved with the Chameleon cloud research infrastructure project and the broader Systems Group at UChicago, contributing to a vibrant research community focused on systems, programming languages, and software engineering.
N. Rich Nguyen is an Assistant Professor in the Department of Computer Science at the University of Virginia (UVA), where he joined in August 2018. He's part of the School of Engineering and Applied Science and is on a teaching track , focusing on making machine learning accessible and engaging for all students. Research and Innovation: Rich Nguyen's research interests include biomedical image analysis , machine learning , and computer science education . He aims to reinvent instructional activities to make them adaptive and engaging by incorporating art and music elements to help everyone learn coding. Notable research contributions include: Floodwatch : A system for flood monitoring using crowdsourced images TuneScope : A digital music creation tool combining SoundScope and Snaps! technology CAD Library : Open-source design tools for educators AI for early sepsis detection : Highlighted in UVA Today Teaching Accomplishments: Before UVA, Rich taught computer science courses at UNC Charlotte for four years to a total of 1,458 students. At UVA, he teaches several courses including: CS 4774: Machine Learning (multiple semesters) CS 2501: Machine Learning for All (launched in Fall 2021) SYS 6016 / SDS 6050: Deep Learning CS 2150: Data and Program Representation (multiple semesters) CS 6316: Machine Learning (Graduate Level) CS 2910: CS Education Practicum (for Teaching Assistants) He previously taught at UNC Charlotte: ITCS 1600: Computing Professionals ITCS 2600: Computing Professionals for Transfer Students ITCS 4156: Introduction to Machine Learning ITCS 2215: Design and Analysis of Algorithms Academic Achievements: Rich Nguyen has received several notable awards and grants: Google Faculty Award for Machine Learning Education with TensorFlow (2019) Best Paper Award at IEEE BigDataSE (2022) Best Poster Award at SITE Conference (2022) CCI Faculty Innovation Award (2018) NSF grants for Smart and Connected Communities (2022) and Computational Thinking (2021) 3 Cavaliers Grant on Coding and Music (2021) Student Mentorship: Rich has mentored numerous students and teaching assistants who have achieved recognition. Notable students include: Joy Qiu - Published in Clinical Infectious Diseases Louisa Edwards and Zach Boner - Invited to Ken Ono Podcast Mike Ferguson - Winner of CS Louis T. Rader Undergraduate Teaching Award He has also served as faculty advisor for HooHacks (UVA's hackathon) and co-founded CharlotteHack at UNC Charlotte. Labs and Collaborations: Rich Nguyen leads the ML4VA (Machine Learning for Virginia) initiative, engaging students in project-based learning to apply machine learning to real-world problems affecting Virginia communities. He collaborates with institutions for symposiums on smart cities, particularly with ASEAN universities, and has partnered with Premier Healthcare for hackathons and with Glen Bull on educational technology projects.
