Dr. Dan Jurafsky is a Professor at the Department of Linguistics within Stanford University's School of Humanities and Sciences. His work spans computational linguistics, natural language processing, and AI ethics, with a particular focus on language model behavior, speech recognition, and ethical implications of anthropomorphism in AI systems. His recent research explores HumT for measuring human-like tone in LLMs, AnthroScore for anthropomorphism detection, and methods for improving low-resource language support through data augmentation and multilingual representation learning. He has also developed open-source tools like string2string for string algorithms. Jurafsky's publications address critical issues in NLP, including grounding gaps in conversational models, causal interpretability in linguistic tasks, and representational biases in multilingual models. His work emphasizes interdisciplinary applications, from educational NLP tools to Sumerian transliteration datasets, while advocating for rigorous statistical power analysis and ethical model evaluation.
Jan Pieter Abrahams is a Professor and Group Leader of the Nanodiffraction group at the Laboratory for Multiscale Bioimaging, Paul Scherrer Institute (PSI) in Switzerland. His work focuses on developing electron diffraction technologies for atomic-resolution imaging of frozen hydrated biological samples, leveraging PSI's detector expertise to advance structural biology beyond conventional microscopy limitations. Current applications target mitochondrial stress mechanisms in neurodegeneration and aging, with active collaborations across international institutions. Research Interests: Abrahams pioneers electron diffraction and cryo-EM methodologies, emphasizing computational-phasing innovations and machine learning integration. His group specializes in: Overcoming dynamical scattering for atomic-level cellular visualization Hybrid pixel detector applications (JUNGFRAU, EIGER) in electron microscopy Deep learning frameworks for diffraction data processing (e.g., DiffraGAN) Structural analysis of protein nanocrystals in disease contexts Mitochondrial protease mechanisms in aging Bacterial cell division and sporulation structures Publication Trends: Recent work (2020–2024) reveals heavy emphasis on AI-driven structural biology, including generative networks for diffraction phasing and lossless data compression. Instrumentation advancements (e.g., Boersch phase shifters) and disease-focused studies (Alzheimer’s amyloid-beta, malaria heme processing) dominate, showcasing interdisciplinary convergence of physics, computation, and biomedicine. Scientific Awards: No specific awards or fellowships documented in source text Advising and Collaborations: Abrahams has mentored PhD students including Thakkar, Pooja; Rheinberger, Jan; Schärer, Martin; and Wennmacher, Julian. His team collaborates with PSI's detector group on sensor development (GaAs/CdTe) and international labs for structural studies. Funding likely stems from PSI instrumentation projects and disease-focused research initiatives. Labs and Teams: He directs the Nanodiffraction group under PSI's Center for Life Sciences, comprising scientists (van Genderen, Latychevskaia) and postdocs (Blum). The lab integrates cryo-EM, electron diffraction, and computational modeling to visualize cellular processes at nanometer scales, with strong ties to detector engineering and pharmaceutical structural analysis.
Fariba Moghaddam is an Ordinary Professor at HES-SO Valais-Wallis - Haute Ecole d'Ingénierie, leading the Power & Control orientation under the Institut Systèmes industriels. She specializes in renewable energy systems, control engineering, and educational technology, with a focus on solar energy optimization, remote laboratories, and sustainable development initiatives in the Global South. Her work spans academic leadership, research, and pedagogical innovation. Education: PhD in Control Engineering from École Polytechnique Fédérale de Lausanne (EPFL), 1996. Additional academic background in electrical and mechanical engineering. Research Interests: Solar tracking systems, photovoltaic-thermal (PVT) integration, machine learning applications in energy systems, remote experimentation platforms, and collaborative educational infrastructure. Recent projects include the Solar Energy Optimization via Remote Platforms and DEAR MENA (Digital Education & Research for MENA). Grants & Projects: Solar Energy Optimization (2022-2026): Swiss National Science Foundation-funded project developing cost-effective sun tracking solutions. DEAR MENA (2019-2024): Cluster fostering digital education partnerships between Swiss and MENA institutions. Sustainable Laboratories (2020-2021): Platform for redistributing lab equipment to emerging economies. MOOLs (2018-2021): Massive Open Online Laboratories for global engineering education. Labs & Infrastructure: Spearheaded the Remote Real-Time Research Platform for control engineering, enabling global access to lab equipment via secure web interfaces. Collaborates on IoT-integrated systems for remote experimentation and industrial automation. Awards: None explicitly listed, but recognized for contributions to sustainable energy and educational equity through numerous funded projects.
