J. Cole Smith is the Dean of the College of Engineering and Computer Science at Syracuse University and holds the academic rank of Professor. He joined Syracuse in 2019 with a focus on advancing undergraduate and graduate education, research initiatives, and diversity, equity, and inclusion efforts. Previously, he earned a PhD in Industrial and Systems Engineering from Virginia Tech (2000) and a BS in Mathematical Sciences from Clemson University (1996). His research focuses on mathematical optimization, particularly mixed-integer programming and combinatorial optimization, with applications in network interdiction, logistics, national security, healthcare, and sports. His work has been supported by agencies such as the National Science Foundation, Office of Naval Research, and Defense Advanced Research Projects Agency. Recent research trends emphasize network interdiction under uncertainty, bilevel optimization, and large-scale optimization challenges. His publications in top journals like Operations Research and Mathematical Programming reflect these themes. Awards include INFORMS Fellow (2023) and recognition from Virginia Tech's Grado Department of Industrial and Systems Engineering. While his dean duties reduce direct research involvement, he remains active through collaboration with PhD students. His team's work addresses real-world problems in security, logistics, and healthcare, leveraging advanced algorithmic techniques.
Massachusetts Institute of TechnologyUnited States
Mark P. Kritzman is a Senior Lecturer in Finance at the MIT Sloan School of Management. He concurrently serves as President & CEO of Windham Capital Management LLC and Senior Partner at State Street Associates. His roles include board memberships at the Institute for Quantitative Research in Finance, Investment Fund for Foundations, and editorial boards of journals like the Journal of Investment Management and Financial Analysts Journal. Education: MBA from New York University and Chartered Financial Analyst (CFA) designation. His research focuses on investing strategies , risk management , and predictive analytics , with recent work addressing federal spending's impact on inflation, bubble detection, and NBA draft prospect evaluation. He has authored six books, including Puzzles of Finance and The Portable Financial Analyst . Key publications from 2023–2025 explore themes like transparent predictive modeling, volatility forecasting, and algorithmic alternatives to neural networks. His work bridges academia and industry, emphasizing practical applications of quantitative methods. Awards : 2025 James R. Vertin Award, 2013 Peter L. Bernstein Award, multiple article honors. Grants/Advising : No explicit student advisees listed; professional contributions focus on institutional advisory roles. He leads Windham Capital Management and actively contributes to editorial boards, shaping discourse in finance and quantitative research.
Adam Finkelstein is a Professor in the Department of Computer Science at Princeton University, where he has been a faculty member since 1997. He holds a PhD and Master's in Computer Science from the University of Washington and a dual degree in Physics and Computer Science from Swarthmore College. Finkelstein is renowned for his interdisciplinary work at the intersection of computer graphics, audio processing, and machine learning, and he co-organized the Art of Science exhibition at Princeton. Education: PhD, Computer Science, University of Washington MS, Computer Science, University of Washington BA, Physics and Computer Science, Swarthmore College His research spans audio processing (e.g., speech enhancement, voice conversion, audio metrics), computer graphics (e.g., line drawing algorithms, stylized rendering, image manipulation), and machine learning (e.g., self-supervised learning, differentiable programming). His work often bridges technical and creative domains, exemplified by collaborations at Pixar and Adobe Creative Technologies Lab. Recent publications highlight advancements in audio super-resolution and voice conversion using deep learning frameworks, as well as stylized line rendering for animated 3D models. His contributions to perceptual audio metrics and shader optimization further underscore his impact on human-centric computational systems. Scientific Awards: NSF CAREER Award Alfred P. Sloan Fellowship Fellow of the Association for Computing Machinery (ACM) Finkelstein has secured foundational grants for his research and actively mentors students, though no specific advisees are listed. He also explores collaborative tools for internet music performance, reflecting his broader interest in distributed systems and user interfaces.