Jose Israel Rodriguez is an Associate Professor in the Department of Mathematics at the University of Wisconsin-Madison. His research bridges applied algebraic geometry and algebraic statistics, focusing on nonlinear algebra, maximum likelihood estimation, monodromy, and polynomial systems in engineering and science applications. Primary Affiliation: Department of Mathematics , UW-Madison Additional Affiliations: Department of Electrical & Computer Engineering , Institute for Foundations of Data Science Research Interests : Applied algebraic geometry for nonlinear eigenvalue problems and kinematics Algebraic statistics in nearest point problems and likelihood geometry Numerical methods for monodromy, Galois groups, and polynomial optimization Teaching and Mentorship : Co-organized the Collaborative Undergraduate Research Laboratory (CURL) for Spring 2020 Advises PhD students Julia Lindberg and Zinan Wang , with Bernd Sturmfels as his own PhD advisor Developed software tools like Multiregeneration and Decomposable Sparse Polynomial Systems Academic Contributions : Authored over 20 peer-reviewed publications in journals like SIAM Journal on Applied Algebra and Geometry, Foundations of Computational Mathematics, and Journal of Symbolic Computation Organized international conferences including Monodromy and Galois Groups in Enumerative Geometry and SIAM AG19 Active member of the SIAM community and developer of the Matroids Day seminar
Cristian Cadar is a Professor in the Department of Computing at Imperial College London, leading the Software Reliability Group . His research focuses on improving software reliability and security through practical techniques in software engineering, computer systems, and program analysis. Education: Ph.D. in Computer Science, Stanford University M.Eng. in Computer Science, MIT B.S. in Computer Science and Mathematics, MIT His research interests center on software engineering and software security , particularly symbolic execution , dynamic symbolic execution (DSE) , and multi-version execution . Current work explores techniques for scalability, constraint solving, and runtime security in software systems. Key trends in his recent articles include optimizing symbolic execution for testing, addressing path explosion in constraint-based test generation, and advancing multi-version execution for dynamic software updates. His publications also cover program analysis , automated testing , and formal methods for software reliability. Awarded prestigious honors such as the Humboldt Research Award (2024) , ERC Consolidator Grant (2018) , and IEEE New Directions Award (2022) . Other accolades include the BCS Roger Needham Award (2019) and SIGOPS Hall of Fame (2018) . Cadar supervises PhD and postdoctoral researchers in software reliability and security. His group has secured grants from the ERC and EPSRC , including a Consolidator Grant (2018) and Early-Career Fellowship (2013) . He actively contributes to conference organizing committees and editorial boards. The Software Reliability Group at Imperial College, led by Cadar, specializes in techniques like KLEE and EXE for automated testing. Their work has been adopted by industry partners such as Fujitsu, IBM, and Microsoft, particularly in runtime security tools like WIT .
Liang Zhao, PhD, MAS, MBA, is a Professor in the Department of Bioengineering and Therapeutic Sciences within the Schools of Pharmacy and Medicine at the University of California, San Francisco (UCSF). Prior to joining UCSF, he served as director of the Division of Quantitative Methods and Modeling (DQMM) in the Office of Research and Standards in the Office of Generic Drugs in the Center for Drug Evaluation and Research (CDER) at the U.S. Food and Drug Administration (FDA) from 2015 to 2024. His professional career spans over 19 years with experience at Pharsight, Bristol Myers Squibb (BMS), MedImmune, and the FDA. Dr. Zhao's research focuses on pharmacometrics, drug delivery modeling, and artificial intelligence-based tools that impact drug development and regulatory decision-making. His work encompasses mechanistic models for brain drug delivery, regulatory science modeling and simulation, AI-driven drug discovery and development, drug interactions, biological availability, generic drugs, clinical pharmacology, therapeutic equivalency, computer simulation, and FDA regulatory processes. He has pioneered innovative approaches including model master files for model sharing and model-integrated evidence for generic product development and approval. His research integrates machine learning tools into pharmacometrics to advance drug delivery and bioequivalence assessment methodologies. Dr. Zhao has published over 120 articles and book chapters in prestigious journals. His recent publications demonstrate strong focus on applying advanced modeling techniques, machine learning algorithms, and pharmacometric approaches to solve complex problems in drug development and regulatory science. His work shows consistent innovation in developing quantitative methods to enhance bioequivalence assessment, improve drug product characterization, and support regulatory decision-making for generic drugs. FDA Group Recognition Award, FDA, 2024 Gary Neil Prize for Innovation in Drug Development, American Society for Clinical Pharmacology & Therapeutics (ASCPT), 2023 Commissioner's Special Citation, FDA, 2021 Humanitarian Award, Victims' Rights Foundation, 2020 30+ FDA CDER team and Individual Awards, CDER, FDA, 2011 Academic Award for Executive MBA Class 2009, Judge Business School, University of Cambridge, 2011 Dr. Zhao leads the Zhao Lab at UCSF, which advances drug development and regulatory science through cutting-edge research in pharmacometrics, drug delivery modeling, and artificial intelligence. His work bridges academic research with regulatory applications, demonstrating leadership in translating scientific innovations into practical regulatory frameworks. His experience across industry, regulatory agencies, and academia provides a unique perspective on drug development challenges and opportunities.