Nabil Ouerhani is a Professor in Computer Science at Haute Ecole Arc // HES-SO, leading the 'Interaction Technologies' research group focused on Human-Machine Interaction (HMI), Human-Robot Interaction (HRI), and Industry 4.0 applications. His work integrates IoT, cyber-physical systems, and multi-agent systems to develop innovative industrial solutions. Education: BSc HES-SO in Computer Science - Haute Ecole Arc - Ingénierie Key Research Areas: Collaborative robotics and AI-driven automation Thermal error prediction in machine tools IoT-enabled energy efficiency solutions Virtual/Augmented Reality for industrial safety Recent Projects: BonsAPPs (EU H2020): AI-as-a-Service platform for Deep Edge applications Decolleteur 4.0 (Innosuisse): AI-based Swiss-Type lathe programming system TherMoMac : Hybrid thermal error prediction for machine tools Grants & Funding: BonsAPPs: CHF 5,750,000 (EU H2020) Decolleteur 4.0: CHF 1,217,809 (Innosuisse) ECOMAC25: CHF 439,062 (Innosuisse) Research Collaborations: Industry partners: Tornos SA, NVISO SA, ST Microelectronics Academic partners: University of Bologna, ETH Zurich, SUPSI
Makhlouf Shabou Basma is a Professor at the Haute école de gestion de Genève (HES-SO), leading the Master in Information Sciences program. She specializes in archival science, digital preservation, and information governance with a focus on sustainable data practices and AI integration. She is actively involved in interdisciplinary research addressing environmental impacts of data management, appraisal processes in the AI era, and computational methods in archival science. Her work spans over 20 years, with leadership in projects such as Arch’Eco (ecological costs of data lifecycle), InterPARES Trust AI (AI-driven archival appraisal), and Ask ArchiLab (AI conversational assistant for archives). She collaborates internationally with institutions like the University of Geneva, CICR, and UNESCO, and has secured over CHF 2 million in research funding. Key research interests include: sustainable digital preservation, environmental impact of data practices, computational archival science, AI ethics in appraisal, and interdisciplinary knowledge management. She has published extensively on topics such as eco-design of archives, maturity models for appraisal processes, and digital heritage preservation. Her teaching and research bridge practical archiving challenges with cutting-edge technologies, emphasizing ethical and sustainable solutions for information governance in both public and private sectors.
Prof. Alexandros Kalousis holds the position of Ordinarius HES Professor at the Geneva School of Economics and Management (HES-SO). His primary affiliation is within the Department of Management Information Systems under the School of Economics and Services. His research focuses on machine learning, data mining, and their applications in biomedical systems, cybersecurity, and generative modeling. Key projects include SimGait (SNSF-funded), which develops neuromechanical models for pathological gait analysis using machine learning, and RAWFIE (EU-funded), creating a mixed network testbed for autonomous vehicle experimentation. He has also led projects on time series forecasting, olfactory modeling for perfume creation, and metric/kernel learning optimization. Notable contributions span graph generative models like DGAE and GLAD, cybersecurity implications of LLMs, and reproducible indoor positioning systems. His work integrates theory-driven models with deep learning, emphasizing interpretability and extrapolation capabilities. Research collaborations include EPFL's Biorobotics Lab, Geneva University Hospitals, and industry partners like Firmenich. Total funding exceeds CHF 3M across projects like SimGait (CHF 2.1M) and RAWFIE (CHF 623K).
Frédéric Montet is a Researcher and Doctoral Student at the Fribourg School of Engineering and Architecture (HES-SO), part of the iCoSys Institute for Complex Systems. His primary role involves leading and contributing to projects focused on smart building technologies, energy efficiency, and machine learning applications. He is the Principal Applicant of the ongoing FACILITY 4.0 project (2019-2021), which develops data-driven solutions for building management and facility optimization using AI and IoT technologies. Research interests include: Machine Learning for energy systems, predictive modeling in smart buildings, radon gas monitoring infrastructure, and integration of large language models into control systems. His work bridges theoretical data science with practical applications in renewable energy management and industrial automation. Key contributions include the BBData 2.0 platform for smart building data integration, predictive domestic hot water temperature modeling in district heating systems, and benchmarking zero-shot time series forecasting models. His projects often involve collaboration with industry partners to co-create scalable ICT solutions. Current focus areas: Optimizing PV installations at grid level, predictive maintenance systems, and ethical challenges in LLM-based control systems for shared appliances.
