Keval Vora is an Associate Professor at the School of Computing Science, Simon Fraser University. His research focuses on scalable solutions for modern data analytics systems, particularly in graph processing and distributed computing. He leads the Parallel Data and Computing Lab (PDCL), developing systems like Peregrine , GraphBolt , and GraphBolt . Contact: TASC1 9419, keval@sfu.ca. Education: PhD in Computer Science from the University of California, Riverside (2017). Previously worked at Morgan Stanley on low-latency trading software. Teaching: Courses include Distributed Systems (CMPT 431) and Special Topics in Networks and Systems (CMPT 982). Advises graduate and undergraduate students on projects involving distributed systems and graph analytics. Research Interests: Parallel/Distributed Computing, Irregular Big Data Processing, High-Performance Computing. His work emphasizes efficient techniques with provable guarantees for large-scale systems. Software Contributions: Peregrine (pattern-based analytics), GraphBolt (dynamic graph processing), and Lumos (disk-based graph processing). These systems address challenges in scalability, efficiency, and real-time data handling.
Prof. Dr. Martin Kronbichler is a faculty member at the Faculty of Mathematics , Ruhr University Bochum , leading the Numerics group. His research focuses on higher-order finite element methods, multigrid techniques, and high-performance computing for complex fluid and solid mechanics problems. Key Research Areas: Higher-order finite element methods, iterative solvers, multigrid algorithms, exascale mathematical software, and computational fluid dynamics. Notable Projects: EU-funded dealii-X (exascale digital twins), BMBF PDExa (optimized PDE solvers for exascale), and DFG grants for cut-discontinuous Galerkin methods and geometric multigrid. Publications Trends: Recent works emphasize matrix-free operators for hyperelasticity, diffuse-interface models for additive manufacturing, and multigrid smoothers for higher-order elements. Scientific Awards: Recipient of the Humboldt Research Award for his contributions to numerical methods and HPC. Team: Collaborates with researchers like Dr. Shubham Kumar Goswami, Dr. Richard Schussnig, and Natalia Nebulishvili.
Mikko Valkama is a Professor at the Department of Communications Engineering , part of the Faculty of Information Technology and Communication Sciences at Tampere University . His research focuses on advanced wireless communication systems, positioning technologies, and integrated sensing and communication (ISAC). He holds an Orcid ID ( 0000-0003-0361-0800 ) and can be reached at mikko.valkama@tuni.fi . Research interests span 5G/6G networks , RF antenna design , deep learning for signal processing , and millimeter-wave systems . He leads projects on positioning algorithms (e.g., mmWave SLAM, NLOS mitigation), ISAC architectures, and hardware-efficient transmitter linearization. Notable contributions include works on DECT-2020 NR standards, phase-based localization, and RIS-assisted systems. In 2025 alone, his group published over 30 articles on topics such as: Antenna array design for Ka-band and wideband applications Machine learning for power amplifier predistortion Bistatic radio SLAM and mmWave mapping Covert transmission and physical-layer security His work bridges theoretical advancements with practical implementations, often validated through experimental setups (e.g., TUJI1 dataset for indoor localization). No scientific awards were explicitly listed in the provided texts.
Dr. Amelia Simpson holds the position of Honorary Associate Professor at the Australian National University (ANU), specializing in constitutional law with a focus on discrimination principles and federalism. She earned her BA Hons and LLB Hons from ANU, followed by an LLM and JSD from Columbia University. She is a practicing Barrister and Solicitor in the High Court of Australia. Her research critically examines constitutional frameworks, particularly regarding state residence discrimination under Section 117 and interstate free trade jurisprudence. Her work has been cited in landmark High Court and Federal Court rulings, solidifying her reputation as a leading scholar in public law. Amelia’s contributions include co-authoring Hanks Australian Constitutional Law: Materials and Commentary (2016) and contributing to the Oxford Handbook of Australian Constitutional Law (2018). Her research on social equality and functionalist approaches to non-discrimination has advanced scholarly discourse on constitutional values. Notably, her 2017 paper Social Equality in Australia critiques the historical neglect of equality principles in constitutional interpretation. Her publications span constitutional doctrine, class actions reform, and environmental policy, reflecting her interdisciplinary approach. She ranks among Australia’s top 20 most prolific legal scholars in high-impact journals (2000–2010). Her work bridges theoretical analysis with practical legal challenges, influencing both academia and judicial practice.