Ashley M R Montanaro is a Professor of Quantum Computation at the School of Mathematics, University of Bristol . Active in quantum computing research since at least 2014, they lead projects at the intersection of quantum algorithms , computational complexity , and quantum information theory , affiliated with the Bristol Quantum Information Institute. Research interests focus on quantum algorithm design , computational complexity analysis , and quantum simulation . Key work includes developing variational quantum algorithms for phase transition detection, Hamiltonian simulation techniques, and quantum-classical hybrid methods for solving complex problems in physics and optimization. Recent publications demonstrate expertise in: Quantum phase diagram simulation with low-depth circuits Quantum speedups for constraint satisfaction problems Quantum communication complexity of machine learning tasks Quantum-enhanced optimization heuristics Hamiltonian simulation with time-dependent product formulas Quantum algorithm complexity analysis Scientific awards include: EPSRC Fellowship (2014-2019) - "New insights in quantum algorithms and complexity" Active in quantum software development through projects like: "Quantum Algorithms from Foundations to Applications" (ERC-2018-COG) "Quantum Computing and Simulation Hub" (2019-2024) "Prosperity Partnership in Quantum Software" (2019-2023)
Peter Doerschuk is a Professor in the Department of Electrical and Computer Engineering at Cornell University's College of Engineering. He joined Cornell in July 2006 after serving on the faculty at Purdue University in both Electrical and Computer Engineering and Biomedical Engineering. His educational background includes: B.S. in Electrical Engineering, MIT (1977) M.S. in Electrical Engineering, MIT (1979) Ph.D. in Electrical Engineering, MIT (1985) M.D., Harvard Medical School (1987) Peter Doerschuk's research focuses on biological and medical systems through the lens of computational nonlinear stochastic systems. His work spans biomedical imaging , signal and image processing , statistical modeling , and computational inverse problems in biophysics . He develops high-performance algorithms and software systems that integrate accurate physical models with computational efficiency. His research addresses problems across multiple spatial scales—from 3D virus reconstruction using electron microscopy to modeling whole-body ethanol pharmacokinetics. The recent publications highlight a strong trend in computational biomedical imaging and physiological modeling . Key areas include 3D reconstruction of heterogeneous biological structures, cryo-EM dynamics analysis, and physiologically based pharmacokinetic modeling. The work consistently combines advanced statistical and machine learning methods with domain-specific physical models, particularly in virology and neurovascular physiology. His scientific awards and honors include: Fellow, American Institute for Medical and Biological Engineering (AIMBE) University Faculty Scholar, Purdue University Motorola Excellence in Teaching Award Ernst A. Guillemin Thesis Prize (MIT) Department of Biomedical Engineering Faculty Service Award (Purdue) Dr. Doerschuk has advised graduate students, including Keyuan Xu, whose M.Eng. thesis at MIT received the prestigious Ernst A. Guillemin Thesis Prize. His research has been supported through academic grants and collaborations with institutions such as The Scripps Research Institute and Indiana University School of Medicine. He has developed parallel software systems for high-performance computing applications in biophysics and biomedical signal processing. His research has involved collaboration with multiple labs and teams, including work with Professor J. E. Johnson at The Scripps Research Institute on virus structure determination and with Professor S. J. O’Connor at Indiana University on ethanol pharmacokinetics modeling. These interdisciplinary teams integrate expertise in engineering, medicine, and computational science to solve complex biomedical problems.