Yun-chien Chang is the Jack G. Clarke Professor in East Asian Law at Cornell Law School , where he also serves as Director of the Clarke Program in East Asian Law & Culture. His work bridges law and economics , comparative property law , and empirical legal studies , with a focus on property theory, corporate governance, and judicial behavior. Research Themes : Property law efficiency and convergence/divergence between civil and common law systems Economic analysis of eminent domain, adverse possession, and takings compensation Empirical studies of judicial decision-making, settlement patterns, and legal aid participation Machine learning applications in comparative legal analysis Publication Trends : Recent work (2021-2025) emphasizes legal origins theory , judicial ideology , and behavioral economics in law Foundational papers (2010-2016) explore property customs , constitutional mechanisms , and economic modeling of legal institutions
Ueli Schilt is a Research Associate and Doctoral Student at the Lucerne School of Engineering and Architecture, part of the Lucerne University of Applied Sciences and Arts (HSLU). His work focuses on thermal energy systems, renewable generation, and energy efficiency in Swiss urban and regional contexts. He is affiliated with the Institute of Mechanical Engineering and Energy Technology (IME), specifically within the Thermal Energy Storage research group. Role: Research Associate & Doctoral Student Institution: Lucerne University of Applied Sciences and Arts (HSLU) School: School of Engineering and Architecture Institute: Institute of Mechanical Engineering and Energy Technology (IME) Research Focus: Thermal energy storage, multi-energy system optimization, renewable integration Ueli Schilt’s research explores the integration of thermal energy storage in multi-energy systems, solar PV expansion, and heating system retrofits. His work emphasizes temperature considerations, load forecasting, and sensor technology validation. Key projects include decentralized renewable generation in Swiss regions and the SENSHOEK initiative for adaptive heating controls. Recent publications highlight advancements in air quality monitoring, heat pump consumption analysis, and communal energy planning tools. While no scientific awards are explicitly listed, his contributions to peer-reviewed journals and international conferences indicate active academic engagement. Collaborations with Philipp Schütz and other researchers underscore interdisciplinary teamwork in energy modeling and policy support.
Markus Schmidiger is a Lecturer at the Lucerne School of Business, part of the Lucerne University of Applied Sciences and Arts. He is affiliated with the Institute of Financial Services Zug (IFZ) and the Competence Center Real Estate (CC Real Estate). His academic work focuses on real estate management, digital transformation in construction, and sustainable urban development. Schmidiger also leads the MAS Real Estate Management program and contributes to industry research through projects like the Digitalisierungsbarometer for real estate. Education: PhD in Economics (HSG), Master in NLP (Society of NLP), Corporate Real Estate Manager (ebs) Research Areas: Real estate digitalization, circular economy in construction, machine learning for property valuation Projects: NISMO (Spatial ML for real estate), Digitalisierungsbarometer, sustainable development in Uri canton, smart city initiatives His industry mandates include roles on the boards of Utilita Foundation (non-profit housing investments) and Seraina Investment Foundation's Investment Committee, alongside serving on AST Migros Pension Fund's real estate committee. Schmidiger actively engages in media commentary and professional networking through his Immobilienblog and LinkedIn profile.
Prof. Dr. Siegfried Handschuh is a Full Professor for Data Science and Natural Language Processing at the Institut für Informatik (ICS-HSG), University of St. Gallen. His research focuses on advanced NLP techniques, financial text analysis, and AI-driven solutions in cybersecurity and education. He leads projects like CS-AWARE-NEXT, enhancing cybersecurity awareness in public institutions. Prof. Handschuh has authored over 100 publications, with recent work emphasizing transformer optimization, generative AI applications, and educational tools for argumentative writing. His team collaborates with companies like Rheasoft and Peracton on AI-powered financial analytics and cybersecurity systems. Education: Doctorate in Computer Science, specialized in knowledge representation and semantic web technologies. Research Interests: Data science, machine learning, financial NLP, cybersecurity, and AI in education. His work bridges academia and industry, addressing real-world challenges in finance, cybersecurity, and educational technology through innovative AI frameworks.