Graham Dobereiner is an Associate Professor and Robert L. Smith Early Career Professor in the Department of Chemistry at Temple University's College of Science and Technology. He received his Ph.D. from Yale University (2011) and completed postdoctoral research at MIT (2012-2014) after earning his B.S. from Brandeis University (2007). His research group develops novel homogeneous transition metal catalysts for synthetic chemistry applications spanning fine chemicals manufacturing, petrochemical processing, and drug discovery. The work integrates organometallic chemistry principles, combining organic molecular diversity with inorganic compound reactivity. Research areas include catalytic isomerization, oxidative synthesis, ligand design, and mechanistic studies of transition metal complexes. Analysis of his recent publications demonstrates strong emphasis on reaction mechanism elucidation, catalyst design for stereoselective transformations (particularly Z-selective isomerizations), and development of novel catalytic systems for sustainable synthesis. His group employs computational and experimental approaches to advance synthetic methodology.
Prof. Dr. Michael Gröschel is a Professor of Business Informatics at the Faculty of Computer Science, Mannheim University of Technology. His expertise spans Business Process Management (BPMN, Process Mining), Digital Transformation, and innovative Business Models. He teaches courses on BPM, project management, and e-business, often collaborating with industry clients through student projects. As a consultant, he specializes in BPMN training and IT-driven business strategy. His work emphasizes practical applications of IT tools like RPA and low-code platforms. Recent publications focus on RPA bot performance evaluation, AI in automotive trade, and business model-IT alignment. Prof. Gröschel’s research bridges academic insights with real-world challenges, particularly in leveraging technology for business innovation. He has authored books and articles on business intelligence tools, mobile business strategies, and digital customer care. His consulting services include executive coaching for academic career advancement and enterprise IT management best practices. Office: Building A, Room 007c | Phone: +49 621 292-6764 | Professional website available for further engagement opportunities.
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.
Xiaofeng Shao is a Professor of Statistics & Data Science at Washington University in St. Louis, with a joint appointment in the Department of Economics. He holds a PhD from the University of Chicago and previously served at the University of Illinois at Urbana-Champaign for 18 years. He is a Fellow of the Institute of Mathematical Statistics and the American Statistical Association. His research focuses on econometrics, time series analysis, change-point detection, high-dimensional statistics, nonparametric methods, and functional data analysis. Recent work emphasizes object-valued time series modeling and machine learning applications in high-dimensional and imaging data. Notable contributions include the dependent wild bootstrap method and self-normalization techniques for time series inference. Key awards include Fellowships from leading statistical societies. His publications span over 20 years, addressing topics like change-point detection in climate projections, statistical methods for COVID-19 infection trends, and high-dimensional dependence testing.
Stephen A. Vavasis is a Professor in the Department of Combinatorics and Optimization at the University of Waterloo, part of the Faculty of Mathematics. He holds a PhD in Computer Science from Stanford University (1989) and has held academic positions at Cornell University (1989–2006) before joining Waterloo. His research focuses on continuous optimization, data science, first-order methods, scientific computing, and computational mechanics. Current teaching includes courses on convex optimization and portfolio optimization methods. He has served as Associate Dean of Computing (2017–2020) and Interim Director of Data Science graduate programs. His work is supported by NSERC grants. Notable awards include the Hertz Fellowship, Churchill Scholarship, and Guggenheim Fellowship. Vavasis's research emphasizes applications of optimization to clustering, machine learning, and fracture mechanics. His publications span convex optimization frameworks, algorithmic analysis of gradient methods, and numerical methods in mechanics. Recent work explores unifying analyses of first-order optimization algorithms and robust optimization techniques for high-dimensional data problems.
Prof. Dr. Ingo Scholtes is Chair of Machine Learning for Complex Networks at Julius-Maximilians-Universität Würzburg's Center for Artificial Intelligence and Data Science (CAIDAS). His research spans network science, graph machine learning, and computational social science, with applications in software engineering, ecology, biology, and physics. He received a Juniorfellowship from the German Informatics Society (2014) and an SNSF Professorship (CHF 1.5Mio, 2018). Current affiliations: JMU Würzburg (since 2021), University of Zurich (2018-2024), Bergische Universität Wuppertal (2019-2021) Research focus: Higher-order network modeling, temporal graph analysis, AI for collaborative systems, causality-aware machine learning His recent publications demonstrate strong trends in temporal network analysis , graph neural networks for time-series, and higher-order models across software engineering and social science domains. He co-chairs multiple international workshops on complex networks and serves as associate editor for EPJ Data Science and Advances in Complex Systems. Key scientific contributions: Foundational work on higher-order network models published in Nature Physics Methodological innovations in temporal network visualization (HOTVis) and path-based analysis (pathpy) As both educator and organizer, he leads the Computational Social Science Section at GI e.V., mentors across disciplines, and develops tools like git2net for collaboration analysis. His work bridges theoretical foundations with practical applications in network science.