Dr. Alireza Nili is a Senior Lecturer in Service Science at QUT's School of Information Systems within the Faculty of Science. His expertise spans digitization of customer-centric services, AI/chatbots, IoT/IIoT, and sustainable technologies. He holds a PhD from Victoria University of Wellington and has coordinated large-scale courses like IT Systems Design (IFB103), achieving top teaching scores. Nili's research focuses on service ecosystems, trust in digital services, and public/retail sector innovations. He has secured over $1.4M in industry grants for projects involving Cisco, Amazon, and Services Australia. His awards include the 2023 Educator of the Year and multiple top conference paper recognitions. Nili supervises PhD students at Level 3 mentoring status and contributes to major conferences as track chair/associate editor. Research highlights include frameworks for chatbot governance, IOT in agriculture, and AI ethics. His work appears in IEEE Software , Communications of the ACM , and MIT Sloan Management Review . Current projects address consumer trust in AI technologies and spatial data systems. Nili's interdisciplinary approach combines design science with empirical methodologies to bridge theory and practice in digital service innovation.
Elias Jarlebring is a Professor in Numerical Linear Algebra at the Department of Mathematics, KTH Royal Institute of Technology, Stockholm. He has held the position of Full Professor since 2021, following his tenure as Associate Professor (2013-2021) and Dahlquist Research Fellow (2011-2013). His research focuses on numerical analysis, numerical linear algebra, matrix computations, and scientific computing. Jarlebring develops linear algebra algorithms to solve problems from various fields including systems and control, acoustics, electromagnetics, data science, quantum mechanics, and quantum chemistry. He is a core developer of NEP-PACK, a scientific computing software package for nonlinear eigenproblems. His recent publications demonstrate significant contributions to computational methods for nonlinear eigenvalue problems, matrix functions, and parameterized linear systems. The research shows a clear trajectory toward increasingly complex applications in quantum computing, data science, and wave propagation problems. Project grant, Swedish research council (2019) Ruth och Nils-Erik Stenbäcks foundation, junior grant (2019) Göran Gustafsson Prize for junior researchers (2014) Project grant for junior researchers, Swedish research council (2014-2018) Professor Jarlebring has supervised numerous PhD students including Vilhelm Peterson Lithell, Gustaf Lorentzon, Siobhán Correnty, Parikshit Upadhyaya, Emil Ringh, Antti Koskela, and Giampaolo Mele. He has received multiple research grants from the Swedish Research Council and serves as editor for BIT Numerical Mathematics, Linear and Multilinear Algebra, NACO Numerical Algebra Control and Optimization, and CALCOLO. He is actively involved in the numerical linear algebra community as a member of ILAS (International Linear Algebra Society), GAMM Activity Group on Numerical Linear Algebra, and the Nordic Numerical Linear Algebra Association. He also contributes to open source projects, particularly in the Julia programming language ecosystem.
Kenneth Ross is a Professor in the Computer Science Department at Columbia University in New York City. His primary appointment is within the Department of Computer Science, with affiliations including the Foundations of Data Science Committee. His work bridges theoretical database research and practical system implementation. His research focuses on database systems with particular expertise in query processing, query language design, data warehousing, and architecture-sensitive database system design. Additional research spans computational biology, especially analysis of large genomic data sets. Current projects include Linear Algebra Operators in Databases for machine learning workloads and Repeats and Somatic Mutation analysis in genomics. His work consistently addresses the intersection of hardware capabilities and database system design. Ross leads the Database Research Lab at Columbia, which has produced significant work on query optimization, GPU database processing, and hardware-conscious database systems. His recent publications demonstrate strong focus on adapting database systems to modern hardware including GPUs, SIMD processors, and persistent memory. His scientific recognition includes: Packard Foundation Fellowship Sloan Foundation Fellowship NSF Young Investigator Award Distinguished Faculty Teaching Award (2008) Ross actively advises undergraduate engineering students (juniors with last names P-Z) and has taught foundational courses including Introduction to Databases and Programming and Problem Solving for over two decades. His teaching portfolio shows consistent engagement with both theoretical concepts and practical implementation challenges in computer science education.