Christian Poellabauer is a Professor at Florida International University (FIU) in the Knight Foundation School of Computing and Information Sciences, serving as Interim Associate Dean for Research and Graduate Studies in the College of Engineering & Computing. He holds a Ph.D. from Georgia Institute of Technology (2004) and a Diplom-Ingenieur from TU Vienna (1998). His research focuses on mobile sensing, data analytics, and healthcare technologies, leading the MOSAIC Lab which develops solutions for healthcare, IoT, and smart cities. He previously led the Mobile Computing Lab at the University of Notre Dame and held leadership roles in data science institutes. Research Interests: His work spans digital biomarkers for neurodegenerative diseases, speech analysis for mental health, wearable device authentication, and wireless sensor networks. The MOSAIC Lab addresses challenges like real-time sensor data analysis on constrained devices and translating insights into clinical applications. Teaching: He has taught courses on Operating Systems, Mobile Computing, and Smart Health at both FIU and Notre Dame. Recent courses include COP4610 (Operating Systems Principles) and COP5614 (Graduate Operating Systems) at FIU. Service: He serves as Associate Editor for IEEE Transactions on Network Science and Engineering, and has organized conferences like ICNC 2023 and IEEE MASS 2021. His academic service includes roles on editorial boards and technical program committees for major conferences in distributed computing and networking. Advising & Labs: Advises current Ph.D. students in areas like multi-modal sensing for affective computing and mental health crowdsensing. Past students have pursued roles in academia and industry (e.g., Rose-Hulman Institute of Technology, Facebook, Microsoft). The MOSAIC Lab collaborates on projects like digital clinical outcome assessments and motor impairment detection.
Rima Alaifari is an Assistant Professor for Applied Mathematics at ETH Zürich, specializing in inverse problems, applied harmonic analysis, and scientific machine learning. She is an associated member of the ETH AI Center and will assume a full professorship at RWTH Aachen University in 2025. Her work focuses on stability analysis, regularization, and operator learning, with applications in phase retrieval and robustness of neural networks. Alaifari holds a Ph.D. in Mathematics from Vrije Universiteit Brussel (2014), where she studied under Prof. Ingrid Daubechies and Prof. Michel Defrise. She completed her M.Sc. in Applied and Industrial Mathematics at Johannes Kepler University, Linz (2010). Her academic career includes postdoctoral fellowships at ETH Zurich (2014–2016) and a Marie Curie-funded position (2016). Her research interests span inverse problems, phase retrieval, stability in machine learning, and operator learning. Notable projects include SNF-funded work on phase retrieval (2019) and collaborations on adversarial robustness in medical imaging (e.g., CT reconstruction). She has mentored PhD students Tandri Gauksson and Matthias Wellershoff, and postdocs Francesca Bartolucci (now at TU Delft) and Jesse Railo (Finnish Inverse Prize winner). Teaching includes courses on inverse problems, time-frequency analysis, and robustness of deep neural networks. She actively participates in international conferences, delivering plenary talks at venues like the International Conference on Computational Harmonic Analysis (2022) and ICERM (2023). Her work bridges mathematical foundations and practical applications, emphasizing stability and robustness in computational methods.
Huazheng Wang is an Assistant Professor in the School of Electrical Engineering and Computer Science at Oregon State University. His research focuses on reinforcement learning, information retrieval, and trustworthy AI. He received his Ph.D. from the University of Virginia (2021) and B.E. from the University of Science and Technology of China (2015). He holds awards including the 2025 EECS Fabulous Teacher Recognition and SIGIR 2019 Best Paper Award. His work addresses challenges in robust reinforcement learning, adversarial attacks on bandit systems, and applications in scientific discovery. Education: Ph.D., Computer Science, University of Virginia (2021) B.E., Computer Science and Technology, University of Science and Technology of China (2015) Research interests emphasize developing efficient algorithms for reinforcement learning, multi-armed bandits, and their applications in recommendation systems, protein optimization, and security. Notable contributions include provably efficient risk-aware reinforcement learning frameworks and adversarial attack analysis on bandit systems. Recent work includes NSF-funded research on neural bandits (IIS-2403401) and publications in top venues like ICML, NeurIPS, and AAAI. His lab explores embodied LLM agents for team cooperation and federated collaborative online monitoring frameworks.