Brian Hie is an Assistant Professor of Chemical Engineering at Stanford University , a Dieter Schwarz Foundation Stanford Data Science Faculty Fellow , and an Innovation Investigator at Arc Institute . He leads the Laboratory of Evolutionary Design , focusing on the intersection of biology and machine learning . His prior roles include a Stanford Science Fellow in the Stanford University School of Medicine and a Visiting Researcher at Meta AI . Education: Ph.D. , Electrical Engineering and Computer Science , Massachusetts Institute of Technology (2021) Bachelor’s Degree , Stanford University Research Interests: Brian’s work bridges machine learning and computational biology , with a focus on protein engineering , single-cell RNA sequencing , and viral evolution . His Evolutionary velocity framework predicts protein evolutionary dynamics across timescales, while his Scanorama algorithm enables efficient integration of heterogeneous single-cell datasets. He also develops structure-informed language models for antibody optimization and uncertainty-aware ML for biological discovery. Publication Trends: His recent work (2023) emphasizes structure-based inverse folding for antibody evolution, evolutionary scale modeling , and unsupervised optimization . Earlier studies (2022-2021) cover evolutionary velocity , multi-modal single-cell analysis , and viral escape prediction using natural language analogies. Scientific Awards: Stanford Science Fellow (2021) National Defense Science and Engineering Graduate Fellowship (2019) Advising: He mentors doctoral students including Brandon Ameglio , Garyk Brixi , and Chang M. Yun , with a focus on biological design and computational methods . Labs & Collaborations: His lab collaborates with Bio-X and the Institute for Human-Centered Artificial Intelligence (HAI) , and he maintains affiliations with Sarafan ChEM-H and Stanford Data Science .
Professor Janet McColl-Kennedy is a leading figure in marketing and service science at The University of Queensland's Business School. She serves as Program Lead for Innovation Pathways (FaBA) and co-founded the Service Innovation Alliance (SIA) research hub, focusing on customer experience, AI, digital transformation, and sustainability. With Fellow status in the Academy of the Social Sciences in Australia and the Australian and New Zealand Marketing Academy, she holds international recognition including Research.com's World's Best Business Scientists ranking and the Christopher Lovelock Career Contributions to the Services Discipline Award (2025). University of Queensland Business School Honorary Visiting Professor, Cambridge Service Alliance Research Collaborator, University of Cambridge Her research spans customer experience management, service ecosystems, digital transformation, and healthcare services. She has pioneered frameworks for customer value co-creation and service recovery strategies, integrating AI and behavioral science. Her 2019 paper on customer experience insights was implemented by a major B2B organization, while her work on value co-creation improved outcomes at Lutheran Community Care. Recent publications focus on AI impacts in food and beverage services, sustainable service ecosystems, and digital health interventions. She has secured over $89 million in competitive grants, including multiple ARC Linkage and Discovery Projects, and leads cross-disciplinary teams with institutions like Cambridge University and Arizona State University. 2024 - ARC College of Experts appointment 2023 - Bo Edvardsson Industry Impact in Services Award 2022 - Elected Fellow of Academy of the Social Sciences in Australia 2021 - Clarivate Highly Cited Researcher 2018 - Ranked most influential marketing academic in Australia With over 220 publications and a Google Scholar H-index of 62, she has mentored 15 PhD students and examined theses at multiple universities. Her teaching spans 30+ years across undergraduate, postgraduate, and executive programs in Australia, USA, Italy, China, and Korea, with awards for blended learning and corporate education.