Dr. Adegboyega Ojo is a Professor at the School of Public Policy and Administration (SPPA), Carleton University , holding the prestigious Canada Research Chair (Tier 1) in Governance and Artificial Intelligence since 2014. His work bridges digital government , AI governance , and smart city development , with a focus on public sector innovation and policy analytics . Research Interests Smart Cities and Urban Digital Infrastructure Open Government Data and Transparency Blockchain Applications in Public Administration AI Ethics and Algorithmic Governance Electronic Participation and Citizen Engagement Publication Trends : His research emphasizes interdisciplinary approaches to digital governance, often involving data-driven policy analysis , smart city frameworks , and open data platform design . Collaborations span institutions in Ireland, Poland, and the U.S., with citations exceeding 5,200. Scientific Awards : Canada Research Chair in Governance and AI (Tier 1) Labs & Teams : Collaborates with the Insight Centre for Data Analytics (Ireland), Delft University of Technology , and United Nations University on projects like OpenGovIntelligence and BOLD (Big and Open Linked Data).
Yves Wautelet serves as an Associate Professor at the Faculty of Economics and Business at KU Leuven, where he conducts research in conceptual modeling, business process management, and digital transformation. His work addresses critical challenges at the intersection of information systems engineering and business strategy, with particular focus on sustainability-driven modeling approaches and IT governance frameworks. His primary research interests include: Conceptual modeling methodologies and frameworks Business process management and information systems design Digital transformation strategies and implementation IT governance and business-IT alignment Sustainability-driven modeling for circular economy Agile software development practices and methods Requirements engineering with user stories Wautelet's recent publication record demonstrates significant scholarly productivity with numerous 2024-2025 publications spanning conceptual modeling frameworks for sustainability (Circulise), tools for identifying ambiguity in user stories (AmbiTRUS), and approaches to align strategic and operational agility. His work bridges theoretical foundations with practical applications across diverse domains including healthcare, circular economy, software development, and organizational transformation. His research consistently applies model-driven approaches to solve complex real-world problems, often integrating sustainability considerations into information systems engineering. As a promotor and co-promotor, Wautelet currently supervises multiple doctoral research projects including: Automatic generation of conceptual models from textual descriptions (2024-2028) Sustainability-Driven Modeling Assistant for Twin Transition in Vietnam (2024-2028) Home Care Business Process Management using Distributed Ledger Technologies (2024-2028) Teaching Modeling Skills in BPMN formalism (2021-2025) His research is conducted through the Information Systems Engineering Research Group (LIRIS) at KU Leuven's Brussels campus, where he contributes to advancing model-driven approaches for addressing contemporary business and technological challenges.
Marcin Jurdzinski is an Associate Professor (Reader) in the Department of Computer Science at the University of Warwick , UK. He has been a faculty member since 2004 and is a core member of the Foundations of Computer Science and Discrete Mathematics and its Applications research groups. University: University of Warwick School: Faculty of Science Department: Department of Computer Science Position: Associate Professor (Reader) Email: Marcin.Jurdzinski@warwick.ac.uk Office: CS2.19 His research lies at the intersection of algorithms, game theory, automata, and logic , with a strong emphasis on formal verification , model checking , and theoretical computer science . He is best known for his foundational work on parity games , including the development of small progress measures and discrete strategy improvement algorithms. The recent publications reveal a consistent focus on computational complexity in games and verification. Key themes include stochastic games, timed automata, bisimilarity, and quantitative analysis . His work often bridges theoretical insights with practical verification challenges, especially in real-time and probabilistic systems. He has supervised several PhD students and hosted postdoctoral researchers such as Laure Daviaud and Alexander Kozachinskiy. He has led EPSRC-funded projects including Solving Parity Games in Theory and Practice and Counter Automata: Verification and Synthesis . PhD Students: Aditya Prakash, Thejaswini K. S., Michail Fasoulakis, John Fearnley, Michal Rutkowski, Ashutosh Trivedi Postdocs: Laure Daviaud, Alexander Kozachinskiy He is actively involved in the academic community, serving on the steering committee of the Highlights of Logic, Games and Automata conference and on program committees for major venues such as CONCUR, ICALP, and LICS. He has also organized workshops including FORMATS and ICALP co-located events.