Ares J. Rosakis is the Theodore von Kármán Professor of Aeronautics and Mechanical Engineering at the California Institute of Technology (Caltech), where he served as Chair of the Division of Engineering and Applied Science from 2009-2015 and previously as Director of the Graduate Aerospace Laboratories (GALCIT). He has held numerous prestigious visiting professorships including at Nanyang Technological University, Northwestern University, Columbia University, Oxford University, and École Normale Supérieure in Paris. Rosakis earned his B.A. and M.A. in Engineering Science from Oxford University in 1978, followed by his Sc.M. (1980) and Ph.D. (1982) in Engineering (Solid Mechanics) from Brown University. He joined Caltech as an Assistant Professor in 1982, was promoted to Associate Professor in 1988, and to full Professor in 1993. In 2004, he was named the Theodore von Kármán Professor, one of Caltech's most distinguished named chairs. Rosakis is globally recognized as the foremost expert in dynamic failure mechanics of solid materials. His pioneering contributions span the dynamic failure of metals, composites, and interfaces. He invented Coherent Gradient Sensing (CGS) interferometry, a novel optical method sensitive to gradients of optical path differences that has been widely adopted in fracture mechanics and thin film stress measurements. His research encompasses dynamic shear-dominated rupture of heterogeneous materials, rupture mechanics of crustal earthquakes (where he experimentally discovered 'intersonic' or 'supershear' ruptures), and reliability of thin films and in-situ wafer level metrology. His work bridges engineering science, materials mechanics, and geophysics with remarkable interdisciplinary impact. His recent publications demonstrate a strong focus on earthquake mechanics and laboratory simulations of seismic events, particularly supershear earthquake ruptures. The research connects fundamental fracture mechanics with real-world geophysical phenomena, revealing how laboratory-scale experiments can illuminate the physics of large-scale earthquakes. His work has established critical links between theoretical models, experimental observations, and geological field evidence. Rosakis has received numerous prestigious awards including: 2024 Foreign Member of the Royal Society, UK 2023 Honorary PhD from National Technical University of Athens 2023 Honorary Degree of Doctor of Engineering from University of Illinois 2021 Zdeněk P. Bažant Medal for Failure and Damage Prevention 2018 Timoshenko Medal from ASME 2016 Elected to the National Academy of Sciences 2011 Elected to the National Academy of Engineering Throughout his distinguished career at Caltech, Rosakis has mentored numerous graduate students and postdoctoral researchers, many of whom have become leaders in their fields. His research has been continuously supported by major grants from the National Science Foundation, Department of Energy, and other federal agencies, focusing on dynamic fracture, earthquake mechanics, and advanced optical measurement techniques. He has served on numerous editorial boards and advisory committees for major scientific organizations. At Caltech, Rosakis leads research in the Graduate Aerospace Laboratories (GALCIT), where he has established world-class experimental facilities for studying dynamic fracture and earthquake mechanics. His laboratory features high-speed imaging systems capable of millions of frames per second, infrared diagnostics for temperature field measurements, and specialized equipment for simulating earthquake ruptures at laboratory scale. His research group combines experimental, theoretical, and computational approaches to address fundamental questions in solid mechanics and their applications to geophysics and materials engineering.