Haozhe Zhang is a Postdoctoral Researcher in the Data Systems and Theory (DaST) group at the Department of Informatics, University of Zurich, supervised by Prof. Dan Olteanu. His career bridges theoretical research and practical implementation in database systems. Education: DPhil in Computer Science, University of Oxford (2023) MSc in Computer Science, University of Oxford (2017) BSc in Computer Science, University of Nottingham (2016) Research Focus: Haozhe's work centers on database theory, emphasizing incremental view maintenance and cardinality estimation . His research explores efficient algorithms for dynamic relational data, theoretical foundations of conjunctive queries, and robust cardinality estimation techniques like LpBound. Publications & Trends: His contributions include theoretical analyses of conjunctive queries under updates, practical systems like F-IVM for analytics over evolving data, and worst-case optimal algorithms for triangle counting. Recent work at SIGMOD 2025 and ICDT/AMW workshops highlights advancements in dynamic query evaluation and cardinality estimation guarantees. Scientific Recognition: Best Paper Award, SIGMOD 2025 Best Paper Award, ICDT 2019 Teaching Contributions: Instructor, Foundations of Data Sciences (UZH, Fall 2024) Teaching Assistant for Foundations of Data Sciences (UZH, Fall 2020–Fall 2023), Efficient Algorithms (UZH, Spring 2021–Spring 2025), and Modern Data Analytics (UZH, Fall 2023).
Catalin Starica serves as a Full Professor at the Institute of Information Management within the Faculty of Economics at the University of Neuchâtel, Switzerland. His academic profile centers on quantitative methodologies applied to financial and accounting domains, with institutional affiliation clearly anchored in the university's economics faculty structure. His research program aggressively bridges statistical theory and financial practice, emphasizing Applied Statistics , Machine Learning in Finance , and Earnings Quality assessment. Core investigations dissect the price-earnings relationship dynamics, intangible asset valuation challenges, and the evolving role of accrual accounting in modern markets. Methodologically, he pioneers AI-driven approaches to financial statement analysis while maintaining rigorous econometric foundations. Analysis of his 2016-2025 publications reveals a pronounced shift toward computational finance, with machine learning techniques increasingly deployed to model earnings-price associations and intangible investment impacts. His work consistently challenges conventional accounting paradigms, particularly regarding earnings quality measurement and the relevance of traditional accrual systems in technology-driven economies. The trajectory indicates growing emphasis on AI integration and structural changes in financial data interpretation.
Rafael Medina Morillas is a researcher at the Embedded Systems Laboratory (ESL) at Ecole Polytechnique Fédérale de Lausanne (EPFL), where he focuses on computer architecture and hardware acceleration for edge AI systems. His research addresses the memory wall problem through innovative architectural designs that improve energy efficiency and performance in data-intensive applications. His primary research interests include: Compute-near-Memory architectures Hardware acceleration for machine learning Edge AI systems Chiplet architectures and interconnects Wireless communication for computing systems Medina Morillas' publication record demonstrates significant advancements in memory systems and hardware acceleration. His work on SideDRAM shows up to 83% EDAP reduction compared to state-of-the-art designs, while his research on wireless communication achieves up to 2.64x speedup for deep neural networks. His recent publications focus on structured pruning techniques for transformers, co-design frameworks for edge AI, and thermal management solutions for heterogeneous systems. His research is supported by collaborations with IMEC, Université de Bordeaux, and HEIG-VD, as well as funding from EC H2020 projects and the ACCESS-AI Chip Center. These partnerships enable comprehensive exploration of architectural innovations across different technology domains. As evidenced by his doctoral thesis 'System-aware Architectural Co-design to Tackle the Memory Wall,' Medina Morillas takes a cross-layer approach to system design, integrating hardware and software optimizations to address fundamental bottlenecks in modern computing systems. His work demonstrates how system-aware architectural design can achieve improvements in runtime, energy consumption, and thermal behavior for data-intensive applications.
Sebastien Houde serves as a Professor at the Faculty of Business and Economics (HEC-UNIL) of the University of Lausanne, where his research bridges institutional design, economic policy, and environmental sustainability. His work critically examines how regulatory frameworks shape market behaviors and societal outcomes. His primary research domains include: Environmental Economics: Focusing on green building policies and their socioeconomic ripple effects Institutional Analysis: Investigating how norms and policies evolve within regulatory ecosystems Machine Learning Applications: Developing data-driven models for policy impact forecasting Well-Being Metrics: Quantifying mental health correlations with sustainable housing initiatives Recent publications reveal a concentrated focus on Swiss residential markets, where he pioneers methodologies to measure economic externalities of environmental regulations. His interdisciplinary approach synthesizes econometric modeling with behavioral insights, establishing new frameworks for evaluating sustainability transitions in real estate sectors. This work demonstrates growing integration of computational techniques in policy economics. Houde actively contributes to academic discourse through research participation and publications centered on institutional transformation, though specific grant details or collaborative projects remain undocumented in available sources.