Jason Cong is the Volgenau Chair for Engineering Excellence and Distinguished Chancellor's Professor in the Computer Science Department at UCLA's Samueli School of Engineering. He directs the Center for Domain-Specific Computing (CDSC) and the VLSI Architecture, Synthesis, and Technology (VAST) Laboratory, and serves as Associate Vice Provost for Internationalization and Co-Director of UCLA/PKU Student and Scholar Program. Dr. Cong's research spans electronic design automation, customizable computing for machine learning and big-data applications, quantum computing, and highly scalable algorithms. His work has produced over 500 publications with more than 41,000 citations and an H-index of 106. His recent work focuses on quantum computing compilation, domain-specific acceleration for AI workloads, and high-level synthesis optimization techniques that leverage machine learning. His publication trend shows a strong emphasis on quantum computing and machine learning acceleration in recent years, with numerous papers on quantum layout synthesis, LLM acceleration, and high-performance FPGA implementations. His team has developed frameworks like TAPA for task-parallel dataflow programming and RapidStream for automated parallel implementation of FPGA designs. Member of National Academy of Engineering (2017) IEEE Robert N. Noyce Medal recipient (2022) Phil Kaufman Award recipient (2024) ACM Chuck Thacker Breakthrough Award recipient (2024) 18 Best Paper Awards across major conferences Multiple 10-Year Retrospective Most Influential Paper Awards Dr. Cong has graduated 50 PhD students, many of whom are now faculty at major research universities or hold key positions at leading tech companies. He has led over 100 research projects funded by DARPA, NSF, SRC, and industry sponsors. His entrepreneurial activities include founding three successful companies (Aplus Design Technologies, AutoESL, and Falcon Computing Solutions), all acquired by major EDA players. His VAST Laboratory continues to push boundaries in domain-specific computing, with active research in quantum computing, AI acceleration, and high-performance FPGA implementations.
Minjie Chen is an Associate Professor of Electrical and Computer Engineering and the Andlinger Center for Energy and the Environment at Princeton University, serving as Acting Associate Director for Research at the Andlinger Center. He leads the Princeton Power Electronics Lab (PowerLab), which focuses on developing fundamental and novel power electronics solutions for a wide range of applications from mW-scale energy harvesting to MW systems in renewable energy integration. Dr. Chen received his Ph.D. in Electrical Engineering and Computer Science from MIT in 2015 and his B.S. in Electrical Engineering from Tsinghua University in 2009. Before joining Princeton as an Assistant Professor in February 2017, he was a postdoctoral associate at MIT Research Laboratory of Electronics. His research spans power electronics, magnetics design, and machine learning applications in energy systems. The PowerLab develops advanced power conversion architectures that enable order-of-magnitude higher power density through high-frequency designs, addressing circuit timing, parasitics, magnetics, and thermal management challenges. Their work targets applications ranging from portable devices to data centers and renewable energy systems. The research group has produced a remarkable series of high-impact publications, with seven IEEE Transactions on Power Electronics Prize Papers in seven consecutive years (2016-2023). Their recent work increasingly integrates machine learning techniques with power electronics, exemplified by the MagNet project which redefines how power magnetics are studied and modeled. NSF CAREER Award, 2019 IEEE PELS Richard M. Bass Outstanding Young Power Electronics Engineer Award, 2023 Power of Associations Silver Award from ASAE for MagNet project, 2024 Multiple IEEE Transactions on Power Electronics Prize Papers (2016-2023) Princeton Engineering Commendation List for Outstanding Teaching (2019, 2020) Dr. Chen advises approximately 15 graduate students who have received numerous awards including the IEEE PELS John G. Kassakian Fellowship, Princeton SEAS Honorific Fellowship, and multiple IEEE conference best paper awards. His research is supported by significant grants from NSF, DOE ARPA-E, Princeton Innovation Fund, C3.ai DTI, and industry partners including Intel, Google, and pSemi. The lab's MagNet project has become a major international initiative with a $60,000 prize pool challenge. The PowerLab maintains strong industry connections and has launched several collaborative projects with Intel, Google, and pSemi. Their MagNet project has evolved into an international challenge with participation from over 40 teams worldwide, demonstrating the growing impact of their approach to machine learning for power magnetics modeling.
Naoki Yoshinaga is a tenured Associate Professor at the Institute of Industrial Science, The University of Tokyo, with extensive experience in natural language processing and computational linguistics. He has held academic positions since 2008 and currently leads research on pragmatic NLP models and multilingual systems. PhD in Computer Science, The University of Tokyo (2005-2008) MSc in Information Science (2000-2002) BSc in Information Science (1996-2000) His research focuses on mechanistic interpretability in NLP models, multilingual/multimodal NLP , and efficient model design using trie structures and conjunctive features. He also investigates knowledge acquisition from social data and evaluation metrics for language generation . Recent publications include work on neuron empirical gradient analysis (ACL-25), multilingual knowledge representation (EACL-24), and compact embedding methods (CoNLL-24). His research has been funded by multiple grants, including the University of Tokyo Excellent Young Researcher program and JSPS fellowships. Committee Special Award, Association for NLP (2023) JSAI SIG Research Award (2022) Best Interactive Award, DEIM Forum (2019, 2016) He developed widely-adopted NLP tools like pecco (fast classification library), RenTAL (LTAG-to-HPSG grammar converter), and J.DepP (Japanese dependency parser). His lab emphasizes strong equivalence in formalism comparisons and pragmatic model design .