Joshua B. Gross is an Associate Professor in the Department of Biological Sciences at the University of Cincinnati, where he has been conducting research since 2010. His work focuses on evolutionary biology, particularly using the Mexican cavefish ( Astyanax mexicanus ) as a model system to study adaptation to extreme environments. Dr. Gross received his academic training at prestigious institutions: Ph.D. in Organismic and Evolutionary Biology from Harvard University (2005) M.S. with Distinction in Biological Sciences from University of Denver (2001) B.A. in Psychology from Miami University (1995) Dr. Gross's research explores the genetic and developmental basis of evolutionary changes, with a focus on how organisms adapt to extreme environments. His primary model system is the Mexican cavefish ( Astyanax mexicanus ), which exists in both surface-dwelling (with eyes and pigmentation) and cave-dwelling (blind and depigmented) forms. His work integrates quantitative genetics, transcriptomics, and phenotypic analysis to understand the genetic changes underlying cave adaptation, including both regressive traits (like eye loss) and constructive traits (like enhanced taste systems). Analysis of Dr. Gross's recent publications reveals a strong focus on sensory adaptation and craniofacial evolution in cavefish. His work has increasingly incorporated genomic and transcriptomic approaches to understand how cavefish adapt to low-oxygen environments, changes in sensory systems (particularly taste and lateral line), and craniofacial modifications. There's a clear trajectory toward understanding the integration between different biological systems, such as how sensory neuromasts influence skeletal development. Dr. Gross has received several notable awards and recognitions: National Academies Education Fellow in the Life Sciences (2014-2015) Young Investigator Winner, Sigma Xi, University of Cincinnati Chapter (2016) Honorable Mention, Excellence in Doctoral Mentoring Award Nominated for 2018 Dean's Award for Innovative Instruction Young Anatomist's Publication Award from the American Association of Anatomists (2004) As a principal investigator, Dr. Gross has secured substantial funding from the National Science Foundation and National Institutes of Health, including multiple R01 grants from NIH and major awards from NSF. His current projects include "The developmental basis for sensory-skeletal integration: The osteo-inductive role of neuromasts" (NSF IOS-2205928, 2022-2026) and "The constructive evolution of gustation: Molecular, organismal and environmental attributes of taste tuning" (NSF DEB-2343857, 2024-2028). He has mentored numerous undergraduate and graduate students through research projects and has been recognized for his teaching excellence, particularly in Human Genetics. Dr. Gross leads a research laboratory focused on evolutionary and developmental biology at the University of Cincinnati. His team employs a multidisciplinary approach combining field work in Mexican caves, laboratory experiments, genomic analysis, and developmental studies. He has organized international scientific meetings, including the Astyanax International Meeting, fostering collaboration among researchers studying cave-adapted organisms worldwide.
Björn Brandenburg is a researcher at the Max Planck Institute for Software Systems (MPI-SWS) in Kaiserslautern, Germany. His work focuses on real-time systems, scheduling algorithms, and operating system design, with a particular emphasis on predictable resource allocation and performance guarantees in multiprocessor and cyber-physical environments. His research interests include real-time response-time analysis (e.g., PROSA ), locking protocols for multiprocessor systems, side-channel mitigation in cloud environments, and the verification of real-time scheduling policies. He has contributed to foundational studies on deadline failure probabilities, self-suspending tasks, and predictable real-time Linux implementations. Scientific awards include recognition for outstanding papers on TimerShield (2017) Offline Equivalence (2017) . His work intersects with practical systems like LITMUSRT and ROS 2, aiming to bridge theoretical guarantees with real-world applications in safety-critical and distributed real-time systems.
Ananth Grama is the Samuel D. Conte Distinguished Professor of Computer Science and Associate Director of the Center for Science of Information at Purdue University. He holds a faculty position in the Department of Computer Science, College of Science. His research focuses on parallel computing, distributed systems, machine learning, and their applications in complex systems such as materials modeling and clinical analytics. He teaches advanced courses like CS525 (Parallel Computing) and CS314 (Numerical Methods). Research interests span parallel algorithms, fault-tolerant learning, quantum machine learning, and data-driven healthcare analytics. Recent work addresses fundamental limits of generative models, online learning under noisy conditions, and clinical outcome predictions. His projects include DOE-funded research on critical element recovery and NIH grants for hearing assessment technologies. Notable contributions include over 50 peer-reviewed publications since 2022, with recent papers appearing at ICLR, NeurIPS, and ICML. Current postdocs include Changlong Wu (collaborating with Wojciech Szpankowski) and Luopin Wang (with Nadia Atallah). He advises seven graduate students and oversees multidisciplinary research teams.
