Start Time | End Time | Plan |
---|---|---|
Day 1: Feb 15 | ||
8:30 | Taking the Bus in front of HH main door | |
8:30 | 9:45 | Traveling to Skansen |
Chair: Sina Entekhabi | ||
9:45 |
10:10 | Introduction |
10:10 | 11:10(+10) | Keynote Speaker 1 - High5 |
11:20 | 11:30 | Break |
11:30 | 12:30 | ITE Knowledge Tree - Part 1 |
12:30 | 14:00 | Lunch Break |
14:00 | 14:50 | ITE Knowledge Tree - Part 2 |
14:50 | 15:00 | Break |
Chair: Tiago Cortinhal | ||
15:00 | 15:50 | Speed Dating - Part 1 |
15:50 | 16:00 | Break |
16:00 | 16:50 | Speed Dating - Part 2 |
16:50 | 18:00 | Break |
18:00 | 19:00 | City Walk |
19:00 | 20:30 | Dinner |
Start Time | End Time | Plan |
---|---|---|
Day 2: Feb 16 | ||
7:00 |
Breakfast (for those who stayed in the hotel from the previous night) | |
8:30 | Taking bus in front of HH main door | |
8:30 |
8:45 | Traveling to Skansen |
Chair: Sina Entekhabi | ||
9:45 | 10:00 | Introduction |
10:00 | 10:50(+10) | Keynote Speaker 2 - Joakim Rosell |
11:00 | 11:10 | Break |
Chair: Anna Vettoruzzo | ||
11:10 | 11:30(+5) | Galina Sidorenko - Emergency braking with ACC: how much does V2V communication help? |
11:35 | 11:55(+5) | Ece Calikus - Who’s Left Out? Improving equity in access to public transport |
12:00 |
12:20(+5) | Pablo Del Moral - Hierarchical Multi-class Classification for Fault Diagnosis |
12:30 | 14:00 | Lunch Break |
Chair: Ali Nada | ||
14:00 | 14:20(+5) | Anna Vettoruzzo – Meta-training a DANN with sharing weights |
14:25 | 14:45(+5) | Nesma Rezk - MOHAQ: Multi-Objective Hardware-Aware Quantization of Recurrent Neural Networks |
14:50 | 15:00 | Break |
15:00 |
15:50 |
Poster Presentations:Tiago Fernandes Cortinhal, Kunru Chen, Kevin Hernandez Diaz,Pererik Andreasson, Felipe Eduardo Valle Quiroz, Ali Nada, Martin Torstensson |
15:50 | 16:00 | Break |
16:00 | 16:50 | Keynote Speaker 3 - Babak Rostamzadeh |
16:50 | 19:00 | Break |
19:00 | 20:30 | Dinner |
Start Time | End Time | Plan |
---|---|---|
Day 3: Feb 17 | ||
7:00 |
9:00 | Breakfast |
Chair: Tiago Cortinhal | ||
9:00 | 9:50(+10) | Keynote Speaker 4 - Sonja Buchegger |
10:00 | 10:10 | Break |
Chair: Emmanuella Budu | ||
10:10 | 10:30(+5) | Sundas Munir - Pre-deployment Analysis of Smart Contracts |
10:35 | 10:55(+5) | Sina Entekhabi - Locality-based Test Selection for Autonomous Agents |
11:00 | 11:10 | Break |
Chair: Martin Torstensson | ||
11:10 | 11:30(+5) | Ali Amir Ahmadi - Deep learning prediction models based on EHR trajectories: A systematic review |
11:35 |
11:55(+5) | Nasrin Taghiyarrenani - Adversarial Contrastive Semi-Supervised Domain Adaptation |
12:00 | 12:20(+5) | Eduardo Kochenborger Duarte - SafeSmart: A VANET system for faster responses and increased safety in time-critical scenarios |
12:30 | 14:00 | Lunch Break |
Chair: Tiago Cortinhal | ||
14:00 | 14:20(+5) | Abid Ali - Implication of Agentive-Tech: From Tool to Butler in Healthcare Context (Patient and Healthcare Professional Perspectives) |
14:25 | 14:45(+5) | Abdallah Alabdallah - The Concordance Index decomposition - A measure for a deeper understanding of survival prediction models |
14:50 | 15:00 | Break | Chair: Sundas Munir |
15:00 | 15:20(+5) | Mohammed Ghaith Altarabichi - Evolutionary Discovery of Survival Loss Function for Neural Networks |
15:25 | 15:45(+5) | Felix Rosberg - Anonymizing Faces without Destroying Information |
15:50 | 16:30 | Closing Event & Final Remarks |
16:30 | Taking bus and returning to HH |
Sina Entekhabi is a 3rd year Ph.D student at Halmstad University. He is working on software safety testing in the domain of autonomous agents. He is now focused on efficient generation of test data for testing autonomous agents according to a designed Domain Specific Language (DSL) for defining locality-based test selection constraints.Here is a list of his publications.
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Tiago Cortinhal, is currently a 3rd year Ph.D. student. His research focus is Computer Vision with a special interest in generative models and domain adaptation. His most recent work focus on bridging the gap between the LiDAR and RGB domains by introducing a generative domain adaptation model that focus on exploiting semantic segmentation as the middle man. You can check his work here. |