Dr. Sonia Petrone is a Full Professor of Statistics at Bocconi University's Department of Decision Sciences. She earned her PhD in Statistics from Bocconi University and has held academic positions at the University of Pavia and University of Insubria before joining Bocconi. Her extensive international experience includes research visits across North America, Latin America, Europe, India, and Russia. Her research specializes in Bayesian statistics, with contributions to foundational theory, predictive modeling, Bayesian nonparametrics, and stochastic processes. She currently directs the Bocconi Summer School in Advanced Statistics and Probability and previously led the PhD program in Statistics (2011-2018). Her research portfolio demonstrates consistent focus on Bayesian nonparametric methods, predictive modeling, and applications to complex data structures. Recent work explores urn processes, time series analysis, and network modeling using innovative Bayesian approaches. Awards & Honors: IMS Medallion Lecture Award (2018) ISBA Foundational Lecture Award (2016) Fellow of International Society for Bayesian Analysis Fellow of Institute of Mathematical Statistics Fellow of European Laboratory for Intelligent Systems Fellow of Bocconi Institute of Data Science She has held editorial leadership positions as Editor of Statistical Science (2020-2022) and Bayesian Analysis (2010-2014), and served as President of the International Society for Bayesian Analysis (2014).
Matteo Maffei is a Full Professor at TU Wien, leading the Security and Privacy group. He joined in 2017 after 11 years at Saarland University's CISPA. He holds a Ph.D. in Computer Science from Ca’ Foscari University of Venice (2006). He coordinates the TU Wien Cybersecurity Center, the SecInt Doctoral School, and the FWF Special Research Program SPyCoDe. His research focuses on formal methods for security and privacy, blockchain technologies, and web security. Roles: Full Professor, Coordinator of TU Wien Cybersecurity Center, Module Head of Christian Doppler Lab for Blockchain Technologies (CDL-BOT), Board Member of Vienna Cybersecurity and Privacy Research Cluster (ViSP). His work emphasizes formal verification of cryptographic protocols, smart contracts, and decentralized systems. Key achievements include ERC Advanced (2024) and Consolidator (2018) Grants, and leadership roles in conferences like IEEE Computer Security Foundations Symposium (CSF). Research Interests: Formal methods, smart contracts, blockchain scalability, web security, privacy-preserving protocols, and decentralized systems. His recent projects include optimizing Lightning Network channels, secure multi-hop payments, and AI-driven robustness verification. Publications: Over 200 publications in top venues like CCS, IEEE S&P, and CRYPTO. Recent work includes advancements in blockchain interoperability (e.g., Alba bridges), light client protocols (Blink), and neural network verification techniques. Grants & Awards: ERC Advanced Grant (2024), ERC Consolidator Grant (2018), DFG Emmy Noether Fellowship (2009). Led projects funded by EU Horizon, FWF, and industry partners like ABC Research GmbH. Advising & Labs: Supervised over 20 PhD/Master theses. Active in the Christian Doppler Lab for Blockchain Technologies and the SPyCoDe SFB. Collaborates with institutions like SBA Research and Stanford University.
Paolo Tonella is a Full Professor and Director of the Software Institute at the Faculty of Informatics, Università della Svizzera italiana (USI) in Lugano, Switzerland. He also holds an Honorary Professorship at University College London (UK) and previously led the Software Engineering group at Fondazione Bruno Kessler (Italy). His research focuses on software testing, analysis, and AI-driven systems. He has authored over 200 peer-reviewed papers and 100 journal articles, with an H-index of 72. He teaches courses in Data and Software Engineering and Informatics, including Information Modeling, Probability & Statistics, and Knowledge Search. Key contributions include foundational work on web application testing (ICSE MIP award), evolutionary testing techniques (eToc/EvoSuite tools), and reverse engineering of object-oriented systems. He led the ERC-funded PRECRIME project on anticipatory testing. His recent work addresses AI dependability, autonomous systems testing, and deep learning fault analysis. Scientific awards include the ICSE MIP Award (2001) and ERC Advanced Grant (2018). He has served on editorial boards for major journals like IEEE Transactions on Software Engineering and ACM TOSEM. Current roles include leadership in the Software Institute and organizing the SIESTA summer school.
Prof. Dr. Franziska Jahnke is a Professor in the Faculty of Mathematics and Computer Science at the University of Münster, affiliated with the Institute for Mathematical Logic and Foundational Research. She specializes in model theory and its applications to algebra, particularly valued fields, set theory, and arithmetic definability. Her research bridges foundational mathematics and algebraic structures, with a focus on henselian valuations, NIP fields, and combinatorial aspects of valued fields. Education: Diplom in Mathematics from Albert-Ludwigs-Universität Freiburg (2009), DPhil in Mathematics from the University of Oxford (2013). Academic roles include Junior Professor at Münster (2017–2024), and a visiting position at the University of Amsterdam (2023–2024). Research Interests: Model theory of fields, arithmetic definability of valuations, perfectoid fields, and connections to infinite combinatorics. Key contributions include work on Ax-Kochen-Ershov principles, NIP fields, and classification conjectures in strongly dependent fields. Recent Activities: Organized workshops on non-archimedean geometry and model theory of valued fields. Recipient of the Teaching Prize 2022 and a fellow of the Daimler und Benz Stiftung. Deputy Equal Opportunity Representative in her department. Supervision: Advised multiple PhD students (e.g., Blaise Boissonneau, Simone Ramello) and postdocs. Taught courses on algebra, logic, and model theory, including lectures on valued fields and stability theory.
Angel Xuan Chang is an Associate Professor at Simon Fraser University's School of Computing Science, where she leads research at the intersection of natural language processing, computer vision, and 3D scene understanding. She holds the prestigious Canada CIFAR AI Chair position and is affiliated with multiple research groups including 3DLG, GrUVi, SFU NatLang, SFU AI/ML, and VINCI. PhD in Computer Science, Stanford University MSc in Computer Science, Stanford University M.Eng in Electrical Engineering and Computer Science, MIT BSc in Computer Science and Engineering, MIT Professor Chang's research primarily focuses on connecting language to 3D representations of shapes and scenes, with particular emphasis on grounding language for embodied agents in indoor environments. Her work spans natural language processing and understanding, linking natural language with visual and 3D representations, multimodal grounding of language, embodied AI, and machine learning applications for biodiversity monitoring through the BIOSCAN project. She has developed methods for synthesizing 3D scenes and shapes from natural language and created various datasets for 3D scene understanding. Her recent publications reveal a strong trend toward integrating language understanding with 3D scene generation and manipulation, with increasing focus on practical applications in embodied AI and biodiversity monitoring. The research shows progression from foundational work on text-to-3D scene generation to more sophisticated approaches for evaluating semantic coherence in generated scenes and developing efficient methods for zero-shot scene modeling. Canada CIFAR AI Chair TUM-IAS Hans Fischer Fellow (2018-2022) Best paper award at 3DV 2025 for 'An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion' Professor Chang actively advises numerous graduate students who appear as first authors on her publications, indicating a strong mentoring program. Her research is supported through multiple channels including the CIFAR AI Chair position and likely various research grants supporting her BIOSCAN-related work and 3D scene understanding projects. She has been involved in organizing multiple workshops at major conferences including ICML, CVPR, and ICLR. Her research is conducted through several interconnected groups: 3DLG (3D Language and Graphics), GrUVi (Graphics, Vision, and Interaction), SFU NatLang (Natural Language Processing), SFU AI/ML, and VINCI. These groups work collaboratively on problems spanning language grounding, 3D scene understanding, embodied AI, and biodiversity applications, creating a rich interdisciplinary research environment.
